{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import requests\n",
    "from IPython.display import display, Markdown\n",
    "from random import sample\n",
    "from maayanlab_bioinformatics.plotting.bridge import bridge_plot\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from matplotlib_venn import venn2, venn3\n",
    "from supervenn import supervenn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking L1000 Consensus Signature Methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides code and visualizations for benchmarking different methods of generating consensus signatures from the L1000 signatures. \n",
    "\n",
    "The current implementation examines the performance of consensus signatures generated by taking the mean, median, and weighted average of L1000 Characteristic Direction signatures downloaded from SigCom LINCS; however, this notebook is designed to work with any number of consensus and signature computation methods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = pd.read_csv('similarity_matrices/mean_pearson.txt.gz', sep='\\t', index_col=0)\n",
    "medians = pd.read_csv('similarity_matrices/median_pearson.txt.gz', sep='\\t', index_col=0)\n",
    "wavgs = pd.read_csv('similarity_matrices/weightedavg_pearson.txt.gz', sep='\\t', index_col=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Consensus Signature Similarity Matrices\n",
    "For each consensus signature generation method, we generated consensus signatures for all L1000 CRISPR KO genes and chemical perturbations. We then pre-computed the Pearson Correlation Coefficients between each CRISPR KO signature and each chemical perturbation signature. \n",
    "\n",
    "The reasoning is that we expect the consensus siganture for a given drug to have a non-random correlation to the consensus CRISPR KO signatures for its targets, representing a common pharmacological effect across different cell line contexts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Pearson Correlation Coefficients for Mean Consensus Signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AKT1</th>\n",
       "      <th>KRAS-2B</th>\n",
       "      <th>BRAF1</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>MYC</th>\n",
       "      <th>BRD4</th>\n",
       "      <th>MCL1</th>\n",
       "      <th>CDK4</th>\n",
       "      <th>BCL2L2</th>\n",
       "      <th>MAPK1</th>\n",
       "      <th>...</th>\n",
       "      <th>BRDN0003677177</th>\n",
       "      <th>KIT</th>\n",
       "      <th>SUV420H1</th>\n",
       "      <th>TGFBR2</th>\n",
       "      <th>STAG2</th>\n",
       "      <th>SMC1A</th>\n",
       "      <th>SMC3</th>\n",
       "      <th>RAD21</th>\n",
       "      <th>NIPBL</th>\n",
       "      <th>PDS5B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afatinib</th>\n",
       "      <td>0.039288</td>\n",
       "      <td>0.087599</td>\n",
       "      <td>0.052460</td>\n",
       "      <td>0.032641</td>\n",
       "      <td>0.035939</td>\n",
       "      <td>-0.025099</td>\n",
       "      <td>0.078779</td>\n",
       "      <td>-0.097733</td>\n",
       "      <td>-0.041715</td>\n",
       "      <td>0.158596</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002491</td>\n",
       "      <td>0.042092</td>\n",
       "      <td>0.017690</td>\n",
       "      <td>0.065971</td>\n",
       "      <td>-0.018110</td>\n",
       "      <td>-0.025007</td>\n",
       "      <td>-0.052941</td>\n",
       "      <td>0.069486</td>\n",
       "      <td>0.015613</td>\n",
       "      <td>0.017151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>erlotinib</th>\n",
       "      <td>-0.048258</td>\n",
       "      <td>0.151482</td>\n",
       "      <td>0.064425</td>\n",
       "      <td>0.070239</td>\n",
       "      <td>0.023104</td>\n",
       "      <td>-0.088843</td>\n",
       "      <td>0.093194</td>\n",
       "      <td>-0.155576</td>\n",
       "      <td>-0.023462</td>\n",
       "      <td>0.156218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>-0.004992</td>\n",
       "      <td>0.016802</td>\n",
       "      <td>0.035774</td>\n",
       "      <td>0.012185</td>\n",
       "      <td>-0.030883</td>\n",
       "      <td>0.022719</td>\n",
       "      <td>0.058391</td>\n",
       "      <td>-0.038472</td>\n",
       "      <td>-0.020328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neratinib</th>\n",
       "      <td>-0.011519</td>\n",
       "      <td>0.116096</td>\n",
       "      <td>0.040359</td>\n",
       "      <td>0.142860</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>-0.008306</td>\n",
       "      <td>0.020023</td>\n",
       "      <td>0.050044</td>\n",
       "      <td>-0.008112</td>\n",
       "      <td>0.098631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036109</td>\n",
       "      <td>0.012717</td>\n",
       "      <td>0.047148</td>\n",
       "      <td>0.063692</td>\n",
       "      <td>0.006152</td>\n",
       "      <td>-0.044527</td>\n",
       "      <td>-0.061279</td>\n",
       "      <td>-0.005293</td>\n",
       "      <td>0.047986</td>\n",
       "      <td>0.039911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lapatinib</th>\n",
       "      <td>-0.039902</td>\n",
       "      <td>0.088429</td>\n",
       "      <td>0.025822</td>\n",
       "      <td>-0.064870</td>\n",
       "      <td>0.025288</td>\n",
       "      <td>-0.023845</td>\n",
       "      <td>-0.065937</td>\n",
       "      <td>-0.203769</td>\n",
       "      <td>-0.046839</td>\n",
       "      <td>0.103114</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.068757</td>\n",
       "      <td>-0.055504</td>\n",
       "      <td>0.081767</td>\n",
       "      <td>0.159499</td>\n",
       "      <td>0.063188</td>\n",
       "      <td>-0.008917</td>\n",
       "      <td>-0.013766</td>\n",
       "      <td>-0.073146</td>\n",
       "      <td>-0.032068</td>\n",
       "      <td>0.078218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pazopanib</th>\n",
       "      <td>-0.115736</td>\n",
       "      <td>-0.041087</td>\n",
       "      <td>-0.118093</td>\n",
       "      <td>-0.012833</td>\n",
       "      <td>-0.101394</td>\n",
       "      <td>-0.125349</td>\n",
       "      <td>0.088811</td>\n",
       "      <td>-0.107420</td>\n",
       "      <td>0.022588</td>\n",
       "      <td>-0.083674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029134</td>\n",
       "      <td>-0.076045</td>\n",
       "      <td>-0.013759</td>\n",
       "      <td>0.112378</td>\n",
       "      <td>-0.040245</td>\n",
       "      <td>-0.020721</td>\n",
       "      <td>-0.001347</td>\n",
       "      <td>0.060826</td>\n",
       "      <td>-0.008466</td>\n",
       "      <td>0.015619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desvenlafaxine</th>\n",
       "      <td>-0.057500</td>\n",
       "      <td>0.009144</td>\n",
       "      <td>-0.066686</td>\n",
       "      <td>0.003290</td>\n",
       "      <td>-0.030215</td>\n",
       "      <td>-0.072164</td>\n",
       "      <td>-0.003146</td>\n",
       "      <td>-0.197048</td>\n",
       "      <td>-0.072740</td>\n",
       "      <td>0.045427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007904</td>\n",
       "      <td>0.034718</td>\n",
       "      <td>0.037131</td>\n",
       "      <td>0.015089</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.033563</td>\n",
       "      <td>0.094683</td>\n",
       "      <td>-0.032039</td>\n",
       "      <td>-0.065282</td>\n",
       "      <td>-0.019728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taltirelin</th>\n",
       "      <td>-0.042235</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>-0.056294</td>\n",
       "      <td>-0.071240</td>\n",
       "      <td>-0.068729</td>\n",
       "      <td>0.006262</td>\n",
       "      <td>-0.007184</td>\n",
       "      <td>-0.146936</td>\n",
       "      <td>0.024411</td>\n",
       "      <td>-0.011845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023191</td>\n",
       "      <td>0.027566</td>\n",
       "      <td>0.020941</td>\n",
       "      <td>0.041642</td>\n",
       "      <td>0.006416</td>\n",
       "      <td>0.015843</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.071191</td>\n",
       "      <td>-0.130223</td>\n",
       "      <td>0.043829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rupatadine</th>\n",
       "      <td>-0.022242</td>\n",
       "      <td>0.049211</td>\n",
       "      <td>0.023811</td>\n",
       "      <td>-0.114862</td>\n",
       "      <td>0.048122</td>\n",
       "      <td>0.022194</td>\n",
       "      <td>-0.085811</td>\n",
       "      <td>-0.142641</td>\n",
       "      <td>-0.163349</td>\n",
       "      <td>0.033877</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017072</td>\n",
       "      <td>0.130603</td>\n",
       "      <td>-0.108800</td>\n",
       "      <td>-0.105444</td>\n",
       "      <td>0.034300</td>\n",
       "      <td>-0.143470</td>\n",
       "      <td>-0.031385</td>\n",
       "      <td>-0.017706</td>\n",
       "      <td>0.077739</td>\n",
       "      <td>0.066941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talinolol</th>\n",
       "      <td>-0.049888</td>\n",
       "      <td>-0.001736</td>\n",
       "      <td>-0.074130</td>\n",
       "      <td>-0.082681</td>\n",
       "      <td>-0.020887</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>-0.103481</td>\n",
       "      <td>-0.076028</td>\n",
       "      <td>0.104234</td>\n",
       "      <td>-0.035430</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001946</td>\n",
       "      <td>0.048554</td>\n",
       "      <td>-0.040367</td>\n",
       "      <td>0.193269</td>\n",
       "      <td>0.006213</td>\n",
       "      <td>-0.077561</td>\n",
       "      <td>-0.123392</td>\n",
       "      <td>0.098631</td>\n",
       "      <td>0.028672</td>\n",
       "      <td>0.081624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suvorexant</th>\n",
       "      <td>-0.043768</td>\n",
       "      <td>-0.184991</td>\n",
       "      <td>-0.036125</td>\n",
       "      <td>-0.044292</td>\n",
       "      <td>-0.097146</td>\n",
       "      <td>-0.075767</td>\n",
       "      <td>-0.061297</td>\n",
       "      <td>-0.024928</td>\n",
       "      <td>0.114663</td>\n",
       "      <td>-0.057395</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081464</td>\n",
       "      <td>-0.105453</td>\n",
       "      <td>0.052560</td>\n",
       "      <td>0.107027</td>\n",
       "      <td>0.093611</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>-0.081600</td>\n",
       "      <td>-0.000702</td>\n",
       "      <td>-0.026095</td>\n",
       "      <td>0.001112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 7489 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AKT1   KRAS-2B     BRAF1      BRAF       MYC      BRD4  \\\n",
       "afatinib        0.039288  0.087599  0.052460  0.032641  0.035939 -0.025099   \n",
       "erlotinib      -0.048258  0.151482  0.064425  0.070239  0.023104 -0.088843   \n",
       "neratinib      -0.011519  0.116096  0.040359  0.142860  0.078508 -0.008306   \n",
       "lapatinib      -0.039902  0.088429  0.025822 -0.064870  0.025288 -0.023845   \n",
       "pazopanib      -0.115736 -0.041087 -0.118093 -0.012833 -0.101394 -0.125349   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine -0.057500  0.009144 -0.066686  0.003290 -0.030215 -0.072164   \n",
       "taltirelin     -0.042235  0.012455 -0.056294 -0.071240 -0.068729  0.006262   \n",
       "rupatadine     -0.022242  0.049211  0.023811 -0.114862  0.048122  0.022194   \n",
       "talinolol      -0.049888 -0.001736 -0.074130 -0.082681 -0.020887  0.012472   \n",
       "suvorexant     -0.043768 -0.184991 -0.036125 -0.044292 -0.097146 -0.075767   \n",
       "\n",
       "                    MCL1      CDK4    BCL2L2     MAPK1  ...  BRDN0003677177  \\\n",
       "afatinib        0.078779 -0.097733 -0.041715  0.158596  ...       -0.002491   \n",
       "erlotinib       0.093194 -0.155576 -0.023462  0.156218  ...        0.003109   \n",
       "neratinib       0.020023  0.050044 -0.008112  0.098631  ...        0.036109   \n",
       "lapatinib      -0.065937 -0.203769 -0.046839  0.103114  ...       -0.068757   \n",
       "pazopanib       0.088811 -0.107420  0.022588 -0.083674  ...        0.029134   \n",
       "...                  ...       ...       ...       ...  ...             ...   \n",
       "desvenlafaxine -0.003146 -0.197048 -0.072740  0.045427  ...        0.007904   \n",
       "taltirelin     -0.007184 -0.146936  0.024411 -0.011845  ...       -0.023191   \n",
       "rupatadine     -0.085811 -0.142641 -0.163349  0.033877  ...       -0.017072   \n",
       "talinolol      -0.103481 -0.076028  0.104234 -0.035430  ...       -0.001946   \n",
       "suvorexant     -0.061297 -0.024928  0.114663 -0.057395  ...       -0.081464   \n",
       "\n",
       "                     KIT  SUV420H1    TGFBR2     STAG2     SMC1A      SMC3  \\\n",
       "afatinib        0.042092  0.017690  0.065971 -0.018110 -0.025007 -0.052941   \n",
       "erlotinib      -0.004992  0.016802  0.035774  0.012185 -0.030883  0.022719   \n",
       "neratinib       0.012717  0.047148  0.063692  0.006152 -0.044527 -0.061279   \n",
       "lapatinib      -0.055504  0.081767  0.159499  0.063188 -0.008917 -0.013766   \n",
       "pazopanib      -0.076045 -0.013759  0.112378 -0.040245 -0.020721 -0.001347   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine  0.034718  0.037131  0.015089  0.000156  0.033563  0.094683   \n",
       "taltirelin      0.027566  0.020941  0.041642  0.006416  0.015843  0.018180   \n",
       "rupatadine      0.130603 -0.108800 -0.105444  0.034300 -0.143470 -0.031385   \n",
       "talinolol       0.048554 -0.040367  0.193269  0.006213 -0.077561 -0.123392   \n",
       "suvorexant     -0.105453  0.052560  0.107027  0.093611  0.000125 -0.081600   \n",
       "\n",
       "                   RAD21     NIPBL     PDS5B  \n",
       "afatinib        0.069486  0.015613  0.017151  \n",
       "erlotinib       0.058391 -0.038472 -0.020328  \n",
       "neratinib      -0.005293  0.047986  0.039911  \n",
       "lapatinib      -0.073146 -0.032068  0.078218  \n",
       "pazopanib       0.060826 -0.008466  0.015619  \n",
       "...                  ...       ...       ...  \n",
       "desvenlafaxine -0.032039 -0.065282 -0.019728  \n",
       "taltirelin      0.071191 -0.130223  0.043829  \n",
       "rupatadine     -0.017706  0.077739  0.066941  \n",
       "talinolol       0.098631  0.028672  0.081624  \n",
       "suvorexant     -0.000702 -0.026095  0.001112  \n",
       "\n",
       "[839 rows x 7489 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Pearson Correlation Coefficients for Median Consensus Signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AKT1</th>\n",
       "      <th>KRAS-2B</th>\n",
       "      <th>BRAF1</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>MYC</th>\n",
       "      <th>BRD4</th>\n",
       "      <th>MCL1</th>\n",
       "      <th>CDK4</th>\n",
       "      <th>BCL2L2</th>\n",
       "      <th>MAPK1</th>\n",
       "      <th>...</th>\n",
       "      <th>BRDN0003677177</th>\n",
       "      <th>KIT</th>\n",
       "      <th>SUV420H1</th>\n",
       "      <th>TGFBR2</th>\n",
       "      <th>STAG2</th>\n",
       "      <th>SMC1A</th>\n",
       "      <th>SMC3</th>\n",
       "      <th>RAD21</th>\n",
       "      <th>NIPBL</th>\n",
       "      <th>PDS5B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afatinib</th>\n",
       "      <td>0.051767</td>\n",
       "      <td>0.133398</td>\n",
       "      <td>0.080351</td>\n",
       "      <td>0.063201</td>\n",
       "      <td>0.107135</td>\n",
       "      <td>0.018604</td>\n",
       "      <td>0.112896</td>\n",
       "      <td>-0.073323</td>\n",
       "      <td>0.004723</td>\n",
       "      <td>0.157686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006165</td>\n",
       "      <td>0.044201</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.104882</td>\n",
       "      <td>-0.057247</td>\n",
       "      <td>-0.010636</td>\n",
       "      <td>0.033024</td>\n",
       "      <td>0.081134</td>\n",
       "      <td>0.005840</td>\n",
       "      <td>-0.010548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>erlotinib</th>\n",
       "      <td>0.002502</td>\n",
       "      <td>0.176474</td>\n",
       "      <td>0.071451</td>\n",
       "      <td>0.105725</td>\n",
       "      <td>0.075541</td>\n",
       "      <td>-0.044170</td>\n",
       "      <td>0.102680</td>\n",
       "      <td>-0.130693</td>\n",
       "      <td>0.022231</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>-0.011169</td>\n",
       "      <td>0.051464</td>\n",
       "      <td>0.091472</td>\n",
       "      <td>-0.026796</td>\n",
       "      <td>-0.022035</td>\n",
       "      <td>0.092110</td>\n",
       "      <td>0.076800</td>\n",
       "      <td>-0.046434</td>\n",
       "      <td>-0.045664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neratinib</th>\n",
       "      <td>0.009636</td>\n",
       "      <td>0.154788</td>\n",
       "      <td>0.055518</td>\n",
       "      <td>0.158173</td>\n",
       "      <td>0.134390</td>\n",
       "      <td>0.026031</td>\n",
       "      <td>0.063275</td>\n",
       "      <td>0.065081</td>\n",
       "      <td>-0.003457</td>\n",
       "      <td>0.134173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043940</td>\n",
       "      <td>0.009929</td>\n",
       "      <td>0.058189</td>\n",
       "      <td>0.085934</td>\n",
       "      <td>-0.014226</td>\n",
       "      <td>-0.058900</td>\n",
       "      <td>-0.009731</td>\n",
       "      <td>0.023470</td>\n",
       "      <td>0.042463</td>\n",
       "      <td>0.021765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lapatinib</th>\n",
       "      <td>0.025341</td>\n",
       "      <td>0.094168</td>\n",
       "      <td>0.030544</td>\n",
       "      <td>-0.066402</td>\n",
       "      <td>0.056962</td>\n",
       "      <td>0.025915</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>-0.180667</td>\n",
       "      <td>0.028163</td>\n",
       "      <td>0.097402</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025873</td>\n",
       "      <td>-0.045500</td>\n",
       "      <td>0.071522</td>\n",
       "      <td>0.216329</td>\n",
       "      <td>0.020691</td>\n",
       "      <td>-0.006198</td>\n",
       "      <td>0.081951</td>\n",
       "      <td>-0.032450</td>\n",
       "      <td>-0.028261</td>\n",
       "      <td>-0.006342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pazopanib</th>\n",
       "      <td>-0.076459</td>\n",
       "      <td>0.026644</td>\n",
       "      <td>-0.091446</td>\n",
       "      <td>0.038801</td>\n",
       "      <td>-0.049903</td>\n",
       "      <td>-0.090547</td>\n",
       "      <td>0.103126</td>\n",
       "      <td>-0.108055</td>\n",
       "      <td>0.177348</td>\n",
       "      <td>-0.036792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026215</td>\n",
       "      <td>-0.064031</td>\n",
       "      <td>-0.022605</td>\n",
       "      <td>0.171841</td>\n",
       "      <td>-0.041509</td>\n",
       "      <td>0.012598</td>\n",
       "      <td>0.030378</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>-0.020205</td>\n",
       "      <td>0.015649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desvenlafaxine</th>\n",
       "      <td>0.035313</td>\n",
       "      <td>0.074652</td>\n",
       "      <td>-0.049751</td>\n",
       "      <td>-0.003208</td>\n",
       "      <td>0.005777</td>\n",
       "      <td>-0.042920</td>\n",
       "      <td>0.061258</td>\n",
       "      <td>-0.153651</td>\n",
       "      <td>0.054393</td>\n",
       "      <td>0.055375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049694</td>\n",
       "      <td>0.029920</td>\n",
       "      <td>0.050870</td>\n",
       "      <td>0.078416</td>\n",
       "      <td>-0.020032</td>\n",
       "      <td>0.033176</td>\n",
       "      <td>0.130389</td>\n",
       "      <td>-0.019806</td>\n",
       "      <td>-0.008313</td>\n",
       "      <td>-0.022432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taltirelin</th>\n",
       "      <td>0.022628</td>\n",
       "      <td>0.056814</td>\n",
       "      <td>0.019259</td>\n",
       "      <td>0.006777</td>\n",
       "      <td>-0.005549</td>\n",
       "      <td>0.044637</td>\n",
       "      <td>-0.015711</td>\n",
       "      <td>-0.105024</td>\n",
       "      <td>0.110464</td>\n",
       "      <td>0.024544</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006071</td>\n",
       "      <td>0.035309</td>\n",
       "      <td>0.072777</td>\n",
       "      <td>0.108833</td>\n",
       "      <td>-0.015532</td>\n",
       "      <td>0.066210</td>\n",
       "      <td>0.127457</td>\n",
       "      <td>0.037998</td>\n",
       "      <td>-0.103694</td>\n",
       "      <td>-0.061299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rupatadine</th>\n",
       "      <td>0.017681</td>\n",
       "      <td>0.051315</td>\n",
       "      <td>0.040323</td>\n",
       "      <td>-0.085913</td>\n",
       "      <td>0.072831</td>\n",
       "      <td>-0.009402</td>\n",
       "      <td>-0.066040</td>\n",
       "      <td>-0.147568</td>\n",
       "      <td>-0.081792</td>\n",
       "      <td>0.065457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011651</td>\n",
       "      <td>0.128499</td>\n",
       "      <td>-0.124422</td>\n",
       "      <td>-0.070918</td>\n",
       "      <td>0.035924</td>\n",
       "      <td>-0.099524</td>\n",
       "      <td>0.033638</td>\n",
       "      <td>-0.026681</td>\n",
       "      <td>0.044997</td>\n",
       "      <td>0.044457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talinolol</th>\n",
       "      <td>-0.043222</td>\n",
       "      <td>0.027401</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>-0.003828</td>\n",
       "      <td>-0.024398</td>\n",
       "      <td>0.045217</td>\n",
       "      <td>-0.013021</td>\n",
       "      <td>-0.010395</td>\n",
       "      <td>0.168474</td>\n",
       "      <td>-0.038179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078951</td>\n",
       "      <td>-0.011128</td>\n",
       "      <td>0.068661</td>\n",
       "      <td>0.237796</td>\n",
       "      <td>-0.010255</td>\n",
       "      <td>-0.018282</td>\n",
       "      <td>0.021292</td>\n",
       "      <td>0.018570</td>\n",
       "      <td>0.018735</td>\n",
       "      <td>-0.009164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suvorexant</th>\n",
       "      <td>-0.019009</td>\n",
       "      <td>-0.090110</td>\n",
       "      <td>-0.073949</td>\n",
       "      <td>-0.042468</td>\n",
       "      <td>-0.082291</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>0.037124</td>\n",
       "      <td>-0.028563</td>\n",
       "      <td>0.227746</td>\n",
       "      <td>-0.078382</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039910</td>\n",
       "      <td>-0.119415</td>\n",
       "      <td>0.041700</td>\n",
       "      <td>0.166488</td>\n",
       "      <td>0.046186</td>\n",
       "      <td>0.066971</td>\n",
       "      <td>-0.041845</td>\n",
       "      <td>-0.040430</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>-0.011008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 7489 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AKT1   KRAS-2B     BRAF1      BRAF       MYC      BRD4  \\\n",
       "afatinib        0.051767  0.133398  0.080351  0.063201  0.107135  0.018604   \n",
       "erlotinib       0.002502  0.176474  0.071451  0.105725  0.075541 -0.044170   \n",
       "neratinib       0.009636  0.154788  0.055518  0.158173  0.134390  0.026031   \n",
       "lapatinib       0.025341  0.094168  0.030544 -0.066402  0.056962  0.025915   \n",
       "pazopanib      -0.076459  0.026644 -0.091446  0.038801 -0.049903 -0.090547   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine  0.035313  0.074652 -0.049751 -0.003208  0.005777 -0.042920   \n",
       "taltirelin      0.022628  0.056814  0.019259  0.006777 -0.005549  0.044637   \n",
       "rupatadine      0.017681  0.051315  0.040323 -0.085913  0.072831 -0.009402   \n",
       "talinolol      -0.043222  0.027401  0.004059 -0.003828 -0.024398  0.045217   \n",
       "suvorexant     -0.019009 -0.090110 -0.073949 -0.042468 -0.082291 -0.002317   \n",
       "\n",
       "                    MCL1      CDK4    BCL2L2     MAPK1  ...  BRDN0003677177  \\\n",
       "afatinib        0.112896 -0.073323  0.004723  0.157686  ...        0.006165   \n",
       "erlotinib       0.102680 -0.130693  0.022231  0.126003  ...        0.012645   \n",
       "neratinib       0.063275  0.065081 -0.003457  0.134173  ...        0.043940   \n",
       "lapatinib       0.009600 -0.180667  0.028163  0.097402  ...       -0.025873   \n",
       "pazopanib       0.103126 -0.108055  0.177348 -0.036792  ...        0.026215   \n",
       "...                  ...       ...       ...       ...  ...             ...   \n",
       "desvenlafaxine  0.061258 -0.153651  0.054393  0.055375  ...        0.049694   \n",
       "taltirelin     -0.015711 -0.105024  0.110464  0.024544  ...       -0.006071   \n",
       "rupatadine     -0.066040 -0.147568 -0.081792  0.065457  ...        0.011651   \n",
       "talinolol      -0.013021 -0.010395  0.168474 -0.038179  ...        0.078951   \n",
       "suvorexant      0.037124 -0.028563  0.227746 -0.078382  ...       -0.039910   \n",
       "\n",
       "                     KIT  SUV420H1    TGFBR2     STAG2     SMC1A      SMC3  \\\n",
       "afatinib        0.044201  0.035481  0.104882 -0.057247 -0.010636  0.033024   \n",
       "erlotinib      -0.011169  0.051464  0.091472 -0.026796 -0.022035  0.092110   \n",
       "neratinib       0.009929  0.058189  0.085934 -0.014226 -0.058900 -0.009731   \n",
       "lapatinib      -0.045500  0.071522  0.216329  0.020691 -0.006198  0.081951   \n",
       "pazopanib      -0.064031 -0.022605  0.171841 -0.041509  0.012598  0.030378   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine  0.029920  0.050870  0.078416 -0.020032  0.033176  0.130389   \n",
       "taltirelin      0.035309  0.072777  0.108833 -0.015532  0.066210  0.127457   \n",
       "rupatadine      0.128499 -0.124422 -0.070918  0.035924 -0.099524  0.033638   \n",
       "talinolol      -0.011128  0.068661  0.237796 -0.010255 -0.018282  0.021292   \n",
       "suvorexant     -0.119415  0.041700  0.166488  0.046186  0.066971 -0.041845   \n",
       "\n",
       "                   RAD21     NIPBL     PDS5B  \n",
       "afatinib        0.081134  0.005840 -0.010548  \n",
       "erlotinib       0.076800 -0.046434 -0.045664  \n",
       "neratinib       0.023470  0.042463  0.021765  \n",
       "lapatinib      -0.032450 -0.028261 -0.006342  \n",
       "pazopanib       0.055902 -0.020205  0.015649  \n",
       "...                  ...       ...       ...  \n",
       "desvenlafaxine -0.019806 -0.008313 -0.022432  \n",
       "taltirelin      0.037998 -0.103694 -0.061299  \n",
       "rupatadine     -0.026681  0.044997  0.044457  \n",
       "talinolol       0.018570  0.018735 -0.009164  \n",
       "suvorexant     -0.040430  0.001560 -0.011008  \n",
       "\n",
       "[839 rows x 7489 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Pearson Correlation Coefficients for Weighted Average Consensus Signatures"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AKT1</th>\n",
       "      <th>KRAS-2B</th>\n",
       "      <th>BRAF1</th>\n",
       "      <th>BRAF</th>\n",
       "      <th>MYC</th>\n",
       "      <th>BRD4</th>\n",
       "      <th>MCL1</th>\n",
       "      <th>CDK4</th>\n",
       "      <th>BCL2L2</th>\n",
       "      <th>MAPK1</th>\n",
       "      <th>...</th>\n",
       "      <th>BRDN0003677177</th>\n",
       "      <th>KIT</th>\n",
       "      <th>SUV420H1</th>\n",
       "      <th>TGFBR2</th>\n",
       "      <th>STAG2</th>\n",
       "      <th>SMC1A</th>\n",
       "      <th>SMC3</th>\n",
       "      <th>RAD21</th>\n",
       "      <th>NIPBL</th>\n",
       "      <th>PDS5B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afatinib</th>\n",
       "      <td>0.043842</td>\n",
       "      <td>0.126407</td>\n",
       "      <td>0.057370</td>\n",
       "      <td>0.032816</td>\n",
       "      <td>0.029585</td>\n",
       "      <td>-0.028552</td>\n",
       "      <td>0.083929</td>\n",
       "      <td>-0.060740</td>\n",
       "      <td>-0.009248</td>\n",
       "      <td>0.137992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.042848</td>\n",
       "      <td>0.012697</td>\n",
       "      <td>0.050618</td>\n",
       "      <td>-0.010374</td>\n",
       "      <td>-0.023753</td>\n",
       "      <td>-0.073939</td>\n",
       "      <td>0.054797</td>\n",
       "      <td>0.041644</td>\n",
       "      <td>0.011173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>erlotinib</th>\n",
       "      <td>-0.039662</td>\n",
       "      <td>0.189926</td>\n",
       "      <td>0.074773</td>\n",
       "      <td>0.068886</td>\n",
       "      <td>-0.016643</td>\n",
       "      <td>-0.104045</td>\n",
       "      <td>0.089787</td>\n",
       "      <td>-0.118480</td>\n",
       "      <td>-0.011179</td>\n",
       "      <td>0.151351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024561</td>\n",
       "      <td>-0.004225</td>\n",
       "      <td>0.017831</td>\n",
       "      <td>0.038363</td>\n",
       "      <td>-0.012422</td>\n",
       "      <td>-0.029911</td>\n",
       "      <td>0.008076</td>\n",
       "      <td>0.060249</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>-0.023663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neratinib</th>\n",
       "      <td>-0.022374</td>\n",
       "      <td>0.097546</td>\n",
       "      <td>0.053648</td>\n",
       "      <td>0.128124</td>\n",
       "      <td>0.074055</td>\n",
       "      <td>-0.012890</td>\n",
       "      <td>0.045100</td>\n",
       "      <td>0.084469</td>\n",
       "      <td>0.005544</td>\n",
       "      <td>0.066128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062011</td>\n",
       "      <td>0.014258</td>\n",
       "      <td>0.041622</td>\n",
       "      <td>0.073326</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>-0.054571</td>\n",
       "      <td>-0.081777</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>0.080846</td>\n",
       "      <td>0.051861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lapatinib</th>\n",
       "      <td>-0.046097</td>\n",
       "      <td>0.110954</td>\n",
       "      <td>0.027357</td>\n",
       "      <td>-0.071855</td>\n",
       "      <td>-0.079702</td>\n",
       "      <td>-0.019023</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>-0.203612</td>\n",
       "      <td>-0.047021</td>\n",
       "      <td>0.129413</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.078217</td>\n",
       "      <td>-0.047301</td>\n",
       "      <td>0.072616</td>\n",
       "      <td>0.162382</td>\n",
       "      <td>0.071773</td>\n",
       "      <td>-0.021629</td>\n",
       "      <td>-0.022790</td>\n",
       "      <td>-0.072636</td>\n",
       "      <td>-0.011988</td>\n",
       "      <td>0.083381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pazopanib</th>\n",
       "      <td>-0.133967</td>\n",
       "      <td>-0.065964</td>\n",
       "      <td>-0.134439</td>\n",
       "      <td>-0.008608</td>\n",
       "      <td>-0.182686</td>\n",
       "      <td>-0.121940</td>\n",
       "      <td>0.096863</td>\n",
       "      <td>-0.107326</td>\n",
       "      <td>0.055106</td>\n",
       "      <td>-0.109153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044837</td>\n",
       "      <td>-0.087218</td>\n",
       "      <td>-0.022356</td>\n",
       "      <td>0.109515</td>\n",
       "      <td>-0.007274</td>\n",
       "      <td>-0.021698</td>\n",
       "      <td>-0.019016</td>\n",
       "      <td>0.050318</td>\n",
       "      <td>-0.014645</td>\n",
       "      <td>0.006664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desvenlafaxine</th>\n",
       "      <td>-0.066152</td>\n",
       "      <td>0.028200</td>\n",
       "      <td>-0.040536</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>-0.084393</td>\n",
       "      <td>-0.069483</td>\n",
       "      <td>-0.014510</td>\n",
       "      <td>-0.205238</td>\n",
       "      <td>-0.077648</td>\n",
       "      <td>0.031597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012706</td>\n",
       "      <td>0.032006</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>0.017247</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>0.037844</td>\n",
       "      <td>0.078058</td>\n",
       "      <td>-0.027733</td>\n",
       "      <td>-0.072112</td>\n",
       "      <td>-0.040837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taltirelin</th>\n",
       "      <td>-0.038543</td>\n",
       "      <td>0.019442</td>\n",
       "      <td>-0.049489</td>\n",
       "      <td>-0.088412</td>\n",
       "      <td>-0.120527</td>\n",
       "      <td>0.014203</td>\n",
       "      <td>-0.031268</td>\n",
       "      <td>-0.173947</td>\n",
       "      <td>0.037578</td>\n",
       "      <td>0.005174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021278</td>\n",
       "      <td>0.028144</td>\n",
       "      <td>0.015788</td>\n",
       "      <td>0.032516</td>\n",
       "      <td>0.021326</td>\n",
       "      <td>0.009114</td>\n",
       "      <td>0.037747</td>\n",
       "      <td>0.079069</td>\n",
       "      <td>-0.139897</td>\n",
       "      <td>0.030299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rupatadine</th>\n",
       "      <td>-0.017826</td>\n",
       "      <td>0.057744</td>\n",
       "      <td>0.025905</td>\n",
       "      <td>-0.111355</td>\n",
       "      <td>0.091760</td>\n",
       "      <td>-0.000953</td>\n",
       "      <td>-0.107360</td>\n",
       "      <td>-0.104506</td>\n",
       "      <td>-0.193811</td>\n",
       "      <td>0.043774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001984</td>\n",
       "      <td>0.140903</td>\n",
       "      <td>-0.125506</td>\n",
       "      <td>-0.087973</td>\n",
       "      <td>0.013074</td>\n",
       "      <td>-0.135349</td>\n",
       "      <td>-0.011436</td>\n",
       "      <td>-0.009831</td>\n",
       "      <td>0.091532</td>\n",
       "      <td>0.052763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talinolol</th>\n",
       "      <td>-0.071532</td>\n",
       "      <td>-0.014545</td>\n",
       "      <td>-0.091004</td>\n",
       "      <td>-0.086301</td>\n",
       "      <td>-0.081596</td>\n",
       "      <td>0.046822</td>\n",
       "      <td>-0.065511</td>\n",
       "      <td>-0.085525</td>\n",
       "      <td>0.107279</td>\n",
       "      <td>-0.034005</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005736</td>\n",
       "      <td>0.045796</td>\n",
       "      <td>-0.041015</td>\n",
       "      <td>0.182636</td>\n",
       "      <td>0.036970</td>\n",
       "      <td>-0.086812</td>\n",
       "      <td>-0.135329</td>\n",
       "      <td>0.105089</td>\n",
       "      <td>0.016583</td>\n",
       "      <td>0.083552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suvorexant</th>\n",
       "      <td>-0.069449</td>\n",
       "      <td>-0.215272</td>\n",
       "      <td>-0.072690</td>\n",
       "      <td>-0.082030</td>\n",
       "      <td>-0.168155</td>\n",
       "      <td>-0.055780</td>\n",
       "      <td>-0.002163</td>\n",
       "      <td>-0.046884</td>\n",
       "      <td>0.148673</td>\n",
       "      <td>-0.085423</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067647</td>\n",
       "      <td>-0.108374</td>\n",
       "      <td>0.057096</td>\n",
       "      <td>0.107738</td>\n",
       "      <td>0.105224</td>\n",
       "      <td>-0.008234</td>\n",
       "      <td>-0.062274</td>\n",
       "      <td>-0.020499</td>\n",
       "      <td>-0.013064</td>\n",
       "      <td>-0.000444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 7489 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AKT1   KRAS-2B     BRAF1      BRAF       MYC      BRD4  \\\n",
       "afatinib        0.043842  0.126407  0.057370  0.032816  0.029585 -0.028552   \n",
       "erlotinib      -0.039662  0.189926  0.074773  0.068886 -0.016643 -0.104045   \n",
       "neratinib      -0.022374  0.097546  0.053648  0.128124  0.074055 -0.012890   \n",
       "lapatinib      -0.046097  0.110954  0.027357 -0.071855 -0.079702 -0.019023   \n",
       "pazopanib      -0.133967 -0.065964 -0.134439 -0.008608 -0.182686 -0.121940   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine -0.066152  0.028200 -0.040536  0.003049 -0.084393 -0.069483   \n",
       "taltirelin     -0.038543  0.019442 -0.049489 -0.088412 -0.120527  0.014203   \n",
       "rupatadine     -0.017826  0.057744  0.025905 -0.111355  0.091760 -0.000953   \n",
       "talinolol      -0.071532 -0.014545 -0.091004 -0.086301 -0.081596  0.046822   \n",
       "suvorexant     -0.069449 -0.215272 -0.072690 -0.082030 -0.168155 -0.055780   \n",
       "\n",
       "                    MCL1      CDK4    BCL2L2     MAPK1  ...  BRDN0003677177  \\\n",
       "afatinib        0.083929 -0.060740 -0.009248  0.137992  ...        0.029999   \n",
       "erlotinib       0.089787 -0.118480 -0.011179  0.151351  ...        0.024561   \n",
       "neratinib       0.045100  0.084469  0.005544  0.066128  ...        0.062011   \n",
       "lapatinib      -0.040734 -0.203612 -0.047021  0.129413  ...       -0.078217   \n",
       "pazopanib       0.096863 -0.107326  0.055106 -0.109153  ...        0.044837   \n",
       "...                  ...       ...       ...       ...  ...             ...   \n",
       "desvenlafaxine -0.014510 -0.205238 -0.077648  0.031597  ...        0.012706   \n",
       "taltirelin     -0.031268 -0.173947  0.037578  0.005174  ...       -0.021278   \n",
       "rupatadine     -0.107360 -0.104506 -0.193811  0.043774  ...       -0.001984   \n",
       "talinolol      -0.065511 -0.085525  0.107279 -0.034005  ...       -0.005736   \n",
       "suvorexant     -0.002163 -0.046884  0.148673 -0.085423  ...       -0.067647   \n",
       "\n",
       "                     KIT  SUV420H1    TGFBR2     STAG2     SMC1A      SMC3  \\\n",
       "afatinib        0.042848  0.012697  0.050618 -0.010374 -0.023753 -0.073939   \n",
       "erlotinib      -0.004225  0.017831  0.038363 -0.012422 -0.029911  0.008076   \n",
       "neratinib       0.014258  0.041622  0.073326  0.010700 -0.054571 -0.081777   \n",
       "lapatinib      -0.047301  0.072616  0.162382  0.071773 -0.021629 -0.022790   \n",
       "pazopanib      -0.087218 -0.022356  0.109515 -0.007274 -0.021698 -0.019016   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "desvenlafaxine  0.032006  0.041190  0.017247 -0.000311  0.037844  0.078058   \n",
       "taltirelin      0.028144  0.015788  0.032516  0.021326  0.009114  0.037747   \n",
       "rupatadine      0.140903 -0.125506 -0.087973  0.013074 -0.135349 -0.011436   \n",
       "talinolol       0.045796 -0.041015  0.182636  0.036970 -0.086812 -0.135329   \n",
       "suvorexant     -0.108374  0.057096  0.107738  0.105224 -0.008234 -0.062274   \n",
       "\n",
       "                   RAD21     NIPBL     PDS5B  \n",
       "afatinib        0.054797  0.041644  0.011173  \n",
       "erlotinib       0.060249 -0.008020 -0.023663  \n",
       "neratinib      -0.008257  0.080846  0.051861  \n",
       "lapatinib      -0.072636 -0.011988  0.083381  \n",
       "pazopanib       0.050318 -0.014645  0.006664  \n",
       "...                  ...       ...       ...  \n",
       "desvenlafaxine -0.027733 -0.072112 -0.040837  \n",
       "taltirelin      0.079069 -0.139897  0.030299  \n",
       "rupatadine     -0.009831  0.091532  0.052763  \n",
       "talinolol       0.105089  0.016583  0.083552  \n",
       "suvorexant     -0.020499 -0.013064 -0.000444  \n",
       "\n",
       "[839 rows x 7489 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"Pearson Correlation Coefficients for Mean Consensus Signatures\"))\n",
    "display(means)\n",
    "display(Markdown(\"Pearson Correlation Coefficients for Median Consensus Signatures\"))\n",
    "display(medians)\n",
    "display(Markdown(\"Pearson Correlation Coefficients for Weighted Average Consensus Signatures\"))\n",
    "display(wavgs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with Known Drug-Target Pairs\n",
    "### Ranking CRISPR KO consensus signatures by similarity to each drug consensus signature\n",
    "Because some drugs may have both inhibitory and agonist effects depending on the target, we rank targets for each drug in both ascending (low to high similarity) and descending (high to low similarity) order. \n",
    "\n",
    "We expect that for drugs with inhibitory targets, the consensus CRISPR KO signatures for those targets should have high similarity to the drug perturbation signatures. Conversely, the CRISPR KO signatures of agonist targets should have low similarity to the corresponding drug perturbation signature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "asc_drug_ranks = {'mean': {}, 'median': {}, 'wavg': {}, 'random': {}}\n",
    "desc_drug_ranks = {'mean': {}, 'median': {}, 'wavg': {}, 'random': {}}\n",
    "for drug in means.index: \n",
    "    asc_drug_ranks['mean'][drug] = means.loc[drug].sort_values(ascending=True).index.tolist()\n",
    "    asc_drug_ranks['median'][drug] = medians.loc[drug].sort_values(ascending=True).index.tolist()\n",
    "    asc_drug_ranks['wavg'][drug] = wavgs.loc[drug].sort_values(ascending=True).index.tolist()\n",
    "    asc_drug_ranks['random'][drug] = sample(means.columns.tolist(), len(means.columns))\n",
    "\n",
    "    desc_drug_ranks['mean'][drug] = means.loc[drug].sort_values(ascending=False).index.tolist()\n",
    "    desc_drug_ranks['median'][drug] = medians.loc[drug].sort_values(ascending=False).index.tolist()\n",
    "    desc_drug_ranks['wavg'][drug] = wavgs.loc[drug].sort_values(ascending=False).index.tolist()\n",
    "    desc_drug_ranks['random'][drug] = sample(means.columns.tolist(), len(means.columns))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction of drug-target associations from Pharos\n",
    "Using [Pharos](https://pharos.nih.gov/), we pre-constructed a list of drug-target associations by querying all ligands in Pharos, filtering [only to drugs](https://pharos.nih.gov/ligands?facet=Type!Drug) (n=1782), and downloading the table of all associated target symbols for each drug. \n",
    "\n",
    "Here, we import the generated table and identify the drugs which overlap with the L1000 chemical perturbations (n=720).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "720 drugs overlap\n"
     ]
    }
   ],
   "source": [
    "pharos = pd.read_csv('pharos_query_results.csv')\n",
    "pharos = pharos.dropna(subset=['Ligand Name', 'Symbol'])\n",
    "pharos = pharos.sort_values(by=['Ligand Action']).drop_duplicates(subset=['Ligand Name', 'Symbol'], keep='first')\n",
    "pharos_drugs = set(pharos['Ligand Name'].tolist()).intersection(asc_drug_ranks['mean'].keys())\n",
    "print(len(pharos_drugs), 'drugs overlap')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the overlapping drugs between Pharos and the L1000 dataset, we can store all targets associated with the drug in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug2target = {}\n",
    "drugtarget2action = {}\n",
    "for d in pharos_drugs:\n",
    "  if type(pharos[pharos['Ligand Name'] == d]['Symbol']) == str: \n",
    "    drug2target[d] = [pharos.loc[d]['Symbol']]\n",
    "    drugtarget2action[f\"{d}-{drug2target[d][0]}\"] = pharos.loc[d]['Ligand Action']\n",
    "  else:\n",
    "    drug2target[d] = pharos[pharos['Ligand Name'] == d]['Symbol'].tolist()\n",
    "    for s in drug2target[d]: \n",
    "      drugtarget2action[f\"{d}-{s}\"] = pharos[(pharos['Ligand Name']==d) & (pharos['Symbol']==s)]['Ligand Action'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "asc_rankings = []\n",
    "desc_rankings = []\n",
    "for drug in drug2target.keys(): \n",
    "  for method in ['mean', 'median', 'wavg', 'random']:\n",
    "    asc_indices = np.nonzero(np.isin(asc_drug_ranks[method][drug], drug2target[drug]))[0]\n",
    "    desc_indices = np.nonzero(np.isin(desc_drug_ranks[method][drug], drug2target[drug]))[0]\n",
    "    for i in range(len(asc_indices)):\n",
    "      asc_rankings.append([method, drug, asc_indices[i], asc_drug_ranks[method][drug][asc_indices[i]]])\n",
    "    for i in range(len(desc_indices)):\n",
    "      desc_rankings.append([method, drug, desc_indices[i], desc_drug_ranks[method][drug][desc_indices[i]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "asc_rankings_df = pd.DataFrame(\n",
    "  data=asc_rankings,\n",
    "  columns=['Method', 'Drug', 'AscRank', 'Target']\n",
    ")\n",
    "\n",
    "desc_rankings_df = pd.DataFrame(\n",
    "  data=desc_rankings,\n",
    "  columns=['Method', 'Drug', 'DescRank', 'Target']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "asc_rankings_df['Action'] = asc_rankings_df.apply(\n",
    "  lambda row: drugtarget2action[f\"{row.Drug}-{row.Target}\"], axis=1\n",
    ")\n",
    "desc_rankings_df['Action'] = desc_rankings_df.apply(\n",
    "  lambda row: drugtarget2action[f\"{row.Drug}-{row.Target}\"], axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DescRank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wavg</th>\n",
       "      <td>3617.909457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3621.042791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>3635.453333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>3747.283101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DescRank\n",
       "Method             \n",
       "wavg    3617.909457\n",
       "mean    3621.042791\n",
       "median  3635.453333\n",
       "random  3747.283101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(desc_rankings_df.groupby(['Method']).mean(numeric_only=True).sort_values(by='DescRank'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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></th>\n",
       "      <th>DescRank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method</th>\n",
       "      <th>Action</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">mean</th>\n",
       "      <th>GATING INHIBITOR</th>\n",
       "      <td>192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC MODULATOR</th>\n",
       "      <td>1306.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>2926.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTIAL AGONIST</th>\n",
       "      <td>2944.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC ANTAGONIST</th>\n",
       "      <td>3130.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOCKER</th>\n",
       "      <td>3384.564706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE MODULATOR</th>\n",
       "      <td>3468.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGONIST</th>\n",
       "      <td>3559.194286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INHIBITOR</th>\n",
       "      <td>3687.469602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTAGONIST</th>\n",
       "      <td>3709.403361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVERSE AGONIST</th>\n",
       "      <td>4158.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODULATOR</th>\n",
       "      <td>4555.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVATOR</th>\n",
       "      <td>4717.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPENER</th>\n",
       "      <td>4776.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEGATIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>5743.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">median</th>\n",
       "      <th>GATING INHIBITOR</th>\n",
       "      <td>377.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC MODULATOR</th>\n",
       "      <td>435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>2313.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTIAL AGONIST</th>\n",
       "      <td>2572.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC ANTAGONIST</th>\n",
       "      <td>2697.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE MODULATOR</th>\n",
       "      <td>3123.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOCKER</th>\n",
       "      <td>3481.752941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGONIST</th>\n",
       "      <td>3564.691429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INHIBITOR</th>\n",
       "      <td>3717.691824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVERSE AGONIST</th>\n",
       "      <td>3747.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTAGONIST</th>\n",
       "      <td>3828.865546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODULATOR</th>\n",
       "      <td>4457.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPENER</th>\n",
       "      <td>4576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVATOR</th>\n",
       "      <td>4732.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEGATIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>5739.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">random</th>\n",
       "      <th>ALLOSTERIC MODULATOR</th>\n",
       "      <td>1141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATING INHIBITOR</th>\n",
       "      <td>2741.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE MODULATOR</th>\n",
       "      <td>3145.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTIAL AGONIST</th>\n",
       "      <td>3453.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVATOR</th>\n",
       "      <td>3552.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTAGONIST</th>\n",
       "      <td>3634.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOCKER</th>\n",
       "      <td>3673.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGONIST</th>\n",
       "      <td>3738.171429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEGATIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>3770.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INHIBITOR</th>\n",
       "      <td>3778.750524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVERSE AGONIST</th>\n",
       "      <td>3897.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>3944.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODULATOR</th>\n",
       "      <td>4212.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC ANTAGONIST</th>\n",
       "      <td>5198.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPENER</th>\n",
       "      <td>6083.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">wavg</th>\n",
       "      <th>GATING INHIBITOR</th>\n",
       "      <td>249.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC MODULATOR</th>\n",
       "      <td>1350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>2888.485714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTIAL AGONIST</th>\n",
       "      <td>2956.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLOSTERIC ANTAGONIST</th>\n",
       "      <td>3003.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGONIST</th>\n",
       "      <td>3478.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOCKER</th>\n",
       "      <td>3519.447059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INHIBITOR</th>\n",
       "      <td>3677.138365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POSITIVE MODULATOR</th>\n",
       "      <td>3680.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTAGONIST</th>\n",
       "      <td>3708.424370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVERSE AGONIST</th>\n",
       "      <td>4402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPENER</th>\n",
       "      <td>4512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVATOR</th>\n",
       "      <td>4548.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MODULATOR</th>\n",
       "      <td>4708.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEGATIVE ALLOSTERIC MODULATOR</th>\n",
       "      <td>5761.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         DescRank\n",
       "Method Action                                    \n",
       "mean   GATING INHIBITOR                192.000000\n",
       "       ALLOSTERIC MODULATOR           1306.000000\n",
       "       POSITIVE ALLOSTERIC MODULATOR  2926.400000\n",
       "       PARTIAL AGONIST                2944.666667\n",
       "       ALLOSTERIC ANTAGONIST          3130.166667\n",
       "       BLOCKER                        3384.564706\n",
       "       POSITIVE MODULATOR             3468.428571\n",
       "       AGONIST                        3559.194286\n",
       "       INHIBITOR                      3687.469602\n",
       "       ANTAGONIST                     3709.403361\n",
       "       INVERSE AGONIST                4158.142857\n",
       "       MODULATOR                      4555.500000\n",
       "       ACTIVATOR                      4717.500000\n",
       "       OPENER                         4776.500000\n",
       "       NEGATIVE ALLOSTERIC MODULATOR  5743.250000\n",
       "median GATING INHIBITOR                377.666667\n",
       "       ALLOSTERIC MODULATOR            435.000000\n",
       "       POSITIVE ALLOSTERIC MODULATOR  2313.857143\n",
       "       PARTIAL AGONIST                2572.266667\n",
       "       ALLOSTERIC ANTAGONIST          2697.166667\n",
       "       POSITIVE MODULATOR             3123.285714\n",
       "       BLOCKER                        3481.752941\n",
       "       AGONIST                        3564.691429\n",
       "       INHIBITOR                      3717.691824\n",
       "       INVERSE AGONIST                3747.714286\n",
       "       ANTAGONIST                     3828.865546\n",
       "       MODULATOR                      4457.500000\n",
       "       OPENER                         4576.000000\n",
       "       ACTIVATOR                      4732.750000\n",
       "       NEGATIVE ALLOSTERIC MODULATOR  5739.500000\n",
       "random ALLOSTERIC MODULATOR           1141.000000\n",
       "       GATING INHIBITOR               2741.000000\n",
       "       POSITIVE MODULATOR             3145.285714\n",
       "       PARTIAL AGONIST                3453.600000\n",
       "       ACTIVATOR                      3552.000000\n",
       "       ANTAGONIST                     3634.205882\n",
       "       BLOCKER                        3673.764706\n",
       "       AGONIST                        3738.171429\n",
       "       NEGATIVE ALLOSTERIC MODULATOR  3770.250000\n",
       "       INHIBITOR                      3778.750524\n",
       "       INVERSE AGONIST                3897.285714\n",
       "       POSITIVE ALLOSTERIC MODULATOR  3944.057143\n",
       "       MODULATOR                      4212.250000\n",
       "       ALLOSTERIC ANTAGONIST          5198.166667\n",
       "       OPENER                         6083.500000\n",
       "wavg   GATING INHIBITOR                249.333333\n",
       "       ALLOSTERIC MODULATOR           1350.000000\n",
       "       POSITIVE ALLOSTERIC MODULATOR  2888.485714\n",
       "       PARTIAL AGONIST                2956.133333\n",
       "       ALLOSTERIC ANTAGONIST          3003.500000\n",
       "       AGONIST                        3478.800000\n",
       "       BLOCKER                        3519.447059\n",
       "       INHIBITOR                      3677.138365\n",
       "       POSITIVE MODULATOR             3680.714286\n",
       "       ANTAGONIST                     3708.424370\n",
       "       INVERSE AGONIST                4402.000000\n",
       "       OPENER                         4512.000000\n",
       "       ACTIVATOR                      4548.625000\n",
       "       MODULATOR                      4708.250000\n",
       "       NEGATIVE ALLOSTERIC MODULATOR  5761.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(desc_rankings_df.groupby(['Method', 'Action']).mean(numeric_only=True).sort_values(by=['Method','DescRank']))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_color_map = {}\n",
    "fig1 = go.Figure()\n",
    "\n",
    "desc_sub_df = desc_rankings_df[asc_rankings_df['Method'] != 'random'].set_index('Action')\n",
    "for gs in desc_sub_df.groupby(['Action']).mean(numeric_only=True).sort_values('DescRank').index:\n",
    "    fig1.add_trace(\n",
    "        go.Box(\n",
    "            y=desc_sub_df.loc[gs,'DescRank'].tolist(),\n",
    "            name=gs\n",
    "        )\n",
    "    )\n",
    "fig1.update_xaxes(title_text=\"Drug Action\")\n",
    "fig1.update_yaxes(title_text=\"Rank\")\n",
    "\n",
    "fig1.update_layout(\n",
    "    height=600,\n",
    "    width=1000,\n",
    "    title_text=\"Target Gene Consensus Sig Rankings for L1000 Drugs by Action (High to Low Sim)\",\n",
    "    showlegend=False\n",
    ")\n",
    "fig1.show(\"png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bridge Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "inh_drug2target = {}\n",
    "agon_drug2target = {}\n",
    "for k in drugtarget2action.keys(): \n",
    "  if drugtarget2action[k] == 'INHIBITOR': \n",
    "    if k in inh_drug2target.keys(): \n",
    "      inh_drug2target[k.split('-')[0]].append(k.split('-')[1])\n",
    "    else:\n",
    "      inh_drug2target[k.split('-')[0]] = [k.split('-')[1]]\n",
    "  elif drugtarget2action[k] == 'AGONIST': \n",
    "    if k in agon_drug2target.keys(): \n",
    "      agon_drug2target[k.split('-')[0]].append(k.split('-')[1])\n",
    "    else:\n",
    "      agon_drug2target[k.split('-')[0]] = [k.split('-')[1]]\n",
    "  else: \n",
    "    continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = {\n",
    "  'mean': '#88CCEE',\n",
    "  'median': '#DDCC77',\n",
    "  'wavg': '#CC6677'\n",
    "}\n",
    "\n",
    "def drugBridgePlot(drug, method, ranks, dtmapping): \n",
    "  select = pd.Series([x.upper() in dtmapping[drug] for x in ranks[method][drug]])\n",
    "  x, y = bridge_plot(select)\n",
    "  return x/len(x), y \n",
    "\n",
    "def methodBridgePlot(method, ranks, dtmapping): \n",
    "  x = []\n",
    "  y = []\n",
    "  for drug in ranks[method].keys(): \n",
    "    if drug in dtmapping.keys():\n",
    "      x_drug, y_drug = drugBridgePlot(drug, method, ranks, dtmapping)\n",
    "      x.append(x_drug)\n",
    "      y.append(y_drug)\n",
    "  return np.nanmean(np.array(x), axis=0), np.nanmean(np.array(y), axis=0)\n",
    "\n",
    "def fullBridgePlot(ranks, sim, dtmapping):\n",
    "  fig = plt.figure()\n",
    "  for method in ranks.keys(): \n",
    "    x_method, y_method = methodBridgePlot(method, ranks, dtmapping)\n",
    "    if method == 'random': \n",
    "      plt.plot(x_method, y_method, label=method, color='gray')\n",
    "    else:\n",
    "      plt.plot(x_method, y_method, label=method, color=color_dict[method])\n",
    "  plt.axhline(y=0, color='black', linestyle='dashed')\n",
    "  plt.xlabel('Rank')\n",
    "  plt.ylabel('D(r) - r')\n",
    "  plt.legend(bbox_to_anchor=(1.25,1))\n",
    "  # plt.title(f\"Target Gene Consensus CRISPR KO Sig Ranks for L1000 Drugs ({sim})\")\n",
    "  plt.show()\n",
    "  fig.savefig('../../publicationfigs/Figure7B.pdf', format='pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leadingEdge(ranks, drugtype, dtmapping, cutoff=0.01): \n",
    "  fig = plt.figure()\n",
    "  for method in ranks.keys(): \n",
    "    x_method, y_method = methodBridgePlot(method, ranks, dtmapping)\n",
    "    x_method = x_method[x_method < cutoff]\n",
    "    y_method = y_method[:len(x_method)]\n",
    "    if method == 'random': \n",
    "      plt.plot(x_method, y_method, label=method, color='gray')\n",
    "    else:\n",
    "      plt.plot(x_method, y_method, label=method, color=color_dict[method])\n",
    "  plt.axhline(y=0, color='black', linestyle='dashed')\n",
    "  plt.xlabel('Rank')\n",
    "  plt.ylabel('D(r) - r')\n",
    "  plt.legend(bbox_to_anchor=(1.3,1))\n",
    "  # plt.title(f\"Leading Edge of Target Gene Consensus CRISPR KO Sig Ranks ({drugtype})\")\n",
    "  plt.show()\n",
    "  fig.savefig('../../publicationfigs/Figure7A.pdf', format='pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inhibitors = desc_rankings_df[desc_rankings_df['Action'] == 'INHIBITOR']['Drug'].tolist()\n",
    "inh_desc_drug_ranks = {\n",
    "  m: {d: desc_drug_ranks[m][d] for d in desc_drug_ranks[m].keys() if d in inhibitors}\n",
    "  for m in desc_drug_ranks.keys()\n",
    "}\n",
    "fullBridgePlot(inh_desc_drug_ranks, 'Pharos Inhibitors -- High to Low Sim', drug2target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/tjTdGebQL/4bR4V3UDnGEw1sOlVaa449+yc6Xt2EMxTAVVOFUVVJty+Ir66KqqnV+Cv9DFcmz0okCb+5g86Nb9K1ZSdks2jDwKqtw5g5C/d50/C4TBz5qlDp9k66d+/rP6mCUsy77YMEly4c1WsEaE1YPLqzm6Sle6tWhQr8PExF7+jcSNrmaNziUDSLDTgNmOF3MDvgzo8oNqj1mPicg783plK5rmv34Fj6Mnq3M/sVxO9ha40CaTULMUYkOU8g4ZTFL/fGaIlbvGWahwvrPcQydr/nNXseyTgSt9jZlcEe0JfpdShCLoN563+Bt3EXyuGA9MgGNvWMTj7ZQhlWIknTgz+ja9NWfLNnkGjvomvXPlQqlzDjQBtgOZykA0F0KISqyp3LUxnEFelCHT1G5nALqbYO0JpMhZ8ji1cSXrAYPXMmYUuRsvpc7LR8SceengU7TSiWq+nsr6nEMX8mWutRTUZdKYvHd3XjMODSem+/51xbYhkSVuEnJ5wGVHtMzp3qZm7ISUOFY8x7QGQAmBBjS5LzBLEvkmHd3hi21vzV/AoWVuYqyg3XGopl9KAqSeldjfh27WDfuW9lz1nn40gl8UbDeLrDBGIRAvFuKmIRXK1hzD37UQNm2FFOZ/+JBHq7jPNz8Ab9NEVtXmlJEj3YwvLf/gJXuJPE5W/n4Dlr2NmVRQHLfTaLjRip9i7irR1k2jvRHV2ocBhHyxHMZIIMkAbioWpi1bWk5iylpX429owZrKn38p5aN858Iusp6di367an8MPRtElcV4K3Mvdp4M0wRk/yHlCxKegqXHLxRLrTNo/timJp+OCiQMHiF2nr+M/C1rlu6KDLwGMW7ooWQoyNNWvWLL7nnnua3/a2t8UvvvjiBU899dS+2traolYmkeQ8AeyLZHiqMUqV2+Da+YERF04wlCLgUv2KGdjZLNseeBbqarj2w28nqRz5JFbbr0rSoXySS9tgplN4ohG83WH8sTDBWISKWDfuzjDm/oOoAVWpbNMkWRFgaiDEwmOHsVxu9lz3QQ7XNkAky7lT3aya4iHoMoAaoPB930QsSWd7mLjXj6FN7LRNNqM5P+BgRbULc0Dr8nRKOu4NZ4hlC5dc7O2W7ls3uU/JxUTW5ond3SSyNtcPkZghV7WqzmtSN8R6IUTp/fGPf2wci/NIch7nDkWz/GJPlGqPyU2L/CcsKTgSx37/Z1L5wVCmy0UFDFmesf/sQNX9Jhhoyb9PWRojk+5N3p5ohKp4hPpsFG93GNfyhcy66RrOCwXpKYgz0lait8KDt+L0q3L1OJWSjj1f7+/OEO3QBUsuAiSymvcv8DO9Qv7LCTEWTmfKyGg0qm644Ya527Zt886fPz+ZTCZ7/yg1NDScsWHDhu319fXZyy+/fP6RI0dcqVTK+NSnPnX0jjvuaAPw+Xxn33rrrcd++9vfhjwej/3MM880zpw5M3sy8ctfinGsNWHxZGOUCqfBBxaefmJOd4Y58l+/J7RyKaEzlgy7/cnMDhRJVxFO2/idBgsrnQW7g8u963a4ko6W1v1mJ+ppdccyNqumeJgdlJHOYvJpbXpyZjrRMqqFwF3eafG6Oe8r2pSR//Iv/1Ln9XrtvXv3bn3llVe8F1544bJCx3/kkUeapk6dakWjUXX22Wcvu/nmmzunTZtmJRIJ4/zzz4/+4Ac/OPSpT31qxg9+8IO6b33rW0dO5holOY9TXSmLJ3bnBhddv9CPv8BI3ZN18Kn1aMtm5vvfMwoR5gzXlTyRmEpR6TZzjzEFSh2NEOJUp4z805/+5P+bv/mbYwDnnXdeYtGiRfFCx//mN7859b/+678qAVpaWpxbt271TJs2LeZ0OvUNN9wQBli1alXs2WefHTwn6jAkOY9DWmt+1hgla8NNiwO9z7SejvCbO+h8bTP177nspIthCCHEUEbSwi2WU50yciSeeeaZwB//+MfAhg0bdgQCAXvNmjWLE4mEAeBwOHTPY58Oh+OUppWUaU3GoWMJi46kzdoZ3lEZPBTds5+99z2Cd0Y90955yekHKIQQ40DPlJG2nZt06s9//rMX4KKLLoo+8sgj1QCvvfaaZ9euXYP6/rq6usxQKGQFAgH79ddf92zevPkkJ049MUnO49C+SG5cwbzQ6d/DjB88QuO9/4GzMsjC//UxDJfcFxVCTA5333334Ww2q5YsWbJswYIFy7/yla80ANxxxx3HYrGYOW/evOVf/vKXG5YtWxYbuO91110Xzmazat68ecu/+MUvNqxcuXLQNqdDpowch/5zZzcJS/OxZSd9G6OfVGs7O771/1CGYvEXP4W7tnqUIhRCjBcyZWRpyZSRE0TK0hyMZTl3Sv96jMeef4nkkaPHK3fV5qp2FarWlY3GiB88wv6f/BxtWSy6/ZOSmMuUbWeItm8kFTuI6QrhdFXhcFXicOf+VUqeiRZiIpLkPM4c6M6V3Jzb57GcxOGjND++DmWa6Gz/R+mU05Gri11TCSgSh1vIdEWA3OQPi27/ON7pU8fwCsRAtpXCynTnkq3hyC9LEml9hfDRF7CyUQxHBXY2Dv2epFaYziDOfKJ2uKpyL3fP+0oMQ25TCDEeSXIeZ/ZFsjgNaPAf/9EdeeZZDJeTM/7p71CmmZuGsK2DdEdXfv7g3L/atgksmY93+jS8M+qpmN2Awz+qYxjEScqk2jm88/9hZbrJJdsADlclmeQxbCuJN7iQymlr8fjngrbIZsJkU51k0p1k011k051kU50ko01k028Adr/jm45Abyvb4aoalMgN01WS6xZCnJgk53FmXyTDrICzd+KDePNhOje+ybR3re1NtCczDaEonWwmwpFdD6Bti9pZ12Blovmk24k3uIjQ1LfiqehTFV05cLprcLpr8BY4ntYW2XQkl7B7kncq928qfohY11bQ/csBG46KQV3lvYnbcGLbKWwrhbbToMz8emmRC1FskpzHkc6URVfKZnWf+82Hf/UsptfD1Le/tYSRTWyZVCfpxBHSiRbSiRay6TAO58AWaXUuaZkjKydqZeMc2fUAVjZK/aJP9E/Cp0gpE6c71zouRGsbKxPtl7wzqdz7dOIo8fAOtB5ZhcFcC39gi/z416fSIreycZQyMczC81vaVhJQQ64XYiKR5DyO7AtngOP3m2NNBwlv3sb0q96Oo2ISlOAqgUjry7Qd+GXv1z2tyqGSmWF6+7RCqwa0SqswTC/aztDS+CCZVBv1Cz46Kol5JJQycLiCOFxBYPag9Vpr7Gyst/Wu7SyG6cYw3CjThbaz/brSM+nOE7bIHa7K3PUPaJE73VW9H2Ky6S5inVuIdW0hGd0PaAzT17sP2ur9EKHtXJ0Iw/T1a+kPPIdhekdUClZri1T8EJlkO9lUO5l0B9lUF4bDO+C4uZfpKNRfIURxSHIeR/ZFsoRcBlXu3OPph9f9FrPCx5RLLyxxZBNXtOMNnO5a6ua8H5d3ar+WsdYaK5tviaa6elukmVQnmWQ7iUhjrju4D2W4MAwXVjbG1HkfxBtcMNaXNCSlFKbTj+n0w0l8YBjYIs+kjt8PTyeH+hDjwXT4yaRyT9u4vNOorL8Uw3D2tuYzyVaUcuBwVePxz8PhCgH0+R63kojsLvA9dhcYJNczQM5NMrqXeHgXie49vQkfwHQG8/f7W0lEdqHtzBDH7T/wrucDmOGoKPv68GL8kOQ8Tli25kB3huU1bpRSRPfsJ7J1Fw3XXoHpHb2ZmcRxtpUkGW2ictrb8PgHtzSVUrnubWcAKgZPa6m1xrbivfd9e1qkVjpCRfVKKqpWjMVlFN1IWuSFPsRkMxH8NauoqFqBy1N3Sufu+z3u+f72PUeie1+/BNzD4arEX70Sb3AhLu/U3nvs/Y7b24twvLeg532ie++g4yrDeTxxuyoHJPIqTMfgxxqFGIok53HiUCxL2oa5wdyP7PDTv8UR8FO39oISRzZxxSO7ARtvcPEp7a+UwnRUYDoqcFfMGN3gxpHhPsSc7rGH+x5b2URvsrazCdz+2TjdtSdMlCPpReh73H4fwFKdpGLN2FZ8wDEdx1vzBUbQm84gSknRxtHw1a9+darb7dZf+cpXjt16660zt27d6n355Zd3rVu3LnD//ffXBgIBa/PmzRXJZNJ473vf2/md73zn8JNPPhl84IEHan/961/vhVzt7G9/+9tTn3/++cbvfOc7td/73vemBQIBa/ny5XGXy6UffvjhA8W8hqImZ6XUFcD3ABO4X2t994D1buBhYBXQDlyvtW7qs34WsA34mtb6nmLGWu72RbIYwKyAk/CWnXTv3MOM978H0y2PwhRLIrwTw/Tg8Y9uQhFjy3R4MR1e3L7pY3pc20oN6ubvGUEf79qGlY0O2MPI30vv311+/L53aFwWnWl66MmZicOjO2Wkd/q0+JyPDD2hxiWXXBK95557pgLHNm3a5Eun00YqlVJ//OMf/W9961u7P/zhD3dOnTrVymazXHDBBYtfeeUV79VXXx3567/+69mRSMQIBoP2f/7nf1a9//3v72hqanLec8899X/5y1+2VVZW2hdccMGi5cuXJ0bzegopWnJWud+iHwJvBw4Cryml1mmtt/XZ7FagU2u9QCl1A/BN4Po+6/8F+HWxYixXKUvz/ME4DkMRchmEXAaNXWka/A5c2DQ+tR53XQ11l7yl1KFOWFpr4pGdeIMLx+UfRFF6hunG5Z2Gy1v4sUbbzvR53K3v/fpOEpHdRDPdDF10pv99b5enLjeATgBw0UUXxT/ykY9UdHR0GG63W5955pnRF154wffSSy8FfvCDHxx46KGHqh988MHabDarWltbnZs3b/acd955iUsuuSTy2GOPhT760Y92Pvfcc6F777334DPPPBM877zzuqdOnWoBXHvttZ27du0q+r3EYrac1wCNWuu9AEqpx4CrybWEe1wNfC3//kngXqWU0lprpdQ1wD5gVIuJjwd/OZZkc1sapwGZPjUlLm5w0/biRpKHjzLvkx/EcMhdiWJJJ45gZbrxnWKXthDDMQwnLk/dkPfbj4+O7xp0Pz0Z3Uc2vYme5F1RdQZT531w7II/CSdq4RaL2+3WM2fOTP3rv/5r7Zo1a6IrV65MPPvss4H9+/e7Kyoq7HvvvXfqxo0bt9fV1VnXXXfdnGQyaQDceOONHffee++U2tpa64wzzohXVVXZw52rWIr5170B6PtDOQicN9Q2WuusUioM1CilksDfkWt131HEGMtOxtZsOJZiXtDB+xb4SVqacMomltU0OC12rvstFfNnU3n2xBhMVK7i4Z0AeEOSnEVpKMOB01OL01N7gqIzYbLpLgxDbm8NdP7550d/+MMfTv3Rj37UtGrVqsTf//3fz1ixYkW8s7PT9Hq9dnV1tdXc3Oz4wx/+ELr44ou7Aa688sruT3/603P+/d//vfYDH/hAB8BFF10Uu/POO2e2traalZWV1tNPP121dOnS8dutfZq+BnxHax0dZtDGbcBtALNmTYz7gm+0pYhnNefX557V9DoUXkfPo1PPk41EWfCZD8uozyJLRHbi8jXkBjEJUYZyRWeqcbpl0ppCLr744u7vf//70y699NJYMBi03W63vvDCC6Pnn39+YsWKFfH58+evqK+vT69atar35r/D4eCyyy4LP/nkkzVPPPFEE8DcuXMzt99++5HVq1cvDYVC2QULFiRDoZA15IlHSTGT8yGg7zDHGfllhbY5qJRyACFyA8POA96nlPoWUAnYSqmk1vrevjtrre8D7oPclJHFuIixZNmaV1qSzPA7mOHv/6NJd4Zp+e0LVK0+k4q5E+ODSLmysnGS0f1UTltb6lCEEKfo6quv7s5ms3/p+bqpqWlLz/unnnqqaaj9Hn744QMDR2J/4hOf6LjjjjvaMpkM73znOxdcc801XcWIua9iJufXgIVKqbnkkvANwE0DtlkHfAR4CXgf8JzOTTDdW4tSKfU1IDowMU80VirNm9uaCWxrZqXVxe7fduLwV+CuqcRVXUV4yw7QNg3XXlHqUEcsHtlNJnF03FVZSkQaAY1PurSFEMAXv/jF6f/zP/8TTKVS6uKLL47cfPPNXcU+Z9GSc/4e8ueA35B7lOrHWuutSqm7gA1a63XAA8BPlFKNQAe5BD7h2Zks4Td3kDh4hMShFhKHW0i1doDWLAMyLifGlFoSh1ro6IqAznUKTH3H28bNvMu2neHY3kfy9ZCP619laUCZS3cVhukb1GWvtSYVbybWuYVkdB+mo6JfHWeHO7f/aFVoikd2YJg+3GNUVlMIUd7uu+++g2N9zqLec9ZarwfWD1j2D33eJ4H3D3OMrxUluBLJxuI0/vAhYnv2g1K4p9TgnVGPfcaZbDAqectZs1k6bwrKyN1n1pZFujNMJtyNb3ZDiaMfuUR4J7aVZMrcm3C6q4eo3jR8lSWlDGJd27AyYVAmnoqZZNNhktGmwYlfOfvMZTy4SpPp9A9b5EFrm0R4V/4RKikIIYQojXIdEDYhpTu62P39H5NqbWfOLe+natUZGC4XWmse3N6NZWuWzA/2a/0p08RdWz1uWsw9op2bMRwVVFQtRylzRNWb+s9T3EUq1oy203iDC6moege+0FJMh6/Avl2D9k/FD2FnBzyFl5/ycKgpEh2uIOn4EaxsFF9oSTG/PUIIcUKSnMdI4lALu7//Y6xkinl//VEOT5nFG8eytCZStCYsohnNlXN8GBNgFLZtpYh3bSdQu3rYAh7DVVnSWg/ZVT2yCk3HKzNleos9dBEP78TKdg/Yw8hPdajwBRcOd5lCCFE0kpzHQKzpILu/dz+G08niL36KVwjx6r4YpoIaj8nsgJMGv4Pl1RPjWcVY1za0zuKvPuu0j3U695BzFZqm4vJOLbi+t0LTgO52h7smV1NZCCFKRJLzGDjyX79HmQ4W/91nyARD/OXNMEurnLxnbsWEaCkPFO3YhOkM4R7lSQ5G23AVmoQQokdDQ8MZGzZs2F5fX58dfuvTJyNeiiwbixPZuouat5yNu6aKV1qSWBoumu6dkInZysZIRHbjr14pA6qEEGXBtm0sq+h1Q0aV/PUsss6/bEFbFlXnrqQ7bbOpNcXyGhfVnok5mUKscwtg469eWepQhBCT2M6dO11z5sxZce21185ZtGjR8uuvv37OihUrli5YsGD57bff3jtQpaGh4Yzbb799+rJly5YuWrRo2euvv+4BaGlpMS+88MKFCxYsWH799dfP1vp4nauvfe1rUxcuXLh84cKFy++6664pPeebO3fu8uuuu27OnDlzVlx11VVzf/nLXwbOOeecJbNnz17x/PPPn9TMXNKtXWSdr23CPaUW36wGft+cwNJwYX3RJzQpmWjHJpyeOlze0Z2eTwgxPj399NMzjx07NqpTRk6ZMiV+9dVXDzuhxoEDB9wPPPDAvssuu6zp6NGj5sBpIs8777wEQG1tbXbbtm3b77777rq777576uOPP77/zjvvnH7++edH77nnniOPPfZY6IknnqgFeOGFF3yPPvpozcaNG7drrVm1atXSyy67rLu2ttZqbm72PP7443tXrVrVdOaZZy595JFHajZs2LDj0UcfrfzGN75Rv3bt2j0jvUZpORdRuitC9659VJ+7ku6MZlNbijNqXVS6J2aruef5Y3/VSqn9LYQoufr6+vRll10WA3jooYeqly1btnTZsmXLdu/e7dm8eXNvK+mmm27qBFizZk28ubnZDfDyyy8HPvaxj7UD3HDDDeFgMGgB/OEPf/BfeeWVXcFg0A6FQva73/3uzueffz4A0NDQkFqzZk3CNE0WLVqUuPTSSyOGYXDOOefEDx486D6Z2KXlXESdG94Arales5L/aUmiNVwwbeK0mrPpCCiF6fCjlCLa+QagR2WUthBiYhhJC7dYfD6fDbBjxw7XUNNEAng8Hg3gcDh0Nps95ZaFy+Xq7fs2DKP3uKZpYlnWSR1XWs5F1PHaZrwz60lX17K5LcWZtS5CE6DVbGVjtO7/OQfe/GcOvPENml7/Ks1bvk3Xkedx+RpwempLHaIQQvQqNE3kcPu85S1v6X7wwQdrAJ544olgJBIxAdauXRtdv359ZXd3txGJRIz169dXrV27dmDRhNMmLeciSbW2E29qpuGv3sUrLbkyk+fXl9ekDycq8FF4e5vutlfpOPRbbCtJcMr5ON01uWeFU51kM2FCU99WxIiFEOLknWiayKHcfffdh6+77rp5CxYsWL569epofX19GuCiiy6K33TTTe3nnHPOUoAPfehDrRdeeGFi586do1qoQvUdgTaerV69Wm/YsKHUYfQ6sv45Dj/9Wxb+4//m/kOKxVUu3j2notRh9Yq0vkJ7868wncHeMpY9k0/01KU2HRVkUm2kEy2k40dIdDeSTrTg8c+ldtbVuLzTSn0ZQojTpJTaqLVePZrH3Lx5c9PKlSvbRvOYE9XmzZtrV65cOWfgcmk5F0nHa5upmD+b7baPjJ3g3CknNRag6KIdr2M6/bgrZuYmoYjsJpqJDLm9Ug5c3mlMmXsDFTLgSwghikqScxEkDrWQPHyUGTdcxXPHkswKOJjiK59vtZWNk4zup3LaWqob3tG7XNvZXA3qfP1pKxPF6anB5a3H6a6RoiJCCDFGyidjTCAdr24Cw6B9/lK6WzVvn1VereZEpBHQ+EKL+y1XhgOnp1YGdAkhTpdt27YyDGNi3DctEtu2FWAXWidNoVGmtaZz45sEFs9nY9yk0m2wIOQsdVj9xCM7MEwf7oqZpQ5FCDExbWltbQ3lk48owLZt1draGgK2FFovLedRlmg+TKq1Hc/at3I4ZnH5TG9Z3Z/V2iYR3oU3uFC6qYUQRZHNZj/e0tJyf0tLywqkETgUG9iSzWY/XmilJOdR1rnxTTAMdkybhzujOKOmvLq00/HDWNkovtCSUocihJigVq1adQy4qtRxjGfyiWYU9XRp+xbNZ1vSyZm1Llxm+bSaAeKRnYDCF1xY6lCEEEIMQZLzKOrp0m5fkGuVrqorr1YzQDy8E7dvBqbTX+pQhBBCDEGS8yjq6dLeWDOXZdXlV6rTysZIxZoHjdIWQghRXiQ5jxKtNR0b3iA1ew5Zj5e3NZRXqU6AeHgXoPFKchZCiLImyXmUJJoPk27rYM/MRaye6iHoKr9vbSKyE8NRgdvXUOpQhBBCnED5ZZBxqmPDG2iliC5YzFvKcFpIrW3i4V34govkESohhChz8ijVKNBac+y1N2hvmMNb5lXjHuUR2lrbdB15Dq2t3okpnPnJKZQx+Eeo7SyZVAdg43BVYpgeUrGD2FZcHqESQohxQJLzKIgeOITu6CR29vmsrB3VWcMASCda6DzybMF1pjOQn0WqCm2nyCTbehNzD8P0oAwnoPDKI1RCCFH2JDmPgl0vvI6tFCsuPBOjCNXAktEmAGau+CJgkE135l/5eZTTXaTiBzEMJy5fPRVVZ+L01KGU2bttJtWJq3oapsM36vEJIYQYXZKcT9Pu13dhvfgyiXkLWT09VJRzJKP7cvMtu2sAcLqrinIeIYQQ5UFGBp2G/Tv20/HAT0mHqjjntg8UpYa21ppkdxMe/5xRP7YQQojyVNTkrJS6Qim1UynVqJS6s8B6t1Lq8fz6V5RSc/LL1yilNuVfm5VS1xYzzlNxZN8RjvzoQSyPl+V/+3EClcWpuJVNtWNlu/H45xbl+EIIIcpP0ZKzUsoEfgi8C1gG3KiUWjZgs1uBTq31AuA7wDfzy7cAq7XWZwFXAP+mlCqbLvi2I23su/fHaGWw4H/dSlVdZdHO1XO/WVrOQggxeRSz5bwGaNRa79Vap4HHgKsHbHM18FD+/ZPAZUoppbWOa62z+eUeoKwm7N7+o0cw02lmfu6jTJk5pajnSkT3YZg+nJ7inkcIIUT5KGZybgCa+3x9ML+s4Db5ZBwGagCUUucppbYCbwKf6pOseymlblNKbVBKbWhtbS3CJQzWergV39EjGGsvZsaCGUU/XzKau99cTnNCCyGEKK6yHRCmtX5Fa70cOBf4klJqUNktrfV9WuvVWuvVdXV1YxLXgY3bAZi5amAP/ejLZrrJptrxBOYU/VxCCCHKRzGT8yFgZp+vZ+SXFdwmf085BLT33UBrvR2IAiuKFulJiG/fTTJYWfTubMg9QgXIYDAhhJhkipmcXwMWKqXmKqVcwA3AugHbrAM+kn//PuA5rbXO7+MAUErNBpYATUWMdUTSqTTu/ftg4UIMo/idDsnuJpThxO2bXvRzCSGEKB9FGwGttc4qpT4H/AYwgR9rrbcqpe4CNmit1wEPAD9RSjUCHeQSOMBFwJ1KqQy5OpSf0Vq3FSvWkdq3uREzmyV45tjUp05G9+GpmE1u4LsQQojJoqiPJ2mt1wPrByz7hz7vk8D7C+z3E+AnxYztVLS9uROn6WDuWQuKfi7bSpJOtFBVf1nRzyWEEKK8lO2AsLK0axepWbNxe9xFP1Uyuh/Q8nyzEEJMQpKcR+hY8zE8XZ34li0ak/PlBoMZuP2zxuR8Qgghyock5xFqfj3/CNXZS8fkfMloE+6KBgxj9KegFEIIUd4kOY9QfOtOkpXVY/IIlW1nSMaa5REqIYSYpIZNzkopUyl1z1gEU65SyRTu5v2waGHRz2XbGVqbngBt4Q0Wf+CZEEKI8jPsaG2ttaWUumgsgilX+zY1YloWlUV+hMq2krQ0PkwyupfqGVfiC47N/W0hhBDlZaSPUr2ulFoH/AyI9SzUWv+8KFGVmfY3duB0OJlz5vyinSObDtPS+B+kk63UzbmeQM3ZRTuXEEKI8jbS5OwhV1bz0j7LNDApkjO7d5OaPQeXe/QGZ2ltkUm2kU60kE600N3+F2wrwbQFt+ALFr/7XAghRPkaUXLWWn+02IGUq2QygyfSRXbVWaN2zESkkZY9D6HtTH6Jgcs7jWkLPozbN3DiLiGEEJNNUSuETQThcBQAT9A/asfsOvoChumhetZf4fJOxeWZgjLkRyGEECJHMsIwIp3dAPhCo5OcrWyMRGQ3oakXyX1lIYQQBclzzsOIdeVazv7K0UnOsc4tgI2/euWoHE8IIcTEc9LJWSn1TDECKVeJSG5weqAyMCrHi3Zswumuw+WVaSCFEEIUdiot50k1YikVybWc3cGK0z5WNh0mGW3CX70SpdRpH08IIcTEdCrJ+fVRj6KMZaIxNOCo8J32saKdbwCaCunSFkIIcQInnZy11h8rRiDlyorGsL1elHH6t+djHZtx+RpweepGITIhhBATlQwIG04sDr7T79LOJNtIxQ/ir5JWsxBCiBOT5HwCKUtjJhIYo9KlvRkAf/WZp30sIYQQE5sk5xOIpG1cyTiOwOm1nLXWRDs24/HPweGqHJ3ghBBCTFjDFiFRSs0AbgDeCkwHEsAW4L+AX2ut7aJGWEKRtI0zmcB1ksk5EWkk1rUVhyuEw1UFQCZ5jNCsa4oQpRBCiInmhMlZKfUf5B6degb4JnCM3CQYi4ArgC8rpe7UWv9PsQMthUjKwpmMn3Tpzo5D/00qfojc3CA9DCqqzhjV+IQQQkxMw7Wcv6213lJg+Rbg50opFzBr9MMqD9HuOD6t8YVG3nLOpiOk4gepmv5OQlMuIJvuJJPqxHR4MR2nP7BMCCHExHfCe85a6y1KKVMp9cgQ69Na68bihFZ6sfykF07/yFvO8fB2ACoql2KYblzeaVRULsXjn1OMEIUQQkxAww4I01pbwOx8K3lSSYZzpTtPZkBYPLwdh6sap2dqscISQggxwY10Vqq9wJ+VUuuAWM9CrfW/FCWqMpHuzrWcHf6RPUplW2kSkUYCdedJeU4hhBCnbKTJeU/+ZQCjMwNEmdNak4nmPoc4AyPr1k5070brLBWhpcUMTQghxAQ3ouSstf6/xQ6k3CTzBUhg5C3neNd2DNODJzC3mKEJIYSY4E54z1kp9e9KqYLP/yilKpRSH1NKfbA4oZVWd74ACU4nhmv42+1a28TDO/AGF6OUOQYRCiGEmKiGazn/EPhqPkFvAVrJPee8EAgCPwYKjuQe7yJpG2cigTnCVnMqdhArG6WiUrq0hRBCnJ4TJmet9SbgA0opP7AaqCdXIWy71nrncAdXSl0BfA8wgfu11ncPWO8GHgZWAe3A9VrrJqXU24G7AReQBr6otX7uJK/ttHRndK505wgfo4qHtwEG3uCi4gYmhBBiwhvpPeeoUmorsFVr3TqSfVSub/eHwNuBg8BrSql1WuttfTa7FejUWi9QSt1ArgrZ9UAb8F6t9WGl1ArgN+QqlY2ZXF3tBK66kSXnWNd2PIE5mI7TnyRDCCHE5DbcPWellPqaUqoN2AnsUkq1KqX+YQTHXgM0aq33aq3TwGPA1QO2uRp4KP/+SeAypZTSWr+utT6cX74V8OZb2WOmO23jSiVGNFI7k2onkzyKT0ZpCyGEGAXDFSG5HbgQOFdrXa21rgLOAy5USt0+zL4NQHOfrw8yuPXbu43WOguEgZoB21wH/EVrnRp4AqXUbUqpDUqpDa2tI2rQj1junnMch3/4AiTxrp6qYMtGNQYhhBCT03DJ+UPAjVrrfT0LtNZ7gZuBDxczMACl1HJyXd2fLLRea32f1nq11np1XV3dqJ47Gk9jZtLDJmetNdHON3B6puB0D/xcIYQQQpy84ZKzU2vdNnBh/r6zc5h9DwEz+3w9I7+s4DZKKQcQIjcwrGeqyl8AH9Za7xnmXKNKa02qpzrYMKU7E92NpGIHCNa9ZSxCE0IIMQkMl5zTp7gO4DVgoVJqbr4u9w3AugHbrAM+kn//PuA5rbVWSlWSmy/6Tq31n4c5z6iLZzXGCAqQaK3pPPwspjNEsHbNWIUnhBBightutPZKpVSkwHJF7nnnIWmts0qpz5EbaW0CP9Zab1VK3QVs0FqvAx4AfqKUagQ6yCVwgM8BC4B/6DP47B1a62MjuqrT1FuAhBOX7kxEdpOK7ad21jUoY6SVUIUQQogTG+4559MqdaW1Xg+sH7DsH/q8TwLvL7Df14Gvn865T0ckY+NMnrjlrLWm88jvcLgqCdSsHsvwhBBCTHDDThk5GfVtOQ81ICwR2Ukq1kzltEul1SyEEGJUSXIuIJK2cSfjoBSmzztofc+9ZoermkDtqhJEKIQQYiKT5FxAd9rGl07i8PtQxuBvUTy8nVT8IJX1a2WSCyGEEKNOknMB3RmNN5UYsku7q+UPONw1BGrOGePIhBBCTAaSnAvoqatdKDnbVpJUrJlA9dnSahZCCFEUkpwHsLUmmrZxJOMFC5AkowcAjcc/Z8xjE0IIMTlIch4gntXYgBkvXFc7GWsCFO6KmYPWCSGEEKNBkvMAyawGrSFRuFs7FT2Ay1uPYY7pJFlCCCEmEUnOAyQtjSOVBNseVIBEa4tk7AAe/+wSRSeEEGIykOQ8QMrSQ5buTCda0HZakrMQQoiikuQ8QDI7dOnOZHQ/AO4KSc5CCCGKR5LzAMk+LeeB95yT0f2YziAOV2UJIhNCCDFZSHIeIGVpnInCyTkV24/HPxulVClCE0IIMUlIch4gmdV4Uvlu7T7POWfTYbLpLjzSpS2EEKLIJDkPkLRypTsNtwvD6Ty+vOd+swwGE0IIUWSSnAdIWRp3gbrayVgTynDi9k0vUWRCCCEmC0nOAySzuQFhA0t3pqIHcPtmSj1tIYQQRSfJeYCkpXEOmPTCttKk4ofl+WYhhBBjQpLzAElL4xhQVzsVbwZsSc5CCCHGhCTnAVKWxkjE+xUgOV58ZFapwhJCCDGJOEodQDmxtSaTSqMyGWwzTKJ7Lw5XFcloE07PFEyHb/iDCCGEEKdJknMfaUsTSLYCEE+8yZFdb/auC9SeW6qwhBBCTDKSnPtIWpqKRCcAoRlrqFx4Btl0J1Y6gr/6rNIGJ4QQYtKQ5NxHMqvx5ZOzp7YBX3BhiSMSQggxGcmAsD6SlsaTjALgCtWUOBohhBCTlSTnPpKWxp3oBsBdVVfiaIQQQkxWkpz7SFkaVzIBJpjeQKnDEUIIMUlJcu4jmdU4EgkMr8Iw5FsjhBCiNCQD9ZGyNI5ECsMn4+SEEEKUTlGTs1LqCqXUTqVUo1LqzgLr3Uqpx/PrX1FKzckvr1FKPa+Uiiql7i1mjH0lLY2ZzGL6nMNvLIQQQhRJ0ZKzyk3f9EPgXcAy4Eal1LIBm90KdGqtFwDfAb6ZX54EvgrcUaz4CklmbUhkMf2esTytEEII0U8xW85rgEat9V6tdRp4DLh6wDZXAw/l3z8JXKaUUlrrmNb6T+SS9JhJWhqSGoffO5anFUIIIfopZnJuAJr7fH0wv6zgNlrrLBAGRvyAsVLqNqXUBqXUhtbW1tMMFzKJbsgyaC5nIYQQYiyN6wFhWuv7tNartdar6+pO/7lks7sdAGcgeNrHEkIIIU5VMZPzIWBmn69n5JcV3EYp5QBCQHsRYzohM5or3ekMhkoVghBCCFHU5PwasFApNVcp5QJuANYN2GYd8JH8+/cBz2mtdRFjGpLWGmesCwBXZVUpQhBCCCGAIk58obXOKqU+B/wGMIEfa623KqXuAjZordcBDwA/UUo1Ah3kEjgASqkmIAi4lFLXAO/QWm8rVrxZDa5krnSn1NUWQghRSkWttqG1Xg+sH7DsH/q8TwLvH2LfOcWMbaBkVuNO5Ce9qJS62kIIIUpnXA8IG01JS+NKxsEBTp+/1OEIIYSYxCQ55+VKdyYxvPItEUIIUVqSifKSWRtHIo3ymaUORQghxCQnyTkvaWmMZAbT5yp1KEIIISY5Sc55KUtDwpK62kIIIUpOknNeMm1BUuOUutpCCCFKTJJzXireDRY4AzJSWwghRGlJcs6zI20AOIOBEkcihBBispPk3KO7AwBnqLK0cQghhJj0JDnnqe4uAFxBqasthBCitCQ55xnxfF3tqtoSRyKEEGKyk+ScZ8byyblSkrMQQojSkuScZ8bj4ASH21fqUIQQQkxykpwBW2vMZBIldbWFEEKUAclG5KqDmYk0ylvUGTSFEEKIEZHkTE9d7SxGhdTVFkIIUXqSnIFkNldX26hwlzoUIYQQQpIzQDxjQUJj+mUwmBBCiNKT5AykuztBg0PqagshhCgDkpyBdFeurrYrECxxJEIIIYQkZwCy4VxdbXdlZWkDEUIIIZDkDICVr6vtqZS62kIIIUpPHuwFdHcEAE9VXYkjEUIUg9aacDhMV1cXfr+fUCiE0+ksdVgFxeNxHA4HLpc82jmZSXIGiEUBcIVqShyIEGK0dHR08OKLL3L06FGOHTtGOp3ut76iooJQKERlZWXvvz2vUCiE2z32j1Zu2rSJdevWobXG6/X2i2fge4/HM+bxibEjyRlQ8Ti4wHTJL7sQE8GxY8d4+OGHSafTTJ8+nZUrVzJ16lQqKyuJxWJ0dXXR1dVFOBympaWFnTt3YllWv2P0JMdCCTwUCuHxeFBKjVrMO3bsYN26dcyePZv58+f3xtfa2sru3bvJZrP9tne73b3xzJs3jzVr1oxaLKL0JDkDKi51tYWYKA4fPsxPf/pTHA4Ht912G7W1w880p7UmGo32JsS+ybutrY09e/aQyWT67eN2u0/Y8vb5fCNO3vv27ePJJ59k+vTp3HjjjYO6tLXWxOPxgvF1dnbS3t4+8m+QGBckOQMqngapqy3EuGLbNqlUql8L9sCBAzz66KN4PB4+/OEPU11dPaJjKaUIBAIEAgFmzpw5aH1PcuybGPsmyqampkHd5k6nszdRF0refr8fpRSHDh3iscceo6amhg9+8IMF7zUrpaioqKCiooKGhoZT+G6J8UYyEqASGajyljoMIcRJeOaZZ3j99df7JcH9+/cTCAT48Ic/TCgUGrVz9U2O06dPH7Rea00ymRzUqu15f+jQIRKJRL99TNMkFAoRi8Xw+XzcfPPNeL3yd0jkTPrkrLWGpIXyyf1mIcYL27bZsWMHM2fOZPr06YTDYcLhMDNnzuTaa6/F7x/ban9KKbxeL16vl2nTphXcJpVKFUzelmXxjne8g0AgMKYxi/JW1OSslLoC+B5gAvdrre8esN4NPAysAtqB67XWTfl1XwJuBSzgb7TWvylGjKlMFpKA1NUuC6lUCpfLdUoDbaLRKJs3bwbo14VY6N6f1pq2tjYOHDhAMpnsd9+woqJiVAf6iNHX0xJds2YNK1asKHU4I+J2u5kyZQpTpkwpdShiHChaclZKmcAPgbcDB4HXlFLrtNbb+mx2K9CptV6glLoB+CZwvVJqGXADsByYDjyrlFqkte4/nHIUxLo6QIPhrxjtQ4uToLXmV7/6Vb9uyqFGyg5Mnl1dXbz44ou8/vrrg0a0Ajgcjn7Hi0ajHDhwYFA3Y9/tTzTQJxAISPIusd27d6OUYv78+aUORYiiUFrr4hxYqfOBr2mt35n/+ksAWut/7rPNb/LbvKSUcgAtQB1wZ99t+2431PkCgYBetWpVv2Uf+MAH+MxnPkM8HufKK68ctM8tt9zCeWcuZ/2Dj/PAfz+Nxx/A4XD0vj772c9y/fXX09zczIc+9KFB+3/hC1/gve99Lzt37uSTn/zkoPVf+cpXuPzyy9m0aROf//znB63/p3/6Jy644AJefPFF/v7v/37Q+u9+97ucddZZPPvss3z9618ftP7f/u3fWLx4Mb/61a/49re/PWj9T37yE2bOnMnjjz/Oj370o0Hrn3zySWpra3nwwQd58MEHB61fv349Pp+Pf/3Xf+WJJ54YtP4Pf/gDAPfccw/PPPNMv3Ver5df//rXAPzjP/4jv//97/utr6mp4amnngLgzjvvZP369UQiEfx+P4ZhEAqFuPnmm+nq6uIXv/gFLS0tvfsqpZg+fTqf/vSncTgc3HPPPbS3t+P3+wkGg5imyZIlS/jCF75AV1cXX/rSlzh69CjZbJZsNothGKxcuZIvf/nLzJo1i49//OO96y3LIpvNsmTJEi6//HK6urq47777+o3UVUqxcuVK/uqv/opQKMT/+T//p9/vjcPhGNHv3i233EJbWxvve9/7Bq3/9Kc/Lb97J/jdu+mmm3A6nbS3t5/W796XvvQlXnqp/5+VGTNm8NOf/hSAz3/+82zatKnf+kWLFnHfffcBcNttt7Fr165+68866yy++93vAnDzzTdz8ODBfuvPP/98/vmfc38Gr7vuukEjrS+77DK++tWvAvCud71r0IfI97znPdxxxx0AXHLJJQx0sr97f/zjHzdqrVcP2lCUVDG7tRuA5j5fHwTOG2obrXVWKRUGavLLXx6w76Ahikqp24DbgFMuGJCMR4m5DRKZLPGOjn7rnn76aTo7O7Esi46OjkF/gIv1wWYsaa3p7Oykq6uLdDqNaZq919fc3Ex9fX3Rr/PgwYNEIhGCwWDv6NoZM2b0Jp2DBw+ycePG3sSZzWbxer0kEglisRh1dXV4PB4cjuO/zj6fj8WLFwMwffp0bNvud86FCxdy9tlnA2AYBi6Xq98o2RUrVvCJT3wCgGeffZZoNNp77mw2SzAYJJPJ0NjYSFdXV79jK6V47rnn8Pl8eL1eurq6+v3emKY5ut/AScayLI4cOcKll14qjxCJCauYLef3AVdorT+e//pDwHla68/12WZLfpuD+a/3kEvgXwNe1lr/NL/8AeDXWusnhzrf6tWr9YYNG0453r7PEfYdrNF3AMdQj0oUKlBgmiadnZ29r1QqRTAYHNRFOlYl+g4cOMDBgwfRWve+wuEwu3btoru7G6UUtbW1xGIx4vF4v317WrJDdTMHg0EM49SeE3/11Vf59a9/zcqVK7n66qvHZXdxNpvtHZBUaKRud3f3oA84gUBgyK77UChEOp3mwIED7N+/n+bmZrq7u4cshlFZWVm2pSiLYdOmTTz99NN88pOfHHLwlRg5pZS0nMtQMVvOh4C+DwzOyC8rtM3BfLd2iNzAsJHsO6qGe46w51GJoZJ3c3MzyWSy4LG9Xi9ut5tIJDKoBefz+U54f/N0SvRprdmzZw8vvPACBw4cGLTe6XSyYMECFi9ezMKFC/H5coPi0un0oOvr+bexsZFoNNrvOEqp3g8eQyXvvq3aeDxOc3Mze/fu5dVXX2Xx4sVcddVV4zIxQ+4edU1NDTU1hcu/WpZFJBIpmLybm5vZunXroN+LHqZpMmPGDObNm0ckEuHQoUNs27at4O/Rie7Tl6IUZbE0Njbi9/uZOnVqqUMRomiKmZxfAxYqpeaSS6w3ADcN2GYd8BHgJeB9wHNaa62UWgc8qpT6F3IDwhYCrxYx1mH1fVSivr6+4DapVKr3D69lWVRVVVHZpwZu3ypEAxPfUCX6PB7PkAkvEolw+PBhDh8+zJEjR9Ba9/sDfeDAAY4cOUIwGOSKK67gzDPPxDRNlFIopTAMo2CL1+VyUVdXR11d4YlAelqKhZJ3U1PTCVuKyWSS1tZWIJd4li9fzjXXXHPKLe/xwDRNqqqqqKoqPOuZbdt0d3f3S96maTJr1izq6+v7fbDp2X6oalZHjx5l165dBX+PTpS8R7sUZbHYtk1jYyNLly4dF/EKcaqKlpzz95A/B/yG3KNUP9Zab1VK3QVs0FqvAx4AfqKUagQ6yCVw8ts9AWwDssBnizFSe7S53W6mTp065Cf6kVYhKpS8Ozo62Ldv36CudcMwmDJlCkuWLME0TcLhMO3t7ezZs4dQKMRVV13Vm5RHy0hbioWSd2VlJWeccQazZs2ioaFhUOKZjHpuG4RCIWbNmjWi7YPBIMFgsOB6rXVv/eiBybvnd2NgKUqXy3XC5H0ypSiLqbm5mVQqxcKFC0sdihBFVbR7zmPtdO85jwd9u9bD4XBv116h+41a67L4YyrKj9aaRCJRMHn3vE+lUv32Gfg42sDk3VOKstieffZZXnrpJb74xS/KrEyjRO45lydptowjI+la77utEIUopfD5fPh8voKlKIFBpSj7Ju8TlaLsSdoDx08EAoFRuXXR2NjIzJkzJTGLCU+SsxBiEI/Hg8fjGfIWTTqdHnJw5K5du4jFYv227+mKHyp59zyffiKRSISjR49y+eWXj9p1ClGuJDkLIU6ay+U6YSnKTCbTO+5gYBLfu3cv3d3d/bbvGY8x1KNioVCIxsZGALnfLCYFSc5CiFHndDqHHTQ41LPhBw4cYMuWLYNG/JumSTAYHPIpAiEmEknOQogxZ5om1dXVQ863bNt2wWfD582bJ+MpxKQgyVkIUXYMw+jt2p49e3apwxFizE3cyg9CCCHEOCXJWQghhCgzkpyFEEKIMiPJWQghhCgzkpyFEEKIMiPJWQghhCgzkpyFEEKIMiPJWQghhCgzE2bKSKVUK7D/NA5RC7SNUjjjxWS8Zpic1z0ZrxnkukdittZaaqKWmQmTnE+XUmrDZJvTdDJeM0zO656M1wxy3aWOQ5w66dYWQgghyowkZyGEEKLMSHI+7r5SB1ACk/GaYXJe92S8ZpDrFuOU3HMWQgghyoy0nIUQQogyI8lZCCGEKDMTMjkrpa5QSu1USjUqpe4ssN6tlHo8v/4VpdScPuu+lF++Uyn1zpEesxyM9nUrpWYqpZ5XSm1TSm1VSv2vMbycESnGzzq/zlRKva6UemYMLuOkFel3vFIp9aRSaodSartS6vwxupwRKdI1357/3d6ilPpPpZRnjC5nxE71upVSNfn/v1Gl1L0D9lmllHozv8/3lVJqjC5HjJTWekK9ABPYA8wDXMBmYNmAbT4D/L/8+xuAx/Pvl+W3dwNz88cxR3LMUr+KdN31wDn5bQLArnK67mJcc5/9/hZ4FHim1Nc5VtcNPAR8PP/eBVSW+lqL/PvdAOwDvPntngBuKfW1juJ1VwAXAZ8C7h2wz6vAWwAF/Bp4V6mvVV79XxOx5bwGaNRa79Vap4HHgKsHbHM1uT9EAE8Cl+U/OV4NPKa1Tmmt9wGN+eON5JilNurXrbU+orX+C4DWuhvYTu4PWrkoxs8apdQM4N3A/WNwDadi1K9bKRUC3gY8AKC1Tmutu4p/KSNWlJ814AC8SikH4AMOF/k6TtYpX7fWOqa1/hOQ7LuxUqoeCGqtX9Zaa+Bh4JpiXoQ4eRMxOTcAzX2+PsjghNK7jdY6C4SBmhPsO5JjlloxrrtXvqvsbOCV0Qz6NBXrmr8L/G/AHvWIR0cxrnsu0Ar8R747/36lVEVxwj8lo37NWutDwD3AAeAIENZa/7Yo0Z+607nuEx3z4DDHFCU2EZOzGGVKKT/wFPB5rXWk1PEUk1LqPcAxrfXGUscyxhzAOcCPtNZnAzGgLMdWjBalVBW5VudcYDpQoZS6ubRRCZEzEZPzIWBmn69n5JcV3CbfnRUC2k+w70iOWWrFuG6UUk5yifkRrfXPixL5qSvGNV8IXKWUaiLXhXipUuqnxQj+NBTjug8CB7XWPT0jT5JL1uWiGNd8ObBPa92qtc4APwcuKEr0p+50rvtEx5wxzDFFqZX6pvdov8i1APaS+zTcM4Bi+YBtPkv/ARRP5N8vp//Akb3kBmQMe8xSv4p03Yrc/ajvlvr6xuqaB+x7CeU5IKwo1w28ACzOv/8a8P+V+lqL/Pt9HrCV3L1mRe6+7V+X+lpH67r7rL+F4QeEXVnqa5XXgJ99qQMoykXBleRGFu8BvpxfdhdwVf69B/gZuYEhrwLz+uz75fx+O+kzgrHQMcvtNdrXTW6kpwbeADblX2X1n7gYP+s+6y+hDJNzEX/HzwI25H/evwSqSn2dY3DN/xfYAWwBfgK4S32do3zdTUAHECXXO7Isv3x1/pr3APeSrxYpr/J5SflOIYQQosxMxHvOQgghxLgmyVkIIYQoM5KchRBCiDIjyVkIIYQoM5KchRBCiDIjyVmIU6CUspRSm/KzGf1KKVV5GseKjmJoQogJQJKzEKcmobU+S2u9gtxzpJ8tdUBCiIlDkrMQp+8l8hMHKKXWKKVeyk8e8aJSanF++S1KqZ8rpf5bKbVbKfWtgQdRStXm9333GMcvhCgzjlIHIMR4ppQygcvIT7VIrtrUW7XWWaXU5cA/Adfl151FbmavFLBTKfUDrXVz/jhTgXXAV7TWvxvDSxBClCFJzkKcGq9SahO5FvN2oCehhoCHlFILyZU+dfbZ5/da6zCAUmobMJvcVH9O4PfAZ7XWfxyb8IUQ5Uy6tYU4NQmt9VnkEqzi+D3nfwSez9+Lfi+5usc9Un3eWxz/cJwFNgLvLGbAQojxQ5KzEKdBax0H/gb4Qp/p+nqm37tlpIcBPgYsUUr93agHKYQYdyQ5C3GatNavk5vJ6UbgW8A/K6Ve5yRuG2mtrfz+lyqlPlOUQIUQ44bMSiWEEEKUGWk5CyGEEGVGkrMQQghRZiQ5CyGEEGVGkrMQQghRZiQ5CyGEEGVGkrMQQghRZiQ5CyGEEGXm/wfmAnhqHuvUmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leadingEdge(inh_desc_drug_ranks, 'Pharos Inhibitors -- High to Low Sim', drug2target)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overlap with IDG target gene sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('../../consensus/cp_weightedavg_coeff_mat.gctx', 'r') as f_in:\n",
    "  dex_mask = np.in1d(f_in['0/META/COL/id'].asstr()[:], ['dexamethasone'])\n",
    "  dex_ind = f_in['0/META/COL/id'].asstr()[dex_mask].nonzero()[0]\n",
    "  dex_consensus = f_in['0/DATA/0/matrix'][dex_ind, :]\n",
    "  dex_consensus = pd.Series(\n",
    "    dex_consensus[0],\n",
    "    index=f_in['0/META/ROW/id'].asstr()[:]\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('../../consensus/xpr_weightedavg_coeff_mat.gctx', 'r') as f_in:\n",
    "  mask = np.in1d(f_in['0/META/COL/id'].asstr()[:], ['NR1I2', 'NR0B1'])\n",
    "  ind = f_in['0/META/COL/id'].asstr()[mask].nonzero()[0]\n",
    "  consensus = pd.DataFrame(\n",
    "    f_in['0/DATA/0/matrix'][ind, :],\n",
    "    index=f_in['0/META/COL/id'].asstr()[mask],\n",
    "    columns=f_in['0/META/ROW/id'].asstr()[:]\n",
    "  )\n",
    "  nr0b1_consensus = consensus.loc['NR0B1']\n",
    "  nr1i2_consensus = consensus.loc['NR1I2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEnrichrLibrary(library_name): \n",
    "    ENRICHR_URL = f'https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=json&libraryName={library_name}'\n",
    "    resp = requests.get(ENRICHR_URL)\n",
    "    if not resp.ok: \n",
    "        raise Exception(f\"Error downloading {library_name} library from Enrichr, please try again.\")\n",
    "    return resp.json()[library_name]['terms']\n",
    "\n",
    "chea2022 = getEnrichrLibrary('ChEA_2022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = ['NR3C1', 'NR0B1', 'NR1I2']\n",
    "tf_gene_sets = {t: [] for t in targets}\n",
    "\n",
    "for tf in chea2022.keys():\n",
    "  geneset = []\n",
    "  if any(t in tf for t in targets):\n",
    "    if 'MLEC' not in tf and 'Wistar' not in tf:\n",
    "      tf_gene_sets[tf.split(' ')[0]] += list(chea2022[tf].keys())\n",
    "\n",
    "tf_gene_sets = {f\"{t} Targets\": set(tf_gene_sets[t]) for t in tf_gene_sets.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dex_up_genes = dex_consensus[dex_consensus > 0].sort_values(ascending=False).index.tolist()[:250]\n",
    "dex_down_genes = dex_consensus[dex_consensus < 0].sort_values(ascending=True).index.tolist()[:250]\n",
    "\n",
    "nr0b1_up_genes = nr0b1_consensus[nr0b1_consensus > 0].sort_values(ascending=False).index.tolist()[:250]\n",
    "nr0b1_down_genes = nr0b1_consensus[nr0b1_consensus < 0].sort_values(ascending=True).index.tolist()[:250]\n",
    "\n",
    "nr1i2_up_genes = nr1i2_consensus[nr1i2_consensus > 0].sort_values(ascending=False).index.tolist()[:250]\n",
    "nr1i2_down_genes = nr1i2_consensus[nr1i2_consensus < 0].sort_values(ascending=True).index.tolist()[:250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NR3C1 Targets: 4042 genes\n",
      "NR0B1 Targets: 1210 genes\n",
      "NR1I2 Targets: 631 genes\n",
      "Dex up genes: 250\n",
      "Dex down genes: 250\n",
      "NR0B1 up genes: 250\n",
      "NR0B1 down genes: 250\n",
      "NR1I2 up genes: 250\n",
      "NR1I2 down genes: 250\n"
     ]
    }
   ],
   "source": [
    "for tf in tf_gene_sets.keys(): \n",
    "  print(f\"{tf}: {len(tf_gene_sets[tf])} genes\")\n",
    "print(f\"Dex up genes: {len(dex_up_genes)}\")\n",
    "print(f\"Dex down genes: {len(dex_down_genes)}\")\n",
    "print(f\"NR0B1 up genes: {len(nr0b1_up_genes)}\")\n",
    "print(f\"NR0B1 down genes: {len(nr0b1_down_genes)}\")\n",
    "print(f\"NR1I2 up genes: {len(nr1i2_up_genes)}\")\n",
    "print(f\"NR1I2 down genes: {len(nr1i2_down_genes)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map = {\n",
    "  'NR3C1': '#44AA99',\n",
    "  'NR0B1': '#DDCC77',\n",
    "  'NR1I2': '#332288',\n",
    "  'Dex Up Genes': '#88CCEE',\n",
    "  'Dex Down Genes': '#AA4499',\n",
    "  'NR0B1 Up Genes': '#88CCEE',\n",
    "  'NR0B1 Down Genes': '#AA4499',\n",
    "  'NR1I2 Up Genes': '#88CCEE',\n",
    "  'NR1I2 Down Genes': '#AA4499'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6))\n",
    "vd = venn3(\n",
    "  [\n",
    "    tf_gene_sets['NR3C1 Targets'], \n",
    "    tf_gene_sets['NR0B1 Targets'], \n",
    "    tf_gene_sets['NR1I2 Targets']\n",
    "  ],\n",
    "  (\n",
    "    'NR3C1 ChIP-seq Targets \\n(PC12, MCF10A, BEAS2B)', \n",
    "    'NR0B1 ChIP-seq Targets \\n(mESCs)', \n",
    "    'NR1I2 ChIP-seq Targets \\n(Liver)'\n",
    "  ),\n",
    "  set_colors=(\n",
    "    color_map['NR3C1'],\n",
    "    color_map['NR0B1'],\n",
    "    color_map['NR1I2']\n",
    "  ),\n",
    "  alpha=0.5\n",
    ")\n",
    "\n",
    "for text in vd.set_labels:\n",
    "  text.set_fontsize(8)\n",
    "for text in vd.subset_labels:\n",
    "  text.set_fontsize(8)\n",
    "\n",
    "# Adjust label for NR3C1 targets\n",
    "lbl1 = vd.get_label_by_id(\"A\")\n",
    "x, y = lbl1.get_position()\n",
    "lbl1.set_position((x+0.15, y))\n",
    "\n",
    "# Adjust label for NR0B1 targets\n",
    "lbl2 = vd.get_label_by_id(\"B\")\n",
    "x2, y2 = lbl2.get_position()\n",
    "lbl2.set_position((x2-0.25, y2+0.03))\n",
    "\n",
    "# Adjust label for NR1I2 targets\n",
    "lbl2 = vd.get_label_by_id(\"C\")\n",
    "x2, y2 = lbl2.get_position()\n",
    "lbl2.set_position((x2, y2+0.01))\n",
    "\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.gca().set_axis_on()\n",
    "\n",
    "fig.savefig(\"../../publicationfigs/Figure6.pdf\", format='pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x1440 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf_dex_overlap = {}\n",
    "\n",
    "fig, axs = plt.subplots(6,3, figsize=(14,20))\n",
    "fig.set_facecolor('white')\n",
    "c = 0\n",
    "for tf in tf_gene_sets.keys(): \n",
    "  tf_dex_overlap[f\"{tf} - Dex Up Genes\"] = tf_gene_sets[tf].intersection(dex_up_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(dex_up_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'Dex Up Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#648FFF'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[0][c]\n",
    "  )\n",
    "  tf_dex_overlap[f\"{tf} - Dex Down Genes\"] = tf_gene_sets[tf].intersection(dex_down_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(dex_down_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'Dex Down Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#DC267F'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[1][c]\n",
    "  )\n",
    "  tf_dex_overlap[f\"{tf} - NR0B1 Up Genes\"] = tf_gene_sets[tf].intersection(nr0b1_up_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(nr0b1_up_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'NR0B1 Up Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#648FFF'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[2][c]\n",
    "  )\n",
    "  tf_dex_overlap[f\"{tf} - NR0B1 Down Genes\"] = tf_gene_sets[tf].intersection(nr0b1_down_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(nr0b1_down_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'NR0B1 Down Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#DC267F'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[3][c]\n",
    "  )\n",
    "  tf_dex_overlap[f\"{tf} - NR1I2 Up Genes\"] = tf_gene_sets[tf].intersection(nr1i2_up_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(nr1i2_up_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'NR1I2 Up Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#648FFF'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[4][c]\n",
    "  )\n",
    "  tf_dex_overlap[f\"{tf} - NR1I2 Down Genes\"] = tf_gene_sets[tf].intersection(nr1i2_down_genes)\n",
    "  venn2(\n",
    "    [tf_gene_sets[tf], set(nr1i2_down_genes)],\n",
    "    (tf.replace(' Targets', ' ChIP-seq Targets'), 'NR1I2 Down Genes'),\n",
    "    set_colors=(color_map[tf.split(' Targets')[0]], '#DC267F'),\n",
    "    alpha=0.6,\n",
    "    ax=axs[5][c]\n",
    "  )\n",
    "  c += 1\n",
    "\n",
    "fig.savefig(\"../../publicationfigs/Figure5.pdf\", format='pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<supervenn._plots.SupervennPlot at 0x7f82777ff430>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "supervenn(\n",
    "  [\n",
    "    tf_gene_sets['NR3C1 Targets'],\n",
    "    tf_gene_sets['NR0B1 Targets'],\n",
    "    tf_gene_sets['NR1I2 Targets'],\n",
    "    set(dex_up_genes),\n",
    "    set(dex_down_genes)\n",
    "  ],\n",
    "  [\n",
    "    'NR3C1 ChIP-seq Targets', \n",
    "    'NR0B1 ChIP-seq Targets', \n",
    "    'NR1I2 ChIP-seq Targets',\n",
    "    'Dex Up Genes',\n",
    "    'Dex Down Genes'\n",
    "  ],\n",
    "  chunks_ordering='minimize gaps',\n",
    "  sets_ordering='minimize gaps',\n",
    "  widths_minmax_ratio=0.03\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<supervenn._plots.SupervennPlot at 0x7f8278af20d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w8fFBt27d8PXXX2tdf/r0aWRkZGDDhg04ceKEdIMiKysrAEBaWpoUm5aWpnEJrbOzM1JTU5GWlobU1FQoFAoMGjSoSHmampoiJydHY1lOTg5MTEw0zl66urpKP6tUKiQmJmrtr2rVqrh9+7b0uHv37khNTcXChQvx5MkTAEB8fDw2bdoEOzs76d+RI0c02uUvYCtXriwdBBTUVqlUYsOGDfjhhx9Qo0YNdO3alTc5IiIiAIBarUZwcDDMzc2xZMkSAE/3uX5+fjA1NYWjoyOWLFmCvXv3Ij09Xa/9cWlh8UlERC+YPn06Vq5ciVu3bmldLwgC+vTpg1atWmHGjBkAAHt7e9SoUQPR0dFSXHR0tM4zeLa2tnjnnXewY8eOIuVYq1YtxMXFaSy7fv06XF1dNYrPmzdvSj/fuHEDzs7OWvt7/fXXsW3bNqjVap1jurq6Ijg4GKmpqdK/zMxMTJ48udB8C2vbsWNH/Pnnn7h9+zbq1asn3cSJiIheXqIoYtiwYbhz5w7Cw8NhZmamNU4QBABPC1VD98clicUnERG9wNPTE3379sXixYsLjJs8eTJWrlyJpKQkAMC7776LmTNnIiUlBZcuXcLKlSsxePBgrW0zMjKwfv16jZ1hTk4OsrOzoVarkZubi+zsbI2vcsmvV69e+P3337F3717k5eUhMTERM2fORL9+/TTili5dioSEBCQnJ2PWrFno27ev1v7Gjx+PlJQUBAcHIzY2FqIoIj09XeOGSgMHDsSOHTuwZ88e5OXlITs7GxEREdIdfAtSUNs7d+5g+/btyMzMhIWFBaysrAr87CkREb0cPvjgA1y8eBE7duyAQqGQlp84cQKXL1+GWq3GgwcPMGbMGAQGBkqX2hqyPy5J3LMREZFWX3zxhcZ3fmrTsGFDvPrqq5g3bx6Ap2dMa9euDZVKhYCAAEyYMEHjtu6JiYnS95GpVCokJydr3NV1+PDhUCgUWLduHWbNmgWFQoG1a9dqHdvb2xvr1q3Dp59+iipVqqBVq1Zo0aLFC98d+s4776BDhw7w8PBA7dq1X/hu0GccHBxw/PhxWFpaok2bNtJ3qqWnp2PZsmUAnp693L59O7766itUq1YNrq6umDdvXoFnS58pqK1arcaCBQvg7OyMKlWq4ODBg9KYRET0coqPj8fy5csRFRUFJycnaf8ZFhaGa9euoVOnTrC2toaPjw8sLCw07mlQ2P64tPB7PomICABeuITV1dX1ha8lEUXxhXbPvvAaACwsLLBq1SqsWrXqhbjAwMBCi7TVq1cbdCv4N954A2+88UaBMc2aNcOnn36qV3/Ozs748ccfC4xp0aIFDh48qHVd/q9OAYDBgwdr/KW5oLa6lhMR0ctJpVJp3e8+079/f53rCtoflyae+SQiIiIiIiLZsfgkIiIiIiIi2fGyWyIiqrCev5SYiIiISg/PfBIREREREZHsWHwSERERERGR7Fh8EhERERERkexYfBIREREREZHsWHwSERERERGR7Fh8EhERERERkexYfBIREREREZHsWHwSERERERGR7Fh8EhERERERkewEURRLdMA6tWuLS2fM0Cv2sY0NKleurFfso0ePZImVsw99ZD58CMusLIPaZCsUBrUxNN6Q56Ugcj5nhsybvttv6DwZo62x5rokldTvRnnJg4iIdCvKcU5pKM4xABVdaR2j6mPEiBFYuHChUfu0srIyan+lKSgo6JEoisrnl5uWdCKCKKLO2bN6xV7r0AGNGjXSKzYqKkqWWDn70EfkH3/oPV/PxLzyikFtDI035HkpiJzPmSHzpu/2GzpPxmhrrLkuSSX1u1Fe8iAiIt2KcpxTGopzDEBFV1rHqPowMTExerEYGBho1P7KIl52S0RERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERKXuxo0bcHBwQG5ubmmnQkRERDJh8UlEVA41btwY9erVQ2ZmprRs7dq16N69u/TYwcEBtWrVgkqlgo+PDz7//HPk5eVJ6x8/fowxY8bAzc0NDRo0wPfffy+tO3LkCKpVqwaVSiW1//rrrzVymD17Ntq2bQtHR0fMmTOn0JyvXr2KoUOHwsvLC+7u7nj11Vfx/fffa+Sky5w5czBixAi9t60kffzxx9I81ahRA05OTtLjvn37llgejRs3xsGDB0tsPCIiMr4lS5bAz88PFhYWGDx4sLT8+PHjaN++PapUqYJq1aqhd+/euH37trT+wIEDCAoKgq2tLdzc3F7oNy4uDkFBQahcuTLq1auHffv2lcDWvIjFJxFROZWXl4cVK1YUGBMREYH4+Hj89ttv2LZtG8LCwqR1c+fOxbVr1xAVFYVt27ZhyZIl+Ouvv6T1Tk5OiI+PR3x8PH7//XeEhYVh165d0np3d3dMmzYN7du3LzTX69evo2PHjnB2dsahQ4dw/fp1/Pjjj4iKikJGRkYRtv5/27Z161Zs2bIFP//8c5H6Ka5vvvlGmqePPvoIPXr0kB5v2LBB735Kq3gmIqKyw9nZGZ9//jmGDh2qsTwlJQXvvfce4uLiEB8fD2trawwZMkRar1QqMXToUMybN09rv/3790fjxo3x4MEDzJo1C2+//Tbu3bsn67Zow+KTiKicGjVqFJYuXYqHDx8WGuvh4YEWLVrg3Llz0rL169fj448/hp2dHby8vBAcHIx169Zpba9SqdC8eXNcvnxZWtavXz+0a9cOVlZWhY4/Z84cNGvWDDNnzoSTkxMAoE6dOlixYgVsbW2luM2bN8PX1xdeXl5YsGBBof0+66dly5a4dOmS1vXr1q1D06ZNoVKp0KRJE2zatElaFxYWhlatWqF27dro3bs3bt68Ka2LiIhAy5Yt4e7ujkmTJuGNN97A2rVr9crpmaFDh6JBgwZwd3dHt27dNHIcNWoUPvnkE/Tr1w+1atXCkSNHEB0djaCgIKhUKgwdOhTDhg3DV199JbXZs2cPAgMD4eHhgc6dO+P8+fMAgA8++AAJCQkYMGAAVCoVFi9ejOzsbIwYMQJ16tSBh4cH2rVrh7t37xqUPxERlayePXuiR48eqFq1qsbyzp07o3fv3rCxsUHlypUxatQo/P3339L65s2bIzg4GB4eHi/0eeXKFZw+fRrTp0+HQqFAr1690LBhQ4SHh8u+Pc9j8UlEVE41atQIrVu3xtKlSwuNjYmJwbFjx+Du7g4ASE1NxZ07d+Dt7S3FeHt7axSX+cXGxuLEiRNo2rRpkXI9dOiQxiXBuhw/fhzHjx/Hli1bMH/+fFy5cqXQNpcvX8bx48fRsGHDF9ZlZmbi008/xfr16xEfH49du3ZJcbt27cK3336LNWvW4PLly2jZsiXee+89AMCDBw8waNAgTJkyBVeuXIGbmxv++ecfA7caeP3113HixAlcunQJvr6+GpcOA0B4eDjGjRuHuLg4NGnSBIMGDUK/fv1w9epV9OzZU+NM89mzZzF27Fh88803iImJwaBBgzBw4EA8fvwYy5Ytg4uLC8LCwhAfH48xY8Zg/fr1SEtLQ3R0NGJiYjB//nwoFAqDt4GIiMqeQ4cOaezDC3L+/Hl4eHjA2tpaWubr6yv9AbMksfgkIirHJk+ejJUrV+L+/fta17/22muoVasWWrVqhdatW0uX8Tz7rKiNjY0Ua2Njo3EJbFJSEjw8PODm5oYWLVqgadOmaNmyZZHyTE5OhqOjY6FxEydOhEKhgI+PD7y9vTXO1Grbttq1a2PAgAEYOHAg3nnnHa1xJiYmuHTpErKysuDk5IR69eoBANasWYOxY8fCy8sLpqamGDduHM6dO4ebN29i3759qFevHrp37w4zMzOMGDEC1atXN3i7BwwYAGtra1hYWGDixIk4d+4c0tLSpPWdO3dGixYtYGJign///Re5ubl47733YGZmhm7duqFJkyZS7M8//4xBgwahadOmqFSpEvr16wdzc3OcOnVK69hmZmZISUnB9evXUalSJTRq1EjjwIOIiMqns2fPYsaMGTovsX1eRkaGxlVGAGBra4v09HQ50isQi08ionKsfv366NChAxYtWqR1/f79+xEfH4/Q0FCcPn0ajx49AvD0syEANHY86enpGpfQOjk54dq1a4iLi8O1a9dgaWmJkSNHFinPKlWq4M6dO4XG5S/wFAqFxg2Vnrd//37Exsbi1KlTmDJlCkxMTDRu/rNw4UIolUqEhoZi9erV8Pb2Rr9+/RATEwMAuHnzJj777DN4eHjAw8MDnp6eEEURt2/fRlJSEmrWrCmNJQiCxmN95OXlYcaMGfDz84ObmxsaN24M4OlZ1Wfy95mUlIQaNWpAEARpmbOzs/RzQkICvv/+eylfDw8PJCYmIikpSev4ffr0QVBQEIYPHw5vb2+EhIQgJyfHoG0gIqKy5erVq+jcuTMWLVqEtm3b6tXGyspK4w+fAJCWllYqf5Bk8UlEVM5NmjQJa9eu1bjrXX6CIKBHjx7w8/PD/PnzAQB2dnZwdHTUuOTm3LlzqFu3rtY+bGxs0KtXL+zdu7dIOb766qvYsWNHkdoaIv/Nf8aNGwfg6RnS8PBwnD9/HnXq1JGW16xZE/Pnz8e1a9ekfwkJCWjevDkcHR1x69YtqV9RFDUe6yM8PBy7d+9GeHg4rl+/jjNnzkh9PZO/0HR0dMTt27c11icmJko/Ozs7Y9y4cRr53rx5E7169XqhL+Dpmc+JEyfi6NGj2L17N/bu3WvQDZCIiKhsiY+PR7t27TB16lQEBwfr3c7b2xvXrl3T+INzdHS03pftGhOLTyKics7DwwM9evTAypUrC4wbO3Ys1q5dK52B7Nu3L7755hukpqYiJiYGa9euRf/+/bW2zcjIwNatWzWK05ycHGRnZ0OtViMvLw/Z2dk679g6adIknDx5EtOmTZPGv3btGkaMGKHXDZOK6u7du9i1axcyMzNhYWEBpVIpFWmDBw/GokWLpJsApaWlYfv27QCA9u3b49KlS9i5cydyc3OxYsUKg2/Wk5GRAQsLC1SpUgWPHj3CzJkzC4xv1qwZKlWqhNDQUOTm5mLXrl04ffq0tD44OBirV69GZGQkRFFEZmYm9u7dKx1MVKtWDXFxcVL84cOHceHCBeTl5cHa2hpmZmYwMeFun4ioLMvNzZX2p8/2rbm5ubh16xZee+01jBo16oX7BwCAWq1GdnY2cnJyIIoisrOz8eTJEwCAl5cXGjVqhOnTpyM7Oxtbt27F2bNnpT9eliTuhYiIKoAJEyZIl9Tq0qBBA7Rq1QpLliwB8LQgdHd3R6NGjdC9e3eMGjUKr7/+uhSflJQkXcLauHFjpKSk4IcffpDWjxs3Di4uLtiyZQsWLFgAFxcXbNy4UevY7u7u2L17N27evIk2bdrA3d0dQ4YMQaNGjfS6W25RqdVqLFu2DD4+PvD09MTRo0els79du3bFmDFjMHz4cLi5uaFNmzbSV81UrVoVq1atwowZM1CnTh1cu3YNzZs3N2jsPn36wMXFBT4+PmjdujX8/PwKjDc3N8fq1asRFhaG2rVrY/PmzejQoQPMzc0BPP0ez4ULF2LSpEmoXbs2mjVrhvXr10vtx44diwULFsDDwwNLlizB3bt3MWTIELi7u8Pf3x/+/v7o06ePQdtAREQla+bMmVAoFPj666/xyy+/QKFQYObMmQgNDcW1a9cQEhICKysr6d8zhw4dgkKhQJcuXXDjxg0oFAp06NBBWr9+/XqcOnUK9vb2mDx5MjZv3oxq1aqV+PYJ+S/vKQleHh7int699Yq91qEDGjVqpFdsVFSULLFy9qGPyD/+QJ2zZw1qE/PKKwa1MTTekOelIHI+Z4bMm77bb+g8GaOtsea6JJXU70Z5yYMqju7du6N3794GXepUXB06dMDgwYN13kyJqLwrynFOaSjOMQAVXWkdo+rD398fy5cvN2qfgYGBRu2vNAmC8EgUReXzy3nmk4iIqIz4+++/cefOHeTm5mL9+vW4cOECXnvttdJOi4iIyChMSzsBIiIieurq1av4v//7Pzx69AgqlQqrVq2Ck5NTaadFRERkFCw+iYiI9PDbb7/JPsagQYMwaNAg2cchIiIqDbzsloiIiIiIiGTH4pOIiIiIiIhkx+KTiIiIiIiIZMfik4iIiIiIiGTH4pOIiIiIiIhkx+KTiIiIiIiIZMfik4iIiIiIiGTH4pOIiIiIiIhkx+KTiIiIiIiIZCeIoliiA9apU0dcuXKlXrEZGRmwsrLSKzb5YTIemz7WK9Yi10LvWF2sRWu9cysOQ+bgGUPmAjB8Powxf4b2Y+h8y/HaKc52F/X1UpTnv7QZ+vqTi7Fep0REJJ/y8l5dUsd9pMnQ46CSPG4aPnw4Fi5cWCJjlUdvvPHGI1EUlc8vNy3pRExMTBAYGKhXbEREhN6x635fhytVrugV65XspXesLgGPA/TOrTgMmYNnDJkLwPD5MMb8GdqPofMtx2unONtd1NdLUZ7/0mbo608uxnqdEhGRfMrLe3VJHfeRJkOPg0ryuMnExIR/kCgCXnZLREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSUREZcLCngsRezK2tNMgIiIimZiWdgJERGS4hT0XIic7Bx+FfwRzhTkAIPK3SJzdcxZDlg4BAIT4h8DM0gwQAEulJbxf90aHUR1gUunp3x33Lt2Lc3+eQ3ZGNhTWCjTt0RSvDnpVGiM3JxeH1xzGv3v/Rfr9dFS2qwz3pu4IGBoA+xr2OPfXORzfcBxJMUmo2aCmNK4u2ZnZOLDyAC4evIistCxY2VvBq40XXh38KpR2ygLbXj99HVumb8HH2z8GAPw08icknE+ASSUTmJqbQtVIha4fd4W1g3WR57Sozu45ix1zdwAAxDwRuTm5T+f9P5/99VmJ5LF15lbYVLPB6++/XiLjERGRfBISEjB06FAEBATgs8+e7kf27duH0NBQPHz4EE2bNsXEiRNhY2NTaLtjx47h119/xfXr12Fubo5WrVph5MiRqFy5colvF4tPIqJySlSLOL7xuEbB+LwRP49AVZeqeJDwAKs/XI1qbtXQ9M2mAIAm3ZogcGggzBXmSLuXhrUfrYWDygENAhsAADZ+thFpd9PQK6QXnLyckJOdg7N7zuL6qeuwf8MeChsFWvZtifvx93E98nqBuebm5OLn0T/D0toSAxcMhIPKAY9SHyFyeyRuXbgFL38vg7e/y/guaNq9KR6lPcLGKRvxx6I/0PvL3gb3U1yvdHwFr3R8BcCLRbIh8nLzUMm0krHTIyKicmjRokWoV6+e9Pj69etYsGABZs+eDS8vL8yfPx/ffvstvvjiiwLbAUBmZiaCg4PxyiuvICcnBzNnzsQPP/yA8ePHl8i25Mfik4ionPJ/xx9/h/2NZj2bQWGtKDC2qktVuL7iiqSYJGmZg8pBI0YQBCQnJAMAYk/G4to/1zB6w2jYOtoCACpZVULzXs2l+NrNagN4esa1MNG7o/HwzkMMWjIIFpUtAABWVawQMCRAIy4pJgl7Fu/Bw6SH8GzpiR6f94CZhZm2LiWVbSqjQVADnNxyUuv6hAsJ+H3+73hw4wHMLMzQsENDdBrbCQBw89xN7Fm8B/fi7sHOyQ6dPuoE9ybuAICUxBRsm7kNt6/chou3C6rWqors9Gz0CulV6PY+c/jnwzj922lkpmbCpvrTs5L1A+oDAM78fganfzuNmg1qInp3NPze8kOrfq2wbeY2xJ2Jg0MtB9RuURtxZ+Iw7IdhAIB7cfewe+FuJF5KhNJeiaDhQfB53Qentp3Cv3v+BQTg+MbjcG/ijnfmvYMja4/gxKYTePzoMawdrNH1k67w8PPQO38iIip5+/fvh1KphLe3N27dugXg6VnPVq1awdfXFwAwdOhQDB48GI8ePZLOYGprBwDt2rWTfra0tETXrl2xevXqktugfFh8EhGVU871neHWxA1Hfz1a6KWW9+Lu4Ub0DbQe0Fpj+eGfD+PQmkPIycqBnbMdGnZoCAC4dvIaajaoKRWexXXt5DV4tvSUCk9dzv91HgMXDISphSlWvb8KUbui0OytZgW2yUzNxIUDF1DDq4bW9X8s/AMte7eEb2dfPH70GHev3QUApN1Lw6+f/Iq3vngLni09cf3UdWycshGj1o2C0l6J8GnhcPFxQfC3wUg4n4BfJ/yKum3qGrTdVVyqYMiyIbCqaoUL+y9gy/QtGLNxjHR5cMKFBPi088EnOz+BOk+NbTO3wczSDJ/s/ASpt1Pxy7hfYOv09Dl4kvUEaz9ai6D/C8KAbwbgbuxd/PzRz6juUR1+Pfxw89xNjctu78ffxz/h/2D4j8NhU80GKbdTIOaJBuVPREQlKzMzEz/99BMWLFiA33//XVoeFxcHHx8f6XHNmjVhamqKmzdvom7dujrbaXP27Fm4ubnJtQkFYvFJRFSOBf1fEFaNWIWWfVpqXb988HKIahE52TnwaeeDZj01C7m277ZFm+A2SLqShEuHL8FSaQkAyHqYBauqVkbLMystCzXqai8O82vRuwVsqj39/IpXGy+NM7XP271wN/Yu2QtzS3O4NXFDxzEdtcaZmJog+VYyMlMzobRTwtXHFQBw9o+zqNOqjnTJb+3mteFczxkxx2Lg1sQNty7dwruL34WpuSncGrvBq7XhlwZ7v+Yt/ezTzgeH1x7GrQu3UO/Vp5dEWTtYo0XvFgCennm+GHERH/7yIcwtzVHdvTp8O/si7kwcAODK31dgV8MOjbs1BgDUqFsDDQIb4ML+C6g+rPoLYwuVBOQ+ycW9uHtQ2ithX8Pe4PyJiKhkrVq1Cl26dEG1atU0lmdlZUGp1Lw/gpWVFbKysgps97xTp05hz549+P77742buJ6KVHwKgmAHwB3AZVEUHxk1IyIi0ptjbUd4tfbCkbVH4ODm8ML691e/jyo1q+D8/vPYt2wfnmQ/gam55lu/IAioUbcGrp64igOhB9BpbCcobBV4cPOB0fJU2CiQ8SCj0Lj8Ba+ZhRnS76XrjO08rjOadm+qsSz/zX9UvioMXDAQb055EwdWHsCS/ktgX8MeAcMCULd1XaQmpeL8gfO4/Pdlqb06Vw23Jm5Iv58OhbVCupkTANg52eHhnYd6bzMARO2OwrH1x5B6OxXA07OXjx7+b7dpW/1/Z5YzUzOhzlPDxvF/N4/If+Y5NSkVCecTMLvD7P/lm6eGbydfrWNXdamKTh91QsSPEbh3/R5qN6+NjmM6SsU9ERGVLVevXkVkZCRWrlz5wjqFQoHMzEyNZZmZmVAoFAW2y+/ChQuYOXMmQkJC4OrqatTc9VVo8SkIwkQAV0VR3PLf404ANgFQAkgRBKGLKIon5E2TiIh0Cfy/QCwfvBz+/f21rhcEAT6v++Dy4cs4uOogOn/UWWucOk+NlFspAACPZh44sfEEHt59qFEgFZVHMw/sX7EfT7KeaBR0xpb/5j/PVHWtirdnvA21Wo2LERex8bONmLR7EmwdbeHb0RfdP+3+Qj+pt1ORlZ6lka+hhWfq7VTs+HoH3l38Llx9XGFSyQTLBi2DKOa79FX4349KOyVMKpkg7W4aHGo5vDCmbXVbuDV2w7uL3tU6npC/s/+80uEVvNLhFWRnZmPnnJ3Y9/0+9JzW06DtICKikhEVFYU7d+6gb9++AJ6e7VSr1YiPj0ezZs0QG/u/ryNLTExETk4OXF1dsWvXLp3tVqxYAQCIiYnBZ599hokTJ6Jp06YvDl5C9Pmez6EAzuV7vPi/f9YAFgCYra0RERGVjKouVeHzug9ObCr474Btgtvg9G+nkf4gHWq1Gqe2nUJWWhZEUUTChQSc3HIS7n5Pb7ZTu1lteDT3wIbJG5B4KRF5uXl4nPkYJ7eexOmdpwE8LVZzHudAnad+emnv4xzk5eZpHdu3ky9sq9tiw5QNuBd3D2q1Go8ePsKhNYdw5egV407Ic6L/iEZmSiZMTExgaf30smLBRMArHV/B5b8v4+rxq9K2XD99HQ/vPoRdDTs413PGgdADyM3JRXx0PC4fuVzISJqeZD8BBEhfI3Nm5xnp86bamFQyQf3A+oj4MQJPsp/gXtw9RP8RLa33au2FBzceIHp3NPJy85CXm4dbF27hXtw9AICyihIpiSlS/P34+7h26hpyn+TC1NwUphamEExeLFCJiKhs6NatG8LCwhAaGorQ0FB0794dLVu2xNy5c9GuXTscO3YMZ8+eRVZWFn766Se0bdsWlStXLrAd8PROuZMmTcLo0aPh76/9D9UlRZ/LbmuIongFAARB8ASgAjBbFMVMQRDmAxgnZ4JERFS4gKEBiN4TXWCMY21HqBqpcDTsKNqPao+LBy9i37J9yMvNg7WDNZq/3Vz6/CEA9JnVB4fXHMamqZuQ8SADlW0rw6O5h3SH2ug/orF91nYpflbQLPh28cVbn7/1wtim5qZ4d/G7OBB6AGs/Wous9Kff81m3bV24eLsYaRa0u3r8KvYs3oOcxzmwc7LD29PfhpmFGWwdbdF/Tn/8ufRPbJ62GSaVTFCzfk10ndAVANArpBe2zdyGOZ3mwNXHFb6dfZGdnq33uNXdq8O/nz9C3wuFYCLAt5MvajWsVWCbLuO7YNvMbZjfbT4cajmgYbuGSLyUCACwUFog+Ntg7Fm8B3u+2wNRLcLR01H6rGuTN5pg4+cbMbvDbLg1dkPQ8CDsW7YP9+Pvw6SSCVwbuuKNSW8UcRaJiEhulpaWsLS0lB4rFAqYm5vDzs4OdnZ2GDduHGbNmoW0tDQ0adIEkyZNKrQdAGzcuBGpqamYN28e5s2bBwBwdHQslTveChqX/2gLEITbAOqKopgmCMJgACNFUWz23zozAPdEUbTTd8C6deuKly/r99fjiIgIBAYG6hW77vd1uFJFv7+eeyV76R2rS8DjAL1zKw5D5uAZQ+YCMHw+jDF/hvZj6HzL8dopznYX9fVSlOe/tBn6+pOLsV6nRM8cCD2A5IRkg75qpbj+XPonMpIz8NbUFwt6ooqgvLxXl9RxH2ky9DioJI+b6tati+XLl5fIWOVRUFDQI1EUlc8v1+ey290AVgiC0B3AJwDC863zBXDTOCkSERG93O7F3UPS1STpUujTO09Ld8YlIiIq7/S57HYcgIUAZgE49t/Pz3QCsF6GvIiIiF46Tx49weZpm5F+Px1WVazg39+fxScREVUY+hSfXURRHKpthSiKM42cDxERUZkU9H9Bso9Rs0FNjN00VvZxiIiISoM+l93yYmYiIiIiIiIqFn2KT96XnYiIiIiIiIpFn8tuKwmCEIQCilBRFPcbLyUiIiIiIiKqaPQpPi0A/AjdxacIwMNoGREREREREVGFo0/xmSmKIotLIiIiIiIiKjJ9PvNJREREREREVCy84RARERERERHJTp/i06uglYIgNDVSLkRERERERFRB6VN8Xs7/QBCEmOfWHzBeOkRERERERFQRCaIoFhwgCOmiKFrne5wiiqK9rvWFqeNZW1wxb5pesRmCElZipvFjTe1RuXJlvWJ1efToUbH7kGscQ9vIHa+zn/RUWZ5fQ+P1jTU0B2O0Lc6YpaWs5GyM33MiIpJXSR1PFVd5ybOiKa1jVH2MGDECCxcu1CvWyspK5mzKnqCgoEeiKCqfX67P3W6fr04Le1wgE6jRKveoXrHHTP1lif3bpjcaNWqkV6wuUVFRxe5DrnEMbSN3vC5nD/0uy/NraLy+sYbmYIy2xRmztJSVnI3xe05ERPIqqeOp4ioveVY0pXWMqg8TExO9i8rAwEB5kylHeLdbIiIiIiIikp0+Zz4rC4JwKN9j63yPBQAK46dFREREREREFYk+xeew5x7/+NzjUCPlQkRERERERBWUPsXnOQCPRVE8BwCCIFQH8C0AbwDHAXwsW3ZERERERERUIejzmc9vATjle7wCQJ3//vcGMNf4aREREREREVFFos+Zz/oADgOAIAh2ALoC8BZF8YogCL8BOArgQ9kyJCIiIiIionJPnzOfpgCe/PdzSwC3RVG8AgCiKN4EYCdPakRERERERFRR6FN8ngfQ+7+f+wHY92yFIAg1ATyUIS8iIiIiIiKqQPS57HYSgB2CIPwAIA9Am3zr+gL4W47EiIiIiIiIqOIo9MynKIpHANQC0B6AhyiKl/Ot/h3AOJlyIyKiCq579+5Yu3ZtaadBREREJUCfy24himK6KIqRoiimP7f8siiKifKkRkREhWncuDHq1auHzMxMadnatWvRvXt36bGDgwNq1aoFlUoFHx8ffP7558jLy5PWb9u2DZ07d4arq6tGOwC4ceMGHBwckJubCwD47rvv0KZNG6hUKjRp0gTfffddgfk9efIEc+bMQbNmzVCrVi00btwYY8aMwY0bNwrdtufHHjVqFGrUqAGVSgVPT0/06tULMTExhU+SDI4dOwaVSgWVSoVatWrBwcFBeqxSqZCQkFAiecyZMwcjRowokbGIiKh0rF+/HvXr14dSqUTt2rVx+PBhXLhwAX5+frC3t4e9vT3atWuHCxcuSG0OHDiAoKAg2Nraws3NrfSSf45exScREZVdeXl5WLFiRYExERERiI+Px2+//YZt27YhLCxMWmdvb4/3338fY8aMKXQsURSxdOlSxMbGYsOGDfjxxx+xZcsWnfFDhgzBH3/8geXLl+PatWs4ePAgfH19cejQIf03MJ/Ro0cjPj4eZ8+ehYODA0aNGlWkfoqrVatWiI+PR3x8PI4cOQIAiI2NlZa5uLjo1c+zwpqIiEibP//8E5MmTcJPP/2E9PR0HDp0CB4eHnB2dsbmzZuRnJyM+/fvo3v37ujXr5/UTqlUYujQoZg3b14pZv8iFp9EROXcqFGjsHTpUjx8WPj93zw8PNCiRQucO3dOWhYQEIAePXrAycmpgJZPjRkzBr6+vjA1NUWdOnXQuXNn/PPPP1pjDx48iIMHD+KXX35BkyZNYGpqChsbGwwbNgwDBw6U4hISEtClSxeoVCq8/fbbePDgQaF5VK5cGb169cKlS5e0rr927RreeOMNuLu7w8vLC8OGDZPWxcTEoFevXvD09ESLFi2wbds2aV1ycjIGDBgANzc3tG/fHrNnz0bXrl0LzSe/X3/9Fa1atYJKpULTpk2xevVqad2RI0fQsGFDLF68GA0aNMDo0aORlZWFkSNHonbt2mjVqhUWL16Mhg0bSm1u376NwYMHo27dumjSpIn0h4a//voL3377LbZt2waVSoWAgAAAwLp169C0aVPp7PSmTZsMyp+IiMqOadOm4YsvvkDLli1hYmKCmjVrombNmrCzs4ObmxsEQYAoiqhUqRKuXr0qtWvevDmCg4Ph4eFRitm/iMUnEVE516hRI7Ru3RpLly4tNDYmJgbHjh2Du7t7sccVRRHHjx9H3bp1ta4/ePAgGjdujJo1axbYT3h4OBYvXoxLly7hyZMnem1HRkYGNm/erFGk5Td79mwEBQUhNjYWZ8+exfDhwwEAmZmZ6NWrl1S4rly5EhMnTsTly09vZzBx4kRYWlri/PnzWLx4scYZYn05ODjg119/RVxcHBYvXoypU6ciOjpaWn/37l2kpKTgzJkzWLBgAebNm4cbN24gMjIS4eHh2Lx5sxSrVqsxYMAAeHt7499//8WWLVuwfPly7N+/H6+//jo++ugj9OjRA/Hx8Th48CAyMzPx6aefYv369YiPj8euXbt0zhEREZVteXl5OHXqFO7duwdPT0+4uLhg1KhRyMrKkmLs7OxgaWmJ0aNHY8qUKaWYrX5YfBIRVQCTJ0/GypUrcf/+fa3rX3vtNdSqVQutWrVC69atMXTo0GKPOWfOHKjVarzzzjta1ycnJ8PR0bHQfvr37w9PT08oFAr06NFD46zs85YuXQoPDw80b94cmZmZOj9zamZmhps3byIpKQmWlpZo2bIlAGDv3r2oVasW3nnnHZiamuKVV15Bt27dsH37duTl5WHnzp2YPHkylEol6tevr3EJk746dOgAd3d3CIKA1q1bIzAwEMePH5fWm5iYYNKkSbCwsIBCocD27dsxbtw42NnZwdnZWSqUAeDMmTN48OABJkyYAHNzc7i5uSE4OBhbt27VOb6JiQkuXbqErKwsODk5oV69egZvAxERlb47d+4gJycHmzdvxuHDhxEVFYUzZ85g5syZUkxqaioePnyIJUuWoHHjxqWYrX5YfBIRVQD169dHhw4dsGjRIq3r9+/fj/j4eISGhuL06dN49OhRscYLDQ3Fxo0bsW7dOlhYWGiNqVKlCu7cuVNoX9WrV5d+VigUGjdPet7IkSNx7do1XLhwAWFhYXB3d9e4+U/r1q0BPL1MSRRFtG/fHq1bt5bOYN68eRORkZHw8PCQ/oWHh+Pu3bu4f/8+cnNz4ezsLI2n72c389u3bx86duwIT09PeHh4YN++fUhOTpbWV61aFZaWltLjpKQkjbPD+X9+VkDnz3fhwoW4d++e1rGVSiVCQ0OxevVqeHt7o1+/fqV2UyYiIioehUIB4On9DmrUqAEHBweMHz8eu3bt0ohTKpUYMWIE3n33Xdy9e7c0UtWbPt/zSURE5cCkSZPw2muv4cMPP9S6XhAE9OjRA7t378b8+fMxa9asIo0TFhaGRYsWYceOHRqF2vMCAgKwYsUKJCYmFhhXXM9u/pOfo6Mjvv32WwDA8ePH0atXL7Rq1Qo1a9aEv78/wsPDX+gnLy8PpqamSExMRJ06dQAAt27dMiiXx48fY8iQIVi6dCk6d+4MMzMzBAcHQxRFKUYQhBdyTUxMlC5fzj9mzZo1UatWLZw8eVLreM/3BTw9y/3aa68hKysLX331FcaNG4edO3catB1ERFT67O3t4eLiovFer+19H3j6MY1Hjx7h1q1bGn/ULWt45pOIqILw8PBAjx49sHLlygLjxo4di7Vr10pnJfPy8pCdnY3c3Fyo1WpkZ2cjJydHa9tNmzZh1qxZCA8PL/TW7QEBAQgICMC7776LqKgo5ObmIj09HT/99FORPktpiO3btyMx8ek3gdnZ2UEQBJiYmKBDhw6IjY3Fxo0bkZOTg5ycHJw+fRpXrlxBpUqV0LVrV8yZMwePHj3C5cuXsX79eoPGffLkCR4/foyqVavC1NQU+/btQ0RERIFt3nzzTXz77bdITU3F7du38eOPP0rrmjRpAisrKyxevBhZWVnIy8vDxYsXcfr0aQBAtWrVcPPmTajVagBPP0+6a9cuZGZmwsLCAkqlUueBChERlX1DhgzBd999J90vYOHChejWrRv+/PNPnDlzBnl5eUhLS8P48eNhb2+P+vXrA4DG/lwURWRnZ+PJkyelvDUsPomIKpQJEyYUekltgwYN0KpVKyxZsgQAsHHjRri4uGDChAk4fvw4XFxcMG7cOK1tZ8+ejeTkZLRv31661PXjjz/WOdZPP/2Edu3aYfjw4fDw8EDbtm0RFRUl3ZlVLmfOnEGHDh2gUqkwcOBAzJo1C25ubrC2tsamTZuwZcsW+Pj4oEGDBpgxYwYeP34M4OnnWDMzM9GgQQOMGjVK5+dZdbG2tsbs2bPxf//3f6hduzbCw8PRsWPHAtt88skncHZ2RpMmTdCzZ0+88cYb0qXMlSpVwq+//op///0XTZs2hZeXFz766COkpz/92u0333wTAFCnTh0EBQVBrVZj2bJl8PHxgaenJ44ePYr58+cbOn1ERFRGTJ06Fc2aNYOXlxfq16+Pxo0b47PPPkNqair69+8PW1tb1K5dG7Gxsfjjjz+kj3UcOnQICoUCXbp0wY0bN6BQKNChQ4dS3hpAyH8pUEmo6+kuRs8ueEf8zDFTf7TKPWr02L+r9EajRo30itUlKiqq2H3INY6hbeSO1+Xsod9leX4Njdc31tAcjNG2OGOWlrKSszF+z4nWrVuHX375Bb///nuJjblq1Sps3boVO3bsKLExiUpLSR1PFVd5ybOiKa1jVH34+/tj+fLlesUGBgbKm0wZJAjCI1EUlc8v55lPIiKiUpSUlIQTJ05ArVYjJiYGy5YtM/i7RYmIiMoD3nCIiIioFOXk5ODjjz/GjRs3YGNjg7feessoX4VDRERU1rD4JCIi0qF///7o37+/rGO4urriyJEjso5BRERUFvCyWyIiIiIiIpIdi08iIiIiIiKSHYtPIiIiIiIikh2LTyIiIiIiIpIdi08iIiIiIiKSHYtPIiIiIiIikh2LTyIiIiIiIpIdi08iIiIiIiKSHYtPIiIiIiIikp0gimKJDlinTh1x5cqVesVmZGTAysqqVGPl7EOucQxtk5yahiyY6R2vQI5B8cbox9Ax5ejb1jSvyM95UV8vJfU6M6ayknNZyYOIiHQrL+/V5SXPiqa0jlH1ETJhDBYuXCg95utDU1BQ0CNRFJXPLzct6URMTEwQGBioV2xERESpx8rZh1zjGNombNtunFO76B3vY5JgULwx+jF0TDn67mj1oMjPeVFfLyX1OjOmspJzWcmDiIh0Ky/v1eUlz4qmtI5R9WFiYqJRcPL1oR9edktERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSURERERERLJj8UlERERERESyY/FJREREREREsmPxSUT0EnBzc0P16tWRmZkpLQsNDUVgYKD0WBAEKJVKWFlZoWbNmhg/fjzy8vKk9Y8fP8bQoUNhY2MDJycnLFiwQFoXEREBExMTWFlZSe2nTZumkcPUqVPRsGFDmJqaIiQkpMB8Q0JCYGZmBmtra1hbW8PLywujRo3C7du3izcRRnDq1Cl069YN9vb2sLOzQ4MGDfDZZ58hJSWltFMjIqIK5PHjxxg2bBhUKhWsra3RqFEj7N69GwAQFxcHQRCk/a6VlRW+/PJLjba69tmlicUnEdFLIi8vD4sWLSowJjo6GhkZGTh48CA2bNiAVatWSetCQkIQExOD+Ph4HDhwAHPnzsUff/whrXd2dkZGRgYyMjJw5MgR/Pjjj9i2bZu03tPTE3PnzkXXrl31yrdv375IT09HcnIytm7diqSkJDRt2rRUC9CjR48iMDAQrVu3xqVLl5Camoo//vgDpqamiI6OLrW8iIio4snNzYWrqysOHjyIhw8fYubMmejTpw/i4uKkmNTUVGnfO3XqVGl5Yfvs0sLik4joJTFhwgTMnz8fqamphcZ6enqidevWiIqKkpatWbMGU6dOhb29PerXr4/hw4dj9erVWtu7u7vD398fFy5ckJYNGjQInTt3hrW1tUF5m5mZwdvbGxs2bEC1atXwzTffSOtWrlwJT09PVKlSBd27d0diYiIAYNq0aRg9ejQAICcnB0qlEhMmTAAAZGVlwdLSEsnJydJfjtesWYNatWrBwcEBs2bN0pnLxIkTMWTIEHz66adwdHQEANSqVQvTp0/XOIu8atUq1K9fH/b29ujYsSPi4+OldYIg4IcffkCdOnVgZ2eHkSNHQhTFQtuKoohx48ahevXqsLGxQcOGDXHu3DmD5pKIiMoPpVKJkJAQuLm5wcTEBN26dYO7uzsiIyMLbWvIPrsksfgkInpJ+Pn5ITAwEPPnzy809tKlSzh8+DA8PT0BACkpKbh9+zZ8fX2lGF9fX5w/f15r+5iYGPz9999o2bKlcZIHUKlSJbz55ps4fPgwAGD//v349NNPsXHjRty+fRsqlQr9+vUDAAQEBCAiIgIAcPLkSTg5OeHQoUMAgGPHjqFu3bqoUqWK1PeRI0dw+fJl/PXXX5gxYwYuXrz4wviZmZk4duwYevXqVWCe27dvx1dffYUtW7bg3r17aNu2Lfr3768Rs3PnTpw8eRJnz57Fxo0bsWfPnkLb7t27F4cOHcKVK1fw8OFDbNy4EVWrVi3CTBIRUXl0584dXLlyBd7e3tIylUoFFxcXDBkyBPfv3wdg+D67JLH4JCJ6icyYMQPfffcd7t27p3V9kyZNoFQqUb9+fQQGBuLDDz8EAGRkZAAAbG1tpVhbW1ukp6dLjxMTE2FnZwcbGxt4eXmhRYsWaNOmjVHzd3Z2RnJyMgAgLCwMQ4cORZMmTWBhYYHZs2fj2LFjiIuLQ6tWrRATE4MHDx7g0KFDGDZsGG7duiVdUhwQEKDR77Rp06BQKODr6wtfX1+tl9CmpKRArVbDyclJWjZx4kTY2dlBqVRi5syZAIAffvgBn376KerXrw9TU1NMmTIFUVFRGmc/J0+eDDs7O9SqVQtBQUHSGeaC2pqZmSE9PR2XLl2CKIqoX78+atSoYdT5JSKisiknJwcDBgzAoEGDUK9ePTg4OODkyZOIj49HZGQk0tPTMWDAAAD67bNLC4tPIqKXiI+PD7p164avv/5a6/rTp08jIyMDGzZswIkTJ6QbFFlZWQEA0tLSpNi0tDSNS2idnZ2RmpqKtLQ0pKamQqFQYNCgQUbN/9atW9IZy8TERKhUKmmdlZUVqlatilu3bkGhUMDPzw8HDx7EoUOHEBAQAH9/f/z9999ai8/8BWXlypWlHXd+9vb2MDEx0fjM6dy5c5Gamoq33noLubm5AID4+HiMHTsWdnZ2sLOzQ5UqVSCKIm7dulXoeAW1fe211zBq1CiMHDkS1atXx3vvvafxfBARUcWkVqsRHBwMc3NzLFmyBMDTfZ6fnx9MTU3h6OiIJUuWYO/evUhPT9drn11aWHwSEb1kpk+fjpUrV2oUQ/kJgoA+ffqgVatWmDFjBoCnhVeNGjU0zghGR0drXPqTn62tLd555x3s2LHDaHmr1Wrs2LEDbdu2BfC02M1/NjEzMxMPHjxAzZo1ATy99Hb//v04c+YMmjVrhoCAAOzZswf//PMPXn31VYPHVyqVaNGiBbZs2VJgnKurK5YvX47U1FTpX1ZWFvz9/Qsdo7C2Y8aMQWRkJC5cuIArV65g3rx5Bm8HERGVH6IoYtiwYbhz5w7Cw8NhZmamNU4QBABP95WG7rNLEotPIqKXjKenJ/r27YvFixcXGDd58mSsXLkSSUlJAIB3330XM2fOREpKCi5duoSVK1di8ODBWttmZGRg/fr1Gju6nJwcZGdnQ61WIzc3F9nZ2Rpf5aJLbm4uLl68iP79+yMpKQnjx48HAPTv3x8//fQToqKi8PjxY0yZMgUtWrSAm5sbgKfF588//4wGDRrA3NwcgYGBCA0Nhbu7O6pVq6bHTL1o7ty5WLVqFb7++mvcvXsXAJCQkIDr169LMSNGjMDs2bOlz9Y8fPgQmzZt0qv/gtqePHkSJ06ckG6gZGlpCRMT7saJiCqyDz74ABcvXsSOHTugUCik5SdOnMDly5ehVqvx4MEDjBkzBoGBgdKltobss0sS91pERC+hL774QuM7P7Vp2LAhXn31Vens2vTp01G7dm2oVCoEBARgwoQJ6NSpkxSfmJgofdeYSqVCcnIywsLCpPXDhw+HQqHAunXrMGvWLCgUCqxdu1bn+Bs2bICVlRVsbW3RvXt3VK1aFZGRkXB2dgYAtGvXDl9++SV69eqFGjVqIDY2FuvXr5fa+/v7IysrSzrL2aBBA1haWhbprOczbdq0wf79+3Ho0CF4eXnBzs4OnTp1QmBgoHR33bfeeguTJk1Cv379YGNjAx8fH+l72QpTUNu0tDQMHz4c9vb2UKlUqFq1qnQHXyIiqnji4+OxfPlyREVFwcnJSdrHhoWF4dq1a+jUqROsra3h4+MDCwsLrFu3Tmpb2D67tJiWdgJERCS//N8JBjy9vDM7O1tjWf6v+3gmf9FkYWGBVatWaXz35zOBgYFQq9UF5rB69Wq9b/MeEhKCkJCQQuNGjBiBESNGaF1nZWWFnJwc6bEgCNLZymfc3Nxe2O5nd8nVpUWLFti1a1eBMcHBwQgODta67vnxnp8TXW1ff/11nD17tsBxiYio4lCpVFr3zc88fyf1/AraZ5cmnvkkIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmZlvSAN2/eRGBgoF6xqampsLOzK9VYOfuQaxxD29y5n4xMWOgdr8Rjg+KN0Y+hY8rR927TnCI/50V9vZTU68yYykrOZSUPIiLSrby8V5eXPCua0jpG1YeiREapeARRFEt0QIVCIdatW1ev2Ly8PFSqVKlUY+XsQ65xDG0jd7wx+pEzR31ji7PdRW1bUq8zYyorOZeVPIiISLfy8l5dXvKsaErrGFUfjx8/xrJly0pkrPIoKCjokSiKyhdWiKJYov+8vLxEfR04cKDUY+XsQ65xDG0jd7wx+pEzR31ji7PdRW1bUq8zYyorOZeVPIiISLfy8l5dXvKsaErrGFUfTZs2LbGxyiMAmaKWWpCf+SQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmVavG5ZMkS+Pn5wcLCAoMHD9arTUxMDCwtLTFw4ECjxAYGBsLS0hJWVlawsrLCs+8gjYiIgImJibTcysoKa9asMWg74uLiIAiCRh9ffvmltP6TTz5BnTp1YG1tjXr16uHnn39+oe8nT55g2LBhUKlUsLa2RqNGjbB7925p/V9//YV69eqhcuXKCAoKQnx8PLZu3VrovM6YMQOCIGDfvn0ay/PnamVlhUqVKmH06NE656+g+AsXLsDPzw/29vawt7dHu3btcOHCBa39PH78uMDtfJ6u500bQ15n+sQOHDgQNWrUgI2NDby8vBAaGvpCjLb51dXu+PHjaN++PapUqYJq1aqhd+/euH37doF5FqQ445w+fRqvvvoqrKys4OjoiEWLFhU5D20Kep4Ly+/AgQMICgqCra0t3NzcjJoXUUnT9Xta2PvmwoUL4eHhARsbGzg7O2PcuHHIzc3V6HvRokVwd3eHUqlE/fr1ceXKlRLdNiI5FHacEBoaCk9PT1hZWaFTp05ITEyU1oWEhMDMzEzjeOXatWulsRlGU9jxWkHzoc/7SFmh65hWn2MafY7ptB2v3bp1C2+++SaqVKkCFxcX/PDDD3Js2kurVItPZ2dnfP755xg6dKjebUaOHIlmzZoZNXbJkiXIyMhARkYGLl++rJHfs+UZGRkYNGiQ1vaFbUdqaqrUx9SpU6XlSqUSO3bswMOHD7FmzRqMHTsWR48e1Wibl5cHV1dXHDx4EA8fPsTMmTPRp08fxMXF4f79++jZsye+/PJLJCcnw8/PD3379kXVqlULzCc2NhabNm1CjRo1XliXf3uTkpKgUCjQu3dvnXNXULyzszM2b96M5ORk3L9/H927d0e/fv209pObm6tzO3XR9bw9z5DXmT6xn376KeLi4pCWlobffvsNn3/+OSIjI6X1uuZXV7uUlBS89957iIuLQ3x8PKytrTFkyJBCczU0v8LGuX//Pjp16oT3338fDx48wNWrV9GhQ4ci56FNQc9zYfkplUoMHToU8+bNM2pORKVB1+9pYe+b3bt3x+nTp5GWloZz584hOjoaixcvltaHhobixx9/xO+//46MjAzs3LkTDg4OpbGJREZV0P4jIiICU6ZMwfbt25GcnAx3d3f0799fo33fvn01jlk8PDxKaUuMo6Djr8Lmo7D3kbJE1zGtPsdOhR3T6TpeGzhwINzd3XHnzh38/vvvmDJlCg4cOGDcDXuJmZbm4D179gQAnDp1CgkJCYXGr1+/HnZ2dvD398fVq1eNFltchm7HM9OnT5d+btGiBdq2bYtjx47B399fWq5QKBASEiI97tatG9zd3REZGYkHDx7A29tberMJCQmBg4MDPvzwQ/To0UNnPiNHjsScOXPw4YcfFphfeHg4qlevjrZt2+q1Pc/H29nZwc7ODgAgiiIqVaqk87lQKpU6t7O4Z7kMeX70ifX29pZ+FgQBgiAgNjYWTZs2BaB7fnW169Onj0bcqFGjEBAQoOfW6Z9fYeMsWLAAHTt2xIABAwAAFhYWqF+/fpHz0Kag57lXr14F5te8eXM0b978hbP1ROVRQe8jBb1v1q5dW/pZFEWYmJhI69VqNaZPn47Vq1ejQYMGL8QTlWcF7T+OHTuG3r17S79XU6dORc2aNREbG/tS/A48f/y1c+fOAuejoPeRsubVV19FYGDgC8dlnTt31ojTduxU2DGdtuO1jIwMREREYOPGjTAzM4Ovry/efvttrFq1CkFBQcbctJdWufnMZ1paGr744gssWLDAqLHA079AOzg4oHXr1oiIiJCW3717F46OjnB3d8e4ceOQmZlZpNxVKhVcXFwwZMgQ3L9/X2tMVlYWTp48qXFAos2dO3dw5coVeHt74/z58/D19ZXWKZVK1K5dG9evX9fZftOmTbCwsECXLl0KzXvNmjV49913IQhCobEFxdvZ2cHS0hKjR4/GlClT9Oor/3bqout5KwkffvghKleujHr16qFGjRrSfBY2v7ra5Xfo0KFCXwdFza+gcY4fP44qVarA398f1atXxxtvvIEbN24UK4/CFPQ8G2MeiMqygn5PC3rf/PXXX2FjYwMHBwdER0fj/fffBwAkJCQgISEB586dg6urK9zd3TFt2jSo1eoS3S6ikvD8/kMURWnds5/PnTsnLduxYweqVKkCb29vLFu2rGSTlZm246/C5kPX+0h5Zegxg67jtWdz9fz85Z87Kp5yU3xOnToVw4YNg4uLi1Fj58yZg2vXruHWrVt477338MYbbyA2Nhb16tVDVFQUbt++jf379yMyMhLjx483KGcHBwecPHkS8fHxiIyMRHp6unRW6XkjRoyAr68vOnbsqLO/nJwcDBgwAIMGDUK9evWQkZEBW1tbjRhbW1tkZWVpbZ+eno4pU6bo9Tm++Ph4HDx4UOelxobEp6am4uHDh1iyZAkaN25caF/Pb6c2up63kvL9998jPT0dhw8fRs+ePWFhYaHX/Gprl9/Zs2cxY8aMYl9aWpRxEhISsGbNGixatAg3btzQetmSMRX0PBtrHojKsoJ+Twt633znnXeQlpaGK1euYMSIEXB0dAQA6S/7e/fuxb///osDBw5g3bp1+PHHH0tuo4hKwPP7j06dOmHjxo04e/YssrKypM/xPXr0CADQp08fXLx4Effu3cPKlSsxY8YMrFu3rpS3wji0HX8VNh+A7veR8sjQY4aCjtesra3RunVrfPnll8jOzsbp06cRHh6uMXdUPOWi+IyKisK+ffswbtw4o8YCTy93tba2hoWFBQYNGoTWrVtj165dcHJyQoMGDWBiYgJ3d3fMnTsX4eHhBuVtZWUFPz8/mJqawtHREUuWLMHevXuRnp6uETdhwgScO3cOGzdu1HmWUa1WIzg4GObm5liyZInUf1pamkZcWloaFAqF1j5CQkIQHBys12Wsa9euRZs2beDu7q7HlhYer1QqMWLECLz77ru4e/euzn60bac2up63klSpUiW0adMGCQkJWLZsmd7z+3y7Z65evYrOnTtj0aJFel/qbMxxFAoF3nrrLTRr1gyWlpaYNm0ajh49iocPHxY7l+cV9Dwbex6IyjJdv6dA4e+bderUgbe3t3TJ2LP3/okTJ8LOzg5ubm54//33S/y9kUhO2vYf7dq1w/Tp09GrVy+4ubnBzc0N1tbW0kmIBg0awNnZGZUqVYK/vz/Gjh2LzZs3l+ZmGI2246/C5iO/599HypuiHDMUdrwWFhaG69evw9XVFR988AEGDhyo1wkt0k+5KD4jIiIQFxeHWrVqwcnJCfPnz0d4eDiaNGlSrFhtBEHQONWef3lxL116Vljm72fatGnYvXs39u7dCxsbG63tRFHEsGHDcOfOHYSHh8PMzAzA088MRUdHS3GZmZmIjY3VWQD+9ddfWLx4MZycnODk5ISbN2+iT58+Wv/69/PPP+t91lPfeLVajUePHuHWrVta1+vaTn3oet5KQm5uLmJjY3XO75w5cwpsBzz9y2W7du0wdepUBAcHy5JfYeO88sorGn/80Pdya0MV9DzLOQ9EZVn+39P8CnvfzN+ubt26MDc3L5HfY6LSUND+Y+TIkYiJicGdO3fQq1cv5ObmwsfHR2s/pXnMYGy6jr8MmQ9d7z9lXVGPGQo7XlOpVNi5cyfu3buHEydO4P79+2jevLlcm/HSKdXiMzc3F9nZ2cjLy0NeXh6ys7O13ur5vffeQ2xsLKKiohAVFYURI0aga9eu2LNnT7FiU1NTsWfPHmncsLAwHDp0CJ06dcKBAwcQHx8PURRx8+ZNTJ48GW+++aZB23HixAlcvnwZarUaDx48wJgxYxAYGChdKjt79mz8+uuv2LdvH6pWrapznj744ANcvHgRO3bs0Dir+dZbb+HcuXMIDw9HdnY2ZsyYgVdeeQU1a9bUms9ff/2Fc+fOSXPj7OyM5cuXo0ePHhrjHT16FLdu3SrwLrf6xP/55584c+YM8vLykJaWhvHjx8Pe3l7nTWx0befzCnretNH3daZP7N27d7F+/XpkZGQgLy8Pe/bswbp16/D666/rnN+RI0ciJSVFZ7tbt27htddew6hRozBixIjCprtABeVX2DhDhgzB1q1bERUVhZycHHz55Zdo06bNC5d2F5eu57mw/NRqNbKzs5GTkwNRFJGdnY0nT54YNTeiklDQ72lh75uhoaHSWdALFy5g9uzZeP311wEAlStXRt++fTF37lykp6cjISEBK1asQLdu3UptW4mMSdf+Izs7G+fOnYMoirhx4wbee+89jB07Fvb29gCA7du3IyUlBaIo4p9//sHixYt1HtOVJ7qOvwqbj4LeR8qaZ8dizx+X6XPspOuYrqDjNQC4ePEi0tPT8eTJE/zyyy/Yu3evwR+9owKIolii/7y8vMRnpk2bJgLQ+Ddt2jRp/YEDB0Rtpk2bJg4YMEBjWVFi7969K/r5+YlWVlaira2t2KJFC3Hv3r2iKIriN998Izo7O4sKhUJ0cXERR48eLaalpWkdT9d2/Prrr6Kbm5tYuXJl0cnJSQwODhZv374ttQMgmpubi0qlUvo3a9YsjVzXrVsnAhAtLCw04n755RdRFEXxzz//FOvWrStaWlqKAQEB4vXr18VBgwYVOK/PqFQq8c8//3xh7t577z1x4MCBWudT21zrit+4caNYt25dUalUig4ODmKXLl3E6Ohorf3ExcUVuJ35Ywt63rT1bcjrrKDYAwcOiHfv3hVfffVV0dbWVrS2thZ9fHzEFStWaJ2nZ/MriqK4detWne1CQkJEABrbrVQqtc51YQrKr6Bxnvn+++9FZ2dn0c7OTuzWrZt448YNg8YvLOeCnufC8jtw4MALz01AQECR8iAqTQX9nhb2vjl48GCxevXqYuXKlUWVSiV+8sknYlZWlrT+4cOHYt++fUUrKyvRxcVFnD59uqhWq0t8G4n0pe97dUH7j5SUFLFhw4Zi5cqVRUdHR3Hy5Mlibm6u1LZfv35ilSpVRKVSKdatW1dctGiRbHmWJF3HX4XNR2HvI2WJrmNaXccMhhz/PZP/eE0URXHhwoWig4ODWLlyZbF169biyZMntebWtGlTY29uhQIgU9RSC5Zq8VkYQ37R5YqVsw+5xjG0jdzxxuhHzhz1jS3Odhe1bVnc2RWmrORcVvIgIiLdyst7dXnJs6IprWNUfbD4LJiu4rNcfOaTiIiIiIiIyjcWn0RERERERCQ7Fp9EREREREQkOxafREREREREJDsWn0RERERERCQ7Fp9EREREREQkOxafREREREREJDsWn0RERERERCQ74el3gJbggIKgBpBVooMSEREREREZT2UAj0o7iTJMIYriCyc6S7z4JCIiIiIiopcPL7slIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCIiIiIiItmx+CQiIiIiIiLZsfgkIiIiIiIi2bH4JCKiMkMQhDhBEL4QBCHjv3/ZgiDk5Xt8/r84URCEzHzLMwRBmPjfupD/1o99ru+x/y0PybdsiiAI1/9rnyAIwoYS3WAiIqKXCItPIiIqa46KomgliqIVgBEAjj17LIqid74433zLrURRnJtv3RUA7z7X76D/lgMABEEYBCAYQLv/xvID8JcsW0REREQsPomIqEI6CaCyIAjeAPDf/5b/LX+mGYA9oijGAoAoikmiKK4o8UyJiIheEiw+iYioolqL/539HPTf4/yOA3hXEIQJgiD4CYJQqUSzIyIiesmw+CQiovLqtCAIqfn+dXxu/S8A+guCYAag33+PJaIo/gJgNICOAA4CuCsIwqSSSJyIiOhlZFraCRARERVRE1EUr+paKYriDUEQrgL4CkCMKIo3BUF4PiYMQNh/BWqP/36OEkVxj4x5ExERvZR45pOIiCqynwF8/N//OomimCOK4iYAZwH4lERiRERELxue+SQioopsA4AEAH8/v0IQhMEA7gE4BCATTy+/9QZwogTzIyIiemnwzCcREZVX0c99z+e3zweIopgliuI+URSztLRPAzAFwA0AqQDmAvhAFMUjciZNRET0shJEUSztHIiIiIiIiKiC45lPIiIiIiIikh2LTyIiIiIiIpIdi08iIiIiIiKSHYtPIiIiIiIikh2LTyIiIiIiIpIdi08iIiIiIiKSHYtPIiIiIiIikh2LTyIiIiIiIpIdi08iIiIiIiKS3f8DHwPQWsKI3GoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "supervenn(\n",
    "  [\n",
    "    tf_gene_sets['NR3C1 Targets'],\n",
    "    tf_gene_sets['NR0B1 Targets'],\n",
    "    tf_gene_sets['NR1I2 Targets'],\n",
    "    set(nr0b1_up_genes),\n",
    "    set(nr0b1_down_genes)\n",
    "  ],\n",
    "  [\n",
    "    'NR3C1 ChIP-seq Targets', \n",
    "    'NR0B1 ChIP-seq Targets', \n",
    "    'NR1I2 ChIP-seq Targets',\n",
    "    'NR0B1 Up Genes',\n",
    "    'NR0B1 Down Genes'\n",
    "  ],\n",
    "  chunks_ordering='minimize gaps',\n",
    "  sets_ordering='minimize gaps',\n",
    "  widths_minmax_ratio=0.03\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<supervenn._plots.SupervennPlot at 0x7f827db0b4c0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "supervenn(\n",
    "  [\n",
    "    tf_gene_sets['NR3C1 Targets'],\n",
    "    tf_gene_sets['NR0B1 Targets'],\n",
    "    tf_gene_sets['NR1I2 Targets'],\n",
    "    set(nr1i2_up_genes),\n",
    "    set(nr1i2_down_genes)\n",
    "  ],\n",
    "  [\n",
    "    'NR3C1 ChIP-seq Targets', \n",
    "    'NR0B1 ChIP-seq Targets', \n",
    "    'NR1I2 ChIP-seq Targets',\n",
    "    'NR1I2 Up Genes',\n",
    "    'NR1I2 Down Genes'\n",
    "  ],\n",
    "  chunks_ordering='minimize gaps',\n",
    "  sets_ordering='minimize gaps',\n",
    "  widths_minmax_ratio=0.03\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dex down\n",
      "{'NRP1', 'IL6ST'}\n",
      "Dex up\n",
      "set()\n",
      "NR0B1 down\n",
      "{'NFKBIA'}\n",
      "NR0B1 up\n",
      "set()\n",
      "NR1I2 down\n",
      "set()\n",
      "NR1I2 up\n",
      "{'FGF1'}\n"
     ]
    }
   ],
   "source": [
    "# Dex down vs targets\n",
    "print('Dex down')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(dex_down_genes))\n",
    "# Dex up vs targets\n",
    "print('Dex up')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(dex_up_genes))\n",
    "\n",
    "# NR0B1 down vs targets\n",
    "print('NR0B1 down')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(nr0b1_down_genes))\n",
    "# NR0B1 up vs targets\n",
    "print('NR0B1 up')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(nr0b1_up_genes))\n",
    "\n",
    "# NR1I2 down vs targets\n",
    "print('NR1I2 down')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(nr1i2_down_genes))\n",
    "# NR1I2 up vs targets\n",
    "print('NR1I2 up')\n",
    "print(tf_gene_sets['NR0B1 Targets'].intersection(tf_gene_sets['NR3C1 Targets']).intersection(tf_gene_sets['NR1I2 Targets']).intersection(nr1i2_up_genes))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overlap between all three target gene sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib_venn._common.VennDiagram at 0x7f827dd52580>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "fig.set_facecolor('white')\n",
    "venn3(\n",
    "  [\n",
    "    tf_gene_sets['NR3C1 Targets'],\n",
    "    tf_gene_sets['NR1I2 Targets'],\n",
    "    set(dex_up_genes)\n",
    "  ],\n",
    "  (\n",
    "    'NR3C1 Targets \\n(PC12, MCF10A, BEAS2B)', \n",
    "    'NR1I2 Targets \\n(Liver)',\n",
    "    'Dex Up Genes'\n",
    "  ),\n",
    "  set_colors=(\n",
    "    color_map['NR3C1'], \n",
    "    color_map['NR1I2'],\n",
    "    color_map['Dex Up Genes']\n",
    "  ),\n",
    "  alpha=0.4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ScatterEnv",
   "language": "python",
   "name": "scatterenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "5e9a1c5299caa2f223d30cf4fcf42c44b82f7011cad9fa6e2d99d51b0feb9873"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}