Visualize_SKO_Pathways.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>Pathway</th>\n",
       "      <th>NB</th>\n",
       "      <th>Phenotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fatty acid oxidation</td>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Glycerophospholipid metabolism</td>\n",
       "      <td>19</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Methionine and cysteine metabolism</td>\n",
       "      <td>3</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nucleotide interconversion</td>\n",
       "      <td>42</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sphingolipid metabolism</td>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Pathway  NB Phenotype\n",
       "0                Fatty acid oxidation   2        S1\n",
       "1      Glycerophospholipid metabolism  19        S1\n",
       "2  Methionine and cysteine metabolism   3        S1\n",
       "3          Nucleotide interconversion  42        S1\n",
       "4             Sphingolipid metabolism   2        S1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df = pd.read_csv('KO_data/severity_SKO_RECON_Pathways.csv')\n",
    "der_df.Phenotype= der_df.Phenotype.str.replace('s','Series')\n",
    "der_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fatty acid oxidation', 'Glycerophospholipid metabolism',\n",
       "       'Methionine and cysteine metabolism', 'Nucleotide interconversion',\n",
       "       'Sphingolipid metabolism', 'Starch and sucrose metabolism',\n",
       "       'Transport, extracellular', 'Inositol phosphate metabolism',\n",
       "       'Pyrimidine synthesis', 'Alanine and aspartate metabolism',\n",
       "       'Folate metabolism',\n",
       "       'Glycine, serine, alanine, and threonine metabolism'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.Pathway.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('Var2', 'count')],\n",
       "           )"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Recon pathways reactions for RECON3 subsystems\n",
    "pathway_df = pd.read_csv('RECON_Pathway_Reactions.csv')\n",
    "pathway_df.head()\n",
    "pathway_df_unq = pathway_df.groupby(['Var1']).agg(['count'])\n",
    "pathway_df_unq.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Calculate the percentage of rxns in a pathway\n",
    "for i in range(der_df.shape[0]):\n",
    "    pathway = der_df.iloc[i,0]\n",
    "    rxns_count = int(pathway_df_unq.loc[pathway,:].values)\n",
    "    der_df.iloc[i,1] = der_df.iloc[i,1]/rxns_count\n",
    "    der_df.iloc[i,2] = der_df.iloc[i,2]/rxns_count\n",
    "der_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec51efdc90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iuPMtk/d8YL6Z878FTm37fAO/CXwJOAD8BXBiG+cauIve5wTfbMrl+pXmlt4hg081r9H99M4SalPuZ+gds154Xf7Jkv1vbnI/BVzWptzHbT/I4oesfc+3P1UgSUW19RCNJGlIFrwkFWXBS1JRFrwkFWXBS1JRFrwkFWXBS1JR/we7gP0q0SkjawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "der_df.iloc[:,1].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#der_df = der_df.loc[der_df.iloc[:,1]>3,:]\n",
    "# Calculate the percentage of rxns in a pathway\n",
    "for i in range(der_df.shape[0]):\n",
    "    pathway = der_df.iloc[i,0]\n",
    "    rxns_count = int(pathway_df_unq.loc[pathway,:].values)\n",
    "    der_df.iloc[i,1] = der_df.iloc[i,1]/rxns_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#der_df = der_df.loc[der_df.iloc[:,1]>0.05,der_df.iloc[:,2]>0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "der_T = der_df.pivot_table(values='NB',index='Pathway', columns='Phenotype',aggfunc='mean',fill_value=None,dropna=False)\n",
    "der_T = der_T.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Phenotype</th>\n",
       "      <th>S1</th>\n",
       "      <th>S16</th>\n",
       "      <th>S2</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>S7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pathway</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alanine and aspartate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatty acid oxidation</th>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerophospholipid metabolism</th>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycine, serine, alanine, and threonine metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inositol phosphate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methionine and cysteine metabolism</th>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleotide interconversion</th>\n",
       "      <td>0.256098</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030488</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyrimidine synthesis</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sphingolipid metabolism</th>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starch and sucrose metabolism</th>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport, extracellular</th>\n",
       "      <td>0.050528</td>\n",
       "      <td>0.036199</td>\n",
       "      <td>0.010558</td>\n",
       "      <td>0.011312</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>0.016214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Phenotype                                                 S1       S16  \\\n",
       "Pathway                                                                  \n",
       "Alanine and aspartate metabolism                    0.000000  0.000000   \n",
       "Fatty acid oxidation                                0.002081  0.000000   \n",
       "Folate metabolism                                   0.000000  0.000000   \n",
       "Glycerophospholipid metabolism                      0.115152  0.115152   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000  0.000000   \n",
       "Inositol phosphate metabolism                       0.000000  0.000000   \n",
       "Methionine and cysteine metabolism                  0.065217  0.065217   \n",
       "Nucleotide interconversion                          0.256098  0.256098   \n",
       "Pyrimidine synthesis                                0.000000  0.000000   \n",
       "Sphingolipid metabolism                             0.015038  0.015038   \n",
       "Starch and sucrose metabolism                       0.095238  0.095238   \n",
       "Transport, extracellular                            0.050528  0.036199   \n",
       "\n",
       "Phenotype                                                 S2        S5  \\\n",
       "Pathway                                                                  \n",
       "Alanine and aspartate metabolism                    0.000000  0.000000   \n",
       "Fatty acid oxidation                                0.000000  0.000000   \n",
       "Folate metabolism                                   0.000000  0.000000   \n",
       "Glycerophospholipid metabolism                      0.109091  0.109091   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000  0.000000   \n",
       "Inositol phosphate metabolism                       0.030769  0.030769   \n",
       "Methionine and cysteine metabolism                  0.000000  0.000000   \n",
       "Nucleotide interconversion                          0.000000  0.030488   \n",
       "Pyrimidine synthesis                                0.000000  0.047619   \n",
       "Sphingolipid metabolism                             0.015038  0.015038   \n",
       "Starch and sucrose metabolism                       0.047619  0.047619   \n",
       "Transport, extracellular                            0.010558  0.011312   \n",
       "\n",
       "Phenotype                                                 S6        S7  \n",
       "Pathway                                                                 \n",
       "Alanine and aspartate metabolism                    0.076923  0.000000  \n",
       "Fatty acid oxidation                                0.002081  0.002081  \n",
       "Folate metabolism                                   0.101695  0.000000  \n",
       "Glycerophospholipid metabolism                      0.109091  0.109091  \n",
       "Glycine, serine, alanine, and threonine metabolism  0.042553  0.000000  \n",
       "Inositol phosphate metabolism                       0.000000  0.000000  \n",
       "Methionine and cysteine metabolism                  0.000000  0.000000  \n",
       "Nucleotide interconversion                          0.018293  0.256098  \n",
       "Pyrimidine synthesis                                0.000000  0.000000  \n",
       "Sphingolipid metabolism                             0.015038  0.015038  \n",
       "Starch and sucrose metabolism                       0.000000  0.000000  \n",
       "Transport, extracellular                            0.016214  0.016214  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['S1', 'S16', 'S2', 'S5', 'S6', 'S7'], dtype='object', name='Phenotype')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "der_T_1 = der_T.loc[:,['S2', 'S6', 'S5', 'S16', 'S1', 'S7']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "der_T_1.columns = ['A549_0.02', 'A549_0.2_ACE2',  'A549_2','A549_2_ACE2','NHBE_2','Calu3_2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lung1_pathways = der_T_1.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alanine and aspartate metabolism',\n",
       " 'Fatty acid oxidation',\n",
       " 'Folate metabolism',\n",
       " 'Glycerophospholipid metabolism',\n",
       " 'Glycine, serine, alanine, and threonine metabolism',\n",
       " 'Inositol phosphate metabolism',\n",
       " 'Methionine and cysteine metabolism',\n",
       " 'Nucleotide interconversion',\n",
       " 'Pyrimidine synthesis',\n",
       " 'Sphingolipid metabolism',\n",
       " 'Starch and sucrose metabolism',\n",
       " 'Transport, extracellular']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung1_pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MjojjCvv4NSlIv1pp5v+HwMYRcW9u527AK6Rg8iKa0xk4OCIeKNTxUt7PnXXKHwJcFhEX5O8nSdoUWKGJff05Iv6d93Mmaax2iIj+edk5pAC75mjgjIi4NH9/MY/TlZJ+FxHvSPoIGBMRn6R1RMRrwBmFenpJ+h6wM3BXYfnrwG/zefacpG+SLiD+PJPGtwvwZEQ8mr8Pr1Pm8oi4Otd/cm7jbRHRLy87HegvadGIGN3EPs06zDPdZmYVioheEbFO4adX+1uxIbAG8ChpNrEtF5JmTmuz5j8izd4BrEyaXb2r/qasTZrFHpPTF8YC7wNfApYvlBtRC7iz5YF5SXnotT5+RArmVy7t46FCmbGkGfFimUm1gDsbmeuuXXB0Le4nu79Qxx2kQHuYpD6Sdpf0uVL5QXnfxTbNV+rjoNI2I4HFC234uNSX9+v0pT0fk45nrY6X837aqqNrcZ+Ftjej2J/asXu6tGzhwl2HtYGja+dBPheuAhYm3TmpS9I8ko6WNEjS23m77UkXQ0UPl+7YPAQsJenzzJzxPQ/4uaT/KqUGbVynTDNjAp8ed7OZzjPdZmatMxQI0i3xT0TEMABJ49vZ/grgNKV86jWB0cDteV3dtIWCTsBTpBnvsncKfy6nvdTqrZf20tEHLKe0sX2nOsumKRcRY3Ie7kak1JqjgJMlrRsRIzvQjg/r1F9rQ6NxnFUfKC32Jxos61T4PAHoW6euUQ320x04HDiYFMCOJaWkdCRwneHxjYhbJXUBtiHdObhFUt+IKOaXd3RMzGY6n1xmZi0SEW+TguTfaDpeDRgR7wA3kNJK9gJ651lngMGkBx/L+bU1A0mpCqMjYmjp5502toF0oTAZKD44OQ8pX3dwqez6hTILk/Kyn222f7lst9KybsX9RMSUiLg7Io4Cvk2and22UH61vO9imyYDLzbZhsF8mo8MQJ6hXY1p+9tIJ2DdQh1fB75G2+PxLIXxy8rfZ5aBwLfqnAdDI6J2YTSZlIde1A34V0RcERFPkcb0m3Xq/04pd319YGREfMBMGt+IGJ3bsQfwK2B3SfM3u73ZZ8Ez3WZmrXUAKYXiCUk9SDnJU0i3/Ffn05nrtlxIenhwXtLDb8Ans8BnA6dImgTcC3wFWDsiziM9ONcd6CfpOFIO7TKkFJXzI+KFejuLiHGSzgNOlTQaGEbKHV+C9FBa0TGSRpHSKI4jBW4def3hGUBfSU+QxmFrUp7v9gCStiWlidxLmp3fFPgcUweynYFLJJ1ICnJPBS5s68HVOv19QVI/0gOC+5AeTDyJ9IrHjvRlCnCWpINJrwb8C/A/6udzQ3oo8XJJjwEDSMf2O0x9F2JmORG4WdLLpFz+KaQLpPUi4ohcZjiwoaQrSWlBo4EhwI75Tsto4CBSytKTpfq/Rur7uaRg+nekhx9nyvjmYzuQNJ6dSefHSxExqaMDYVYlB91mZi0UES9JWpOUGvFHUuD7ISlwPJepH3irZwDpAcWXI6I8e3sU8C7pDSZLk/JWL8/7HS9pI1IQ2hf4Aik47p+3aeTI/HkpKf/6SWDriHi9VO73wJmkN4v8D9i22WA3t/EmSQeRLg7OIuVvHxAR/8pF3gN+TAroFyLNtO4dEfcVqrkn77t/LnM9cAQds2fe/z9JOfYPkPrbkfdqTyIFk5eTcp4fBrZv6+00EXGt0isFT8rt/ifwZ2CPDra9XRFxm6QfkM6T7qSgewhTv83jOOAC0hjPT0oL+RMpyL6VdCHRm3QxV87F7kOaJX+ElMZxMemio2ZGx7c2tsuR3q7zMLBdk9uafWbU3u84OL3/i7NqzpoZ4z/8uNVNsBmw0LxzfobbEZsu315u9QzJr1V7DTgoIvpUua9mSdqEFOQu1so3QUjqDSwaEdu2V7biduwBnBMR/u2iZnMxz3Sbmc2GlH65yBKk1I4J1H8IzszMZhEOus3MZk9fJ+VTjyC9h3pyi9tjZmYNOOg2M5sNRcRw2n8tYEtExABmgbblN1m0XET0xr/t0GyuN+cnVJqZmZmZtZiDbjMzMzOzijnoNjMzMzOrmINuMzMzM7OKOeg2MzMzM6uYg24zMzMzs4o56DYzMzMzq5iDbjMzMzOzijnoNjMzMzOrmINuMzMzM7OKOeg2MzMzM6uYg24zMzMzs4opIhoWeGbE2MYFZnMrfHWRVjehUkPfGNvqJlTq8dffaXUTbAbttObXW92ESi3QGbW6DWZm1nqe6TazlpnTA24zM7MaB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmb2CUl7SBrb6nbUSOouaXir22H1SQpJP53BOnpIeqat72ZzCgfdZmYtJGkxSedKGi5pkqQ3Jd0laYtCmeGSureynTbnkrRsDp7XaXVbsp7Axq1uhNnM1rnVDTAzm8tdDywE/AoYCixOCji+MrN3JGm+iJg8s+ud20iaNyI+bHU75lQRMRaYZe62mM0snuk2M2sRSV8ENgR+HxF3RcTLEfFYRPSMiGtymQFAF+CMPBsZeflXJF0taYSkCZL+J2nPUv0DJJ0nqaekUcADefnn8/LXJU2U9KykHUvbbibpGUnjJPWXtFw7fTlM0qBc/jVJF+X+1dbvIWlse/VKOkLSG7ns5cAiTYzjcZJezncK3sjbFcfgnFL53pJuLnyXpMMlvZDrGCHplLyuNgu8s6S7JU0A9s3rtpf0dN7mVUlHS1Kh3u3zmEyQ9I6keyQtUVi/naQn8jEYJukkSfM16GdtDLeR9Jyk8ZL+KekLkn6a2/++pCskLVjq3xGSXsxteVrSroWqh+XPx3JfB+Tt1pV0u6TRkj6QdL+kDeo07auSbsnteblUN5JWk3RnYRx6S/pCg36W001WU7r784GkMZL+K2nTvG6T3OZt8lhOkHSfpKUlbZzLjpV0s6SZfiFr1hEOus3MWqc2o/dDSQu0UWZ7YARwIrBk/gFYABgIbAusApwNXCBps9L2uwIiBfe/zEHhraTZ9D2BlYHDgOIM+PzAUcBewAbAF4Hz2+nLx8AhuS27AOsBfyuVaVivpJ8DfwKOB9YCns9ta5OkHYDuwAHAiqTxeLSdtpadDBwLnJLb/zPg1VKZU4BzSeN1k6S1gb7ADcBqwO9z336T2/VV4BrgMqArsBFwRaHdWwF9gHPyPvcCfprb0sj8wOHAL4DNgHWAfwC7AzsAPyaNwQGFbf5EupNyYG7/KaRz5Qd5/Xr5c2vS+bV9/v653OYNc5mngH9LWrTUphOAfwJrAL2Ay5VTVSQtBPyHdJ6vB/wE+C5wSTv9LLoKeD1vvybQA5hYpw2HAN8BvgRcCxwH7ANsQhrjHh3Yp9lM5/QSM7MKSdqH9B9/Ta+I6AUQEVMk7QFcCOwj6UnSbHTfiHgkl3lH0kfAmIh4o1ZJRLwGnFGsV9L3gJ2BuwrLh0XE4YX2bEEKeFeJiGfz4pdKze4MHBgRz+dtegKXSuoUER/X62dEnFX4OlzSEUA/SbsXtmmv3kOAyyLiglz+pDyjuUK9fWZdSAHZ7Tnl4xXg8QblpyJpEeBQ4JCIqAWCQ4GHSkX/FhH/KGx3GnBPRByfFw2RtCJwJOli42vAvMA/IuLlXKb4cODRwBkRcWn+/qKkI4ErJf0uIqKNJpfH8Krc/iUiYnRe1g/YFDhT0sKkC5ctI+K+XMcwSeuRgvBbgFF5+dulc+zu0lgdRArstwauLKy6oc4xO4R0wfcL0t2K3SJiTK5nH6C/pBUiYmgb/SzqAvSMiOfy93rbHFvrn6TzScdg7YgYmJddRrqoMWsZz3SbmVUoInpFxDqFn16l9deTArTtSDPQ3wUelvSHRvVKmienMwyS9LbSG0e2B75eKvpE6fuawOuFgLueSbWgLhtJCiC/2EZ5JH1P0h1KqRljSDPA8wFf7UC9XZk22C1/L+tLmvUfJuliST+TNH872xStTJo9vqudcuVAvis5XafgfmApSZ8H/gvcCTwj6XpJ+0tarFB2beDonPowNh+/q4CFmXrMyspj+CbwRi3gLixbvNC/BYD/lPa1P7B8ow5LWlzSBZKGSHofGJPrLZ9j9Y7ZyvnPXYFBtYA7e5B0Z2RlmvNn4CKl9J6jJX2rTplBhT+/mT+fLi1bHLMWctBtZtZiETExIu6IiBMj4rvAxUAPNcjvJaVUHE6a7d6MdGv/JlKgWzSu9F20b0q5ifmz7v8ZkrqQZkyfJaVmrE1Kl6DUng7V24yIeBVYiZRn/QFwJvBEnuGFFNyV+zxvsflN7qreOLY1Gx0R8RGwZf4ZRErveEHS6rlMJ1JKxBqFn2+TUmRGTVPjp+qNYfmhzuDTMa19blfa1yq5bY1cBqxLmkn/bt5uBNOeY400HKdmKoiIHuS0ntyOQZL2KhUrjkHk7crLHPNYS/kENDOb9QwmpRHU8rwnA/OUynQD/hURV0TEU8CLwDebqHsgsKSkrjOrsaS84vmAQyPioYgYQpq976hngfVLy8rfp5EvWm6JiENJQeIqwP/l1aP4NA++ZvXCnwcDk0gXLh0xmHQMiroBI2qzupE8FBEn5HaNBGoPrA4EvhURQ+v8lAPrGVHrX5c6+6mlvdTy+eudY3/LY/s/0kx3eSyh/jGr3UkZDKwu6XOF9d8lxR+N7rZMJSJeiIi/RsQPSBeleze7rdmswjndZmYtkt+m0Jf0UNkgUlCzDnAEcFdEfJCLDgc2lHQlKb1gNDAE2FFSN2A0cBCwHPBkO7u9C3gEuF7SobmeFYCFI+Km6ezKC6Qg6hBJN5CCrkOmo56zSQ/hPQYMIOXgfgd4p60Nck58Z1KfxpKC2g9zmwDuBs6S9EPSg5n7AsuQxpSIGCPpbOAUSZOAe0mva1w7Is5r0NYzSW/76EFKC1mXdOfhD7ld6wObA7eRUhvWzPsdnLc/EbhZ0svAdaQZ7FWB9SLiiIaj1AG5fz2Bnvkh2ntJOdbrAx/ndKe3gAnAVkq/iGhiRLxPOjd2lfQIKe3ldKZ+4LZm+9Ix24x03CA9LHoC6bgeR3rI8QJSHni7+dxKb2HpSfp7MhxYgnQx8EjHRsKs9TzTbWbWOmOBh4GDgXuA/5HeXnEVn86IQnoLwzKk2exa6sGfSG/puJUUSI0jBTgN5QcWtyHlI19Jmm08m46lDJTrHJT7cBgpqNyblP7S0XquJb1h4iTSxcNqpHzeRt4jpW7cR3pQcQdg+4iovQbvksLPA6Qxv7FUx1HAaaQ3mDxLenf60u20dSAplWaHvN9T80/t9YTvk2bbbyZdAJwJ/DEirszb3wb8gPTA46P55/ekB0FntmNJ49qddI7dkds9LLdlCvBb0nEbCfTL2+1FCtCfIL2J5RLyxUpJj1zfIFKu+J4R8ViuezywFfB5Uh/7kXK+y+khbfmIFKhfRrpoujFv3/CtNmazIrX9gHTyzIixTeVcza5W+Gq7r4CdrQ19Y87+/QKPv97mBJjNBnZas/w81pxngc5N5wybmdkczDPdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVTRDQsMHEKjQvM5gY8P6rVTajUJist1uommM3VFuiMWt0GMzNrPc90m5mZmZlVzEG3mZmZmVnFHHSbmZmZmVXMQbeZmZmZWcUcdJuZmZmZVcxBt5mZmZlZxRx0m5mZmZlVzEG3mZmZmVnFHHSbmZmZmVXMQbeZmZmZWcUcdJuZmZmZVcxBt5mZmZlZxRx0m5mZmZlVzEG3mZmZmVnFHHSbmZmZmVXMQbeZmZmZWcUcdJuZmZmZVcxBt5nZbEBSSPrpTK6zh6RnZmadnyVJi+Zx2aTVbbGZQ9IeksbOhHqGS+re1nezVnDQbWY2C5C0pqSPJD3wGe62J7DxZ0g7M+UAACAASURBVLg/K5jei56ZFZhWaRa8oFsXOLfVjbC5m4NuM7NZw69JQcGqkrp+FjuMiLER8fZnsS+bmqR5W92GuUlEjIqI8a1uh83dHHSbmbWYpAWBXYALgX8Av2pim1MlPS9pQr51frqkBQrre0h6RtJOkl6UNEbSTZIWLZcpfO8t6WZJB0t6TdK7ki6VtFChjCQdkeucIOlpSbu209Z1Jd0uabSkDyTdL2mDUpmQtI+kvpLGSXqpXG+u5wlJEyU9CXyniXHaWtJ9uS/vSLqtfFEj6ThJL0uaJOkNSZcX1g2QdL6ks3Md70o6Q1KnQpldJT2Wx/it3IelCus3yf37vqRHJU0G9gWOB1bJ60LSHrn8YZIG5XF4TdJFkr5Yqwu4FFi4sF2PvG4+SadJGpG3fUzSVu2MzwBJ50k6M4/PqHz855f0d0nvSXpF0m6l7ZaSdE1hTG6RtGJet8f09K1U/3aShuRj3V/SN0rr95U0VNLk/PnrdvpZTjfZt1D/qHxedM7ran8Pjsznw/tKf986Kf2deSsvP7LRPs3KHHSbmbXeT4GXI2IQcAXwS7U/EzoO2AvoChwA7AQcXSqzLLAj8BNgS2BN4KR26t0QWBXYvLDtwYX1fyJdFBwIrAycAlwg6QcN6vxc7teGwHrAU8C/VbgAyI4D+gGrA9cCl0jqAiBpYeAW4CVgHeD3pPSY9iwMnJX3uwnwPvAvSfPlencAupPGcEVgW+DRUh2/IP1/uQEpWN4HOKSwfj5SkLl63n5R4Oo6bTkNOAb4Vu7nmcDzwJL559pc7uNc/yqki7H1gL/ldQ/mdeML29XG4VJSutAuwGrAZbmvqzceIn4BjCFdxJxKGq+bgCGksb4MuEjS1wCULsL6AxPz/jYAXgfuzOuunc6+1cxPGs89c93zADdKUt7/T4BzcjtXBc4GzpW0XTv9JG+/DvB34ARgJdK5/p9SsY2A5UjnzH7AEcC/c9u6AT2AUyWt3cw+zQAUEQ0LTJxC4wKzuQHPj2p1Eyq1yUqLtboJZnO1BedVLUir6RURvYplJN0D/CsieubAYhhweERcXygTwM8i4h/19iNpP6B7RKyQv/cgBaZLRMT7ednRwJ6lMj+NiFXz997AZsByETElL7swf988B76jgS0j4r7Cvs8CvhkR329mTHIfRwK/i4grC/07NSKOyt87Ax8A+0TElZL2AU4Hlo6IsbnMrqRgftOIGNDkvhfO9W4cEfdLOowUSK8aER/WKT8A+BqwUuT/MCUdA+wXEUu3sY9vAc8Cy0TEiDw73Z801sVj2oPC+Ddo89akIH3BiPg4zxqfExGLFMosD7wALBsRrxSW3wSMjIgD2qh7ADB/RGyQvwt4C3goIn6Yl81LusjbJSL+IWkv4CjSMa+NyTx5u/0j4roZ7NulQLeIeCCX6UK62NoqIu5Ueu7h+YjYq1BPb2CFiOiWvw/PY9Sz/F3S9nkfS0fEmDpt6k36e7BsRHyUlz0OzBcR3y6Um2ofZu3xTLeZWYUioldErFP4KQfcKwD/B1yVywfQB9i7Ub2SfqqUpvGG0kN1fwG+Xir2ci3gzkYCi7fT5MG1gLvONisDCwD/kTS29gPsDyzfoK2LS7og385/nzSrunid9g6q/SG3YVRh312BQbWAO3uonb4gaXlJVymlw3wAvEn6v6+27765T8MkXSzpZ5LmL1XzcC24LOx3KUmfz/tYS1I/pRSVMcDjuVy5f4/TBEnfk3SHUprIGOAG0mz6VxtsthYgYHDp2PyABscmK457kILnpwvLPgTe5dNjsTZpFnhMYT/vA19qb19N9u1jCncbIuJl0nm4cl7UFSg/cHx/YX177gBeJh3zPpJ2l/S5UpnBtYA7e5PCmBSWtff3yewTnVvdADOzudzepNvnr+S755CCJyQtExGvljeQtD5wDen2+KHAe8APmTbdojxzG7Q/2dJom9rndsArpXLTzBIXXAYskds6HJgE3EUKtprdt5g+/wJeI81mvwZMAQbX9h0Rr0paiTSzuTkpLeJ4Sd+JiHHtVZ5nzm8D7gR2IwWsiwL3MW3/mqmvCymN5kJSus3bpID66jr1FXUijde6TDuOE9rZbb1xb+88eIqU0lT2Tls7mYG+1VPvLnxTd+YjYoyktUgpJFuQZu1PlrRuRIzMxTo6JmbtctBtZtYiOYVid9J/+jeXVl9Bymk9sc6m/we8FhF/LNTVpap2FgwmBcxdIuLuDmzXDfhtRNwCIGkJUp5vR/e9u6SFC8Hw+o02kPQV0qzogRHRPy9bi9L/fRExkRQM3iLpVOAN0hjfnot8R5IKs93rk1I2Psg5vYsCf4iIYXkf2zfZp8mkC66idUgB6KGF1IZtm9juSdKFyVdrfa3QQGBnYHREvNdGmentG6RAdl1S/jqSvk5K8Xk2r3+WdE5dUtimG+kcaUq+k3I3cLek40kXS9sCvRpuaDYDHHSbmbXOD0gB24XlV/dJugbYX9KfIuLj0nZDSOkNvyClOmxFCoIqlWcIewK13PN7gUVIQejH5dSZUnt3lfQI6cHG00lBWUdcRXoI9BJJJ5KCsPKDo2XvknLQfy3pVWAp4AzSbDfwyZs2OgOPAGNJD49+SMqPrvkacJakc0kPKP6O9EAppBn/ScBvJP2dFOT/keYMB7rkC4FXSGk3L5CCzkMk3UAa20PqbLeApC1Iwfb4iBgiqQ/QW9LhpMD4y6QHAV+KiBuabFMz+pAePu0n6bjc9mWAHwHnR8QLM9A3SMfnLEkHk2bp/wL8j3Q3AdIx7CvpCdKF0dakh0GbutjJgf7ypPP3HWBT0sO+zzbazmxG+baImVnr/Aro38a7svsCXUgpD1OJiH+RAo+zSPm4W5Bu138WjiW9uaE7KRC6A9iB9PBnW/YiBedPkNJiLiEFZU3Ludzbkt4wMpCUStPwlW35YmVH4NvAM6Q3VhxLCpJr3iMdh/tymR2A7Wuz1lkf0qztI6TUiItJgSARMYp0t+LHpJnW44HDmuzW9aQ3YtxFyl/fOb/B5uBcx2BS+tFUv0kxIh4EzielZYwivVkD0p2RS0kXNc+R7p5sRMpfnmny+643Ij3c2Dfv6zJSTve7M9K3bBLpAuty0ph3Ih2TyPu/CTiIlK40ONd5QP570Yz3SMfrztz27sDexYeDzargt5f47SVmVqEFOk93LrLNAvLbPZ6JiN+0ui1mNnvzTLeZmZmZWcUcdJuZmZmZVcwPUpqZmbUhIjZpdRvMbM7gmW4zMzMzs4o56DYzMzMzq5iDbjMzMzOzijnoNjMzMzOrmINuMzMzM7OKOeg2MzMzM6uYg24zMzMzs4o56DYzMzMzq5iDbjMzMzOzijnoNjMzMzOrmINuMzMzM7OKOeg2MzMzM6uYg24zMzMzs4p1bnUDWu2l98e1ugmV2oTFWt0EMzMzs7meZ7rNzMzMzCrmoNvMzMzMrGIOus3MzMzMKuag28zMzMysYg66zczMzMwq5qDbzMzMzKxiDrrNzMzMzCrmoNvMzMzMrGIOus3MzMzMKuag28zMzMysYg66zczMzMwq5qDbzMzMzKxiDrrNzMzMzCrmoNvMzMzMrGIOus3MzMzMKuag28zMzMysYg66zczMzMwq5qDbzGw2JmlZSSFpnVa3ZVYgqbekm2ewjqnG1GNsZjODg24zsxbKQWLU+Vmj4n3OUGBaNUkDJJ3T6nZkrwJLAk+1uiFmNvvq3OoGmJkZdwK7lZaNbkVDbFoR8RHwRqvbYWazN890m5m13qSIeKP0MwVA0vySzpL0pqSJkh6W1K2tiiTNI+liScMkTZD0gqQjJHXK63sAuwM/KMyqb5LXLSXpGknv5p9bJK3YqOF5+/0l9ZM0XtIQSZtKWlrSbZLGSXpK0lql7b4r6Z68zWuSzpP0+byuN7AxcGChjcu217dS/cfkMRsr6VJJCxbWdXRMy+km80r6q6SRkiZJelXSqYXywyUdl+8ojMnrd5T0xTy+Y3Pbt2w0tmY2Z3HQbWY2azsd2BHYC1gTeBr4j6Ql2yjfCXgN+DnQFTga+AOwZ17fE7iONLu+ZP55UNJCQH9gIing3QB4Hbgzr2vkGOAaYHXgceBq4GLg3NzmkUDvWmFJqwG3A//M22wPrAFckoscDDwEXFpo46tN9K1m41zvZsAOwJbAaYX1HR3Tst8CPwF2AlbMdT1fKnMI8CiwFmm8LwOuAv6d+3ovcKWkBZrcp5nN5hx0m5lVSNI+kh4v/OxTp9jWefaz9nNr3nZhYH/gyIi4JSKeBfYD3gQOrLe/iPgwIo6LiMciYnhEXAecD+yc148FJjD17PpkUgApYM+IGBQRzwH7AosA27bTzcsj4uqIeAE4GVgCuC0i+kXEEFKQu5qkRXP53wHXRsSZEfFCRDyS+7mDpMUj4n1gMjC+0MaP2utbwUe5H89ExG3AkcC+khaenjGtowswBLgvIl6JiAcj4tJSmdsi4tw8JscD8wNDI+LyiBgK/BFYDFi1yX2a2WzOOd1mZhWKiF5Ar3aK3QsUg/EJ+XN5YF7ggUJ9H0l6CFi5rcok7QfsTQoOF8x1vNxOG9YGlgPGSCouXyi3o5FBhT+/mT+frrNscVKu+trACpJ2LDY7fy4PvNXWjprs26B8cVHzEDBfoR8dHtOS3sAdwBBJt5Nmr2+NiI+LbSjUP1bSeNoeEzObCzjoNjNrvfF59rOsFohGnXX1lpED2bOA7sCDwAekGdyftNOGTqS3c+xUZ9077Wz7YZ121VvWqfB5EfCXOnW91tZOZqBvU1VTalNR3TGdplDEQEnLAlsD3yOljvxX0haFwPvD8mY0HhMzm8M56DYzm3UNJaVZdANegvSgJCnf+qo2tukGPBIRn7xuT1J5pnoyME9p2UBSmsboiHhvxpve0EBglTYuNGrqtbGZvkFKZVk4Isbl7+vn+l4s1N2RMZ1GRIwB+gJ984OfDwMrkNJOzMym4StsM7NZVA4azwNOlfR9SV3z9yVIDynWMwRYS9I2klaUdCzpwcKi4cCqklaStKikeYE+pJSHfpI2lrScpI0kndneG0ymw2nAepLOl7SmpBUkbSvpglIb18tvDlk0v6Gkmb5BmlC6RNIqkrYATgUujIhx0zmmU5F0mKSdJXWVtAKwC2nWfcR0jYaZzRU8021mNms7Mn9eCnwReBLYOiJeb6P8BaS3Y1xFSqW4HjiT9KaOmguBTUhvGlkE2DQiBkjaiBSg9gW+QHrrSH/g3ZnYHyJiUN7Xn4B7SDPaLwE3For1JKVtDCblbi/XZN/Idf4vt32hXO6IwvqOjmnZGNLDoCuS0kSeBLaJiPFNbm9mcyFFNE5hmziluRy32dUljw5vdRMqtdd6y7a6CWZztQU6o/ZLmZnZnM7pJWZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVU0Q0LDBxCo0LmLXQE8PebXUTbAatvdyXWt2ESi3QGbW6DWZm1nqe6TazlpnTA24zM7MaB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZmZmYVc9BtZmZmZlYxB91mZmZmZhVz0G1mZmZmVjEH3WZmZmZmFXPQbWZmZmZWMQfdZmZzAUk9JD0zo2WmY7/LSgpJ68zMej9Lkm6W1LvV7egoScMldW91Oz5rknpLunkG65jqvJ0TzmNrPQfdZmYtlAOEkHRRnXWn53UdCiDyNj+djub0BDaeju0aeRVYEnhqJtc7V5jBYG9d4NyZ3abPiqQBks5pdTsyn8c2wxx0m5m13qvAjpIWri2Q1BnYDXjls2pERIyNiLdncp0fRcQbETFlZtZr7YuIURExvtXtmBP4PLaZwUG3mVnrDQJeAH5eWPYDYCIwoFxY0p6SBkuaKGmIpEMldcrrhudiffMM6fDStjtJelHSGEk3SVq0sG6q9BJJnSQdK+lVSZMkPS3pR4X1tVnYHSTdIWl8btcWdcrUbtNvkr9vJumRvM3jktYqtfO7ku7J61+TdJ6kz7c1gJLmkXSxpGGSJkh6QdIRtXHJZXrnVJGDc53vSrpU0kKFMgvlcmMlvSnpD23ts7T/9SXdLWmcpPcl3SXpa5J+KeltSfOXyveR9M/852Uk9ZP0Tu7vc5J2ykWH5c/H8rgNKNTR5nmQ10+VXpK330dS39zOlyTtWmrXUpKuyWPzrqRbJK3YTt9D0v65D+NzWzaVtLSk2/K+nurIMVZK59kYODDXH/lcavc4F+o/Jh/Dsfk4L1hYN7+ks/L6iZIeltStQR/L5/G8kv4qaaTS341XJZ1aGvvj8rk0Jq/fUdIX8/iOzW3fstHY2pzFQbeZ2azhYmCvwve9gEuBKBaS9GvgZOA4oCtwOHAkcEAusm7+/DXpdvi6hc2XBXYEfgJsCawJnNSgTQcDv8v1rwbcCNwgaY1SuZOAvwKrA48B10hapFFngVOA3wNrAW8DfSQp93E14Hbgn7nO7YE1gEsa1NcJeI104dIVOBr4A7BnqdyGwKrA5nw6FgcX1vcEtgB2ADYjjdFGjToiaXWgPzAU+D9gfeA6oDPQN7eteLHyhbzfi/Oic4GFgE2BVYBDgPfyuvXy59ak47l9rqO986AtxwH9SON6LXCJpC65zoVyPyaSAt4NgNeBO4sXJm04Brgm1/s4cHXu37mkMRwJ9C6MQXvH+GDgIdLfgSXzz6s0f5w3zvVuRjqWWwKnFdafTjr+e+X2PQ38R9KS7fSz5rekY7gTsGKu6/lSmUOAR0nn+HXAZcBVwL9zX+8FrpS0QJP7tNmcIqJhgYlTaFzArIWeGPZuq5tgM2Dt5b7U6iZUbsF5tS+wT2FRr4joVfuSZ/QWJaWSjAS+DYwBXib9Z34isGhEbJvLvwIcHRFXFOo4BNgnIlbO3wP4WUT8o1CmBynIXSIi3s/Ljgb2jIgVCmV+GhGr5u+vARdExImFegYAIyJiV0nLkmZi94uIC/L6pYARwIYRcX+hzLoR8bikTUiB3dYRcVve5v+A+4FlImKEpMuBDyPiV4X9rgE8mdv/VjNjn2ce14mIzQtjvRmwXC1NQNKF+fvm+ULhbWCviOiT1y+S+3NTROzRxn76AMtHxPptrD8HWCEits7f9weOB5aOiCmSBgHXR8QJdbadavwKy5s5D4YD50REz/w9gFMj4qj8vTPwQd7mSkl7AUcB34wcHEiaB3gL2D8irmujf+V6VyUFsYdHxJ/zsk1Ix32xiBjdzDHO59ozEfGbevstbFfvOP84j+/YvGxX0kXAl/Nm7wJ7R8TlhX4OAa6OiGPqnLfl738lXSBtXhurUpuGAw9FxM75+yKkv9d/i4jf5mVT1dmojzZn6NzqBpiZzclygN2riXLvSrqRNPP2HjAgIl7Jk78ASFoMWAa4QNJ5hc07A6J9L9cC7mwksHi9gvk2/9eAB0qr7ge+X1o2qFQnbdXbxDYjgLWBFSTtWGxS/lyeFATWa/N+wN5AF2BBYF7SxUvR4FJe7kjgO4W65yPNsAIpz13S0+30ZU3SXYC2XAgMlLR0RIwgHePLCu04Gzhf0tbAXcCNEfFEW5XN4HnwybjngH8Unx6rtYHlgDHF8440C798s/UCb+bPp+ssWxwYzXQeY2j6OA+qBdzZQ6RjW+vHvBTO7Yj4SNJDwMpt7bekN3AHMETS7aTZ61sj4uNiGwr1j5U0nrbHxOYCDrrNzGYdl5BuQY8lpQGU1VIC9wMenI76Pyx9D9pPM6x3t7O87JN6IyJywNZevcW21OrrVPi8CPhLne1eq1dZDt7OArqTxuYD4EBSCkBb+63tu7bfZi5c6u6+0cqI+K+kgcAekm4C1gF2Lay/WNJtpIuZzYEHJZ0SET3aqHJGzoNG/e9EejvHTkzrnQ7UGw2WTfcxhg4d50Zqx6uZc7uuiBiYZ6q3Br5H+nv7X0lbFALvemPdaExsDueg28xs1nEXMJmUbnJTeWVEvJlTPpav3RZvw4fAPDPSkIj4QNJIoBtwd2FVN2DwjNTdhIHAKhExtAPbdAMeiYhPXjEnqb3Z2bKhpLFbH3gp17EwKQf8xXba+7126r4QOIJ0bB+IiKnyf/MMeC+gl6QjSTnNPUjnAxSOZwfOg44aCOwMjI6I99orPBP21d4xnsy053Gzx3k1SQtHxLj8ff1cX+04Ts511Y7zPKQc9qua7UBEjCHl7PfNKS0PAyuQ0lTMpuGg28xsFpFnib9Net5mUhvFegB/k/Qe6Zb2vKQHtZaKiFNymeHAZpLuASZFxPQ+/HAGcKKkF4AnSLOzG5JSA6p0GvCwpPOBC0i5sN8CtouIfdvYZghpJnkbUvC8E+lhuqb7nlMALgZOy2kXI0l3HNq7gDkjt7cX8HfSg4gbArdHRO2Vj1cDfwb2J81Qf0LS2cCtuQ+fJ82e1i5s3gImAFvlPOGJOUWoB+2fBx3VhzSD3E/ScaTXVS5Degj0/Ih4YTrrraeZYzwcWC/PKI8lzbY3e5w7kx4SPZGUJnUqcGEtCM9pOadKGk3Kqz4UWIIm32su6TDSQ6ZPkS7UdiHNuo/oyCDY7EfSlyOivTs/dfmWhpnZLCQixkTEBw3WX0TKCd4N+C9wH+lBzWGFYoeT3oTxKunBtOn1V1JAeTrwDOkW/g4RUekvCImIQaQ3hiwL3EPq5yl8mgNbzwWkN0RcRXqDyrLAmdOx++6kB/5uzJ/PkN4y0ai9T5HSQr5Fmu18hBQMFtNuxuT2Tc6fRZ2Av5EC7TtI/dw9bzeF9KaMvUkXAf3y8mbOgw7J7/TeiDT72xd4jpQ28SU6cPHS5L6aOcY9SeM1GBgFfJ3mj/M9wP/49FjeTbrTUHNkrudSUuD8bdLDva832YUxpDf7PEqatV8D2MbvRZ8rPKL02s3v19641Cy/vcRma357yextbnh7yQKdpztP2OYwkm4lvfnl161ui5lNnxxob0666F2P9OrN3hHRblqRZ7rNzMwqJOnLkn5Oelf02a1uj5lNv0juyK+D3Jt0V+pRpV/0tEGjbZ3TbWZmVq2BpPdD/yEinmmvsJnNuiR9hfR8y26kdKiDSL/kaQ1SWtZybW3roNvMzKxCEbFsq9tgZjPNQ8AVwI/zW4dqHs8PBrfJQbeZmZmZWXNWqvdbSAEi4rRGGzroNjMzMzNrzqKSjgBWARaoLYyI9t7V7wcpzczMzMya1If0Os3lgBNI75N/rJkNHXSbmZmZmTXnKxFxMfBhRNwTEXuRfuPp/7N333FyVeUfxz/fEDqKCEoVgjTpvUpTUEFBMegPaVJUunQJqEAoQoCgiIAQOoiCdBCk9yodQoDQAoQaWkgn5fn9cc7AzTA7M5vdm9ndfN+v176Suffcc55zZ3b3mTPPvduQy0vMzMzMzJpT+aNXb0v6EemPVi3SzIFOus3MzMzMmnOspLlJf/n3b8CXgQOaOdBJt5mZmZlZc26LiPHASOA77TnQSbeZmZmZWXMGS3oXuBe4B7g/IkY2c6AvpDQzMzMza0JELAlsCzwDbAE8JenJZo71SreZmZmZWRMkLQJ8G9gAWBl4FrivmWOddJuZmZmZNed10n25j4uIPdpzoMtLzMzMzMyasypwEbCdpAclXSTpV80cqDb+fPxnPh43uX6Dbm5iz54evXup1SGU6oPRn7Y6hFKNmTCp1SGUbqF5Zm91CKX62ly9e/Y3oZnZDEbSXMD6pBKTHYCIiD6NjnN5iZm1TE9PuM3MrGeR9CgwK/AAqZZ7w4h4rZljnXSbmZmZmTVn84gYMS0HOuk2MzMzM2vOJ5K2A/pQyKMj4uhGBzrpNjMzMzNrzrWkv0b5GDChPQc66TYzMzMza84iEbHZtBzoWwaamZmZmTXnAUkrTsuBXuk2MzMzM6tD0jNAkHLnXSS9QiovEemWgSs16sNJt5mZmZlZfVt0tAMn3WZmZmZmdVTuxS3p4ojYsbhP0sXAjjUPLHBNt5mZmZlZc5YvPpA0E7B6Mwc66TYzMzMzq0PSYZJGAStJ+iR/jQLeI91GsHEfEVG3wcfjJtdv0M1N7NnTo3cvtTqEUn0w+tNWh1CqMRMmtTqEUs0Ifwb+a3P17tnfhGZmMxBJx0fEYdNyrGu6zczMzMyaEBGHSZoHWAqYrbD9nkbHOuk2MzMzM2uCpF8D+wGLAE8C6wAPAt9tdKxrus3MzMzMmrMfsCbwWkR8B1gVGNHMgU66zczMzMyaMz4ixgNImjUingeWaeZAl5eYmZmZmTVnuKSvANcAt0r6CHirmQO90m1mZtZikh6SNLCtx9PSh5l1voj4aUR8HBH9gcOBc4GtmjnWSbeZWYtIigZfF7Q6xrJI2kPS+y0ae7Z8fjv8Z53NbMaQf27sL+k0SbtL6h0Rd0fEdRHR1P2LXV5iZtY6Cxb+vwVwdtW2cbUOkjRzREwsM7AySZq51TE0o7uf5/aSJKD3jDRns3a4EJgI3AtsDixHuqiyaV7pNjNrkYh4p/IFfFy9LSJGSvpWXpX9uaS7JY0HdpI0v6TLJL0paaykwZK2L/afyw3+IukkSR9KekfScTm5qrTZJh87TtIHku6UNG/eN0DSo5L2kjQ8j3N5vkdt5fiZJB2d90+Q9KSkHxb214p/d+DvwLyFVf1Dmz1vecXp5Dz3MZIelvTdwv4/5Xi+Wth2dT4fvYFhefP1eeznq+a7m6RXgQmSekvaUtL9kj7O5+hGSUtVxbSopEvz/rGSHpO0fmF/X0lPSBov6RVJ/dvz5iM/d/tUbatbTiJplxzHqHz8pZIWKOzfLM//B5IecGAfzQAAIABJREFUAyYAGzUbk9kMZrmI2CEizgJ+BmzQ3g6cdJuZdQ8DgL8AywI3ArMDDwE/AlYgJbEXFhO9bFdgJLA2cBDQj1x/KGkx4BLgzNzvxsClVccvA/w0j/MDYCXgrML+Q4B9c98rATcD10patk781+U4PiSt7C8I/K35U8ElwFrANnnMy4D/FsY8EniT9MkBknYDNgW2j4hJpNt9AeyYxy6es28BPwH6AqsAk4E5gJOANYBNgE+B63ICj6QvA/cACwA/zjEdDyjv/zFwXp7/csBueewj2zHnaTEz8HtgZdJzvghwcY12A0jPx7LAEyXHZNZdffYJUP450m4uLzEzK1FO+HYrbBoUEYOmoas/R8Q1Vdv+Uvj/6ZK+B/wCuK+w/fGIODb//0VJe5ASx6uBhUmLL1fk1XaAZ6rGmBX4ZUS8neezN3CLpEUj4nXgYOC4iLgst+8naSNSEv7rtuKX9AkQhXGbImk5UgK5UES8W+lb0veB3wAHRsSkvOr/hKTjSW8K9omIl3P7yj11P64xfm9gx4j4sLDtsmIDSbuQ3jCsAjwK7AR8BdgqIj7OzV4qHPJH4E8RcVF+/IqkPwBn5H2lqHqdvZJXyp+QNF9EFOvp/xgRt5UVh1kPsXL+uQXpDfXs+bFIP8u+3KgDJ91mZiXKic+0JNnVHi0+yKusfyB9zLkwMAspQf5v1XFPVz1+C/h6/v8jpPrEFyTdAtwKXBkRHxTaD6sk3NmDpF8y38qlIl8F7q8a4z5gvXrxd8DqpDcKLxeqZCDNfULlQUS8JOlg0ir+VRFxfpP9v1qVcCNpaeAY0gr5fHz+KfGipHmtCjxWSLiLxyrvX1FScWW7F+mX9jwR8VGTsbWLpLWAI0gr7/NUxV1MujvruTHrsSJipo724aTbzKx7GFP1+A/A3sD+wLN5/8mk5LOo+qK4AGYCiIiJkr4DrAt8H9gTGCDp2xHxXBMxVbLeqLGvelt1/NOqF2lOqzYxxoak8pDF1PxFkVP1kZPm/wIvkFbuK29AniW90YHPz0MtyjH/Ebi2xv5PamyrZUqNcdqsCVe6j/DNwPXA9qTV/YWB2/g87orOem7MrA7XdJuZdU/rA1dHxD8j4ingFWDp9nYSEVMi4v6IOJK0ivwR8PNCkz7Fi++AdUjJ7vO5vOMDpq6JrsQ2pMHQn5KT/3Z6nJRszhcRL1V9fbYiL2k7Ui36xqRa66MKfUyi8OajgYWAbwLHRMQd+c3IPEydAD8OrJYT3alExBTgSWDpGvG+FBGTm5z3CAp3tpE0J7BU281ZnlTy0i8i7s1/NW/+JscysxJ4pdvMrHsaCvxI0rqkO58cSEoQX2u2A0kbkBLkW4H3SOUTCzJ1wjyBdIHmIcCXgNNI5Rqv5/0Dgd/nu308Rbpwc3VglwbDDwPmzvXfzwJjIqLmLRKLIuIZSVcCl0g6iJTQzgd8FxgSEdfnC0TPAA6OiPsk7QTcJOmmiLgn13wPBzaV9DDpzzp/oTQke490fneXNIJUmnECU6+yXwT8Drgm12q/Tbp48f2IuJeU8F8p6U3gStKq9YrAKhHx+0Zzzu4g3bXmxhxPo4swXyV9IrCvpLNJF9se0eRYZlYCr3SbmXVPR5LqtW8F7iIlh1e0s4+PSSvBN5KS+OOBP0REsZ8XSCUKN+axhjD1haEnAacCpwCDSfev3aqJ8pQ7gfOBq0iruPvBZ7ftG9/g2O2BfwJ/zvFdR1qBf11SL9IdOu6PiDMAIuL2HN/FkubOfRwA/BB4g3QXmJpyScovSHdLGZz76UdKnCttRpJKWd4nnadnSOU/U/L+60h3RNmMVD/9EOkC1KbfIAFHAw8AN5DKXW6mzqcJEfEW6Q3QL3K7w0gXt5pZiyiiVine5z4eN7l+g25uYs+eHr171Ss17P4+GN3UH4HqtsZMmKa7EnUbC80ze6tDKN3X5urdbb8JJQ0ANo2INabjmJcBs0XET6bXmGZm04PLS8zMrEuQNBPpD058u9WxmJl1NifdZmbWJeSLChdqdRxmZmVw0m1mZjVFRNN/mt3MzOrzhZRmZmZmZiVz0m1mZmZmVjIn3WZmZmZmJXPSbWZmZmZWMifdZmZmZmYlc9JtZmZmZlYyJ91mZmZmZiVz0m1mZmZmVjIn3WZmZmZmJXPSbWZmZmZWMifdZmZmZmYlc9JtZmZmZlYyJ91mZmZmZiXr3ajBx2MnTo84WmaBuWdrdQilemfk+FaHUKpFvjp7q0MwMzMza8gr3WZmZmZmJXPSbWZmZmZWMifdZmZmZmYlc9JtZmZmZlYyJ91mZmZmZiVz0m1mZmZmVjIn3WZmZmZmJXPSbWZmZmZWMifdZmZmZmYlc9JtZmZmZlYyJ91mZmZmZiVz0m1mZmZmVjIn3WZmZmZmJXPSbWZmZmZWMifdZmZmZmYlc9JtZmZmZlYyJ91mZmZmZiVz0m1mNoORdJek07pCP5L6Sxrc0Vg6StIwSQe3Oo6uStLOkkZ3Qj9TnWefd5uROOk2M2shSRdI+s90HrYvcFghBic+naCzEtMydZU3OQVrAme0Ogiz6aF3qwMwM7PpKyI+bHUMZgARMaLVMZhNL17pNjPrQior35L2k/SmpI8knS9pjkKbWSWdIuldSeMlPSRp/cL+mSWdKuktSRMkvSFpQGH/Z2Uhku4CFgNOkhSSotCur6RnCn38QZLaMZedJY2WtKWkoTnWOyV9s0bbX0h6WdIoSddImq+wr5ekw3MME3JMP6k6/ghJr+X970i6qGq+Z0r6az6fH0k6SVL178DZJJ0l6RNJwyX9rmqMAyU9LWlMfm7OkfSVvG9j4Hxgzsp5lNQ/75tF0gm5zzGSHpH0gwbn7i5Jf5d0sqQPJY3Ir4lZJZ0u6WNJr0vaseq4hSVdWpjnDZKWqjwfwJHA8oUYd240t6r+6z6XknaX9JKkT/O/v2kwz+pyk90L/Y+QdLOk3nlf5XujX36OR0oakF8f/SW9l7f3qzemWas46TYz63o2AFYANgW2AX4K7FfYf2LeviuwKvAMcJOkBfP+ffMxvwCWym1faGOsvsBw4GhgwfyFpNWBy4GrgBWBQ0klKfu0cy6zkhK9XYB1gZmAq6uS9z6FeX4/z+lPhf37Ab8D+uVYrgaukrRKjnVr4GBgrzzfLYD/VcWxPel33rrA7sBuwP5VbQ4gncvVgBOAEyWtW9g/JR+zPLAdsBbwt7zvgbxvLJ+fx4F53/nARvmYFYELgeslrVzrhFXFPApYGxgAnAJcAwwF1sj9nCNpoXwe5gDuBMbn8dYF3gZuy/suA04mvRYqMV7WxNwq6j6Xkn4KnJbjXAH4K3CGpC0bzJN8/BrA6cBRwDKk1/9NVc02BBYHNgb2AA4BbsyxrQ/0Bwbk169Zl+LyEjOzEknajZTgVQyKiEENDvsE2DMiJgHPSboc2AQ4XtKcwJ7AryPihjzGHsB3gb2BP5JWrocC90ZEAK+TksIviIgPJU0GRkXEO4VdBwJ3R8SR+fHQvGLajy8mY/X0BvaLiPtzrDsCr+T53FZos3NEjMxtBpESu4qDgYER8c/8+AhJG+btO+T5vg3cEhET83wfrYrjbWDffD6el7R0nuOfC21uiYjKhaF/k7RvjvNBgIg4pdB2mKRDgGsl7RQRn0oamZp9fh4lLQFsC/SJiNfz5tMkbUpK/veqc+6ejYj+uZ8/k974TIyIv+ZtR5Oej/WAK0hvsgTskueJpN2B94AtIuLfSjXnk6qe60Zzm5K3N3ouDwYuLpzDoTn57QdcX2eeFYsCY4DrImIU8BrwVFWbkcDeETGZ9DweBCwUEZsVxjwU+A7wWBNjmk03Xuk2MytRRAyKiDUKX40SboAhOeGueAv4ev7/EsDMwP2FMSaTEsPl8qYLgFVICcjpkn5Uo5SikWWLY2T3AQtL+nI7+plCYdU5Il4jzWe5QpvXKgl39tl881gLtRFLpY/LgdmAVyWdK+nnkmatav9QJRHNHqwxl6erjimedyR9V9KtuUxkFOlTgFmABWpPHUir5gKGKJXajM6J749Iz2U9n8WTY3+PtBJf2TYR+KgQ4+qkVeBRhXFGAvM0GqvJuTV6Ltt6zSxHc24lJdqvSrpE0k6SvlTVZkh+vVe8S+GcFLZ9HbMuxkm3mVnXM7HqcfD5z2sVtlULgIh4nFSy8ft83IXAre1MvNXGGG2N3RH15ltvzMp83yCVI+xO+pTgZOCx/KlAp8QhaTHgBuA54OekBHfX3G6WOn32yv2sSXojVPlatnB8e+Kpd656AU9WjbMKsDRwVluDdGButbT5PDU8MK1urwb8H+nTisNIq9kLFZq195yYdRl+UZqZdS8vAZ+S6lcBkDQTqcZ2SGVbRIyKiMsjYk/Squp3gSXb6PNTUn1u0ZDiGNn6wPCcHDWrFynhrMS6KGnl+rlmDo6IT0irqbViKc53fETcEBEH5PGWB75daL92VR35OsBbuf9mrEFKQA+IiAcjYmieR1Gt8/gE6Q3MAhHxUtXXm02O3azHSc/x+zXGqtyxplaMzcwNGj+Xz9HgeWokIiZFxB0RcRiwEjAnqUbfrNtzTbeZWTcSEWMk/Z10sdj7wKukCwDnJ9/vWNKBpBrmJ0mrgNuRVoCHt9HtMGADSf8AJkTE+6TV4keU7sDxT1KydRBp9bw9JgGnSNoPGAf8BXiWz+u5m3EScLSkF0l1ujuQLjZdHT67K0dv4GFgNOmizInAi4U+FspxnEG6mPF3wLHtiOFFUtK5v6SrSEl79YWYw0h3QPkeKdkeGxFDJV0CXJDrjx8Hvkq6EPCViLiqHTE0cgmprvpaSUeQVou/AfwEODMiXswxLiZptbx/VJNzg8bP5UnA5ZIeA24BNiNdDNq3meAlbUEqg7kH+JBUl/0lmnyDZtbVeaXbzKz76Qf8m3RXjCdJK4KbRcTbef8oUlL5P1KStwqweUSMbaO/I0jJ2cvACPisROXnwNbAYNLdMwaQ7k7RHhNIdyK5iJQU9wL6VtVXN3IqKaE7McfyU2DriHgy7/8Y+BVwb96/dR7j1UIfl5BWeB8GzgbOJSWNTYmIp0l3UTmQtHL7a1KCW2zzAHAm8C/SeTwk79qF9FydCDwP/Id0F47Xmh2/yRjH5n5fIdW5P08qLZqHVPsNcCXpbh+35xi3bWZuWd3nMiKuAX5LehM4JPe5V0Q0cxElpOdxK1IS/3yO4dcRcW+Tx5t1aWr0c2/YB+M7u3avS1lg7tlaHUKp3hk5vtUhlKqnP3/W/c3Wm6bva93T5BXo0yJirhbHcRcwOCLae7tDM7NO45VuMzMzM7OSOek2MzMzMyuZy0t6eHmCy0vMWmtGLi8xM7PPeaXbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK1rtRgwXmnm16xGElGT1uUqtDKNfcrQ7AzMzMrDGvdJuZmZmZlcxJt5mZmZlZyZx0m5mZmZmVzEm3mZmZmVnJnHSbmZmZmZXMSbeZmZmZWcmcdJuZmZmZlcxJt5mZmZlZyZx0m5mZmZmVzEm3mZmZmVnJnHSbmZmZmZXMSbeZmZmZWcmcdJuZmZmZlcxJt5mZmZlZyZx0m5mZmZmVzEm3mZmZmVnJnHSbmZmZmZXMSbeZWQ8kqY+kkLRGB/vpL2lwgzanSbqr8PgCSf/pyLhdmaS7JJ3W3ccws+nLSbeZWQvlBDXy10RJr0gaKGnODnb9BrAg8GQH+xkIbNTOY/YDdujguC0naeP8vMzXguH7Aoe1YFwzK0nvVgdgZmbcBuwIzAxsAJwDzAnsOS2dSZolIj4F3uloYBExGhjdzmNGdnTcGV1EfNjqGMysc3ml28ys9SZExDsR8UZE/BO4BNhKyUuSDi42lrRUXoFdLT8OSXtLukrSGOC46vKSwqrt5pIekzRO0r2SFpG0kaSnJI2W9B9J8xbGmqq8RNJMeSX+o/x1CjBTVXxTlZfkUokzJB0n6X1J7+U+ehXazCLpBEnDJY2R9IikH9Q7aZI2lPRQjnukpIclrSBpTkmfSPpZVfvv5U8T5i+cn60l3SpprKQhkr6X2/YB7syHjshtLyh016sj85E0s6RTJb0laYKkNyQNqDpnpxUe95X0dH7ePpR0t6T5650fM+tanHSbmXU944CZIyKAc4Fdq/bvCjwZEY8Xth0J3AisCJxep++jgP2BtYF5gMuAI4DdgI2B5YH+dY4/CPgNsDuwLinh3r6JOW0PTALWA/bJMWxT2H8+qYxluzyHC4HrJa1cqzNJvYFrgfuAlfN8/gpMjogxwL+ofd7+ExHvFrb9CTg19/EIcKmkuUjlOVvnNsuTSnX268T57Av8FPgFsFQ+9oU25roAcGnuY1lgQ+DiWm3NrOtyeYmZWYkk7UZKaCsGRcSgOu3XIiVqt+dN5wNHS1onIh6SNBPwS+D4qkMvi4hzCv30aWOIwyPi3tzmTOBvwOqVBF7ShcDP2jgWUnJ5YkT8O7ffD6i7Ip0NiYgj8v+HSvoNsAnwL0lLANsCfSLi9dzmNEmbkpL7vWr092XgK8D1EfFy3vZ8Yf/ZwEOSFo6INyXNA2wF/Lyqn79ExPV5Lr8nndtVIuI+SZUSj/ci4v1Ons9iwFDg3vzm6nXggdqnjoVIpUdXRMRreVvdi1vNrOtx0m1mVqKcYLeZZGebSRpN+pk8M2kF97f5+HdyqcauwEPAZsC8pBKUokebDOnpwv8rK77PVG37eq0DJc1NWvF9sLItIqZIehj4RjvGBXirMM5qgIAhkoptZgXuqNVZRHyYyz1ulnQ76U3K5RHxRt7/qKRngJ2A40hvZD4C/lsnrrfyvzXn38nzuQC4lZSw30L6lOK/ETGlxlhPker+B+e2t5ES8BFNxGlmXYTLS8zMWu8eYBVgGWC2iOgbEe8V9p8DbCNpDlLyfVVEfFTVx5gmx5pY+H8ARET1tjJ+N0yselwcp1d+vCbpPFS+luWLJSKfdxCxC6ms5B7gx6QEtrjqfg6wS/7/rsAFETG5rbjyinMlnlLnkz9Z6AP8Pre/ELi1WBdeiGsy8P389TTwK+DFtkpvzKxrctJtZtZ6YyPipYh4rSoBrrgJ+ATYA9gSOG+6Rpflu5K8DaxT2aa0lLtWB7t+grQyvEA+D8WvNxvE9FREnBARGwN3kVa2K/4BLCxpH9Lq8/ntjOvT/O9MdVt9UVPziYhREXF5ROwJ/Aj4LrBkrQ4jeTAijiIl828xdQ25mXVxLi8xM+viImKypPNIddxv8nm9dyv8FThM0lBSWcpepJKTt6e1w4gYKukS4AJJBwGPA18lXdj5SkRcVX2MpMVJ9dHXkc7JN4GVgL8X+h0p6XLgZOCeiHixnaG9Rlqx/pGk64Fx+RaKHZ6PpANJ5+xJ0qr5dqQ3VsNrzHUdYFPgZlL5z6qkcp4h7ZyPmbWQV7rNzLqH84BZgPMLZRCtcDJpxfgc4GHS75Hq+vJpsUvu90TSBZH/Id2l47U22o8FlgYuJ12QeGGO44SqdueSztu57Q0or0ofSbrDybtAe/5CZKP5jAJ+B/yPlJSvAmweEWNr9DUS+Hbu40XSc3BMRPyjnVMysxZSo5/d4yfRyh/u1kEvvdOuv2nR7Sy5wFytDsGsrtl6o8atGpO0NnA/8M3CHTGsAUnbAGcBC7WR0JqZTRcuLzEz68IkzUoqJTgWuNoJd3PyRad9SBcqnu2E28xazeUlZmZd27akP5oyL3Bgi2PpTg4h3WrvQ+CYFsdiZubykp7O5SVmrdVZ5SVmZta9eaXbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK1rvVAbTaqyPGtDqEUi25wFytDsHMzMxshueVbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjOzbkLSMEkHd2J/IelnndVfq0m6S9JprY6jFkkXSPpPJ/fZJz+Ha3Rmv60kaY08pz7tPG5nSaPLiapzlPEa6A4667mp/vnX2T8Pp4ferQ7AzGxGJ2l+4FBgC+AbwCfAS8C/gPMjoqxkYkHgo5L6boW+wMRWBzEdvUF6Dt9vdSDTk6RhwGkRMbDVsbTTfoBaHcS0ktQf+FlErNDqWLI1gTGtDqI9nHSbmbVQXtG7n5RoHw48TfoUcmngl8AHwD/LGDsi3imj3+lN0iwR8WlEfNjqWKaniJgM9IjnsBUk9QYmR0RMj/EiYuT0GGdGEREjWh1De7m8xMystf4OTAHWiIhLI2JIRAyOiKsiYivSavcXSDqv+qNqSb0kvS7pwPxYkg6S9KKkCZKGSzq+0P6z8pJCqcLWkm6VNFbSEEnfqxpjOUk3SBol6T1J/5K0QHsmLGlFSbdL+iT385Sk7zQ7RuVjekn9JA0HhuftU5WX5I+f/yjprDzWcEm/q4plbkmD8jijJN3d3nINSfPmGIdLGifpWUm7NDhmM0n3SvpI0oeSbpa0bGF/w+ejurxE0sb58SaSHs7HPCpptaqx18vzHCvpTUl/l/Tlds65w/EX+nle0nhJ95LebNYb9y5gMeCk3H9U7d9E0mBJYyTdKWnxwr7+ed/Okl4GJgBz5u+TQyS9nJ+/ZyTtUNXvipJuy/s/zK/BuQv7K6/J/fI5/UjS+ZLmqG5TnIukMyQdJ+n9/BocKKlXoc0skk7Ir60xkh6R9ING5yg/pyfnWEfkuGaVdLqkj5V+TuxYddzCki7NsX+k9D24VN63M3AksHzlvOdtSDpQ0tM5vjclnSPpKzXi2lLS0Pxc3ynpm1X7d5f0kqRP87+/aTDP6nKT3Qv9j8ivyd5Vz08/Se9IGilpgNLPzP753L8jqV+9MTvKSbeZWYtI+irwA+D0iKj5MWmdVbizgc0kLVjY9j1gAeDi/Pg40ur58cDywM9JJQn1/Ak4FVgZeAS4VNJcOd4FgXuAwcBawKbAXMB1xUShCf8E3s59rAr0B8a3c4yNgJWAzYBN6ox1APAMsBpwAnCipHXzWAJuABYmlfasmse+o+q8NjIb8HjuY3ngr8BZkurFNSdwSp7jxsBI4HpJs1S1a/P5qON4UrnSaqRPSi7Jc0XSisAtwHW5z77AKsB5zUy0M+OX9A3gGuDWHMPfgBMbjNuX9CbraFJpTfF5mhU4DNgVWBf4CnBm1fGLA9uRvhdWJr3ujgV+BewNLEc6f2dJ+lGOcw7gJmB0nu9PgfX44jnbAFiB9JrdJrfbr8F8tgcm5f72AfbPx1acT3qtbwesCFxIOs8rN9HvKGBtYADpuboGGAqskfs5R9JChTnemc/HRqTz9zZwW953GXAy8AKfn/fL8lhTctzL5zjXIj2XRbOSkvZdct8zAVcXXpc/BU7Lca5A+h46Q9KWDeZJPn4N4HTgKGAZ0nNwU1WzDUnP/8bAHsAhwI05tvVJP4cGSFq9mTGnhctLzMxKJGk3YLfCpkERMSj/fylSjecLVccMJyUMAP+IiD2q+42IByU9D+xE+qUKKdm4LiJG5MTmAGD/iKgkBy8BDzYI+S8RcX2O4/ekEpdVgPuAPYGnIuKz1SBJvwQ+JP0i/1+DvisWAwZGxPOFuCqaHWM8sGtETGgw1i0RUVn9/pukfUlJ+oPAd/LcvhYR43Kbw/Mv+h1pnAACEBFvAicVNg2S9F1gW+D2No65svhYaWX8E1LCcl9hV73noy2HR8Sd+Zijc9uFScnq74DLIuLkwth7Ak9I+npEvNd4xp0W/57A68C++c3l85KWBo6pM+6HkiYDo2qUR/UG9o6IF/J4A4HzJfWKiCm5zSzAjhHxbm4zJ3Ag8P2IuDe3eVXSWqQk/AZSAjtXPm5UPm434E5JS0ZE5fX7CbBnREwCnpN0Oem19tmnSzUMiYgj8v+H5tXdTYB/SVqC9BrqExGv5zanSdoU2B3Yq06/z0ZE/xzrn0lvwiZGxF/ztqOBfqRk/wrgF6SfRbtU3uhL2h14D9giIv6tdDHkpOrzHhGnFB4Ok3QIcK2knQrnvTewX0Tcn/veEXglz/U24GDg4sL36tCc/PYDrq8zz4pFSfXd1+Xn6DXgqao2I0mvj8mk19pBwEIRsVlhzENJPxcea2LMdnPSbWZWopxgD2rYcGobkFaCBpFWUdtyNukX74C8av4T0uoapBW7WWkj6avj6cL/38r/fj3/uzqwoWrfiWAJmk+6/0xaZdspx3dlIQFvdozBTSTcMPV8IM2pOJ85gBF5wa1itjxWUyTNREpqtiElt7OSkru76hyzBCm5XBv4GumT516k5KGt+Kufj7a0dcxw0pyXlFRcTa1MfglSktVQJ8W/LPBQ1ac5jd4U1jOhknAXxpuZ9Aa2Uu8/vJJwZ8uRnu+bNHWpyszAsEKcT1cS7uwB0grvcnz+pnFITriL46/dIOZ6r8/VSM/NkKrX56zAHc32GxEh6T3SJz6VbRMlfcTU3wuLA6OqxpqDBt8L+Q3mYaTzNDfpZ9cspE/dKs/5FAo/HyLiNUlvkc7fbfnY6k8O7gN+3GCeFbeSEu1XJd1M+jTnqqrnbEhOuCveBT6u6uddGn9/TTMn3WZmrfMSEMC3ihsj4lUASWMbHH8xcIKk9UmlEe+TftnAtN8l4bO7f+Rf1vB5KWIv0spfrdt0vVtjW00R0V/SJcDmpPLcp6/9AAAfO0lEQVSaIyXtkVfkmx2j2bsWVN/NJJh6Pu+S3uRU+6TJ/iHFehCplOAZUhnCcdT/5X098CZpxfJNUonBEFKyUlTv+WhLcc6VRLI453OAv9Q47s0G/RZ1RvydfSePSVWPq+cOX3zdVPZtSVp1L6rErkJf1Yrb673W2tLo9Rmku3RUtxtHfbX6bTTWk6QV72ptXqAsaTHS9+vZwBGkcqbVSNeiVL8WGql1jpu6yDUiRildu7AhqczuMOA4SWtGRCXxb+856XROus3MWiQiPpB0C7CPpL+199aA+aP2q0hlJasCFxRWcoaQLhTbBHixk0J+HPg/4LWI6NCt+SLiRVJcp0r6O/Br0kpXp43RhMeB+YEpEfFKB/pZH7g+Ii6Gz2rFl+aLq2jk/fOSVvb2LpSBrMb0+Z38OLB8oSSi3Tox/iHA1pJUWO1ep4njPiWtpnaGyvfJYhHR1urxEGBXSV8qrJyuR0rOnuukOGp5gpTwL1A5zyV6nFTK8n5E1HzdUvu8r0FKrg+o/OyRtEWNY3uR3jw8kNssCizE5+fvOdL3UXG1e33SuW9K/pThDtI1GUeSS2No/yeNpfGFlGZmrbUX6WfxY5K2Vbpzx9KStiVd6DW5/uGcTao5XZl00RWQVn5IFyMdL2kXSUtIWivX706r00kfH18maW1J35S0qdLdP77UTAeSZle6g8LGSne4WJupf7l2eIx2uI10u8ZrJW0uaXFJ60o6SlKt1e+2DAU2kbS+pG+RLghbvE77j0ifSvxG0pKSNiJd8Fe9UluGE4C1JJ0padU8/haSzmpHH50V/5lAH+AUScso3UnnC9cv1DAM2EDpbhvztXPMqeTvk4HAQEm75vmsImmPXLcNcAlphfwipbuYbAicRSpfmOY3L03ENjSPfYGkn+XvhTUkHSypbycPdwnpU59rJW2Uvxc2VLoDylK5zTBgMUmrSZpP0qykN869gP3zMduSLqqsNon0PK8raRXShZzPkr4HIV0TsaOkvSUtJem3pJ9rTV1XkV/D++XX9GKkCzq/RLlvitrNSbeZWQvlFdZVSVfaH0Na3XqcdHHXGdT+BVZ0F6lW966IeLlq32GkJOtw0i+fK4FFOhDrW8C3SfWZN5F+aZ5OWimcAFPdtm7jNrqZDMxD+qX7AnA1qY73wGbH6Cx5dfWHpNWxs3M8/ybd/aDykXTl1or963R1LKle9b+ku5+MISUxbY07hVT/vRLpLi2nk56jTp1fG2M/TfoIvg9wN+lis+MplO4o3VKvzb8K2Vnx54sD+5LuQPMU6cLfQ5s49AjSH5F6GeiMezUfTrpzxcGk19utwNbAqznOsaQyqC+TnudrSa/ZXTth7EZ2Ib2ZPhF4HvgP6fl7rTMHyXPckHRx4+V5rAtJ36uVP6B1JeluH7eTzvu2+fW0H+n7dwjpE6tapWETSHeyuQh4mJR/9q18whER1wC/Jb0GhuQ+96pchNuEj4GtSEn88zmGXxcuju0S1Oie8OMnNVdP0129OqJb/TGjdlv8a3O2OgSzGdpsvcv9C3SSZifV1P42ItpM9KYXpbtYDACWqfMxdbehdJ/nl4ENKnde6OkkHQX8DFi56sJAM+sA13SbmXVDSvesnp+0MjSOtDrVFfwQ6NcTEu7sh8BFM0rCnf0Q2McJt1nn8kq3V7rNrERlrXTnj/5fJZWW/Coibql7gJmZtZRXus3MuqGIGEbn33LNzMxK4gspzczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZIqIug3GT6J+g25uSoP5d3e9pFaHYB3Q01+fM4I5ZvY3oZmZeaXbzMzMzKx0TrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zcysNJJ6SwpJW9Vps0Bus37JsQyXtH+DNuMl7TA9xjKzGYuTbjOzFpJ0QU44q79WafL4JWu1l/QPSdeUE3XzImISsCDw31bHAqwKDOrMDiUdK+nJ6TGWmXVvvVsdgJmZcRuwY9W291sRSBki4p1WxwAQESN64lhm1j14pdvMrPUmRMQ7VV+TACT9UNJ9kj6W9KGk/0paJu/rDbyY+3gir3jfJulYYHvgJ4WV8/Ul3SPplOLAkubJJRU/rhWYpK9JujSXS4yVNFjSL6va9JJ0iKQXJU2Q9IakYyoxVpeXSFpb0hN53MeBNRudIEmzSTpV0nv5uAclrVfYf5SktyTNV9h2uaRHJM2cH09V8iFp6XxOxkt6XtLmNcYdKGmopHGSXpU0QNKsed+vgT8AKxfO8w5tjLWYpGsljZb0iaQrJS1U2H+spCclbS/pldzmKklfbXRuzKx7cNJtZta1zQn8mZSYfgcYC1wvaeacmK+b221KKuP4OTAAuBK4KW9bEHgYOBvYXtIshf63Az4Cbmxj/NmBR4EtgBWA04BzJW1caHMCcCjwJ2A5YBvgrVqdSfoScAPwArA68HtgYOPTwMnA1sDOwGrAc8BNkubP+48GhgHn5nF2BTYHto+IiTXi6AVcA0wG1gF+DRwDzFzV9JM85rLAPsAOea4AlwCnAM/y+Xm+oo2xrge+CmxEeq6+AVwlSYWmSwB9gZ/k2NfMMZlZD+DyEjOzEknaDditsGlQRFTX+m4maXTh8b0RsTlARFxe1d8uwMekhPUhPi9D+aBYxiFpPNC7atsVwKnAj/k8OdwVuLCysl4tIl5n6qT4TEmbAr8A7pI0N7AvsHdEXJDbvAw8UKs/UhmNgF0jYizwrKTjgfPbaI+kL5PO4U4RcWPethvwXWBPoH9ETJa0PfCUpBOAvYEDImJoG91uBiwNfD8ihuc+DwburJr/0YWHwyQNICXfR0XEOEljgEkNSmh+QHozsnhEvJHH2p70xmMj4K7crhewc0SMym3OAbat06+ZdSNOus3MSpQT7EYX1N3D1In5uMp/JC1FWsVdG5iPlJgJWJSUdLcnlnGS/kFKtK+QtBJp1bjNxC6XsBxGWkFfGJg1f92amywPzALc3mQYywJP5YS74sEGxyxJ+n11f2EukyQ9REpmK9telXQAcA5wXUSc3SCO1ysJd/YQEMVGkrYhvalYApgrxzGlQby1xnqjknDnWF+U9F6O/668eVgl4c7eAr7ezrHMrIty0m1m1npjI+KlNvbdALwK/IaUhE0BhpAS3WlxNvC4pIWBX5FW1dtaDQboB+wH7A8MBkaTyknmzvvVxnFtaW/74jFRY1/1tg1JJSOLSpolIj5tRxxR3K50C8NLgCOBW0ifMPwUOK750D8bq1bsVG2vLoMJXAZq1mP4m9nMrIvK9cpLAcdGxO0R8Rwp2S3+7K4klTNVHf5pjW1ExNPA46QkfnvgvAZhrA9cGxH/iIgnSaUjyxT2D85jbdLUpNIbhpUlzV7Ytk6DY14EJuVYgM9W4NfJ/VW2/R/wf6Ta93mBYxvEsWjxYsYacXwbeC0i/hQRj0TEi0CfqjY1z3MbY32jEOtSpFXsIW0eZWY9ipNuM7Ou633gQ2A3pftxbwycztTlDe8AE0h14fPn+mdIFxWulO/QMV9OUivOJpWMzAJMVTNew1Dge5LWk7Qs8HfSRYAARMRI0sWVJ0raSdI3891Jdm+jv3/kf8+VtJykH+RY2hQRnwBnASdJ2izHcRbpwsS/A0haBDgT6BcR9wK/BA6Q9J02ur0ZeAm4WNLKkr5Nql0vntuhpGR5W0lLSNqHlNQXDQMWl7RKPs+ztjHWEOASSatLWpO0gv6/iLi73tzNrOdw0m1m1kVFxGTSnUBWI60o/42UoE4stPmUVP6xB6n85Kq86yzSCvFjwAimXsX9F2nl+NKIGNMgjKNyHzcDd5NKLC6ravM7UsLaH3ielMgvRA05gd6CVMv8BKlU5ZAGMQAcTLojy0XAk/n4zSLivXx3kIuBR0jniIi4K8d0kaR5asQxGdiK9Mbjf6QLOfsz9bm9GvgL6eLTJ4GNSaUmRZeTSk/uIp3nn9cYawqwJekuMXcDdwBvkO5UYmYzCEW0VWaWjJ/UZh1ajzClwfy7u16alvJJ6yp6+utzRjDHzF3vmzCXOQwD1ouIh1scjpnZDMEXUpqZzSDyH4lZkHQf7/854TYzm35cXmJmNuPYiLTCvRrQVs21mZmVwOUlPfzje5eXdG89/fU5I+iK5SVmZjb9eaXbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK5qTbzMzMzKxkTrrNzMzMzErmpNvMzMzMrGROus3MzMzMSuak28zMzMysZE66zczMzMxK1rtRg9HjJ02POMzMeqQ5Zm74Y9bMzGYAXuk2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuM7MeQFJI+llH25RB0l2STmvrcZN9DJN0cIM2oyXtPI1hTneSNs7PyXwd7KfD59fMyte71QGYmc3IJH0NOAr4IbAg8DEwGBgQEbd28nALAh91cp/Toi8wsZ3HrAmMKSGWDsuJ/mkRMVerY8mm5fyaWcmcdJuZtdaVwBzAr4CXgK8DGwHzdvZAEfFOZ/c5LSLiw2k4ZkQZsfRE03J+zax8Li8xM2sRSV8BNgAOjYjbI+K1iHgkIgZGxKWFdsMk9Zf0j1xC8U4bpRZflXS5pDGSXpG0Q9V4n5WXSOqTH28t6VZJYyUNkfS9qmN+JOkFSeMl3SPpF/m4PoU2fSU9I2mCpDck/UGS6sy7uhyi4fyqy0skLZn7GZ/j26LB6UbSBZL+I6lfHmOkpAGSeuXx38vb+1UdN7ekQXn/KEl3S1oj79sYOB+YM5+XkNQ/79tB0iP5mPfyc7NwjdDWkfRknstjklavGr+j57evpKcljZP0YY5//ryvv6TBknbK53i0pPMlzSJprzzeB5L+LMk5g1kH+BvIzKx1RuevH0uarUHbA4HngNWAI4HjJPWtanMEcC2wMnAZcJ6kxRr0+yfg1HzMI8ClkuYCkLQocBVwQ95/KnBi8eCcIF6e260IHAocBuzTYNxpmV9lzF7A1aTfYesCuwL9gVmbGGdDYHFgY2AP4BDgxnzs+rmfAZXENye3NwALA1sAqwL3AHdIWhB4ANgfGEsq31kQGJjHmiXPZeV87HzAv2rENBDoB6wBvALcIGmOPH6Hzq+kBYBLgQuBZfP8L65q1gf4SY5xa+DnpNfRmsD3gV8DvwV+2syYZlaby0vMzEokaTdgt8KmQRExCCAiJuV64LOB3SQ9AdwPXB4RD1d19XBE/Cn/f6ikNUmJ6lWFNhdHxD/yuIcD+5FW0l+rE+JfIuL6fMzvgV8CqwD3AXuSksCDIiKAFyQtTUrUKw4E7o6IIwuxLUVKIv9WZ9xqzcyvYlNgOWDxiHg9x74/cG8T44wE9o6IycDzkg4CFoqIzQpjHwp8B3gs/7sK8LWIGJfbHC5pS2DHiDhR0kggqst3IuK8wsNXJO0JPCdpkYgYXth3TETcnOexCzAc2A44h46f34WAmYErIqLyOhhc1WYmYJeIGAkMlnQTqcRp4Yj4NMd8fz4XVzYxppnV4JVuM7MSRcSgiFij8DWoav+VpMRoS+C/wHrAQzkBLnqwxuPlqrY9Xeh3EjCCVCNez9OF/7+V/60c8y3gkZxwV1S/GViW9Eah6D5gYUlfbjB2UTPzK475ZiXhLsQ1pYlxhuSEu+Jd4JmqNu/y+TlYnVRzPyKXXoyWNBpYAVii3kCSVpN0raTXJI0CHs27Fq1q+tncI2J0jqcy946e36eA20jJ9JWS9lS6eLfo9ZxwV7wLDM0Jd3Fbo9eSmdXhpNvMrMUiYnxE3BoRR0fEesC5QH9Js7Szq+o7VgSNf85/dkwhua4co9xHPfXaNDp2WrVZz9yEWueo3nnrRUo4V6n6+hZweJsBSnMCN5PKTnYklWpUVtPb87x26PzmNxjfz19Pky7YfVHSyoVm7T0nZjYN/A1kZtb1DCGV/xXrvNeparMOqQa6TM+RksWitaoeDyHVQhetDwyPiFHtGKs98xtCWun9RlVcZfxOexyYH5gSES9Vfb2X23xKKtEo+haphvv3EXFPRDxP2yvFn809J+sr8PncO3x+I3kwIo4iPZ9vAds0c6yZdR7XdJuZtYikeUkXyZ1HWoUcRbqY7hDg9oj4pNB8HUmHAVeQLgL8JbB9ySGeCRwoaSCp7nx5YPe8r7LKejLwSL5jxz9JSd1BQHV5TCPtmd9twPPARZIOAGYH/gJMaueYzbiNVN5xraRD8rgLkFatb4uIe4FhwGxKd355grS6/TowAdhH0umkMpFj2hjjj5JGkJLhI0hJ/D/zvg6dX0nrkGrgbyat2K8KfIOUzJvZdOSVbjOz1hkNPES64PFu4FngOFJyVb0S+WdgJVJSdyxwRERcUWZw+cK7rYEfk2qDDyD9IR+A8bnN46S7XWxN/qM++au9fxGx6flFxBTSnTR6kWq5L8rHTGjnmA3lkpsfAneQ3ni8APwbWIZcAx8RD5DeoPyLVEd/SL6v+E7AVqQE90jSRZG1HEpKrh8HlgK2iIgxue+Ont+RwLeB/wAv5nGOqVxwa2bTj6a+PuaL3h89qayaPDOzHm++uXp3pP4YSPeoJv3Fw4GN2pZN0n7A0cA8OfntjD6H0UXmZ2ZWFpeXmJlZmyTtTbp/9whS7fHhwAWdlXCbmc0onHSbmVk9S5Lqh+cl3T/6TNJKt5mZtYPLS8zMStQZ5SVmZtb9+UJKMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSOek2MzMzMyuZk24zMzMzs5I56TYzMzMzK5mTbjMzMzOzkjnpNjMzMzMrmZNuMzMzM7OSKSJaHcNUJO0WEYNaHUdZevr8oOfP0fPr3nr6/MzMrGvqiivdu7U6gJL19PlBz5+j59e99fT5mZlZF9QVk24zMzMzsx7FSbeZmZmZWcm6YtLd02ste/r8oOfP0fPr3nr6/MzMrAvqchdSmpmZmZn1NF1xpdvMzMzMrEdx0m1mZmZmVrKmkm5J80gaL+mUwradJX0s6cn8dXWN45aRNFbSwKptd0h6On99r4nxt5T0vKSXJF0maY422s2f+54iaYSktfP2XSR9mucwTtJ7kpZoVaySekm6UtILkp6SdGt1PHX6PzHP5WtV22eWdLSkoZKelfScpJPz9o3z3J4sfF2Uj/uJpMckDcnn7Z5Cn9PlOZa0nqQHcgxDJJ0kqVdhf8tef41iq3PcTJLelHRNjX1LS7pa0iuSBkt6SNJWed8FkoZXPVc/y/tOz6+tpyTdL2kNScNyH8XzNUzSCrmvfarGHiipf/5/9eviGUnbFNrelWMsxrJenTlXXkuD82vwoEbnqRn1Xtt1jvnC3Ntot0t+HVTmv2+D9ktLujM/D4MlnS9p9mmZl5mZzWAiouEXsA9wJ/Au/H975x5sVVXH8c9PLjcjkAzFAQlpHFKMQfCBTJOhUTAYYzD2GgLhAgENjJZNI9MYUaMOTdrDMYfUREF6qNmExDCY5hAEGngrzEdkc0OFYiYdgh485Nsfv7Vl38PZ+5wL3XtQfp+ZPbMfa/329+y1zj2/9Vu/tS/N6dx04KGSOt2AJ4AfArfkzv8GmJr2BwMvAz1K7PQE/gYMTsd3AwsLyt4DPJK0vgr8GTCgBdgAnJT7PI81Sis+2LmySE+J/SZgB/A4cF3FtfuBnwK90nF3/H3EPYHLgM0FNi8B+icN64CDwOVd3MZDc8/sbcD6rP5x0P9KtZXUmwD8FvgHcEbufL/UR6ZWnLs67d8LzC+x2T23/yLQlrZpuXJtSfcRtoBbgEVpv12/AM4D9ub65RPAhFqftbIvpf3e+Pfv0nrrl9gt7NsldQqfY0W5Uzi8tqUX8FdgWEn5QcCItH8S8BPgK8f6GWOLLbbYYnvrb/Wml8wAbgS24s5iPSwAVgF/qjh/PrAGQNI23DkeX2JnPO4YbEvHS4BPFZT9JDAwaX0aaAYuAgTslHQoldsInNUorZIOSVpZoqeIK3BHayHeJgCY2WBgEjBL0p50jwOS7pS0t8ygpCcl7Uj2vga8BkysQwv8n56bpGeyZyZpH9BK++fRsP5Xh7YiZuDt/zNgau78POBXkpbn7rFT0rJaBiWtknQgHW4EBqT9RcAiM2uuQ1cZvYHduX7ZIXJ9CUm7geeo71kVUta3gfeY2a/N7Ok0C/H5Ahvtot75Y0n/lJStJu+BO/SFq8sltUlqTfuHgKeO9TMGQRAEJwb1TJOfD7wLj64uJefsAaPTtOw6M/tors4wYBzw7SomtwCTU7kLgXMo/9EaiEefMrYD766isw8e3eyd03pyruwbWoHFwMpGaa3C/ExPDWYASyWtB5rNbGQ6PwLYJum1krrnVaQJLMwu5Nr4FTyaOzRXryva+A3MrC9wFfCLCm2N6n+F2krKnQ5cDjxQRfMFwJM1brWgoq2GVykzP6djc9o+V8sWcHXF9axfPI8/42srrt9WoaVvDe0AmNm5wKhk81go69ttwIclXQCMBGab2ZCO3sDMrjSzP+Lf3W9K2lpnvbfjbVvPdzcIgiA4wakn0j0TWJaiQQ8Do8zsTDyKOFDScPyH+h4zG5LyLO8C5kp6vYq96cCHkgPwRXy6/kCVckdDU4XWd+IOW17rFmA0sLzBWgEwsy8BQ4AbapTri6cDPJhO3cdhZ87quNWzkobntq/nrs3En9fPccdtRCPa2Mx64Q7MrVk0keOk/xVoK2Iq8IikPZI2AE1mNiozVetewOKKtvpdhZZP4wOHvJN9A3C9mfUsswVURtSzfnEuPiD4Xnq+GddUaNlVS7yZ9cP70rws8n0MlD2vHsAPzGwrnj7WH5/J6BBp1ul9wHuBqWZ2Tk1RZk3Aj4HHJYXTHQRBENSkqeximq6eDPzXzLIIWXc8f/TmrJykVjNbj0eb/gWcDaw2M3DH18zsFEmzJf0F+FjuHs/i09BFbMejhhkDgZeqlNuTPs+snNZueP7l3ele83HHdQ0wDM+3bYTWzN58/PmOkfTvErvgEcomYGvS2gS8w8y+gKfSDDazU2tEu6tpaAY+g+e27gZuouvbGPMFp6uAtZJuzWlrdP+rqq0GLcDpZtaWjnvjA6RN+KBvZEG9mpjZJLyNxkj6e/qMSHrBzFYD1x2tbUnPJc3v5/DgrqP6+gK/xCPGDxytlhxlfftmPD9+uqSDZrYWn92q5CDtAwzVyiBpu5k9hefLv1AkyMy6ASvwVKzShZdBEARBkFEr0j0ReF7SAEmDJA0CxgIt+WiYmZ2FTyX/QdJ2Saflyn8HuEvS7FS2ryVPwcymA/uAx0o0rAEuNs/tBJiLT9tX07oLuCPddwqwExhrZmea2WxgDu78XNhgreT0jJX0aonNjBZgYqZV0gA8n/SqlHO8Evh+ishmb8+4tkrks5IpeErJNdmzoIvb2MxOxhfAbpK0MHep4f2vRFtR+ZG4k90vp2Eo8InkvN8BjDGzybk6/c3ss3XYngB8Cxgnqa1KkUV4znivWrYK7PfHo73bapUtqN8HeBS4PRvoHitlfRsfUL2UHO6hwKUFZl4ELk51+5EbGKc0mGz/tHStML3E/C0x9wKvAzNz+eBBEARBUEpppBt39FbkT0jamH54lpvZGXgUCeDLdUy7gy+Eu97MhP8YTir74ZK0Jzmoq1KEqZWUd5qchNVp2rwFd0g+YmbTgP/gCyuX42+w+CCwH397xS58kdsljdCanIcleA7po8kH3Cepqh7zVx+eypH5sSvwQcT9wDTgq8AWM9uPD6hW404lpNzdXN0dkq7A0xKagTlmNidd+26q3yVtjKeQXAb0MbNx6dyDwAdocP8r0ibppoLyLcCP8jYlvWJmrcDHJS0zs9HAN8zsRjwyvxdfZ5CxwMxm5Y6XSFqC54fvBx7KItzkBs6SXjaz5XjaTL3k+0Uz/iaOfD+5LenMWFiSTrEAd9rb9SVJSzugpxpFfXsxsNTMpuBtua6g/p34M/s9vrA2n1M/x8zG4ilGhg8Y1pZoGY8PVJ9JegA2SJp3tB8uCIIgODGIfwMfBEEQBEEQBJ1M/EfKIAiCIAiCIOhkaqWXdBlmthJfeJhnu6R638vcZXSW1rQIrdrU9sNq/7aRNyXHcxt3VFtK/6j2Hw+nV75t5K2GmW3myL8dmyTNbYSezsDMluDrBPIclHRRI/QEQRAEb34ivSQIgiAIgiAIOplILwmCIAiCIAiCTiac7iAIgiAIgiDoZMLpDoIgCIIgCIJOJpzuIAiCIAiCIOhkwukOgiAIgiAIgk7mfxQPnpZjGoiOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(der_T_1,\n",
    "                   col_cluster=False,\n",
    "                   row_cluster=True,\n",
    "                   mask=False,\n",
    "                   #standard_scale=0,\n",
    "                   yticklabels=True,\n",
    "                   cmap=\"Blues\",\n",
    "                   #square=True,\n",
    "                   #figsize=(12,(.3*(len(enrich_coverage_all.index))))\n",
    "                  )\n",
    "g.cax.set_visible(False)\n",
    "g.ax_row_dendrogram.set_visible(False)\n",
    "g.ax_col_dendrogram.set_visible(False)\n",
    "g.ax_heatmap.set_xticklabels(g.ax_heatmap.get_xmajorticklabels(), fontsize = 11, rotation=0)\n",
    "g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_ymajorticklabels(), fontsize = 14, rotation=0)\n",
    "#g.savefig('output_summ/SKO_Metabolic_Pathways.svg', dpi=400,bbox_inches='tight')\n",
    "g.savefig('Figs/SKO_Lung_1_Metabolic_Pathways.svg', dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#der_T.to_csv('output_summ/Differentially_Expressed_Reactions_Up.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lung study 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>Pathway</th>\n",
       "      <th>NB</th>\n",
       "      <th>Phenotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fatty acid oxidation</td>\n",
       "      <td>2</td>\n",
       "      <td>Calu3_12h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Glycerophospholipid metabolism</td>\n",
       "      <td>18</td>\n",
       "      <td>Calu3_12h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Inositol phosphate metabolism</td>\n",
       "      <td>2</td>\n",
       "      <td>Calu3_12h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nucleotide interconversion</td>\n",
       "      <td>2</td>\n",
       "      <td>Calu3_12h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sphingolipid metabolism</td>\n",
       "      <td>2</td>\n",
       "      <td>Calu3_12h</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pathway  NB  Phenotype\n",
       "0            Fatty acid oxidation   2  Calu3_12h\n",
       "1  Glycerophospholipid metabolism  18  Calu3_12h\n",
       "2   Inositol phosphate metabolism   2  Calu3_12h\n",
       "3      Nucleotide interconversion   2  Calu3_12h\n",
       "4         Sphingolipid metabolism   2  Calu3_12h"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df = pd.read_csv('KO_data/timeseries_RECON_SKO_Pathways.csv')\n",
    "#der_df.Phenotype= der_df.Phenotype.str.replace('s','Series')\n",
    "der_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Calculate the percentage of rxns in a pathway\n",
    "for i in range(der_df.shape[0]):\n",
    "    pathway = der_df.iloc[i,0]\n",
    "    rxns_count = int(pathway_df_unq.loc[pathway,:].values)\n",
    "    der_df.iloc[i,1] = der_df.iloc[i,1]/rxns_count\n",
    "    der_df.iloc[i,2] = der_df.iloc[i,2]/rxns_count\n",
    "der_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec5121c0d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "der_df.iloc[:,1].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the percentage of rxns in a pathway\n",
    "for i in range(der_df.shape[0]):\n",
    "    pathway = der_df.iloc[i,0]\n",
    "    rxns_count = int(pathway_df_unq.loc[pathway,:].values)\n",
    "    der_df.iloc[i,1] = der_df.iloc[i,1]/rxns_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#der_df = der_df.loc[der_df.iloc[:,1]>0.05,der_df.iloc[:,2]>0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "der_T = der_df.pivot_table(values='NB',index='Pathway', columns='Phenotype',aggfunc='mean',fill_value=None,dropna=False)\n",
    "der_T = der_T.fillna(0)\n",
    "der_T_2  =der_T.loc[:,['Calu3_4h', 'Calu3_8h','Calu3_12h', 'Calu3_24h', 'H1299_4h',  'H1299_12h','H1299_24h', 'H1299_36h']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lung2_pathways = der_T_2.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Phenotype</th>\n",
       "      <th>Calu3_4h</th>\n",
       "      <th>Calu3_8h</th>\n",
       "      <th>Calu3_12h</th>\n",
       "      <th>Calu3_24h</th>\n",
       "      <th>H1299_4h</th>\n",
       "      <th>H1299_12h</th>\n",
       "      <th>H1299_24h</th>\n",
       "      <th>H1299_36h</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pathway</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fatty acid oxidation</th>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerophospholipid metabolism</th>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Phenotype                       Calu3_4h  Calu3_8h  Calu3_12h  Calu3_24h  \\\n",
       "Pathway                                                                    \n",
       "Fatty acid oxidation            0.002081  0.002081   0.002081   0.002081   \n",
       "Glycerophospholipid metabolism  0.109091  0.109091   0.109091   0.115152   \n",
       "\n",
       "Phenotype                       H1299_4h  H1299_12h  H1299_24h  H1299_36h  \n",
       "Pathway                                                                    \n",
       "Fatty acid oxidation            0.002081   0.002081   0.002081   0.002081  \n",
       "Glycerophospholipid metabolism  0.218182   0.218182   0.218182   0.218182  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T_2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(der_T_2,\n",
    "                   col_cluster=False,\n",
    "                   row_cluster=True,\n",
    "                   mask=False,\n",
    "                   #standard_scale=0,\n",
    "                   yticklabels=True,\n",
    "                   cmap=\"Blues\",\n",
    "                   #square=True,\n",
    "                   #figsize=(12,(.3*(len(enrich_coverage_all.index))))\n",
    "                  )\n",
    "g.cax.set_visible(False)\n",
    "g.ax_row_dendrogram.set_visible(False)\n",
    "g.ax_heatmap.set_xticklabels(g.ax_heatmap.get_xmajorticklabels(), fontsize = 12, rotation=45)\n",
    "g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_ymajorticklabels(), fontsize = 14, rotation=0)\n",
    "\n",
    "g.ax_col_dendrogram.set_visible(False)\n",
    "#g.savefig('output_summ_/SKO_Metabolic_Pathways.png', dpi=400,bbox_inches='tight')\n",
    "g.savefig('Figs/SKO_Lung_2_Metabolic_Pathways.svg', dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#der_T_2.to_csv('output_summ_/Differentially_Expressed_Reactions_Up.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge DER from the 2 studies\n",
    "der_T = pd.concat([der_T_1, der_T_2], axis=1, join=\"outer\")\n",
    "der_T = der_T.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Glycerophospholipid metabolism', 'Transport, extracellular',\n",
       "       'Nucleotide interconversion', 'Starch and sucrose metabolism',\n",
       "       'Methionine and cysteine metabolism', 'Inositol phosphate metabolism',\n",
       "       'Sphingolipid metabolism', 'Folate metabolism',\n",
       "       'Phosphatidylinositol phosphate metabolism',\n",
       "       'Alanine and aspartate metabolism', 'Pyrimidine synthesis',\n",
       "       'Glycine, serine, alanine, and threonine metabolism',\n",
       "       'Fatty acid oxidation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T.mean(axis=1).sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x468 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(der_T*100,col_cluster=False, row_cluster=True,mask=False,\n",
    "                   annot_kws={'fontsize':'x-large'},\n",
    "                   #standard_scale=0,yticklabels=True,#square=True,\n",
    "                   cmap=\"Blues\",\n",
    "                   cbar_kws={'label':'Reactions per Pathway [%]'},#,\"orientation\": \"horizontal\"},\n",
    "                   cbar_pos=(0.1, .6, .02, .2),\n",
    "                   figsize=(15,(0.5*(len(der_T.index)))))\n",
    "#g.cax.set_visible(True)\n",
    "g.ax_row_dendrogram.set_visible(False)\n",
    "g.ax_heatmap.set_xticklabels(g.ax_heatmap.get_xmajorticklabels(), fontsize = 15, rotation=75)\n",
    "g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_ymajorticklabels(), fontsize = 15, rotation=0)\n",
    "plt.setp(g.ax_heatmap.get_xticklabels(), rotation=90)\n",
    "g.ax_heatmap.axvline(6, 0, 1, linewidth=15, c='grey')\n",
    "#g.ax_col_dendrogram.set_visible(False)\n",
    "g1 = g\n",
    "g.savefig('Figs/SKO_Metabolic_Pathways.svg', dpi=400,bbox_inches='tight')\n",
    "g.savefig('Figs/SKO_Metabolic_Pathways.png', dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>A549_0.02</th>\n",
       "      <th>A549_0.2_ACE2</th>\n",
       "      <th>A549_2</th>\n",
       "      <th>A549_2_ACE2</th>\n",
       "      <th>NHBE_2</th>\n",
       "      <th>Calu3_2</th>\n",
       "      <th>CALU3_4H</th>\n",
       "      <th>CALU3_8H</th>\n",
       "      <th>CALU3_12H</th>\n",
       "      <th>CALU3_24H</th>\n",
       "      <th>H1299_4H</th>\n",
       "      <th>H1299_12H</th>\n",
       "      <th>H1299_24H</th>\n",
       "      <th>H1299_36H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pyrimidine metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein digestion and absorption</th>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One carbon pool by folate</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycine, serine and threonine metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ferroptosis</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          A549_0.02  A549_0.2_ACE2    A549_2  \\\n",
       "Pyrimidine metabolism                      0.000000       0.017544  0.017544   \n",
       "Protein digestion and absorption           0.011111       0.000000  0.011111   \n",
       "One carbon pool by folate                  0.000000       0.100000  0.000000   \n",
       "Glycine, serine and threonine metabolism   0.000000       0.025000  0.000000   \n",
       "Ferroptosis                                0.050000       0.000000  0.050000   \n",
       "\n",
       "                                          A549_2_ACE2    NHBE_2   Calu3_2  \\\n",
       "Pyrimidine metabolism                        0.017544  0.017544  0.017544   \n",
       "Protein digestion and absorption             0.022222  0.022222  0.000000   \n",
       "One carbon pool by folate                    0.000000  0.000000  0.000000   \n",
       "Glycine, serine and threonine metabolism     0.025000  0.025000  0.000000   \n",
       "Ferroptosis                                  0.000000  0.000000  0.000000   \n",
       "\n",
       "                                          CALU3_4H  CALU3_8H  CALU3_12H  \\\n",
       "Pyrimidine metabolism                         0.00     0.000       0.00   \n",
       "Protein digestion and absorption              0.00     0.000       0.00   \n",
       "One carbon pool by folate                     0.00     0.000       0.00   \n",
       "Glycine, serine and threonine metabolism      0.00     0.000       0.00   \n",
       "Ferroptosis                                   0.05     0.025       0.05   \n",
       "\n",
       "                                          CALU3_24H  H1299_4H  H1299_12H  \\\n",
       "Pyrimidine metabolism                         0.000     0.000      0.000   \n",
       "Protein digestion and absorption              0.000     0.000      0.000   \n",
       "One carbon pool by folate                     0.000     0.000      0.000   \n",
       "Glycine, serine and threonine metabolism      0.000     0.025      0.025   \n",
       "Ferroptosis                                   0.025     0.000      0.000   \n",
       "\n",
       "                                          H1299_24H  H1299_36H  \n",
       "Pyrimidine metabolism                           0.0      0.000  \n",
       "Protein digestion and absorption                0.0      0.000  \n",
       "One carbon pool by folate                       0.0      0.000  \n",
       "Glycine, serine and threonine metabolism        0.0      0.025  \n",
       "Ferroptosis                                     0.0      0.000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Enrichr pathways to make it in 1 fig\n",
    "enrichr1 = pd.read_csv('figs/Lung1_Kegg.csv')\n",
    "enrichr1.index = enrichr1.Term\n",
    "enrichr1 = enrichr1.iloc[:,[5,6,7,8,9,10]]\n",
    "enrichr2 = pd.read_csv('figs/Lung2_Kegg.csv')\n",
    "enrichr2.index = enrichr2.Term\n",
    "enrichr2 = enrichr2.iloc[:,[5,6,7,8,9,10,11,12]]\n",
    "enrichr = pd.concat([enrichr1, enrichr2], axis=1, join=\"outer\")\n",
    "enrichr = enrichr.fillna(0)\n",
    "enrichr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrichr.columns = der_T.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x612 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(enrichr*100,col_cluster=False, row_cluster=True,mask=False,\n",
    "                   annot_kws={'fontsize':'x-large'},\n",
    "                   #standard_scale=0,yticklabels=True,#square=True,\n",
    "                   cmap=\"Blues\",\n",
    "                   cbar_kws={'label':'Enrichment [%]'},#,\"orientation\": \"horizontal\"},\n",
    "                   cbar_pos=(0.1, .6, .02, .2),\n",
    "                   figsize=(15,(0.5*(len(enrichr.index)))))\n",
    "#g.cax.set_visible(True)\n",
    "g.ax_row_dendrogram.set_visible(False)\n",
    "g.ax_heatmap.set_xticklabels(g.ax_heatmap.get_xmajorticklabels(), fontsize = 15, rotation=75)\n",
    "g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_ymajorticklabels(), fontsize = 15, rotation=0)\n",
    "plt.setp(g.ax_heatmap.get_xticklabels(), rotation=90)\n",
    "g.ax_heatmap.axvline(6, 0, 1, linewidth=15, c='grey')\n",
    "#g.ax_col_dendrogram.set_visible(False)\n",
    "g.savefig('Figs/SKO_Enrichr_Pathways.svg', dpi=400,bbox_inches='tight')\n",
    "g.savefig('Figs/SKO_Enrichr_Pathways.png', dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 14)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enrichr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 14)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>A549_0.02</th>\n",
       "      <th>A549_0.2_ACE2</th>\n",
       "      <th>A549_2</th>\n",
       "      <th>A549_2_ACE2</th>\n",
       "      <th>NHBE_2</th>\n",
       "      <th>Calu3_2</th>\n",
       "      <th>Calu3_4h</th>\n",
       "      <th>Calu3_8h</th>\n",
       "      <th>Calu3_12h</th>\n",
       "      <th>Calu3_24h</th>\n",
       "      <th>H1299_4h</th>\n",
       "      <th>H1299_12h</th>\n",
       "      <th>H1299_24h</th>\n",
       "      <th>H1299_36h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fatty acid oxidation</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycine, serine, alanine, and threonine metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyrimidine synthesis</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alanine and aspartate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phosphatidylinositol phosphate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folate metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sphingolipid metabolism</th>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inositol phosphate metabolism</th>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.030769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methionine and cysteine metabolism</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starch and sucrose metabolism</th>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleotide interconversion</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.030488</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport, extracellular</th>\n",
       "      <td>0.010558</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>0.011312</td>\n",
       "      <td>0.036199</td>\n",
       "      <td>0.050528</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>0.027149</td>\n",
       "      <td>0.070513</td>\n",
       "      <td>0.070136</td>\n",
       "      <td>0.069759</td>\n",
       "      <td>0.131222</td>\n",
       "      <td>0.131976</td>\n",
       "      <td>0.131976</td>\n",
       "      <td>0.131976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerophospholipid metabolism</th>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.218182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    A549_0.02  A549_0.2_ACE2  \\\n",
       "Fatty acid oxidation                                 0.000000       0.002081   \n",
       "Glycine, serine, alanine, and threonine metabolism   0.000000       0.042553   \n",
       "Pyrimidine synthesis                                 0.000000       0.000000   \n",
       "Alanine and aspartate metabolism                     0.000000       0.076923   \n",
       "Phosphatidylinositol phosphate metabolism            0.000000       0.000000   \n",
       "Folate metabolism                                    0.000000       0.101695   \n",
       "Sphingolipid metabolism                              0.015038       0.015038   \n",
       "Inositol phosphate metabolism                        0.030769       0.000000   \n",
       "Methionine and cysteine metabolism                   0.000000       0.000000   \n",
       "Starch and sucrose metabolism                        0.047619       0.000000   \n",
       "Nucleotide interconversion                           0.000000       0.018293   \n",
       "Transport, extracellular                             0.010558       0.016214   \n",
       "Glycerophospholipid metabolism                       0.109091       0.109091   \n",
       "\n",
       "                                                      A549_2  A549_2_ACE2  \\\n",
       "Fatty acid oxidation                                0.000000     0.000000   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000     0.000000   \n",
       "Pyrimidine synthesis                                0.047619     0.000000   \n",
       "Alanine and aspartate metabolism                    0.000000     0.000000   \n",
       "Phosphatidylinositol phosphate metabolism           0.000000     0.000000   \n",
       "Folate metabolism                                   0.000000     0.000000   \n",
       "Sphingolipid metabolism                             0.015038     0.015038   \n",
       "Inositol phosphate metabolism                       0.030769     0.000000   \n",
       "Methionine and cysteine metabolism                  0.000000     0.065217   \n",
       "Starch and sucrose metabolism                       0.047619     0.095238   \n",
       "Nucleotide interconversion                          0.030488     0.256098   \n",
       "Transport, extracellular                            0.011312     0.036199   \n",
       "Glycerophospholipid metabolism                      0.109091     0.115152   \n",
       "\n",
       "                                                      NHBE_2   Calu3_2  \\\n",
       "Fatty acid oxidation                                0.002081  0.002081   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000  0.000000   \n",
       "Pyrimidine synthesis                                0.000000  0.000000   \n",
       "Alanine and aspartate metabolism                    0.000000  0.000000   \n",
       "Phosphatidylinositol phosphate metabolism           0.000000  0.000000   \n",
       "Folate metabolism                                   0.000000  0.000000   \n",
       "Sphingolipid metabolism                             0.015038  0.015038   \n",
       "Inositol phosphate metabolism                       0.000000  0.000000   \n",
       "Methionine and cysteine metabolism                  0.065217  0.000000   \n",
       "Starch and sucrose metabolism                       0.095238  0.000000   \n",
       "Nucleotide interconversion                          0.256098  0.256098   \n",
       "Transport, extracellular                            0.050528  0.016214   \n",
       "Glycerophospholipid metabolism                      0.115152  0.109091   \n",
       "\n",
       "                                                    Calu3_4h  Calu3_8h  \\\n",
       "Fatty acid oxidation                                0.002081  0.002081   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000  0.000000   \n",
       "Pyrimidine synthesis                                0.000000  0.000000   \n",
       "Alanine and aspartate metabolism                    0.000000  0.000000   \n",
       "Phosphatidylinositol phosphate metabolism           0.000000  0.000000   \n",
       "Folate metabolism                                   0.000000  0.000000   \n",
       "Sphingolipid metabolism                             0.015038  0.015038   \n",
       "Inositol phosphate metabolism                       0.030769  0.030769   \n",
       "Methionine and cysteine metabolism                  0.000000  0.000000   \n",
       "Starch and sucrose metabolism                       0.047619  0.047619   \n",
       "Nucleotide interconversion                          0.012195  0.012195   \n",
       "Transport, extracellular                            0.027149  0.070513   \n",
       "Glycerophospholipid metabolism                      0.109091  0.109091   \n",
       "\n",
       "                                                    Calu3_12h  Calu3_24h  \\\n",
       "Fatty acid oxidation                                 0.002081   0.002081   \n",
       "Glycine, serine, alanine, and threonine metabolism   0.000000   0.000000   \n",
       "Pyrimidine synthesis                                 0.000000   0.000000   \n",
       "Alanine and aspartate metabolism                     0.000000   0.000000   \n",
       "Phosphatidylinositol phosphate metabolism            0.000000   0.000000   \n",
       "Folate metabolism                                    0.000000   0.000000   \n",
       "Sphingolipid metabolism                              0.015038   0.015038   \n",
       "Inositol phosphate metabolism                        0.030769   0.030769   \n",
       "Methionine and cysteine metabolism                   0.000000   0.000000   \n",
       "Starch and sucrose metabolism                        0.047619   0.047619   \n",
       "Nucleotide interconversion                           0.012195   0.012195   \n",
       "Transport, extracellular                             0.070136   0.069759   \n",
       "Glycerophospholipid metabolism                       0.109091   0.115152   \n",
       "\n",
       "                                                    H1299_4h  H1299_12h  \\\n",
       "Fatty acid oxidation                                0.002081   0.002081   \n",
       "Glycine, serine, alanine, and threonine metabolism  0.000000   0.000000   \n",
       "Pyrimidine synthesis                                0.000000   0.000000   \n",
       "Alanine and aspartate metabolism                    0.000000   0.000000   \n",
       "Phosphatidylinositol phosphate metabolism           0.025000   0.025000   \n",
       "Folate metabolism                                   0.000000   0.000000   \n",
       "Sphingolipid metabolism                             0.015038   0.015038   \n",
       "Inositol phosphate metabolism                       0.030769   0.030769   \n",
       "Methionine and cysteine metabolism                  0.065217   0.065217   \n",
       "Starch and sucrose metabolism                       0.000000   0.000000   \n",
       "Nucleotide interconversion                          0.000000   0.000000   \n",
       "Transport, extracellular                            0.131222   0.131976   \n",
       "Glycerophospholipid metabolism                      0.218182   0.218182   \n",
       "\n",
       "                                                    H1299_24h  H1299_36h  \n",
       "Fatty acid oxidation                                 0.002081   0.002081  \n",
       "Glycine, serine, alanine, and threonine metabolism   0.000000   0.000000  \n",
       "Pyrimidine synthesis                                 0.000000   0.000000  \n",
       "Alanine and aspartate metabolism                     0.000000   0.000000  \n",
       "Phosphatidylinositol phosphate metabolism            0.025000   0.025000  \n",
       "Folate metabolism                                    0.000000   0.000000  \n",
       "Sphingolipid metabolism                              0.015038   0.015038  \n",
       "Inositol phosphate metabolism                        0.030769   0.030769  \n",
       "Methionine and cysteine metabolism                   0.000000   0.065217  \n",
       "Starch and sucrose metabolism                        0.000000   0.000000  \n",
       "Nucleotide interconversion                           0.000000   0.000000  \n",
       "Transport, extracellular                             0.131976   0.131976  \n",
       "Glycerophospholipid metabolism                       0.218182   0.218182  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "der_T.assign(m=der_T.mean(axis=1)).sort_values('m').drop('m', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "lung1_metabolic_pathways = der_T.index[der_T.iloc[:,:6].sum(axis=1)>0]\n",
    "lung2_metabolic_pathways = der_T.index[der_T.iloc[:,7:].sum(axis=1)>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "!convert -append Figs/SKO_Enrichr_Pathways.png Figs/SKO_Metabolic_Pathways.png Figs/Fig4.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Fatty acid oxidation',\n",
       " 'Glycerophospholipid metabolism',\n",
       " 'Inositol phosphate metabolism',\n",
       " 'Methionine and cysteine metabolism',\n",
       " 'Nucleotide interconversion',\n",
       " 'Sphingolipid metabolism',\n",
       " 'Starch and sucrose metabolism',\n",
       " 'Transport, extracellular']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in lung2_metabolic_pathways if x in lung1_metabolic_pathways]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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