Mito_Barplots_filtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "advanced-small",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "another-electronics",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures'\n",
    "\n",
    "df_sites = pd.read_csv(path + 'Prep_STYsites_cut0_all.csv', low_memory=False)\n",
    "\n",
    "\n",
    "df_prot = pd.read_csv(path + 'Prep_Protein Groups_cut0_notimuted.csv', low_memory=False)\n",
    "df_prot['SubMitoLocalization'] = df_prot['SubMitoLocalization'].astype(str)\n",
    "df_prot['IMPI_new'] = df_prot['IMPI_new'].astype(str)\n",
    "df_prot['Mito_filt'] = ['Mitochondrial' if (df_prot['SubMitoLocalization'][i]!='nan' or('Verified mitochondrial' in df_prot['IMPI_new'][i])==True) else 'Not Mitochondrial' for i in range(len(df_prot))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "independent-damage",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "ID_all_class_one_phos = []\n",
    "#ID_Mito = []\n",
    "ID_Mito_ClassI = []\n",
    "ID_Mito_Quant = []\n",
    "for T in ['B','BAT','H','K','L','S','SKM']:\n",
    "    \n",
    "    Tissues = ['Brain','BAT','Heart', 'Kidney', 'Liver', 'Spleen', 'SKM']\n",
    "    \n",
    "    col = []\n",
    "    Tab = pd.DataFrame()\n",
    "    [col.append(i) for i in range(len(list(df_sites.columns))) if ((df_sites.columns[i].find('Intensity wt')==0)&(df_sites.columns[i].find('_'+T)==(len(df_sites.columns[i])-len(T)-1)))==True]\n",
    "    Tab['Count'] = df_sites.iloc[:,col].count(1)\n",
    "    Tab['Norm_Int'] = df_sites.iloc[:,col].median(1)\n",
    "    Tab['SubMitoLocalization'] = df_sites['SubMitoLocalization']\n",
    "    Tab['IMPI_new'] = df_sites['IMPI_new']\n",
    "    Tab['Proteins'] = df_sites['Proteins']\n",
    "    Tab['Localization prob'] = df_sites['Localization prob']\n",
    "    Tab = Tab[Tab['Count']>0].sort_values(by=['Norm_Int'],ascending= False).reset_index(drop= True)\n",
    "    Tab2 = Tab[Tab['Count']>2].sort_values(by=['Norm_Int'],ascending= False).reset_index(drop= True)\n",
    "    \n",
    "    #all_phos = len(Tab)\n",
    "    all_class_one_phos = len(Tab[Tab['Localization prob'] >= 0.75])\n",
    "    #all_class_one = sum(Tab['Localization prob']>=0.75)\n",
    "    #Tab = Tab[Tab['Localization prob']>=0.75].reset_index(drop= True)\n",
    "    mito = len([i for i in range(len(Tab)) if (('Verified mitochondrial' in Tab['IMPI_new'][i])==True or \n",
    "                                                         #('Predicted mitochondrial' in Tab['IMPI_new'][i])==True or \n",
    "                                                         Tab['SubMitoLocalization'][i]!='[]')])\n",
    "\n",
    "    Tab = Tab.iloc[[i for i in range(len(Tab)) if (('Verified mitochondrial' in Tab['IMPI_new'][i])==True or \n",
    "                                                         #('Predicted mitochondrial' in Tab['IMPI_new'][i])==True or \n",
    "                                                         Tab['SubMitoLocalization'][i]!='[]')]].reset_index(drop= True)\n",
    "    Mito_class_one = sum(Tab['Localization prob']>=0.75)\n",
    "    Tab2 = Tab2[Tab2['Localization prob']>=0.75].reset_index(drop= True)\n",
    "    mito_quant = len([i for i in range(len(Tab2)) if (('Verified mitochondrial' in Tab2['IMPI_new'][i])==True or \n",
    "                                                         #('Predicted mitochondrial' in Tab2['IMPI_new'][i])==True or \n",
    "                                                         Tab2['SubMitoLocalization'][i]!='[]')])\n",
    "    ID_all_class_one_phos.append(all_class_one_phos)\n",
    "    #ID_all.append(all_phos[all_phos['Localization prob'] >= 0.75])\n",
    "    #ID_Mito.append(mito)\n",
    "    ID_Mito_ClassI.append(Mito_class_one)\n",
    "    ID_Mito_Quant.append(mito_quant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "proprietary-firmware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5/5mdgenOuWTn3EZgPdD8xIQvkj9prTgLFizg3HPPpUaNGiGtP3XqVB5//HHOPPNMqlSpwrBhw3j77bcBGD9+PLfeeitXX301xYoVo0aNGpxzzjmBdfv27cvZZ59N6dKl6datG8uWLQPgvffeo2PHjlx99dVERETwwAMPcOjQIb766qvAuvfeey8xMTFUrFiRTp06BdZ9//336du3L3/5y18oW7Ysw4cPPy7mzp07c9lll1GsWLHjWh0qVapEly5dKFOmDJGRkTz66KMsWbIkqGOxe/fuQMtNMDp27Ej9+vUxM1q3bk27du344osvAC+J2LZtG/Hx8URERNCqVSvMjOLFi5OcnMzq1as5evQoderUCbTChCIuLo4XX3yR+fPn07p1a84880xGjRoV9PoXXngh/fr1Y82aNYwcOTLo9bLbr9xs376dOXPm8Pzzz1O2bFnOPPNM7r//fqZPnx6oNz4+nsTEREqVKnVc4ipSUIrEGBznXIKZjQY2A4eAT4AfgL3OubTLHLYCaWf8GsAWf91jZrYPqOTP/yZd1enXyVL79u2PG9x5KmnWrNnJDiEspO9yOFX16tWLyy+/nI0bNx7XPRWMxMREateuHZiuXbs2iYmJgDfeqEOHDtmuW61atcDvZcqU4cCBA1nWWaxYMWrWrElCQkK266ZtMzExkaZNm2aIJ7OaNWtmG9PBgwe5//77mTdvHnv27AEgKSkpqLE6lSpVYtu2bTmWSW/u3LmMGDGCX375hdTUVA4ePMj5558PwIMPPsjw4cNp164dAHfccQdDhgyhQYMGPP/88wwfPpxVq1ZxzTXXMHbsWGJiYoLebpqePXvSs2dPjh49yowZM+jZsydNmjThmmuC62Vv1KgRderUoUyZMkFvM7v9yk18fDxHjx7NkECmpqYG/pbPPPMMQ4cOpXnz5lSoUIFBgwZx6623Bh2XnN6y+sxbunRplmWLRIJjZhXwWl/qAnuBD/C6mApd+nEIBS2Yb0O5ye4PK6H54YcfTnYIuapduzZ169Zlzpw5jB8/PseyWb23YmJiiI+Pp1GjRgBs3rw58GFbs2ZNNmzYEHJMMTExrFixIjDtnGPLli1BtS5Vr16dLVu2BKY3b94c1H6kGTNmDGvXruXbb7+lWrVqLFu2jAsvvBDnXK7bvuqqq/j3v/9N3759cy2bnJxMly5dmDJlCp07dyYiIoIbbrghsJ3IyEjGjBnDmDFjWLlyJW3btuXiiy/myiuv5Oabb+bmm29m//793HnnnQwePDjQapYXERERdO3alVGjRrFy5cqgE5y8yGm/0sv8N6pZsyYlS5Zk165dnHHG8R8x1apV48033wTgyy+/5KqrruLyyy+nQYMGhbYvEj5C+cwrKl1UVwEbnXM7nXNHgf8FLgOi/S4rgFgg7WtjAlATwF8eBexOPz+LdUROeePHj+fTTz+lbNmyOZarWrUqu3fvZt++fYF5PXr04Mknn2Tnzp3s2rWLJ554IjCOp1+/fkycOJFFixaRmppKQkICP//8c67xdOvWjdmzZ7No0SKOHj3KmDFjKFmyZFDjPLp168akSZNYvXo1Bw8eZMSIEbmuk15SUhKlS5cmOjqa33//PaT1R4wYwVdffcWDDz7Ib7/9BniDX+Pi4ti7d2+GskeOHCE5OZkqVapwxhlnMHfuXD755JPA8o8//pj169fjnCMqKorixYtTrFgx1q5dy6effkpycjKlSpWidOnSFCsW+il30qRJzJ49m6SkJFJTU5k7dy6rVq3ikksuCbmuUGS3X5lVrVqVTZs2BQYgV69enXbt2jFo0CD2799PamoqGzZsCHQffvDBB4HB4BUqVMDM8nRcRHJTJFpw8LqmWphZGbwuqiuBpcBnwI3AdOAWYKZffpY//bW//FPnnDOzWcA0MxsLxABnAd+dyB2RoqNmzZohXcqdl/pDFewYjnPOOYcePXpQr149UlJSWL16NY899hj79++ncePGAHTt2jVwRVLz5s0Dg2c3btxI1apVefnllzOMw8lKw4YNeeeddxgwYAAJCQk0adKEjz76iBIlSuQa47XXXsvAgQNp27YtxYoV48knn2Tq1KlB7R/AwIEDufnmm6lcuTIxMTEMGjSIGTNmBLVu/fr1+frrr3nsscdo1KgRx44do06dOvTt25fIyMgMSU5kZCTjxo2jW7duJCcn06lTJ66//vrA8nXr1tG/f3927txJhQoVuPvuu7niiitYvnw5Q4YMYc2aNURERNCyZUveeOONoPcvTfny5Rk5ciRxcXGkpKRQu3ZtXn311UIfu5LdfmXWtWtX3nnnHSpVqkTdunX58ccfmTJlCkOGDOG8884jKSmJevXqMXjwYMC7KmzgwIHs27ePqlWr8sILL1CvXr1C3Rc5PVkwzbmnAjMbAXQHjgH/BW7DGz8zHajoz4tzziWbWSngbeBC4HfgJufcr349jwK3+vUMdM7NzWXThXaAzIxBgwblef0xY8YE1Rwvufvhhx8yjAcREZFTSw7n6Sz7sotKCw7OuWHAsEyzfyWLq6Ccc4eBrtnU8xTwVIEHKCIiIqcMdXyKiIhI2FGCIyIiImFHCY6IiIiEHSU4IiIiEnaU4IiIiEjYUYIjIiIiYUcJjoiIiIQdJTgiYeKuu+7iX//6V4HX26ZNG956660CrzcrZsb69euzXR7KPuYn7k2bNmFmHDt2LPfCp4Frr72WyZMnn+wwAr744gsaNmx4ssOQU5wSHJFs1K5dGzMrtFdWT8/OSp06dShRosRxT7W/8MILMTM2bdoEwGuvvcbQoUMBWLx4MbGxsQV6PE4F6fcxv3755Re6du1K5cqViYqKonHjxowdO5aUlJQCqT8Uffr0CTw2IysjR46kbt26lCtXjtjYWLp37w54TwkvV64c5cqVo3jx4pQqVSow/dRTTxEVFcUvv/ySoa4rr7wyqKeCpzd37lxuueUWwHs2VmE/JmLq1KmB/Uh7hlfadLly5WjVqhVr164t1BiClfl/rU2bNpQqVYrIyEjKly9P06ZNefrpp0lOTi7Q7eb2nikow4cPJyIiIsPx//XXXwPLly1bRtOmTSlTpgxNmzZl2bJlgWXTpk2jevXq1KlTh88++ywwf8OGDbRs2bLQ/9eKzJ2MRU60zZs3F+pJNJRvoHXr1uXdd99lwIABAKxYsYKDBw8WVminpJSUFIoXL14gdW3YsIFLLrmEvn37smLFCqpXr87atWsZMWIESUlJBbKNgjJ58mTefvttFi5cSP369fntt9+YNWsWAKtWrQqUa9OmDXFxcdx2222BeampqfTr14/PP/8cM2P8+PEkJCQwe/bsE74foejZsyc9e/YEvAQiLi4u8IDOouCll17itttu448//gg8e2vBggUsXLjwuKevZ+XYsWNZPom9IO3cuZPKlSsHFU/37t155513jpt/5MgROnfuzMCBA7n77rt5/fXX6dy5M+vWraNYsWIMGTKEH3/8kR9++IEBAwawcuVKAO69916ee+65Avt/zo5acESKgF69ejFlypTA9OTJk+ndu3eGMmnf6P744w+uvfZaEhMTA9+4EhMTSU5OZuDAgcTExBATE8PAgQMzfKucOXMmTZo0oXz58tSvX5958+YFlsXHx3PZZZcRGRlJu3btMrQmzZo1i0aNGhEdHU2bNm1Ys2ZNYFmdOnUYPXo0jRs3Jioqiu7du3P48OHA8meffZbq1asTExPDhAkTjtuff/zjH3To0IGyZcvy2WefZfjWumfPHq677jqqVKlChQoVuO6664L+EBw2bBgtW7Zk7NixVK9eHfASzmnTphEdHX1c+YkTJ3LuuecSGRlJvXr1eP311wPLdu3axXXXXUd0dDQVK1akVatWgSdrjxo1iho1ahAZGUnDhg1ZtGhRUPGl9/3333PNNdcEHrRarVo17rjjjqDWHTJkCElJSbzyyits376dwYMHM2HCBEqVKnVc2cOHDxMXF0elSpWIjo7m4osvZvv27cCf3X1r1qzhrrvu4uuvv6ZcuXKBY5WcnMwDDzxArVq1qFq1KnfddReHDh3K9fjkVeZWk+yO83fffUezZs0oX748VatW5Z///GeW64P3Xl24cCHgJYZPP/009evXp1KlSnTr1o3ff/895DjLli1LmzZtmDVrFl9//XW2iWVal+j48eOpVasWbdu2BbwHmVarVo2oqCguv/zyQEL7xhtvMHXqVJ555hnKlStHp06dAEhMTKRLly5UqVKFunXrMm7cuGxjmzBhAnXr1mXYsGFs3Lgx5H0D7zgeO3aMgQMHUrJkSe69916cc3z66afs3r2bGjVqUL16da666qpAq8+HH35IjRo1uOSSS/K0zVAowREpAlq0aMH+/ftZs2YNKSkpTJ8+nbi4uCzLli1blrlz5xITE8OBAwc4cOAAMTExPPXUU3zzzTcsW7aMn376ie+++44nn3wS8D4IevfuzbPPPsvevXv5/PPPqVOnTqDOadOmMXHiRHbs2MGRI0cYPXo04HXz9OjRg+eff56dO3fSoUMHOnXqxJEjRwLrvv/++8ybN4+NGzeyfPlyJk2aBMC8efMYPXo0CxYsYN26dYEPl/SmTZvGo48+SlJS0nHdIqmpqfTt25f4+Hg2b95M6dKl6d+/f1DHc+HChdx4441BlQU488wz+fjjj9m/f3/gqes//vgj4D30NjY2lp07d7J9+3ZGjhyJmbF27Vpeeuklvv/+e5KSkpg/f36GYxqsFi1aMGXKFJ599lmWLl0aUrN+REQEEydOZOjQocTFxREXF0fLli2zLDt58mT27dvHli1b2L17N6+99hqlS5fOUObcc8/ltdde49JLL+XAgQOBp64PGTKEX375hWXLlrF+/XoSEhJ44okncjw+BSWn43zfffdx3333sX//fjZs2EC3bt2CqvPFF19kxowZLFmyhMTERCpUqMA999yT5xhr1apFs2bN+OKLL3Ist2TJEtasWcP8+fMBb+zTunXr2LFjBxdddFGgVeuOO+6gZ8+ePPTQQxw4cICPPvqI1NRUOnXqxAUXXEBCQgKLFi3i+eefD9SV2eDBg5k+fTo7duygWbNmXHHFFbz99ttZtgx/9NFHVKxYkUaNGvHqq68G5q9atYrGjRtn+Hs2btyYVatWUaVKFXbv3s3WrVtZsGABjRo1IikpiSeffJL/+Z//CfkY5oUSHJEiIq0VZ8GCBZx77rnUqFEjpPWnTp3K448/zplnnkmVKlUYNmwYb7/9NgDjx4/n1ltv5eqrr6ZYsWLUqFGDc845J7Bu3759OfvssyldujTdunUL9LO/9957dOzYkauvvpqIiAgeeOABDh06xFdffRVY99577yUmJoaKFSvSqVOnwLrvv/8+ffv25S9/+Qtly5Zl+PDhx8XcuXNnLrvsMooVK3Zcq0OlSpXo0qULZcqUITIykkcffZQlS5YEdSx2794daLkJRseOHalfvz5mRuvWrWnXrl3gwyoiIoJt27YRHx9PREQErVq1wswoXrw4ycnJrF69mqNHj1KnTp1AK0wo4uLiePHFF5k/fz6tW7fmzDPPZNSoUUGvf+GFF9KvXz/WrFnDyJEjsy0XERHB7t27Wb9+PcWLF6dp06aUL18+1/qdc7zxxhs899xzVKxYkcjISB555BGmT58eqDer41NQcjrOERERrF+/nl27dlGuXDlatGgRVJ2vvfYaTz31FLGxsZQsWZLhw4fz4Ycf5mvQeUxMTK6tQMOHD6ds2bKBxPLWW28lMjIyEMNPP/3Evn37slz3+++/Z+fOnTz++OOUKFGCevXqcfvttwf+Dllp0aIFr776KomJifzjH//g3XffJTY2NkM3Z7du3VizZg07d+7kzTff5IknnuDdd98F4MCBA0RFRWWoMyoqiqSkJIoVK8arr77KjTfeyOjRo3nzzTcZNmwYAwYMYPny5VxxxRVcc801gW6rwqAER6SI6NWrF9OmTWPSpEnHdU8FIzExMcPA5tq1a5OYmAjAli1bcvzwrVatWuD3MmXKcODAgSzrLFasGDVr1iQhISGodWvWrJkhnszSL8/s4MGD3HnnndSuXZvy5ctz+eWXs3fv3qBaOCpVqsS2bdtyLZdm7ty5tGjRgooVKxIdHc2cOXMC3XQPPvggDRo0oF27dtSrV4+nn34agAYNGvD8888zfPhwzjzzTG666abA8Q5Vz549WbhwIXv37g0MtM7um3lWGjVqRJ06dShTpky2ZXr16sU111zDTTfdRExMDA899BBHjx7Nte6dO3dy8OBBmjZtSnR0NNHR0bRv356dO3cC2R+fgpLTcR4/fjy//PIL55xzDhdffDEff/xxUHXGx8fzt7/9LbA/5557LsWLFw902eVFQkICFStWBMgwYHfz5s2BMunf7ykpKQwZMoT69etTvnz5QKtU5osN0secmJgYiDk6OpqRI0cGFXPJkiVp3LgxTZo0oUSJEhmSjvPOO4+YmBiKFy9Oy5Ytue+++/jwww8D+7F///4Mde3fv5/IyEjAG9D+zTffsGTJEsyMpUuX0qdPH3r37s2kSZMYOnRohmSqoCnBESkiateuTd26dZkzZw5///vfcyyb1TfkmJgY4uPjA9ObN28mJiYG8E6sGzZsCDmmzHU659iyZUtQrUvVq1dny5YtGeLJLKdv+mPGjGHt2rV8++237N+/n88//zwQQ26uuuoq/v3vf+daDrzxJV26dOGBBx5g+/bt7N27lw4dOgS2ExkZyZgxY/j111+ZNWsWY8eODYwBufnmm/nyyy+Jj4/HzBg8eHBQ28xOREQEXbt2pXHjxgX+zTciIoJhw4axevVqvvrqKz7++OMM477SZP6bVK5cmdKlS7Nq1Sr27t3L3r172bdvXyCRzen4FJTsjvNZZ53Fu+++y44dOxg8eDA33ngjf/zxB2XLls3QFZOSkhJIyMD7f5g7d25gf/bu3cvhw4dDbjVNs2XLFn744QdatWoFEOg6PnDgALVq1QqUS39sp02bxsyZM1m4cCH79u0LXC2Z9r7L/HeoWbMmdevWzRBzUlISc+bMyTau3bt389JLL9G8eXPatm1LSkoKn332Gd98802265hZIIZGjRqxfPnyDP9zy5cvp1GjRhnWcc7Rv39/xo0bx65du0hJSaF27dpcfPHFLF++PKdDly9KcESKkPHjx/Ppp59StmzZHMtVrVqV3bt3Z2jO7tGjB08++SQ7d+5k165dPPHEE4FxPP369WPixIksWrSI1NRUEhIS+Pnnn3ONp1u3bsyePZtFixZx9OhRxowZQ8mSJbMd55F53UmTJrF69WoOHjzIiBEjcl0nvaSkJEqXLk10dDS///57SOuPGDGCr776igcffJDffvsNgPXr1xMXFxcYV5LmyJEjJCcnU6VKFc444wzmzp3LJ598Elj+8ccfs379epxzREVFUbx4cYoVK8batWv59NNPSU5OplSpUoHLnUM1adIkZs+eTVJSEqmpqcydO5dVq1YV+CDNzz77jBUrVpCSkkL58uWJiIjIMt6qVauydevWwDirYsWKcfvtt3P//fezY8cOwGutSGthyu74gDeQvE+fPvmKO6fj/M4777Bz506KFSsWGBBdrFgxzj77bA4fPszs2bM5evQoTz75ZIYB93fddRePPvpoIHnfuXMnM2fODDm2gwcPsmTJEjp37kzz5s3p0KFD0OsmJSVRsmRJKlWqxMGDB3nkkUcyLK9atWqGy7WbN29OZGQko0aN4tChQ6SkpLBy5Uq+//77LOsfP348derUYcmSJQwbNowtW7YwatQozj333AzlZs6cyZ49e3DO8d133zFu3Dg6d+4MeIPPixcvzrhx40hOTuall14CCAySTvPWW29x0UUX0aRJEypVqsShQ4dYvXo1n332GfXq1Qv6mIRKCY5INmrVqkXDhg0L7ZX+m1uw6tevT7NmzXItd84559CjRw/q1atHdHQ0iYmJPPbYYzRr1ozGjRtz/vnnc9FFFwWuSGrevHlg8GxUVBStW7fO0DKTnYYNG/LOO+8wYMAAKleuzEcffcRHH31EiRIlcl332muvZeDAgbRt25YGDRocd1LMzcCBAzl06BCVK1emRYsWtG/fPuh169evz9dff82mTZto1KgRUVFRdOnShWbNmgWa19NERkYybtw4unXrRoUKFZg2bRrXX399YPm6deu46qqrKFeuHJdeeil33303V1xxBcnJyQwZMoTKlStTrVo1duzYkafBleXLl2fkyJHUqlWL6OhoHnroIV599dUCvxfNb7/9xo033kj58uU599xzad26Nb169TquXNu2bWnUqBHVqlWjcuXKgHcVU4MGDWjRogXly5fnqquuCtxiIbvjA17LxmWXXZavuHM6zvPmzQvcK+i+++5j+vTplC5dmqioKF555RVuu+02atSoQdmyZTNcVXXfffdx/fXX065dOyIjI2nRogXffvtt0DH179+fyMhIqlatysCBA+nSpQvz5s0LKcHt3bs3tWvXpkaNGpx33nnHjR/q168fq1evJjo6mhtuuIHixYvz8ccfs2zZMurWrUvlypW57bbbsh2zc+mllxIfH88HH3xAx44ds71ke/r06TRo0IDIyEh69+7N4MGDA/dEKlGiBDNmzGDKlClER0czYcIEZsyYkeH/f9euXbzwwguBG3SeccYZvPTSS7Rt25a77rqLF198MehjEioLpjn3NFdoB8jMGDRoUJ7XHzNmTFDN8ZK7H374gaZNm57sMEROG0eOHOGCCy5g+fLlREREnOxwpAjI4TydZV+2bvQnIiInXIkSJTLcM0mkoIWc4JhZGefcQf/3SsBNQArwvnMu9DshiYiIiBSwoBMcM4sGpgMVgEvMrDzwA1ATr3loqJm1cs79mn0tIiIiIoUvlEHGTwJtgbT7t98K1AIeAq4AUv0yIiIiIidVKF1U1wMvOueG+dN/A3Y458YAmNnLwD8LOD6REyY1NTVPl/GKiEjhysvzy0I5m58JrAQwsyjgUuCTdMt3ATnfnEPkFFWmTBm2b9+e74cAiohIwXHOkZyczK+//hrUo0PSC6UFJwFIuyPPDUBxIP19r1sCx9+KVKQIqF+/Phs2bMjzrfRFRKRwnHHGGVSuXDmk58dBaAnOR8BAv/XmJuB34CMziwGGAL2Bf4W0dZFTRIkSJY67g6eIiBRdoSQ4D+F1QfUDtgL/cM4dMrPzgXuAd4CCfYqaiIiISB4EPQbHOXfEOXe7c66sc66hc+5Tf9EyINY5d4tzLjmHKuQ0Exsbi5nl65X+9ukiIiLBysuN/s4ALsa7RHwxcBA4XLBhSThISEjI16MowHschYiISKhCuibWzLriDST+EpgGNAJaAVvN7MGCD09EREQkdEEnOGbWDngXWAc8wJ8Pt9oIrACeNrO4Ao9QREREJEShtOA8DizFu2vxlLSZzrk1wF+Br4CBBRmciIiISF6EkuBcCLzrnDvuTmjOuWN4XVYNCyowERERkbwKJcE5AkTksLwScDR/4YiIiIjkXygJzmKgn5mVyrzAzKoDdwNfFFBcIiIiInkWSoLzCFAdWA4MBRxwg5mNBVYBUcCw7FfPHzOLNrMPzexnM1tjZpeaWUUzW2Bm6/yfFfyyZmbjzGy9mS03s4vS1XOLX36dmd1SWPGKiIjIyRPKjf7W4F0Sngjci3cVVX+8gcXrgSudc8sKPsSAF4B5zrlzgAuANXiPiFjknDsLWORPA1wLnOW/7gBeBTCzinhJ2CVAc2BYWlIkIiIi4SOkG/0551YAbfxEoT7eAzc3Oed+K4zg0vjPv7oc6OPHcQQ4YmadgTZ+scl43WiDgc7AFOecA77xW3+q+2UXOOd+9+tdALTHu/xdREREwkTQCY6ZfQo85Zxb5CcIv2da3gl42jnXqIBjBKgL7AQmmtkFwA/AfUBV59w2v8xvQFX/9xrAlnTrb/XnZTc/W+3bt2fXrl353oHC0qxZs5MdQqE7HfZRRETyZunSpVnOzzbBMbMyQOV0s9oA/zGzdVkUL4bXLVQ37yHm6AzgImCAc+5bM3uBP7ujAHDOOTNzBb3hefPmFXSVAWaWe6FcZPeHPRUUxP7Bqb2PIiJyasqpBacs3oM0o/xpBzzvv7JiwIICiiuzrcBW59y3/vSHeAnOdjOr7pzb5ndB7fCXJwA1060f689L4M8urbT5iwspZhERETlJsk1wnHM7zawn3mBcw7uT8X/wrqLKLAWvC2l6YQTpnPvNzLaYWUPn3FrgSmC1/7oFeNr/OdNfZRbQ38ym4w0o3ucnQfOBkekGFrcDHi6MmEVEROTkyXEMjnNuLjAXwMxqA6+la0U50QYAU82sBPAr0Beva+x9M+sHxAPd/LJzgA54V3cd9MvinPvdzP4FfO+XeyJtwLGIiIiEj6AHGTvn+hZmIEFsfxmQ1WjTK7Mo64B7sqlnAjChQIMTERGRU0pOg4wDV02lm86Nc84dl3CIiIiInEg5teDUxRtonKYe3kBjERERkVNaToOM62aarlPo0YiIiIgUgFCeRZUlM4v075kjIiIickoIKcExs5vM7Kl0068Ce4B9ZvaGmYX06AcRERGRwhB0gmNmfYFpePeOwcw6AHcCXwHvALcCDxVCjCIiIiIhCaXFpT/eXX/b+dM9gSNAZ+fcHjM7DPQGRhZohCIiIiIhCqWL6lzgXefcMTMrBlwD/J9zbo+/fClQu6ADFBEREQlVKAnOAaCk/3tLoCL+XY59McDuAopLREREJM9CSXD+C9xuZhcBw4BU4N8A/ry7ga8LPEIRERGREIWS4AwCquA9x+lKYJxzbqOZXYHXPZX2QE4RERGRkyqUZ1GtNLPzgbbAVudcWmvNKrwnck92zv1WCDGKiIiIhCSk+9Y453YDH2SatwMYVZBBiYiIiORHvu9kLCIiInKqUYIjIiIiYUcJjoiIiIQdJTgiIiISdrJNcMxshplVP5HBiIiIiBSEnFpwrgfWmNldJyoYERERkYKQU4LTHtgJvGxmX5jZOScoJhEREZF8yTbBcc59AjQC/gU0A/5rZo+bWcSJCk5EREQkL3IcZOycO+KcGw6cD3wGDAeWmVnLwg9NREREJG+CupOxc2490MHMrgWeAz43s81ZF3X1CzJAERERkVAF/agGMysNXAzUwHuwpmVVrIDiEhEREcmzoBIcM+sOjAZigJXAXeketikiIiJySslxDI6ZNTSzBcA0IBoYDFyk5EZEREROZdm24JjZ/wD3AyWAmcC9zrktJyowERERkbzKqQVnMPAb0Nk59zclNyIiIlJU5JTgPAOc65z76EQFIyIiIlIQckpwmgO6342IiIgUOTklOG2AqicoDhEREZECk+NVVCIiIiJFkRIcERERCTu53ejv72bWIIT6nHPuX/kJSERERCS/cktw/gb8PYT6HN7TxwuFmRUHlgIJzrnrzKwuMB2oBPwA9HLOHTGzksAUoCmwG+junNvk1/Ew0A9Iwbu3z/zCildEREROjtwSnJHAwhMRSJDuA9YA5f3pUcBzzrnpZvYaXuLyqv9zj3OugZnd5JfrbmbnATcBjfAeO7HQzM52zqWc6B0RERGRwpNbgrPGObfkhESSCzOLBToCTwH/NDMD2gI3+0UmA8PxEpzO/u8AHwIv+eU7A9Odc8nARjNbj3c5vB49ISIiEkaCfpr4KeB54CEg0p+uBOx1zh3zp7fiPekc/+cWAOfcMTPb55evAXyTrs7062Spffv27Nq1qyDiLxTNmjU72SEUutNhH0VEJG+WLl2a5fwikeCY2XXADufcD2bW5kRue968eYVWt9eolD/Z/WFPBQWxf3Bq76OIiJyackpwRgDLT1QgubgMuN7MOgCl8MbgvABEm9kZfitOLJDgl08AagJbzewMIApvsHHa/DTp1xEREZEwke19cJxzI5xzK3Na2cwqmFmpgg/ruFgeds7FOufq4A0S/tQ51xP4DLjRL3YL3lPPAWb50/jLP3XOOX/+TWZW0r8C6yzgu8KOX0RERE6sHG/0Z2YRZnanmU3MNL+Vma0CdgEHzGyumdUvzECzMRhvwPF6vDE24/3544FK/vx/AkMAnHOrgPeB1cA84B5dQSUiIhJ+su2i8u8lsxCve+iImd3uD9g9C5gPlMRLElbhtZJ8ZWaNnXPbCzNg59xiYLH/+694V0FlLnMY6JrN+k/hXYklIiIiYSqnFpz78J4m/hBQMd3VSsPxxsG865zr6Jx7CGgGHAMeKcRYRURERIKSU4LTDfjQOTfaOXcQwMxKANfj3bF4dFpB59zvwCTgusILVURERCQ4OSU4ZwFfZJp3KVAW2OacW5Zp2Xq8uwOLiIiInFQ5JTjF8bqd0rvS/5nV4xuigIMFEZSIiIhIfuSU4KwHmmSa9ze87qmPsyh/jb+OiIiIyEmVU4IzHehtZp3NrIyZ3Y/3kModePeTCTCznkA7/rwPjYiIiMhJk9OdjMfitcr8B6/VxoAjwK3OuSMAZvY3oD/QBlgLPFeYwYqIiIgEI9sExzl3xMyuArrjDS5OAt5xzq1OV6wp3n1y3gH+6Zw7VJjBioiIiAQjx4dt+nf5nea/sjISeNw5l1rQgYmIiIjkVb6eJp52fxwRERGRU0mOz6ISERERKYqU4IiIiEjYUYIjIiIiYUcJjoiIiISdoBMcM/vUzK5MN13en3dh4YQmIiIikjfZXkVlZonAD8CP/qsN8Ga6IhH+vAqFF56IiIhI6HK6TPwZvGdR3QA8jHc345fN7HZgGfCrP88VaoQiIiIiIcrpTsbPp/1uZiWBQ3gP2UwCmgP98B7f8JGZLQOWAt8756YWYrwiIiIiuQrqRn/OuWQzA5jnnJsGYGaV8R68+RJQHO+xDbcASnBERETkpMppDM63wH/xxt8s82en745K+/0T59ynhRKdiIiISB7k1ILzGd4YnL8BVfASmifNrAPwExCPxuCIiIjIKSinMThD0n43sxrAFmAlUAa4C6jrL57it/YsBZY65xYWXrgiIiIiuQvqPjjOuQT/1/ecc12ccw3wEhwD5gKHgb7AvEKJUkRERCQEoTxNPB44kG56vz9vonPua/Bu/leAsYmIiIjkSdAJjnOubqbpvfzZTZU2b3/BhCUiIiKSd3oWlYiIiIQdJTgieRQbG4uZ5esVGxt7sndDRCQshTIGR0TSSUhIYNCgQfmqY8yYMQUUjYiIpKcWHBEREQk7SnBEREQk7CjBERERkbCjBEdERETCjhIcERERCTtKcERERCTsKMERERGRsFMkEhwzq2lmn5nZajNbZWb3+fMrmtkCM1vn/6zgzzczG2dm681suZldlK6uW/zy68zslpO1TyIiIlJ4ikSCAxwDBjnnzgNaAPeY2XnAEGCRc+4sYJE/DXAtcJb/ugN4FbyECBgGXAI0B4alJUUiIiISPopEguOc2+ac+9H/PQlYA9QAOgOT/WKTgRv83zsDU5znGyDazKoD1wALnHO/O+f2AAuA9iduT0REROREKHKPajCzOsCFwLdAVefcNn/Rb0BV//cawJZ0q23152U3P1vt27dn165d+Q+8kDRr1uxkh1Down0fw33/REQK09KlS7OcX6QSHDMrB/wbGOic229mgWXOOWdmrqC3OW/evIKuMiB9/HmV3R/2VFAQ+wen7j6G+/6JiBRlRaKLCsDMIvCSm6nOuf/1Z2/3u57wf+7w5ycANdOtHuvPy26+iIiIhJEikeCY91V5PLDGOTc23aJZQNqVULcAM9PN7+1fTdUC2Od3Zc0H2plZBX9wcTt/noiIiISRotJFdRnQC1hhZsv8eY8ATwPvm1k/IB7o5i+bA3QA1gMHgb4AzrnfzexfwPd+uSecc7+fkD0QERGRE6ZIJDjOuS+B7AY8XJlFeQfck01dE4AJBRediIiInGqKRBeViIiISCiU4IiIiEjYUYIjIiIiYUcJjoiIiIQdJTgiIiISdpTgiIiISNhRgiMiIiJhRwmOiIiIhB0lOCIiIhJ2lOCIiIhI2FGCIyJZio2Nxczy9YqNjT3Zu5Gj02EfRU5XReJZVCJy4iUkJDBo0KB81TFmzJgCiqZwnA77KHK6UguOiIiIhB0lOCIiIhJ2lOCIiIhI2FGCIyIiImFHCU4RFhERke8rQGrVqnWyd0NERKTA6SqqIuzo0aMsWrQoX3VceeWVBRSNiIjIqUMtOCIiIhJ2lOCIiIQp3chQTmfqohIRCVO6kaGcztSCIyIiRZJaqCQnasEREZEiSS1UkhO14IiIiEjYUYIjIiIiYUcJjoiIiIQdJTgiIiISdpTgiIiInKJ0pVje6SoqERGRU5SuFMs7teDIKU0PFJVTnd6jIqcmteDIKa0gHih67bXXYmb5qqNWrVrEx8fnq47TUdqHf37UrFmTzZs3F1BEBS/cH3ob7n/DcN+/05kSHAl7R44cYe3atfmqo2HDhgUUzelFCWpwSpQoka99LMz9C/e/YbjvH+Q/iSuqCZwSHBE5pZ0OCWp+9zHc9w9O7X081fcvv0ncqdzCmBONwRE5iTR+Q0ROdWktjPl51a5d+4THrRYckZMo3MdviEjRd6q3UGXntGzBMbP2ZrbWzNab2ZCTHY9IfhTVb1ciIoXptGvBMbPiwMvA1cBW4Hszm+WcW31yIxPJm6L67UpEpDCdji04zYH1zrlfnXNHgOlA55Mck4iIiBQgc86d7BhOKDO7EWjvnLvNn+4FXOKc659N+XlA5RMYooiIiARvl3OufeaZp10XVaiyOmgiIiJyajsdu6gSgJrppmP9eSIiIhImTscE53vgLDOra2YlgJuAWSc5JhERESlAp10XlXPumJn1B+YDxYEJzrlVJzksERERKUCn3SBjERERCX+nYxeViIiIhDklOCIiIhJ2lOCECTN708ycmT2XxbLhZuYyzXNmNvyEBZhx23387ae9UswswczeN7MCvaWumW0ys0kFWWe6um8ws8/NbIeZHTKzeDObYWYh31rAzOr4x6JPIYRaKNL9HRtks3ySmW06wWEFLbv4zexiM/vdzP5rZpWD/V8xs8Vmtriw4i1MwbyXzayNfyyuyrRuPf//bJOZ1ffnLfbLfpXN9ib6y7cWwr5k+740szNO1rnPPw+3LeRthHxuzeqzI93fOrfXpMLcn/xSghMGzKw00M2fvNnMisrg8a7ApcDlwMPAhcAiM4sqwG38DfhXAdYHgJndC/wHWAf0AzoCT/qLC/UkVoT8C+/4Fxlm1hJYiPd3beuc24X3Hn3rpAZWiPLzXvY/ND8HjgGXO+c2pFucBFyaRQJZBrjRX346GcaJOzcEdW7N4bPjR3/9tNff/fn/k2l+gZ9bC1JR+SCUnN0AlAfmAB2A9sDHJzOgIC1zzq33f/8/M0sEFgAtgblZrWBmJZ1zycFuwDn33/yHmaUHgBnOuX7p5n0KvGlm+uIAZPqwOyFCfX9kWrc13v/NT0AH59x+AOfcNwUY4qkoT+9lM2sELAL2AFc65xIzFVkOVAXigOHp5qd9WM7H+5AMa/l5T+ZDsOfWG8jis8N/7wfe92ZWx//116L0/6ATcXi4Be8k0wc45E8XRfv9nxHwZ9eamf3FzOab2QHgfX9ZOzObY2bbzOygma00s0HmPUw1IHMXVbom3BZmNtXM9ptZopmNM7NSIcRaEfgtqwXOudQstne53+R/wMx2m9nL/renHJlZazNbZGZJZvaHfxz+kkW5v5vZN/6x2GtmH5hZrSyOxTtmdpOZrfHrW2pmfw1hv4Nm6bqozKyked0+Y7Mo180/Rhemm5frfvvdIF+aWSfzupOSgbvzGOvVeCf+74Fr0pIbf9lxXRr+MfzZzJLNbJWZHddSla6Z/3oze8nMdvmvd8wsOlPZM8zs4XR1JprZmLT3pH/8dlrWXdBp77Fz8rLvBPlezrTNJsBiYAfQOovkJs3beAlOer2B/wX+yEOsBc68e6JN9Y9vspkty/z3NLMGZva2mW00rwvvVzN71cwqZCo3ycy2mtmlZvaVmR0CnrE/hwg8an927ww/QbsImc6t6YTLZ0eWlOAUcWYWA1wFvOec2wnMADpl/sc7RRX3T+wlzexcYCTeCXNxpnIzgSXA9UDaCb4e3rfHW/Ga1CfjfUt8Kshtvw1swPs2+SpwD15TbrC+A24xswfN7Owgyr8DrPe39xxwu7/dbJlZR7x9PID3IXEzEAl8YWY105W7C/g3sBqv6f9O4C/AEjOLzFRtK2AQMBTojncvqI8zf+AWNP8b7PtAj8xJKNALWJnW2hbsfvvOBsYBLwLX+OuFqiPwEV5XS0fnXI4fvOaNQZmG16Xzd+BZ4AUgu/FjLwDO348RQBd/XnrvAI/59XbE6wroB0yFwPGbCPTOIhG/E1jinPs5tx3NRqjv5YvxWng2A1c453bkUPZtoJ55XX9p56srgSl5jDUUaeeXwAvv/R7gv5++BS4A7sc7x/wI/NvMrk9XNAbYAgzEe589gbcfc7LYbhTeQ5zfBa7F+5umtVRN4s/uncLs9sz13FrEPzuC45zTqwi/gIfwTp6X+tPX+NN3pSsz3PtTZ1jPAcNPUsx9/O1nfiUAF2eOG7gvl/oMr7v1UbxvI8XSLdsETMpi2yMy1fEx8EsI+3A2XhN8Wuy78E5o7bLZ19cyzX8USAHO9qfr+OX6pCuzHliUab3y/rae96fLAfvwbliZvlxd4AgwMNOx2ANUSDevmb/dm/Pxd2yQzfJJwKZ005f55a9JN68KcBR4KJT99uctBlKBJgXwPlwPlMymXIb/FeD/8JLJ9O+zFn65xenmtfHnTc5U30vAYf68D1krv1zvTOV6+vOb+NP1/PdMr3RlGvtlbsrH/2Ow7+U26crsBSrlUOdi4Ev/98/T3v9456vNeF+uJwFb8xp3kH/X7F7D/bLjgZ2Z9wWvO2dZDts4A/irX9eFmd7zDuiczfvoyYLe3yD3PcO5Nd3fIsfPjnRl6/jLbivM+Av6pRacou8WYJ1z7mt/eiGQSNFoavwb3rfB5nh9wauBOf43jvT+k3lFM6tuZq+bWTzeB/lRvIGR0cCZQWx7dqbpFUCtrApmxTn3C97AvdZ4rUbL8PZnvpk9lsUq72eano53km+eVf1mdhZQH5ia6RvoQeBrvMGD4H0TLJ9FuS3Az+nKpfnaObcn3fQK/2fQ+55Xzrn/w2s165Vu9k14x2EqhLTfaTY555blM7TZ/jZzbcHzW58uBj506bpvnDcuYVMO9ae3AiiJNz4FvHEPR4APM+3zJ/7yy/1t/Io3buXOdHXdifcB/b+5xZ6dPLyXZ+O1UjxrZhbEJqYA3cysJF731FSXTddXAUs7v6R/tchUpj1eK8y+TMd+PnCBmZUHMLMSZvaI34V4CO9884VfR+aWu6Oc/DGQwZxbi/JnR1A0yLgIM7NmwHnAqExdDP8L9Dezs/2T16lqpftzIBxm9gneB/NwvO6TNNvSr2TewMdZeM3Gw/E+yA/h/SM/CgQzlub3TNPJeB86QXPOpeB9O/3cjysGmAcMM7OXMyUS2zOtnjZdI5vq05K08f4rs82Zyi3Mpp49maYz7LdzLtn/jApl/FF+vAM8YGZlndcV1Av41DmX9sDbYPc7zbYsyoTqfrwxKMPM7JBzblQOZSvjjWPI/Pckm3mQ9XsN/jzmZwIlyH5MSqV0v78CfGTeeKSNeF14rznnjuQQc65CfC8/j9etNQKvJSq3cU8f4HUhPg40wrvC50TIcH4Bb6xTpjJn4iVdvbOpoxLe+JX/AQbgdU19hXcFWCzeuTbz/85O/3ieTDmeW8PgsyMoSnCKtrRMe7D/yqw3Xr9+keCcO2Rmv+I1u2dYlGm6Pl7XSi/n3DtpM82sUyGHmCPnXKKZvYU3vuIsvA+BNFWBVZmmIfsn2e/2fz5M1snLkUzl+mSqP82pdinu23iXy/7dzL7F+5aZ/htjsPudJvN7Iy8ccAfeB9XTZpbsnHs+m7K78L6hV81iWVUgPg/b342XKLTKZnn6Abxz8FqK7sS72isSeCMP28xRLu9lnHNP+GOBHvaP1/051LXPzGYCQ4Clzrk1BR1vPuzGa4nJLqlNO/Y3AVOcc2mXz2Nm5bJZpyDekwUqi3NrWH12ZEcJThFl3pPQe+ANkBuSRZHngF5mNvSEBpYP5t0foz5Zf1CnV8b/eTTduhF4YxZOCDOr7pzLqvUg7UqWzFeldMMbmJnmJrzxI99ms4m1eB9kjZxzT+cQStq3yQbOucm5xX2yOec2mHfzt154Yz/+IGP3SrD7XdBxpZrZLXgtKc+Z2WHn3GtZlEsxs++BG81seFpXi5ldgjdOIS8Jzjy8D5ko51yOg6T9OF/H+59vBSx0+bwcPw/v5bRYHvG7nf7pH6+cuvhewksgp+Yn1kIwD6+bd5Vz7lAO5cqQ7nzj6xvito4AuV45WRjSn1tD+exw/gCcokoJTtHVEa/5dJBzbnHmhf5J8FW8gYGnqiZmVhlvkHB1oD/eJasv5rLeGrwPkqfMLAXvxJPtN8hCstLMFuJ9o96INw6mA3AX8L5zLnNXSgczexZvXEVzvFaMKc65dVlV7pxzZnYPMNM/Ib2P13pQFe9eFpudc2Odc/vN7EHgZTOrgnep8z68rq/WeINepxXonh+vvZll/hDcl0P5t4GXgfOB/zjnDqQtCHa/CzT6P7edYmY34yU5r/gtExOzKDoM7+84w/8/q4LXXZNlIhDEdheb2bt4Y3DG4rWWpOIlTB2AwZm6C8bjdTVcgHdFVn6F+l5OH/sgP8kZ4h+v4dmU+xL4sgBiLWiP4x3vz83sJbzkugLeVYj1nHO3+uXm4V1ptoI/r4ZsGeK2VgMdzWweXtdxosv+8vr8yuncGspnx2eFFN8JoQSn6LoF75v7B9ksfxcY65fbdIJiClX62HcCK4H2zrn5Oa3knDtiZjfgfSucgjfGYQLe+Iw3CyfU4zyK9yHwBN6HbwrwC943ouezKB+Hd3n2P/C+yb2Jd4O1bDnn5pjZ5f623sL79vcb3g243ktX7nUz2wI8iHcp8hl4XV9f4A0YLWxZJaSrgKXZlH8Pr+ujGl6yk0Gw+10YnHPHzKwb3iWzb5l3b53MZRaaWU+8JON/8T7wBgL35WPTcXhjPG7F2+9kvP/b+WQa2+Oc22lmS/ASxFn52GaaUN/LmQ3AG78WzBimU4pzbrM/HmU43qXUVfC6rVbi3XoizQC8ZCHtNhRz8FpBMnTd5aI/3i0NPsI7XiPIeAPEgpTtudXMZhD8Z0eRTnDSLlMUkUJg3rOlJgJnZR7wKJIX/n1KNuNdMl9kuqBFTjS14IiIFAF+F2RDvJaiYnhXVIlINnQfHBGRoqEjXrdjc+CWbAYGi4hPXVQiIiISdtSCIyIiImFHCY6IiIiEHSU4IiIiEnaU4IiIiEjYUYIjIiIiYUcJjoiIiIQdJTgiIiISdpTgiIiISNhRgiMiIiJhRwmOiIiIhB0lOCIiIhJ2lOCIiIhI2FGCIyIiImFHCY6IiIiEHSU4InLCmdkkM3NBvDb5P9uc7JhFpGgx59zJjkFETjNmdilQP92sVsAdwBvAF+nmbwTqAgucc9tPXIQiUtQpwRGRk87M+gATgb7OuUknNxoRCQfqohIREZGwowRHRE5ZZtYn8xgcM+tiZt+bWZKZ7TOzBWZ2Wab1WpvZ52a218wOmNn/mVmndMvr+PUOz7RedvP7mNl/zeywme30xxBVz1TmfDOb7y8/ZGY/mtmtBXc0RCQUSnBEpMgws9bAe8A24AFgBN5YnoVmVs8v0xCYDRjwCDAYKAvMNLO/5mGbw/C6z9YD9+ONE/ob8LWZVfbLVAY+AWKAJ4GBwH5gvJndnMfdFZF8OONkByAiEoLuwEGgs/MHEJrZAuBD4CLgV6AzXkLzN+fcLr/MdOAr4ELgy2A35idNjwNPO+ceTjf/XeBH4FG8pKctUA3o5Jxb6peZCHwNnJ+P/RWRPFILjogUJVuBSGCcmZ0L4Jxb4Zxr6Jz7MF0ZgJfMrKlfZrdf5sUQt/c3vPPkLDOrnPYCfgP+C1yXaZtPm1krMyvunDvinGuaPjESkRNHCY6IFCUvAZ8D/YHVZvarmY0zswvSlfkAr0WnO7DUzBLN7C0za5WH7aVdyv4VsDPTqzlQC8A59xXwAl5LzufADjObZmYd87BNESkASnBEpMhwzu13zrUGLgVGAUnAAODHtLEuzrmjzrmuQGNgOLAZ6At8bmZDctlE8WymrweuzuIVSGCccwOBs/DG/CwHbgQ+NrPX8rKvIpI/SnBEpMgws7PN7GLn3DfOuSHOuQuARsAeYJBfppaZ/dXvuhrhnGsB1AbWAQ/6VaX4P0tm2kS1TNOb/J9bnHML07+AEsAhf5tVzaytc26Dc+4Z59wVQHW88T53mFlUgR0EEQmKEhwRKUrG4Y2HKZdu3s/AXv5MWh4BFplZjbQCzrmtQEK6MruBY0CTTPV3zzT9kf/zYTOztJlm1gSYhXe1FHgtRIvMrFm6be7Gu/LKpduuiJwguopKRIqSscBc4AszmwwcBm7AGyszwi/zMtAbr0vqdbzWnbZAG7wronDOHTSzmUAXM3sL+Aa4ArgMOJK2MefcSjMbB9wLVDKzGUBFvG6xJGCoX3Qy8E+8LqlXgESgqR/HJOfcgYI+ECKSMyU4IlJkOOc+MbPrgYfxkpVSwEqgh3Nuul9mhZldBQzDu1dOeeAXvKTk5XTV3QkcAP6O13KzCGgNrMq02YF4rUR3AaOBfXjPyxrqnPvZ3+Y2M7sCeMIvVwmIxxsDNKrADoCIBE3PohIREZGwozE4IiIiEnaU4IiIiEjYUYIjIiIiYUcJjoiIiIQdJTgiIiISdpTgiIiISNhRgiMiIiJhRwmOiIiIhB0lOCIiIhJ2/h+U2HruVosZwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#ID_all.append(len(df_sites))\n",
    "ID_all_class_one_phos.append(len(df_sites[df_sites['Localization prob'] >= 0.75]))\n",
    "\n",
    "mito = df_sites.iloc[[i for i in range(len(df_sites)) if (('Verified mitochondrial' in df_sites['IMPI_new'][i])==True or \n",
    "                                                         #('Predicted mitochondrial' in df_sites['IMPI_new'][i])==True or \n",
    "                                                         df_sites['SubMitoLocalization'][i]!='[]')]]\n",
    "#ID_Mito.append(len(mito))\n",
    "\n",
    "ID_Mito_ClassI.append(len(mito[mito['Localization prob'] >= 0.75]))\n",
    "ID_Mito_Quant.append(0)\n",
    "\n",
    "\n",
    "\n",
    "Tissues = ['Brain','BAT','Heart', 'Kidney', 'Liver', 'Spleen', 'SKM','All']\n",
    "#site_dict = {Tissues[i]:{'ID_all':ID_all[i],'ID_Mito':ID_Mito[i],'ID_Mito_ClassI':ID_Mito_ClassI[i],'ID_Mito_Quant':ID_Mito_Quant[i]} for i in range(len(Tissues))}\n",
    "site_dict = {Tissues[i]:{'ID_all_class_one_phos':ID_all_class_one_phos[i],'ID_Mito_ClassI':ID_Mito_ClassI[i],'ID_Mito_Quant':ID_Mito_Quant[i]} for i in range(len(Tissues))}\n",
    "#site_dict_sorted = {sorted(site_dict.items(), key=lambda k_v: k_v[1]['ID_all'], reverse = True)[i][0]:sorted(site_dict.items(), key=lambda k_v: k_v[1]['ID_all'], reverse = True)[i][1] for i in range(len(site_dict))}\n",
    "site_dict_sorted = {sorted(site_dict.items(), key=lambda k_v: k_v[1]['ID_all_class_one_phos'], reverse = True)[i][0]:sorted(site_dict.items(), key=lambda k_v: k_v[1]['ID_all_class_one_phos'], reverse = True)[i][1] for i in range(len(site_dict))}\n",
    "\n",
    "site_dict_sorted = {'All':site_dict_sorted['All'],\n",
    "                       'Brain':site_dict_sorted['Brain'],\n",
    "                       'Spleen':site_dict_sorted['Spleen'],\n",
    "                       'Liver':site_dict_sorted['Liver'],\n",
    "                       'Kidney':site_dict_sorted['Kidney'],\n",
    "                       'SKM':site_dict_sorted['SKM'],\n",
    "                       'Heart':site_dict_sorted['Heart'],\n",
    "                       'BAT':site_dict_sorted['BAT']\n",
    "                      }\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "x = np.arange(len(Tissues))\n",
    "bar_width = 0.3\n",
    "\n",
    "#temp = \"ID_all\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "#b1 = ax.bar(x, res,width=bar_width, label='All STY sites', color = '#737373', ec = 'black')\n",
    "temp = \"ID_all_class_one_phos\"\n",
    "res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b1 = ax.bar(x, res,width=bar_width, label='All Class I STY sites', color = '#737373', ec = 'black')\n",
    "#temp = \"ID_Mito\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "#b2 = ax.bar(x+bar_width, res,width=bar_width, label='Mitochondrial sites', color = '#969696', ec = 'black')\n",
    "temp = \"ID_Mito_ClassI\"\n",
    "res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b3 = ax.bar(x+bar_width, res,width=bar_width, label='Mitochondrial Class I STY  sites', color = '#bdbdbd', ec = 'black')\n",
    "temp = \"ID_Mito_Quant\"\n",
    "res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b4 = ax.bar(x+2*bar_width, res,width=bar_width, label='Mitochondrial Class I STY sites, Tissue ID-rate >50% ', color = '#d9d9d9', ec = 'black')\n",
    "\n",
    "#ax.set_xticks(x + bar_width*1.5)\n",
    "#ax.set_xticklabels(list(site_dict_sorted.keys()), size = 16)\n",
    "\n",
    "ax.set_xticks(x + bar_width)\n",
    "ax.set_xticklabels(list(site_dict_sorted.keys()), size = 16)\n",
    "\n",
    "\n",
    "ax.legend(fontsize = 'large', facecolor='white', framealpha = 1)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='black')\n",
    "#ax.xaxis.grid(False)\n",
    "\n",
    "ax.set_xlabel('Tissues', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_ylabel('# STY sites', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_title('', pad=15)\n",
    "ax.set_facecolor(\"white\")\n",
    " \n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(path_saving+'\\\\Figure_5\\\\STYsites_new.pdf', bbox_inches = \"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "native-cover",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'All': {'ID_all_class_one_phos': 9224,\n",
       "  'ID_Mito_ClassI': 991,\n",
       "  'ID_Mito_Quant': 0},\n",
       " 'Brain': {'ID_all_class_one_phos': 6507,\n",
       "  'ID_Mito_ClassI': 412,\n",
       "  'ID_Mito_Quant': 352},\n",
       " 'Spleen': {'ID_all_class_one_phos': 2541,\n",
       "  'ID_Mito_ClassI': 202,\n",
       "  'ID_Mito_Quant': 125},\n",
       " 'Liver': {'ID_all_class_one_phos': 1279,\n",
       "  'ID_Mito_ClassI': 358,\n",
       "  'ID_Mito_Quant': 295},\n",
       " 'Kidney': {'ID_all_class_one_phos': 1271,\n",
       "  'ID_Mito_ClassI': 395,\n",
       "  'ID_Mito_Quant': 316},\n",
       " 'SKM': {'ID_all_class_one_phos': 849,\n",
       "  'ID_Mito_ClassI': 374,\n",
       "  'ID_Mito_Quant': 298},\n",
       " 'Heart': {'ID_all_class_one_phos': 771,\n",
       "  'ID_Mito_ClassI': 347,\n",
       "  'ID_Mito_Quant': 289},\n",
       " 'BAT': {'ID_all_class_one_phos': 517,\n",
       "  'ID_Mito_ClassI': 295,\n",
       "  'ID_Mito_Quant': 219}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_dict_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "private-avatar",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "fig_row = 0\n",
    "fig_col = 0\n",
    "ID_all = []\n",
    "ID_Mito = []\n",
    "ID_Mito_quant = []\n",
    "for T in ['B_','BAT_','H_','K_','L_','S_','SKM_']:\n",
    "    \n",
    "    Tissues = ['Brain','BAT','Heart', 'Kidney', 'Liver', 'Spleen', 'SKM']\n",
    "    col = []\n",
    "    Tab = pd.DataFrame()\n",
    "    [col.append(i) for i in range(len(list(df_prot.columns))) if df_prot.columns[i].find('Norm_Intensity '+T)==0]\n",
    "    Tab['Count'] = df_prot.iloc[:,col].count(1)\n",
    "    Tab['Norm_Int'] = df_prot.iloc[:,col].median(1)\n",
    "    Tab['GO_CC'] = df_prot['GO_CC']\n",
    "    Tab['SubMitoLocalization'] = df_prot['SubMitoLocalization']\n",
    "    Tab['IMPI_new'] = df_prot['IMPI_new']\n",
    "    \n",
    "    Tab = Tab[Tab['Count']>0].sort_values(by=['Norm_Int'],ascending= False).reset_index(drop= True).reset_index()\n",
    "    Tab['Mito_filt'] = ['Mitochondrial' if (Tab['SubMitoLocalization'][i]!='nan' or('Verified mitochondrial' in Tab['IMPI_new'][i])==True) else 'Not Mitochondrial' for i in range(len(Tab))]\n",
    "                                            #or ('Predicted mitochondrial' in Tab['IMPI_new'][i])==True) \n",
    "                                            #else 'Not Mitochondrial' for i in range(len(Tab)))]\n",
    "    #Tab_2 = Tab[Tab['Mito_filt']=='Not Mitochondrial'].reset_index(drop=True).reset_index()\n",
    "    #Tab_2['Membrane'] = ['Membrane' if (('synapse [GO:0045202]' in Tab_2['GO_CC'][i])==True) #or \n",
    "    #                                    #('membrane [GO:0016020]' in Tab_2['GO_CC'][i])==True or\n",
    "    #                                            #('plasma membrane [GO:0005886]' in Tab_2['GO_CC'][i])==True) \n",
    "    #                        else 'Not Membrane' for i in range(len(Tab_2))]\n",
    "    Tab_quant = Tab[Tab['Count']>2].sort_values(by=['Norm_Int'],ascending= False).reset_index(drop= True).reset_index()\n",
    "    Tab_quant['Mito_filt'] = ['Mitochondrial' if (Tab_quant['SubMitoLocalization'][i]!='nan' or('Verified mitochondrial' in Tab_quant['IMPI_new'][i])==True)\n",
    "                                                  #or ('Predicted mitochondrial' in Tab_quant['IMPI_new'][i])==True) \n",
    "                                                  else 'Not Mitochondrial' for i in range(len(Tab_quant))]\n",
    "    \n",
    "    \n",
    "    Tab['Norm_Int_raw'] =  np.power(2,Tab['Norm_Int'])\n",
    "    Int_all = Tab['Norm_Int_raw'].sum()\n",
    "    Int_Mito = sum([Tab['Norm_Int_raw'][i]  for i in range(len(Tab)) if (Tab['SubMitoLocalization'][i]!='nan' or\n",
    "                                                                         ('Verified mitochondrial' in Tab['IMPI_new'][i])==True)])# or \n",
    "                                                                         #('Predicted mitochondrial' in Tab['IMPI_new'][i])==True)])\n",
    "    Int_NonMito = sum([Tab['Norm_Int_raw'][i]  for i in range(len(Tab)) if (Tab['SubMitoLocalization'][i]=='nan' and\n",
    "                                                                            ('Verified mitochondrial' in Tab['IMPI_new'][i])!=True )])\n",
    "                                                                            #and ('Predicted mitochondrial' in Tab['IMPI_new'][i])!=True)])\n",
    "    \n",
    "    \n",
    "    ID_Num_all = len(Tab['Norm_Int_raw'])\n",
    "    ID_Num_Mito = len([Tab['Norm_Int_raw'][i]  for i in range(len(Tab)) if (Tab['SubMitoLocalization'][i]!='nan' or\n",
    "                                                                         ('Verified mitochondrial' in Tab['IMPI_new'][i])==True)])# or \n",
    "                                                                         #('Predicted mitochondrial' in Tab['IMPI_new'][i])==True)])\n",
    "    ID_Num_NonMito = len([Tab['Norm_Int_raw'][i]  for i in range(len(Tab)) if (Tab['SubMitoLocalization'][i]=='nan' and\n",
    "                                                                            ('Verified mitochondrial' in Tab['IMPI_new'][i])!=True )])\n",
    "                                                                            #and ('Predicted mitochondrial' in Tab['IMPI_new'][i])!=True)])\n",
    "    \n",
    "    Tab_quant['Norm_Int_raw'] =  np.power(2,Tab_quant['Norm_Int'])\n",
    "    ID_Num_Mito_quant = len([Tab_quant['Norm_Int_raw'][i]  for i in range(len(Tab_quant)) if (Tab_quant['SubMitoLocalization'][i]!='nan' or\n",
    "                                                                         ('Verified mitochondrial' in Tab_quant['IMPI_new'][i])==True)])# or \n",
    "                                                                         #('Predicted mitochondrial' in Tab_quant['IMPI_new'][i])==True)])    \n",
    "\n",
    "    ID_perc_Mito = ID_Num_Mito/ID_Num_all*100\n",
    "    ID_perc_NonMito = ID_Num_NonMito/ID_Num_all*100\n",
    "    \n",
    "    Int_perc_Mito = Int_Mito/Int_all*100\n",
    "    Int_perc_NonMito = Int_NonMito/Int_all*100\n",
    "\n",
    "    \n",
    "    \n",
    "    ID_all.append(ID_Num_all) \n",
    "    ID_Mito.append(ID_Num_Mito) \n",
    "    ID_Mito_quant.append(ID_Num_Mito_quant) \n",
    "\n",
    "    counter = counter+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "handled-ranking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ID_all.append(len(df_prot)) \n",
    "ID_Mito.append(len(df_prot[df_prot['Mito_filt'] == \"Mitochondrial\"])) \n",
    "ID_Mito_quant.append(0)\n",
    "\n",
    "Tissues = ['Brain','BAT','Heart', 'Kidney', 'Liver', 'Spleen', 'SKM', 'All']\n",
    "protein_dict = {Tissues[i]:{'ID_all':ID_all[i],'ID_Mito':ID_Mito[i],'ID_Mito_quant':ID_Mito_quant[i]} for i in range(len(Tissues) )}\n",
    "protein_dict_sorted = {sorted(protein_dict.items(), key=lambda k_v: k_v[1]['ID_all'], reverse = True)[i][0]:sorted(protein_dict.items(), key=lambda k_v: k_v[1]['ID_all'], reverse = True)[i][1] for i in range(len(protein_dict))}\n",
    "\n",
    "protein_dict_sorted = {'All':protein_dict_sorted['All'],\n",
    "                       'Brain':protein_dict_sorted['Brain'],\n",
    "                       'Spleen':protein_dict_sorted['Spleen'],\n",
    "                       'Liver':protein_dict_sorted['Liver'],\n",
    "                       'Kidney':protein_dict_sorted['Kidney'],\n",
    "                       'SKM':protein_dict_sorted['SKM'],\n",
    "                       'Heart':protein_dict_sorted['Heart'],\n",
    "                       'BAT':protein_dict_sorted['BAT']\n",
    "                      }\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "x = np.arange(len(Tissues))\n",
    "bar_width = 0.3\n",
    "\n",
    "temp = \"ID_all\"\n",
    "res = [val[temp] for key, val in protein_dict_sorted.items() if temp in val]\n",
    "b1 = ax.bar(x, res,width=bar_width, label='All proteins', color = '#737373', ec = 'black')\n",
    "temp = \"ID_Mito\"\n",
    "res = [val[temp] for key, val in protein_dict_sorted.items() if temp in val]\n",
    "b2 = ax.bar(x+bar_width, res,width=bar_width, label='All mitochondrial proteins', color = '#969696', ec = 'black')\n",
    "temp = \"ID_Mito_quant\"\n",
    "res = [val[temp] for key, val in protein_dict_sorted.items() if temp in val]\n",
    "b3 = ax.bar(x+2*bar_width, res,width=bar_width, label='Mitochondrial proteins, Tissue ID-rate >50%', color = '#bdbdbd', ec = 'black')\n",
    "\n",
    "ax.set_xticks(x + bar_width)\n",
    "ax.set_xticklabels(list(protein_dict_sorted.keys()), size = 16)\n",
    "\n",
    "ax.legend(fontsize = 'large', facecolor='white', framealpha = 1)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='black')\n",
    "#ax.xaxis.grid(False)\n",
    "\n",
    "ax.set_xlabel('Tissues', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_ylabel('# Proteins', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_title('', pad=15)\n",
    "ax.set_facecolor(\"white\")\n",
    " \n",
    "fig.tight_layout()\n",
    "fig.savefig(path_saving+'\\\\Figure_1\\\\Proteins.pdf', bbox_inches = \"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "illegal-mailing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'All': {'ID_all': 7322, 'ID_Mito': 1266, 'ID_Mito_quant': 0},\n",
       " 'Brain': {'ID_all': 4329, 'ID_Mito': 1043, 'ID_Mito_quant': 1021},\n",
       " 'Spleen': {'ID_all': 4476, 'ID_Mito': 1061, 'ID_Mito_quant': 1016},\n",
       " 'Liver': {'ID_all': 2425, 'ID_Mito': 972, 'ID_Mito_quant': 954},\n",
       " 'Kidney': {'ID_all': 2449, 'ID_Mito': 1047, 'ID_Mito_quant': 1036},\n",
       " 'SKM': {'ID_all': 1662, 'ID_Mito': 913, 'ID_Mito_quant': 878},\n",
       " 'Heart': {'ID_all': 1489, 'ID_Mito': 909, 'ID_Mito_quant': 886},\n",
       " 'BAT': {'ID_all': 1559, 'ID_Mito': 958, 'ID_Mito_quant': 935}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_dict_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electric-satin",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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