SMG-bud-count-collagenase-recovery.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='counts'>"
      ]
     },
     "execution_count": 28,
     "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": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20200117-0119-collagenase-recovery-tifs\"\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder='../data/SMG-bud-count-collagenase-recovery/')\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# Group 1: time 0 of collagenase treatment and recovery group\n",
    "# Group 2: time 0 of control group\n",
    "# Group 3: time 24 h of collagenase treatment and recovery group\n",
    "# Group 4, time 24 h of control group\n",
    "# Group 5: time 48 h of collagenase treatment and recovery group\n",
    "# Group 6, time 48 h of control group\n",
    "groups = [\"1\"]*11 + [\"2\"]*11 + [\"3\"]*11 + [\"4\"]*11 + [\"5\"]*11 + [\"6\"]*11\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# Groups of treatment\n",
    "treatment = [\"collagenase\"]*11 + [\"control\"]*11 + [\"collagenase\"]*11 + [\"control\"]*11 + [\"collagenase\"]*11 + [\"control\"]*11\n",
    "df[\"treatment\"] = treatment\n",
    "\n",
    "# Time points:\n",
    "time = [0]*22 + [24]*22 + [48]*22\n",
    "df[\"time\"] = time\n",
    "\n",
    "df.shape\n",
    "df.head()\n",
    "\n",
    "# # Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "# sns.swarmplot(x='time', y='counts', hue='treatment', data=df)\n",
    "sns.barplot(x='time', y='counts', hue='treatment', data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8  3  4  7 10  9  8  3  5 13  7  5  6  5  6  7  9  6  4  5  7  6]\n",
      "[ 3  2  2  4  3  4  2  2  2  4  3 24 24 12 21 25 22 21  6 22 23 13]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "        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>file_name</th>\n",
       "      <th>scrambled_file_name</th>\n",
       "      <th>counts</th>\n",
       "      <th>groups</th>\n",
       "      <th>treatment</th>\n",
       "      <th>time</th>\n",
       "      <th>ratio_of_counts</th>\n",
       "      <th>log2ratio_of_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-17-collagenase-recovery-collagenase20-...</td>\n",
       "      <td>fb7a7b561c6cc4050adf761be6e9a164.tif</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-17-collagenase-recovery-collagenase20-...</td>\n",
       "      <td>d3fda32d683844d50390208c78e1f225.tif</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-17-collagenase-recovery-collagenase20-...</td>\n",
       "      <td>1462cc3b204becb43eeaab1d4740d5e7.tif</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-17-collagenase-recovery-collagenase20-...</td>\n",
       "      <td>b21ba2f7e1b168a4f1b69f9fb89a542a.tif</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-17-collagenase-recovery-collagenase20-...</td>\n",
       "      <td>3a4709d440dcdf17f67ce645bf086547.tif</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           file_name  \\\n",
       "0  2020-01-17-collagenase-recovery-collagenase20-...   \n",
       "1  2020-01-17-collagenase-recovery-collagenase20-...   \n",
       "2  2020-01-17-collagenase-recovery-collagenase20-...   \n",
       "3  2020-01-17-collagenase-recovery-collagenase20-...   \n",
       "4  2020-01-17-collagenase-recovery-collagenase20-...   \n",
       "\n",
       "                    scrambled_file_name  counts groups    treatment  time  \\\n",
       "0  fb7a7b561c6cc4050adf761be6e9a164.tif       8      1  collagenase     0   \n",
       "1  d3fda32d683844d50390208c78e1f225.tif       3      1  collagenase     0   \n",
       "2  1462cc3b204becb43eeaab1d4740d5e7.tif       4      1  collagenase     0   \n",
       "3  b21ba2f7e1b168a4f1b69f9fb89a542a.tif       7      1  collagenase     0   \n",
       "4  3a4709d440dcdf17f67ce645bf086547.tif      10      1  collagenase     0   \n",
       "\n",
       "   ratio_of_counts  log2ratio_of_counts  \n",
       "0              1.0                  0.0  \n",
       "1              1.0                  0.0  \n",
       "2              1.0                  0.0  \n",
       "3              1.0                  0.0  \n",
       "4              1.0                  0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize data by dividing the counts by the time 0 counts for plotting\n",
    "counts_t0 = df.counts.values[:22]\n",
    "print(counts_t0)\n",
    "counts_t24 = df.counts.values[22:44]\n",
    "print(counts_t24)\n",
    "df['ratio_of_counts'] = df.counts / np.concatenate([counts_t0, counts_t0, counts_t24])\n",
    "df['log2ratio_of_counts'] = np.log2(df.ratio_of_counts)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars for the raw counts\n",
    "\n",
    "fig_width, fig_height = 1.7, 1.4\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "ax = sns.swarmplot(x='groups', y='counts', data=df,\n",
    "                   hue='treatment', palette={'control': 'black', 'collagenase': 'red'},\n",
    "                   size=2, order=['2', '1', '4', '3', '6', '5'])\n",
    "ax = sns.pointplot(x='groups', y='counts', data=df, order=['2', '1', '4', '3', '6', '5'],\n",
    "                   ci=95, capsize=.2, errwidth=.7,\n",
    "                   markers='_', scale=.5, join=False, color=\"Gray\")\n",
    "\n",
    "plt.ylim(0, 120)\n",
    "plt.yticks([0, 40, 80, 120])\n",
    "\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"Buds per gland\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '-bud-count.svg'\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hw+jRQU2AkSbU4wTb4tQm/A3a/1RETvk55q0SznRqCvxQSuGFwrmux1i8alkBrHI97gQal0VAxpgwcffdcPvt8PXXTnPleedBcnLO8dq14frrYdu2nET5hz84U64Zv0LdMWa9iCzEqQHlJUD9or4l8FSJAzPGmHCUmOh0rPHl8cfhH3nmGfnyS+je3akdtmxZ5uFFonBoDh2BE4d3LH/GSWhzcSbKDiQbSAd+AT5R1VUFlA9bqnorcGugMmVxT9AYE+EOHYKxY30fO3zYuW/46qvBjSlChDwJqupRYLD3PhFx/8g/VtiOMcYYE7UWLXLmH/Xn44+DF0uECXkS9GOM63FfSKMwxphIUFAv/zAcBRAuwjIJquqYgksZY4wBnPuEFSrAyZO+j1vnGL/CMgl6E5FYnDFsFfDdizQOSMBZfb41cJ2q9g5ehMYYE2I1ajhTnz37bP5jlSo5SyUZn8I2CYpIQ2AscCVOAjQUrvOMMSYKPf20M+3a+PHOKvEA55/vrFFoE1n4FZZJ0LWO3udAI4q+msT+0o/IGGPCnAj8/e/wwAPOWMHKlQtevsmEZxLEWdWgMc4wiT3ApzidZB5x7XsaSAIaApcA1V377ydnEVpjjIk+CQnQunWoo4gY4ZoEL3c97gLOcg2jQET6AJ2Bhar6pWtfdeAdoBfwAPAacDToERtjjIk4/qYrC7WWODW7ce4E6LLc9djdvUNVDwH9gd04NcM7ghSjMcaYCBeuSbCa6zHvrOPrcO4R5lqHxJUoX3Edu7qsgzPGGFM+hGsSPOF6PJ5n/xbXo6+uTu6p01uVSUTGGGPKnXBNgq7+vTTMs3+r67GJiFTMc8ydMKuVVVDGGGPKl3BNgstwmjZv9t6pqntwkp0A5+c5x107tAmljTHGFEq4JsGZrsfLReRtEWnjdexrnCT4uLs2KCL1gb/hdKbZHNRIjTHGRKywTIKq+n/AApxkN4CcxWMBXnI9/gHYJSLf4iS+pq797wUrTmOMMZEtLJOgSz9gDk4i3O3eqaofAjNc+1OALoD7/mAq8O/ghmmMMSZShetgeVT1GNBfRNoBbfIcvgVYgbMifVPgAE7CHKWqARbVMsYYY3KEZRIUkQqqehJAVdfhjA/0UFUFnndtxhhjTLGEZRIE3nNNov2cq/nTGGP8OnLkCG+++SbLli2jcuXKDBw4kO7du4c6LBMBwjUJdsJZQzDvOEFjjMllw4YN9OzZk71793r2vfzyy9x444288cYbxMSEc9cHE2rh+l9HVdfj6pBGYYwJewMHDsyVAN1mzJjByy+/HIKITCQJ1yS40fV4VkijMMaEteXLl7Nu3Tq/xy0JmoKEaxL8G5ANPCUil4Y6GGNMeNq+fXvA4z/++GOQIjGRKlzvCZ4EngBGAXNF5CdgJc4Cu0dwZobxS1VHlXmExpiQO+OMMwIeb9KkSZAiMZEqXJPgUnISnQCnubbCsiRoTBTo2rUr7dq189sketdddwU5IhNpwrU5FJzkJ3meF2YzxkSR//73v9SvXz/f/htvvNGSoClQuNYErQ3DGFMobdq0YePGjbz11lt8/fXXVK5cmeuvv54LLrgg1KGZCBCWSVBVd4Y6BmNM5KhcuTJ33303d999d6hDMREmnJtDjTHGmDJlSdAYY0zUsiRojDEmalkSNMZEvHXr1nHHHXfQpk0bzj33XMaNG8fx48dDHZaJAGHZMcYYYwrr008/5ZprriE9Pd2zb8WKFbz99tssXryYypUrhzA6E+6sJmgi1po1axg6dChXXHEF9913Hxs2bAh1SCbIMjMzueOOO3IlQLdVq1bxn//8JwRRmUhiSdBEpOeee45OnTrx0ksv8cknnzBhwgTatWvHtGnTQh2aCaLFixfz888/+z3+xhtvBDEaE4ksCZqI89133/HQQw/l25+dnc1dd93Fzp02zDRa7N+/P+DxAwcOBCkSE6nsnqAJe0eOHGHOnDns37+fDh068OGHH/otm5WVxfTp03n88ceDGKEJlY4dO5bouDEhTYIicnpZvK+q7iqL9zXB99ZbbzFkyJBcPf0K6uiwe/fusg7LhInWrVvTu3dv/u///s/n8fvvvz/IEZlIE+qaYFks9qWE/nOZUrBy5UpuvfVWsrKycu0/evRowPOaN29elmGZMDNjxgyuueYavvzyS8++xMREnnzySa666qoQRmYiQajvCRZldQhbSSLKPP/88/kSYEGSkpK47bbbyigiE45SUlK48sorSUlJ8ezr1KkT3bt3D11QJmKEusYU6NeqGfAwTqLeBswAVgG/ABlAdaA9cAPQBTgO3Av4XljMRJw1a9YEPC4iqOasr1yxYkVmzpxJ7dq1yzo0E0Jjx44lIyPD83rBggV8/vnnucosX76c888/nyFDhuT67yE+Pt5npyoTvUKaBFX1dV/7RaQ6TjIT4N/Aw6qa6aPoEmC8iDwEPAuMBexOeDlRs2bNgMe9EyDAaaedxnnnnVeWIZkwkJGR4UmCJ06c4Ouvv/ZZLj09naVLl3LNNdcEMzwTYULdHOrPI0B9YJ6qPuQnAXqo6ljgHaAGtqp8uXHzzTcXqfzmzZt57rnnyigaEy7i4+M9265du8jM9P/zsGXLllzl4+PjgxipiQiqGnYbsBXIAq4swjm9gWxgV6jjj4YNaIvTCUkBTU1N1dKWkZGhl112mXpfp6Dt9NNPL/U4TPiaPXt2wP8eatasWaL3f+edd7Rbt25as2ZNPeuss3TixImakZFRStGXX6mpqXn/LdpqGPxu+dpCfU/Qn/qux31FOOew6zFwG5qJGHFxcXz44Ye8/vrrvPHGG+zfv59q1aqxbNkyv+ccPnzY7zFvx48fZ8aMGXz22WfEx8dzzTXX8Kc//Ym4uHD9X8J4O3LkCABdunQhKSmJtLQ0n+V69+7tKQtQpUqVQl9jxIgRjB071vN6//79DBs2jM8++4z333+fmJhwbUgzRRGu/8fvBRoD7YCVhTynq+vRxgiWA8ePH2fcuHG8+eab7N+/n44dOzJ27FhatGhBixYt3LXRfM4991yOHj3qOffgwYN06dKFBx54gN69ewPOOMKLLrqIbdu2ec57++23ueCCC5g3bx7JyclB+Yym+MaNGwc4c4f6S4IJCQlUq1bNUxYo9CQKGzduzJUAvX300Ud8+OGHdq+xnAjXJLgSaAI8IiLvquqRQIVFpCFOT1IFPg9U1oS/EydO0KNHD7799lvPvkWLFrFo0SIuu+wyWrdu7XOybBGhVq1atGrVKtd8kgsWLGDBggW8+OKLDB06lMGDB+dKgG5ffPEFo0eP9vvjZ8qGqjJz5kymTp3KTz/9RIsWLRg6dCh9+vQp8NwvvviCgwcP+jxWv359atWqBcBPP/3E7t27efXVV/nTn/6UazgFwK5du3j11VfZtm0bjRs3LrBFwT020ZQDoW6P9bUB5+Lc38sCvge6+ykXA1wD7HSVTwdahDr+aNgow3uC48eP93uPJy4uTocPH67NmzfPtT8hIUGvvPJKveSSS/yeW7FiRf3+++9VRPyWSUlJ0czMzFL7LKZgt99+u89/izFjxnjKpKWl6fTp0/WWW27RO++8U2+++WYdNWqUVqxY0e+/pYjokCFDtFGjRrn2V6hQQSdMmOB573feeUfj4+PznevvfQG95JJLQvFVRYxIuicY8gD8BgZPeiXCLJwm0gXAf13bIuCA61i2a7s11HFHy1aWSfDcc88N+APUq1cvrVGjRr79Xbt21Xr16gU895FHHimwc83vv/9eap/FBLZgwYKASWzTpk26a9eufH/0ANq0adMC/y3r16/v99i8efN09+7d+RJgYbbHHnss1F9dWIukJBiuzaGo6kgR+R14DKgM1AHyjoJ2zw6zDximqu8GL0JTVrw7MviyceNGn6sDLF++vMCB8omJicTExJCdne3zeO3atW0R1iB68803/R5TVd566y2WLVvGli1b8h3ftm0bFSpU4OTJkz7PF5GAyyyNGzeO888/P9fA+8KoWrUqd911V5HOMeErrLs3qepzQHPgPmA+TrNnGnACZ97RucAdQEtLgOVH165dAx7ftct/36eCplm77LLLuPLKK/0ev/POO63XXxAVtNTR9u3bWbRokd/j/v6YAadjTCD/+9//fN4b9latWrVcrxs3bsy8efNo0KBBwPNM5Aj7/9tV9VdVnaiql6nqGapaSVUrq2ozVb1KVV9V1cAzKpuIMnz4cL8/YM2aNQt47smTJ/0OiL7ooovo0qULr7zyCu3atct3/LLLLmPkyJFFD9gUW6dOnQIed3ds8efUqVN+h7X4Wm3eW0pKCk2aNAlY5o477mDZsmW89tprLFiwgG3bttmsROVNqNtjbYvMjTIeLP/xxx9r3bp1c91XOOOMM3TgwIEB79XUrFlTBwwYoMnJybn29+7dWw8cOOB5/1OnTunMmTP1jjvu0KFDh+pnn31WqvGbwtm1a1e+fyv3VqdOHV27dm3Af+9AHWMAjYmJ8XvsH//4h+7cuVPj4uL8nrtx48ZQf0URye4JGlNCV1xxBbt27WLBggUcOHCAb775hjp16qCqpKSk+O0W36lTJ1q3bk3z5s3Ztm0bffr0oXPnzrRt2zZXufj4eK677jquu+66YHwc40fDhg354IMPuP7663OtEt+oUSM++OAD2rdvT/fu3Vm6dKnP8/v06cN7773n9/0TExPJzMzMd9/v7LPP5v7776dSpUpMmzaNQYMG5Zp+LSYmhkmTJtGyZcuSfUAT9kSdv+rDSkkX21VbVLfMiUhbINX9OjU1NV+iKU1jxozxPN+zZw9vvvlmvg4RzZs3Z+DAgcTGxnr22QrzkSE9PZ0PP/yQ3bt306JFCy677DLPv+OOHTvo0aMH27dvz3VOnz59GDlyJOeff77f9+3cuTNTp05l/PjxLF++nCpVqnDDDTdw55135poUYdu2bUybNs0zTnDQoEG0aNGibD5sFFi/fj1nnnmm964zVXV9qOIJJFyTYDZOFbo4VFWthlvGQpkEwVlYd/Xq1ezevZuEhATatWtHq1at8nVqsSRYPpw4cYK3336bxYsXU6FCBfr160efPn2IiYnh7LPPZtWqVT7PmzZtGrfffnuQozWWBEvIlQSLS1U1tuBipiSCnQT9DZt4/vnnyczMJC4ujnvvvTff8aLMFWki09tvv81NN91E3t+yevXqsXXrVipWrBiiyKJXJCXBcK0x/bOA4zFAFeA04HycJZS2AffgLK5ryhl/yUxEPI+W8KJPdnY2jzzySL4ECLB3714mTJjAww8/HILITKQIyySoqo8VtqyIVAD+BfwFZ/7Qi8sqLhO+MjMzeeutt/j++++pVasWN954I/Xr1y/4RBPRlixZws6dO/0enz59uiVBE1BYJsGiUNWTwDARaQNcBNwFTA5tVKY07dmzhwkTJvDJJ5+gqlx22WXcd999nHbaaQDs27ePGTNmcPRoznDRRx55hPHjx3PPPfeEKmwTBL/88kvA4/v2FWU1NhONIj4JepmMUwv8M5YEy40tW7bwxz/+MdeP3Q8//MAbb7zB559/TlZWFu+8806uBAhOzfDee++lY8eONri5HMtz3ykfX5MiGOMt7GeMKYLdrsfWIY3ClKoHHnjA51/7v/32G1dffTWpqal+l71RVasJlnNnnXUWF154od/jw4YNC2I0JhKVpyTo/pNQApYyEePAgQPMmzfP7/EtW7YEnEcUCm4uM5HvnXfeoWPHjrn2xcbGMmrUKJsMwRSoXDSHikgjYCTO2ML8q62aiPT7778HnCAZ4Jtvvgl43HqMln/16tVj9erVLFy4kK+//prKlStz3XXXcfrpJZpzw0SJsEyCIvKPwhQDkoDTgctdzxV4qwxDM0HUsGFDatWqxW+//Vbs93jyySdLMSITrkSEXr160atXr1CHYiJMWCZBcmp1heVuAv0G6xRTbiQkJPCXv/yl2LO+3HrrrfTt27eUozLGlCfhfE9QirDtAJ4Ceqlq4AXlTEQZOXIkd911V6HX+IuPj6dfv358/PHHvPrqq57B9MYY40u4TpvWqBDFFMgEflfVE2Uckskj2NOm7dixg3nz5rFq1Spee+01v+WqV6/ud4UJY0xw2LRpJaSq/qeAMFGpcePGDB06lL179/Lmm2/mWvbGW48ePYIcmTEmkoVzc6gx+dSrV4877rjD57GEhAT+9re/BTkiY0wkC/skKCJ1ReRvIrJARH4WkZMiclhEtovIHBG5U0Rsmvgo8vzzz3P//ffnWh2gZcuWfPzxx3Tp0iWEkRljIk1Y3hN0E5HHgb8DCe5deYq4gz8I3Kmq7wcrtmhX2vcEVZXs7GyfqwH4c/jwYdatW0flypU566yzrBOMMWFi48aNuaYrbN++/Xnjx4/f6HqZARzv3LlzWCSfsE2CIvIKMIicxLcfWOd6jAFqAe2Bqq7jipMIpwU51KhUGkkwMzOTo0ePcuTIEU6csL5NxpQXaWlprF+f0w+mSZMm65OSkk56FckG/gcsBJZ07tw5ZL3ZwrI5VER6Ae4bP1uAy4A6qtpDVQeoan9V7Y6TCK8HfsZJli+KSKtQxGyK5tixY2zdupV9+/ZZAjQm+sQAXXCWv/tk9erV3UIVSFjWBEXkPeBPwE6gi6oeKKB8I2AFUBOYrqqDyjzIKFeSmuCxY8f46aefitT0aYyJHNnZ2aSlpXleJyYmnoyJiXHPgaiqmpGdnX00MzPzsKqm4zSRPti5c+evgx1rWNYEga44zZtPFpQAwTOk4mmc2uAlZRybKYHMzExLgMZENxGRhNjY2BqJiYlNYmNjKwPxwHOrV69OCXYwYTlOEKjmevy+COcsdz3WKt1QTGk6evRovgSYlJRESkoKSUlJRerckpGRwbFjx4iNjaVSpUqFnlXGGFO2Tp48ycaNGz2vTz/99B8rVqyYDpCdnR2TlpZW4cCBAzVOnjyZFB8f3yA7O/tH1+9Cd+C9YMYarklwJ9AcZ3LslYU8p4br0ZaSDmNHjhzJ9TopKYlGjRoVKfllZ2ezc+dODhzIaSSIj4+nYcOGpKQE/Q9JY0wesbGxuV7HxcVlx8fHu6e0zEpMTMyoWrXq0e3btzc+efJkUlxcXNWMjIxfcVrygpoEw/VP55k4TZvDpfC/joNxmlA/KKugTMmoar5OMCkpKUUe2rBjx45cCRCcWuH27dvzrTBvjAlPIkKNGjUOAMTExFR27e60evXqoI51Ctck+AywHugGvC0ilf0VFJEYEfkXcDXO6vKFWYbJhICvtQGTkpKK9B7p6ekB5wbdt88aAoyJFO5hEyIS79oVCwR18pOQNocWsG7gV0Ab4DrgEhF5F1gF/IZT40txHb8WaAIcB8YCfwQ+KsOwTTH56gxT1FrgsWPHSnTcGBM+vHqMev8QJOD8ngdFqO8JFnbdwOo4zZ2DA5SpCEx0vV+oP5cpI3nvNeRlnWOMMUURDsnC5royhValShXi4uL8riJhHWOMMUUR6iTYJMTXNxEmJiaG0047jR07duQ7lpiYSN26dYMflDEmYoU0Cdq6gaY4atasSXx8PL/88gvHjh0jJiaGGjVqULduXeLj4wt+A2OMcQl1TdCYYqlatSpVq1YtuKAxxgQQ0l4EInJ9EK81MFjXKi0ikiQifxeR/4nIMRE5KiLfi8goEakW6viMMSbShbor3QwR+VZEupfVBUSkh4gsA2aU1TXKgojUxxkS8jTQEUgGKgHtgDHAGhFpE7oITTCkpqYWXCjM7dq1K+onMfBeVsiEl1AnwX8BnYFFIrJIRC4twgwxfolIvIhcLyLLgQU4S3Y8XdL3DRYRiQM+xBkHqcBLwMU48+pNxFmLqzHwkYhUCU2UpiwdOXKEu+++m6uuuirUoRRbdnY2EydOpG3btvlm+IkW6enpjBo1io4dO4Y6FONHqDvG/F1EPgImAxfh/MjvE5GZwHzgc9cyGwVyJYOLgCuBa3Am4RacSbhvU9U1pf4Bys5gnMQNMFxVn/c69rkrub8DNAX+Cjwe5PhMGXvggQeYNm0ajRo1CnUoxfb2228zfPjwUIcRUmPHjuWJJ54IdRgmgJB3jFHVZSLSARgKPALUA4a7tkwR2QxsALYBh4EjQBbO4Pi6QCPgLKAFOTVbAbbj1DRfVVXfg8rC1zDX4w/Ai3kPqup/ReRG4ArgXhF5IgI/ownA3zjISFIePkNJ2XcQ/kKeBAFUNRt4QUSmAIOAu3DufcUDbXGaBQPxbkL9H/BvYKbrfSOKiLQCWrlevh3gM7yOkwSr49SAFwYhPGOMKVfCIgm6uZo+JwGTRKQjcBVOE2knwN8k2oeA1cBS4H1V/aHsIy1T53k9/yJAua+8nl+IJUFjjCmyUHeM8UtV16jqGFW9SFWr4jR9dgTOx1ldoj1QS1VrqGovVX2qHCRAgNZez7f6K6Sq+4A0H+eYCDZ69GhEhNdffx2AnTt3IiKICKNHjwbwvJ4+fTqrV6+mW7duJCUlUaNGDS666CJ2796d6z337NnDiBEjaNeuHVWqVKFixYq0bNmSu+++O9fCp/4sX76cu+++mzPPPJOUlBTi4+OpWbMmZ599NiNHjuSXX37JVX7p0qWICLfddptnX5MmTRARunfv7tnXvXt3RIQ77rgDgE8//ZRLL72U2rVrk5ycTJs2bXjiiSdIS0vznPPpp5/Su3dvatasSVJSEu3atWPs2LEFNjuuXLmS2267jTPOOIOkpCSqVatGly5dGD16tN9VSdyfQ0TYsWMHR48e5R//+Aft27encuXKVK1alXPPPZcJEyZw6tSpXOdOnz4dEWHMmDGefe73uvXWWwPGaoIrrGqCgajqr8CvoY4jCOq7HjOBvQWU/Rmnc0z9AsoFJCK1gVpFPK1pSa5pSm7jxo3ce++9npUzTp48ybZt22jQoIGnzDvvvMMdd9yRbx3HzZs3s3nzZqZMmcLTTz/NQw89lO/9MzIyGDx4sCcheztw4AAHDhxg1apVvPTSSyxcuJBOnToV+7P89a9/5T//+U+ufT/88AOjRo1i4cKFLFmyhMcee4ynn87dyTs1NZURI0awYsUKZs+ene99s7Ozeeihhxg3blyuVUxOnjzJ6tWrWb16NRMnTmTWrFn07NnTb3w//vgjF198MT/++GOu/StWrGDFihW89dZbLF68mMqV/a76ZsKVqtoWRhswD2dYxO+FKPudq+yGEl5ztOt9ir2lpqZqQTIyMnTDhg25toyMjALPiyZ79+7VNWvW6JVXXqmA1qtXT9esWaNr1qzRvXv3qqp6vvOYmBhNSEjQp59+Wr/66iudOnWqTpkyxfNeH3zwgYqIAtqwYUMdO3asfvnll7ps2TKdNGmStmrVyvNeEydOzBfLgw8+6Dl+3nnn6euvv65ffPGFLl68WCdPnqxt2rTxHD/77LM95x09elTXrFmjY8aM8Rz/5JNPdM2aNbplyxZPuQsvvFABrVmzpgLatGlTnTx5sn799df6xhtvaMOGDT3n9+nTRwFt3769vvXWW/rNN9/o9OnTc5VZuHBhvs9w//33e46fe+65+tprr+k333yjS5Ys0dGjR2v16tUV0MTERF25cmWuc5csWeI5t27dugro9ddfrx9//LEuX75cJ0+enOv6I0aM8Jx74MABXbNmjd51112e4+5/x507dxb/P5AIceLECV25cqVnO3bsWKqqrsq7nTp1ak1qaurx1NTU46tyVNdg/uYG82Jl9iGcibjbAUkFlKsPnBPqeAuIcZHrf5p9hSj7javsthJe05JgmPnzn/+sgDZq1CjfMe/v/fnnn/d5/tGjR7VGjRqeBPX777/nK3PixAm9+OKLFdAKFSp4kqyq6v79+zU+Pl4B7datm546dSrf+RkZGdqxY0dPLLt37851/LXXXvMc+/HHH/Od706CgLZs2VIPHDiQ6/iqVatyfdauXbtqWlparjIbNmzwJPrhw4fnOrZs2TLPsSFDhmhWVla+GHbs2OFJcB06dMh1zDsJAjpp0qR85+/atUuTk5M9f7Dk9fjjj3vOjyaRlATD9p5gYYhINxFZj3PvbC1wQEReDjCl2GBgeZDCKy53b9DCrLPo7hUbcb1gTcnFx8czaNAgn8feeOMNzwD1KVOm+JxnNSkpiZdffhlwmgenTZvmOZaamkrTpk2pUKECf//7331OTB4XF0ffvn09r3ft2lXszzJ69Oh8y2B17tyZ008/3fP6qaeeokKFCrnKtG7dmqZNnZb5bdu25TrmbgKtW7cu48eP97nWZKNGjTzj+NauXcvXX3/tM74zzzyTu+++O9/+hg0b0rt3bwD27t0btZMCRLKITYIichZOj8iWwBLgU+AUTqL7n2uoQSRyL41eIWCp3GUKNaFAAJOAM4u4XV3Ca5oSOvPMM0lKSvJ5bP78+QDUqFGD9u3b+32PZs2accYZZwBORxC3Cy+8kB9++IETJ05w+eWX+z3fe+mqkydPFiX8XHr06OFzf/36zu3u+Ph4unXr5rNMzZo1ATz3RsG5F7hgwQIAunXrRmJiot9ru5MY5P4OvF1yySV+z2/SJGdFOO8YTGSImI4xPjyOE39PVV0KICI1gP8ANwNLRKS7qm4KXYjF4p5ksWIhyia7Hg+V5IJajE5HpTC7nSkh7w4wea1duxZwOrAU9t9q+/bt+fZ5n7tv3z62bdvGli1bWLduHStWrODbb7/1HM/OLl6DRMWKFalVy3e/LHfycvdKDVRGNafxZMeOHRw+fBiAOXPmlOg7AALO3JOcnOx5boPjI08kJ8ELgPfcCRBAVQ8AfxaRn4CHgYUi0lVV94QoxuJwr7GYICK1VPW3AGXdvUJ/LuOYTBiqUsX/tLHFaZb7/fff8+37/PPPmTBhAkuWLPF53FcTY1EVpkdlXFzRfqqK2yzp6zNC7kSXl3eC9U7EJjJEchKsQk7CyEVVHxWRZJzpx+aLyPmqejio0RWf91jHpoDPJCgi9QB3W1h5GB9piihQ7cZdI+nWrRsvvPBCod4vNjY21+tHH32Up556Kte+hg0b0qZNGzp16sQFF1zAzp07GTJkSBEjz62oCa4wvGtkw4cPL/TYPFujMvpEchLci7MChT/3A6cDfwLmikivYARVClZ4Pe+G0wPUl/O9ni8ru3BMJEpJSWHfvn0cOnSIDh06FPn8jz/+2JMAW7ZsyRNPPEHPnj2pXr16rnKFTbDB5t3JJiMjo1jfgYkOEdsxBvgEuEhEHvB1UJ12iRtweoOehzP0IOyn5FfVrcA618tbAiwt9WfX4yHg8zIPzESUNm2c6XZ/+OEHfv018O3e5557jmnTprFsWc7fUpMnTwac2uH//d//0b9//3wJEMg3O024aNKkiafTkL/OLm6//fYb//znP5kxYwabN28OQnQmnERyEhyDcy9srIjsFZF8bTKqehK4DCcR/oGcxBHuXnI9ngXkm8pDRAYA7i57r2ghl5sykaOk99rcvRlVNWBtbcmSJTz00EPccccdvPTSS5797uEG1atX99sp5NSpU7z//vue13k7hZTG/cLiSkhI4IILLgBgw4YNLF682G/ZF154gZEjR3LTTTfx5ZdflmocofwOTOFE7L+Qq0fjOTirKVQAEvyUOwz0AJ7HWYIpErwCuNc/fFZEXheRniJygYiMA2a4ju0gghYLNoXn7vFY3C73d9xxBxUrOh2Mn332WZ9J4NChQ7nu591zzz2e5zVq1ABg//79rF69Ot+5p06d4s4772TLli259vn6DCX5HCXhvZbhoEGDfNZaV65cyXPPPQc4n3nAgAGlGkOovwNTsIhNggCquldVb1fV6uTUnnyVS1fV4UAznKWawpqqZuEsk+Se3fgWnDGRnwP3AbHAHuCKCOrwY4qgTp06gNPLceyzz7Lqm2/yDQb3UIWMDMjK+RuvZs2aTJw4EXCSU58+ffjLX/7CwoUL+frrr3nxxRfp2LGjp/lv8ODB/OEPf/Cc7z0I/vLLL2f8+PF8+eWXLFmyhPHjx9O+fft8c4oeOXLE52cAZ6D7ypUrWbNmDcFy6aWXctNNNwHOkImOHTvy9NNP89VXX7Fo0SIee+wxLr74Ys+8qhMmTKBSpUqlGoP3dzBq1ChWrVrFhg0bSvUapoSCOT2NbUXbcGq4I3CWijoCnMRZYPgpICXEsbXFpk0rMytWrPBM+eXebrziCtVDh3Je33ij6q+/qn7/verKlc62ZYvqiROe93nhhRc0ISEh4JR3N954Y75p0dLT0z1TqvnbqlWrplOmTPG8fuSRR3K9x7FjxzxTkrm3Bg0aeI67p03z3pdXUcpceOGF+Y6lp6frbbfdFvBzxMfH64QJE/Kd6z1tmvecrHl5T43mPTeqqjOtWlJSUq7rdevWze97lRc2bZopFap6UlX/paqdVbWKqlZQ1Taq+oiq+l7/xZQLZ7dqxexnnqFTq1YkJSZSJTmZE8eOwVav1bXS0mDnTkj3uiX8+++waRO4Zm+555572LRpEw888IBnKaX4+HgaNGhA3759mT9/Pm+99Va+gegJCQn83//9H+PHj+ecc86hUqVKxMXFkZKSwjnnnMOoUaP44YcfuOOOOzjrrLMAePfdd91/IAHO2LoFCxbQq1cvqlatSoUKFUhMTOT48eNl9r3llZCQwKuvvspXX33FLbfc4llKqUKFCjRv3pwhQ4awdu1ahg0bVibXb9iwIZ9++indunWjUqVKJCcnk55ut/DDiXj/RxsuROSWYpymONOHHcMZPvGDOh1jTBkQkbZAqvt1amoqbdu2DXhOZmZmrntIAM2bNy+TcWIRb+NG8HcPqUIFaNUKvv8e/M3SUrMmNG5cZuEZE0haWhrr16/3vG7duvX65OTkfL/HGRkZsZs3b24BcPLkSfd450s6d+5colmwiiJcf32mU7gJpAPJEpEFwAOqav2eTeTIzPSfAMGp5R044D8BglMjNMYUKFybQ0+5NnBWSijOFgdcijOZdvfghW5MCRWmdSYMW3CMiURhmQRVtQJOj0j3wKMFQD+cGWAq4EwX1hC4CnAvJ604k2pfBPQFXgBO4ExE/V/X5NrGhL/4eKgYYP70hARISYFAk0IHmFfUGJMjLJOgiLQG3sCpzQ1X1T6q+p6q/qSqp9QZ8rBHVeeq6gBgAE4SfAjYqarvq+ownEm2jwC1iJyB8sZA/fr+j9Wr5yRCPysvIAJeSxwZY/wLyyQI/B1IBN5R1ecLKqyqs4FpQCVgpNf+NTgDzwW4tmxCNaYMVKsGZ5wB3uvgJSRAo0Y5yS8jw/e5qrnGDBpj/AvXjjE9cGp2bxbhnFk4C+r2ybN/KfAg0Lg0AjMmaFJSoHp1ZygEQFJSThNoejocCtCBbt8+KMQSRcZEu3BNgu52nt+LcI67O13ee3/upYj8tB0ZE8ZEfN8fLGgKLpuiy5hCCdfm0P2ux7OLcM45ec51q+16DN4IXWPKWp61/4p83BgDhG8S/ALnPt4jrsVjAxKROjj3ERXIOw28u3nUFp415UeVKhBokgEfyx4ZY/IL1yQ4Hieh1QGWi8iVvtbVE8flwNdAPdc5E13HElwzz9zh2v9JkGI3puzFxOTuNJNXKU8EbUx5FZb3BFX1W9diueNwxgN+ABwUkbU4zZ0xOPf4zgKq49QaAUarqnsl9jE4k0+L65xJwYrfmLKwb98+z9ycsZmZ1Dp+HH8jBU/+9BOHTubMUiUi1LVhE8bkE5ZJEEBVJ4jIAeBfQF2cDi8X5ynm/g04iDM92htex5q6ju8D+qrq72UbsTFlyz3rPUD8qVN+EyBAwqlThOO8wMaEm7BNggCq+paIfAD0x1lJ/SycJtJEnNrdWpxmzrdU9Wie0+cCc4C5qmqdYkzE874joAWsWJ4dE4OIEJuVRaWjR6mQlgY//+wMm6hXz4ZPGOMS1kkQQFWPAa+5tqKc90bBpYyJHLmaM7Oz4fBhZ7JtH+Jq1aJeSoqzGoX3oPojR5ztjDOccYjGRLlw7Rjjk4gkiUgDEakvIkmhjseYkImJ8T81Wmws1KkDe/f6n1Vm926bhNsYIqAmKCItgfuBXkCjPMd+AhYDz6vq/0IQnikvjh+HNWtg9WpYuxYOHoRTp3Imq+7QATp3ho4dITk5+PGpQnY23pOhxRw44Pu+YFYW2QcPIgcP+r9vmJHhDKi3ZlET5cI6CYrI34F/ALHg8//nhjirTdwoIk+o6hPBjM9EuOxsWLQIJk2CuXP9Ni3mEhcHV1wBQ4dCjx5OjawsZWY69/IOHICsLDQ2luPJyZxKSKCWezo1X6ft20d8oPUGIfB6hMZEibBNgiLyCPAEOclvHbAC+BUnKdbGmSWmDc7nGC0iaar6XAjCNZFEFWbPhsceg81FXG85MxM++MDZWrSAJ56A/v0DL2tUXFlZsGlTztyhQFxWFlWPHCE90BhBIC4zk1MJCSSeOuW7gL/p2IyJMmGZBF1LKY1xvdwE3OY1/i9v2fNwVpBoCTwlIh+q6pbgRGoizi+/OLW4994r+Xtt3gwDBsCsWU5tsnbtgs8piv37cyVAb4np6QFPzYqN5VilSiT4axKtVctZt9CYKBeuHWOG4dT2fgEu9JcAAVR1GdAdZzxgLHBnMAI0EWjpUmjbtnQSoLc5c5z3/fzz0n3f338PeDgrQFPsiYoVSa9Qgd+rVctdTsRJgKedVkpBFt7SpUsREUSEqVOn5jrWvXt3RITTQhCXiW5hWRPEGRSvwHOq+mtBhVX1FxH5NzAWpwPNQ2Ucn4k0n3wCffs6SxCVhf37oXdvJ8FedlnpvGcBvTdjAjTBVq5ShUrudQezs+HECeexUiWrARrjJVxrgg1cj8uLcM4y12OjgKVM9Fm6tGwToFt6Olx7rXO90lBAz00JsHBuzMGDxMbGOlt8PFSt6kyqbQnQmFzCNQm6u60V5f9Yd602XD+TCYVffoF+/co+AbqlpzvX+7XABoyC1arlf6WIChUKjsMYU6BwTRg7XI/di3COe17RnaUaiYlcqk4nmAMHgnvdAwfgnntK/j4JCU4PVO9xiSLOuMWGDQs+1xhToHC9J7gAOBN4QETeVtWtgQqLSFPgPpz7iAvKPjwTEWbPLv1OMIX17rvO9fv3L9n7VKwIrVtDWhoHf/2VjNhYsmNj4cQJasbHE+9nRpjDCQmc2LvX89pWkTDGt3CtCU4ETgKVgS9FZICI5FsqW0RiReQ6nEV4qwKnXOeaaJedDSNHhjaGkSNLPiBd1ZnNJiODjLg4smJiPKtJHMrb89PlRFISxytW9JTzXn0ir59++onHH3+cTp06Ub16dZKSkmjVqhX33nsvP/74o9+w5s+fT//+/WnYsCGJiYnUqFGDbt26MXbsWI4fL/356rOzs5k1axbXX389Z5xxBpUrVyYxMZG6detyySWXMGnSJNILaAL+5ptvGDhwIPXr1ychIYEGDRpwyy23sGHDBgAqVKiAiDB9+nSf56sqs2fP5qqrrqJ+/fokJiZSq1YtevbsyZQpU8jw8wfJ6NGjEREaN24MwLZt2xg6dChnnHEGFSpUoFatWlx66aV88MEHZfodrFu3jiFDhtCyZUuSkpKoVKkSzZo147bbbuOLL74IeG2AlStXctttt3HGGWeQlJREtWrV6NKlC6NHj+bgwYMFnh+28v6PEi4bzlCHbCDLtR0ElgAzgf/iTJd20HUs27XdGeq4o2UD2uLUvBXQ1NRULUhGRoZu2LAh15aRkVHgecWyYIGqk0JCuy1cWPzPcPiw6rp1qitXqq5cqVmrVumxDRv05z179Oeff9aff/5Z9/70kx7ZtElPfvedpn3/vf62fbvnmPe2d+/efG//3//+V5OTk9X739F7q1ixon744Ye5zjly5IheffXVfs8BtEGDBrpq1ap811uyZImnzJQpU3Idu/DCCz3n5rV3717t3LlzwGsC2qFDBz106JDPr3L8+PEqIj7PS0xM1JkzZ2piYqIC+tprr+U7/9ChQ3rxxRcHvH67du30xx9/zHfu448/roA2atRI586dG/A7v/POO33GX9LvYOrUqRobGxvw3Lvuukuzs7PznZuVlaUPPPCA3+8P0OrVq+tCr//WT5w4oStXrvRsx44dS1XVVXm3U6dOrUlNTT2empp6fFWO6hrE37JwrQmiqq8AtwJHcWaNqQZcAPTDWVrpQtc+AY4Dt7rOMcYZvB4OXnyxeOedOAFbtoDXwrgxqiQfP0699HTq1atHvTp1qHvqFJWPHiXx1CkqpKdT88gR6iUmOse9trxNoZ988gkDBw7k+PHjVK5cmZEjR7JkyRKWLFnCY489RsWKFTlx4gQDBw5k06ZNAGRmZtKvXz8+/PBDADp27Mhrr73GN998w7x587j99tuJiYlhz549XHTRRZ7zSkJVueqqq1i9ejUAN954I++99x7Lly9n3rx5/POf/6RGjRoArF27lqeeeirfe7z11lvcd999qCp16tRh/PjxLFu2jHnz5nH99deTnp7OTTfdxCk/s+tkZGTQp08fFi9eDMCAAQN47733WLFiBZ988gmDBw8mLi6OdevW0bNnT373M77zwIEDDBgwAFVlxIgRLF68mC+//JLHH3+cCq6OTq+88gqffvppqX4HmzZt4u677yYrK4tmzZoxZcoUli1bxpdffsmkSZM8NdSXX36Zd955J1/cDz74IP/5z39QVc4991zPv/mSJUsYPXo01atX59ChQ1xxxRWsWrXK52cPa8HMuMXZcBLdg8B8YDdwAkgD9rj2PQgE9S8H28K8JnjsmGpcnIa8FghOHMeOFf0zbNvmqQH63E6eVN2+3f/xI0f8vnVaWpqefvrpCmhKSopu2LAhX5n58+drTEyMAnrTTTepqlObcP979+3b1+e/3Zw5czw1hq5du+Y6Vpya4EcffeQ5Z8SIET4/z86dOz21q6ZNm+Y6duTIEa1bt64C2rBhQ929e3e+8ydMmJCrVpO3Jvj0008roCKiM2bM8BnDp59+6vncw4cPz3XMXRMENCkpyWctec6cOZ4yAwcOLNXvwH392NhYn5//p59+0urVqyugPXv2zHVs2bJlns81ZMgQzcrKynf+jh07PN9xhw4dVNVqgqVKVX9X1edUtY+qNlTViqqapKoNXPueU9VDoY7ThJE1awo3GXYwZGY6q1IU1dG8a0TncehQ4F6v+/b5PbRw4UJ27doFwJNPPknr1q3zlenduzeXXHIJAIsWLUJV+c9//gNAjRo1ePXVV4nzMXzj2muvZfDgwQAsX76cZcuW5StTFFu2bKFRo0ZUrFiRESNG+Cxz+umn07VrVwDP53KbPXs2+1zfxcSJE33OSDNs2DB69erl872zsrKYONHpZnD11Vdzww03+CzXp08fz7GpU6f6rVUOGjSIzp0759t/7bXXempzqampuY6V9Dv45ZdfAKhUqRJ16tTJd26DBg0YM2YMf//737n11ltzHRs3bhyqSt26dRk/fjwxPu5BN2rUiCeecNYuWLt2LV9//bXPGMNV2CfBwhKRuiJyloicFepYTIi5mo3CRnHiKWhCbn8TY7sdO+b30CeffOK6hPj9UQeYMmUKO3bsYM+ePezbt8/TgeS6666jSpUqfs+76667PM8XLChZZ+0HHniAHTt2cPToUU+S8MXd3JuRkUG2V2ck92dNSUnhyiuv9Hv+nXf6nm3xu+++Y6+rl22PHj0Cxtq7d28Ajh8/zsqVK32Wcf9h4Yu7WfJYnn+7kn4HLVu2BODw4cMMGDCAjRs35jv33nvv5emnn+bGG2/07MvOzvb8+3Xr1o3EAJO2uz87ONPjRZJwHSJRHH8FHsBpNihPn8sUVXFqXmVpzZqin1O9ujPQ35eYmILXNAwwr+hm18oZTZo0oWrVqn7LNfQai7h+/XrP87PPPjvgpdu3b09CQgKnTp3KdV5JuGsgWVlZ7N69m61bt7J582a+++47li1blqv2lJ2d7Sn//fffA9ChQwdiY/N1MPc455xzfO5f6/Xf0r333su9995bqHi3b99Ot27d8u1v1KiR33OSXf+mmX5aMYr7Hdx88808++yz7Nu3j/fff5/333+fFi1a0KdPH3r37s3FF1/suSfpbceOHRw+fBiAOXPmIIVcKWX79u2FKhcuyluyKIP1bEzECbfu2oeK0Vpfp45znq8aX/36TpLctctZbsmXlBS/b/2razabQLWKvA54Nb3WLmC1jNjYWGrUqMHevXtLpet8dnY2b775JtOmTWPFihU+hwHExMTkqv24uZsCa9asGfAa/j7TgWJOtOCvc0xygD9e3ElGnXvuuZTkO6hRowYLFizgpptu8vxRsHnzZjZv3szEiROpWLEiV155Jffffz/nnnuu57zS/uzhqrwlQWMKbioMtuJMYZaQAK1aOQvqHjzojDesWNFJju7kddppsNPHBEkJCRBgYLy/mkYg3j/MhakRZLmSc2FrD/6kpaVx1VVX8dlnn3n2xcbG0rx5c9q2bUuXLl24+OKLefHFF3njjTfyne9OFr6SgzdfiQdyf1dvvvkmZ555ZqHibtCgQcGFCqmk3wFAu3btWLt2LYsXL2bOnDnMmzePna7/dk6cOMHMmTOZNWsWzzzzjOe+o/dnHz58eL77hf4Eal0IR5YETfkTblOGFbAArl8JCdC4MTRq5PQ1zdvEWbWq8955k2y1agEnyq5evTpAkWppKV41y18LmBc1IyODQ67ab0qAGmlhjBw50vPjf+mllzJixAj+8Ic/5Gu++9e//uXz/Jo1a/Lzzz/z22+/BbzO/v37fe73jj8xMZEOHToUIfrSUdLvwE1E6NGjh+fe5tatW1mwYAHvv/8+n332GarKww8/zNVXX03Lli1zffaMjIwiffY0P+tghqNy0zHGGI8S/vCWOlfSKTYR3/f4tm/3Xcv89deATcJt2rQB4Mcff+RogF6oM2fO5PTTT+eiiy7K1SnCX6cPt7Vr13pmT3F3yiiOrKwspkyZ4nmfjz/+mO7du/u8f7V7926f7+H+4f7+++89tVNf1vi5b+v+rqDgDh/ffvst//73v5kzZ47fpFpUpfEdnDx5ku+//z7f/dlmzZoxdOhQFi5cyDPPPAPk7gzTpEkTkpKSgII/+2+//cY///lPZsyY4bnnHCksCZryJwR/rQfUsWPpv+eJEwF7gAZaxeLii5255t3TcPkzd+5cdu/ezbJly+jcubMnIcyaNStg8nT/aHtfqzh+++03z3XatWvnt2PL1q1bPQPJIXcz3uWXXw7AoUOHmDdvnt9rvfnmmz73n3POOZ7mvbfffjtg7XnEiBE8+OCD9OvXz9OhpKRK4zto1KgR7du3554Ak7r36dPH89xdi0tISOCCCy4AYMOGDZ7JAnx54YUXGDlyJDfddBNffvllIT5Z+LAkaMofH+OwQqos4imouSnA8X79+nk6gjz66KM+5wj94osvePvttwHo378/lSpVYtiwYYDTdDh48GCfNav33nvPs2p8hw4d6N69e2E+jU9Vq1b1jEVctmyZz8S7d+9eBgwYkCsW7zF6N910k6dZb/jw4Z4xg95mz57N7NmzfcaQmJjoGfLx+++/c/PNN/vslPLiiy965t/s3bs3TZs2LezHDKg0voPLXIs8f/HFF8ydO9fndf773/96nnuPYxw+fLjn+aBBg3zWNleuXMlzzz0HOJ1wBgwYUKjPFi4sCZryp2NH/+vwBVtcXNnUTAtaHDfA8cTERCZPngw4vSfPPvtsnnnmGb7++msWLlzIww8/TK9evcjOzqZatWqeabgGDx7suZ80c+ZMzjnnHF5//XW+/fZb5s+fz6BBg+jfvz+qSlJSEjNmzChRx5ikpCQuvfRSAH7++WcuvPBCZsyYwTfffMP8+fN55JFHOOuss/jf//6X67wjR454nlepUsUzyP/HH3+kU6dOTJw4kW+++YbPPvuMu+66i4EDB+Y6P2/Mo0aNolWrVgDMmzePLl26MG3aNL799ls+/vhjbrnlFs/QieTkZCZMmFDsz1wW38Hf/vY3EhMTUVX69u3LX/7yFz766CO+/fZbPvjgA2688UaeffZZAM4999xc4yEvvfRSbrrpJsAZMtGxY0eefvppvvrqKxYtWsRjjz3GxRdfzIkTJwCYMGEClSpVKrXPHxTBnJ6mLDdgLK4Jt0MdSzRshPO0aaqqf/qThnzKNHDiKAvZ2arffed/2rSffy7wLaZNm6YJCQn5JkN2b3Xq1NFly5blOufIkSN6+eWX+z0H0MaNG+vKlSvzXa8406bt2LFDGzRoEPB6bdq00TFjxnheL1iwIN+1//GPf/idALpixYo6evRoz+t33nkn3/l79uzRs88+O2ActWrV0s8//zzfud7Tpm3ZssXvv0dZfgezZ8/WChUqBHyP9u3b6759+/LFlZ6errfddlvAc+Pj43XChAmec2zaNGNCbejQUEfgKI3FdX0RgSZNfHeYqVzZGUpRgNtvv52NGzcybNgwWrZsScWKFUlKSqJdu3Y8+uijpKameqbiynnrysydO5ePPvqIa6+91rMsUd26dbnggguYPHky69ato0uXLqXyMRs1asSaNWt48MEHadWqFYmJiZ7r9ejRg5dffpnVq1czZMgQT7Ohr/ucjz32GF999RUDBgzwxFyvXj1uvfVW1q1bx4UXXugp66smU79+fb755htmzJjBlVdeSb169YiPj6dSpUp06tSJxx9/nA0bNnjuoZWm0vgO+vXrR2pqKvfddx/t2rWjcuXKxMfHU7duXXr37s3UqVNZtWqVz2nVEhISePXVV/nqq6+45ZZbPEspVahQgebNmzNkyBDWrl3raS6PNKLqe3xMUC4uUppTC1THWVNQVdX/1BCmVIhIW8AzRUVqaipt27YNeE5mZiZbtmzJta958+Y+56AssexsZ5xdnusFVYsW8MMPAWdvKbH0dPjtN6eTTEyM0zM2JaVsr1kOvffee/Tt2xeAVatW+Zzf0xReWlpart6orVu3Xp+cnHwyb7mMjIzYzZs3twA4efLkD67dl3Tu3Dlo80GH+sZJY5zqdGnM9BK6bG7CT0wMPPkkhPIm/ZNPln0ySkx0Bs0bn15//XXmzp1L06ZNGT16tM+hBQBLliwBnEHoviYUN+VXqJPgLix5mbLSvz/MmgVz5gT/2v36Odc3IZWYmMi7774LQKtWrXzOerJmzRpee+01wJngumLFisEM0YRYSJOgqjYO5fVNOSfiLK77+edQSoOXC6VmzeIvpmtK1RVXXEHdunXZt28fQ4YMYdWqVfTp04fatWvz22+/8fnnn/Pyyy9z/PhxkpKSGDduXKhDNkEW6pqgMWWrdm2YPRv69CneHJ5FlZgI777rXNeEXKVKlZgzZw5XXXUVBw4c4MUXX+RFH3+g1KpVi3feecczFMJED7t7bsq/7t2dJtHizuFZWBUqwHvvgVdPQxN65513Hps3b+aJJ57gD3/4g2cAer169ejatSv/+te/WL9+fYHrBZryyWqCJjpcfjnMn+/cqyvmEjEB1azp1AAtAYallJQURo4cyciRI0MdigkzVhM00aN7d9iwAVxd4UtN376wfr0lQGMikCVBE13c9whnzXLG8ZVEixbO+9g9QGMiliVBE31EnOELP/wACxfCn/5U+LlG4+Kc8gsXwsaNNgzCmAhn9wRN9IqJgZ49ne34cVi7FlavhjVr4NAhpzdpYqKzHmDHjs5qEB06QHJyqCM3xpQSS4LGgJPYunVzNmNM1LDmUGOMMVHLkqAxxpioZUnQGGNM1LIkaIwxJmpZxxhjgOPHj7NmzRpWr17N2rVrOXjwIKdOnSIhIYGUlBQ6dOhA586d6dixI8nWO9SYcsOSoIla2dnZLFq0iEmTJjF37lwyMzMLPCcuLo4rrriCoUOH0qNHD2Js8VpjIpr9H2yijqoya9YsWrduTa9evfjggw8KlQABMjMz+eCDD+jVqxetW7dm1qxZqNqSmMZEKkuCJqr88ssv9OvXjwEDBrB58+YSvdfmzZsZMGAA/fv359dffy2lCI0xwWRJ0ESNpUuX0rZtW957771Sfd85c+bQtm1bPv/881J9X2NM2bMkaKLCJ598Qp8+fThQFssoAfv376d3797MmzevTN7f5LZ+/fqQXr9x48aICOeff35I4zAlZ0nQlHtLly6lb9++pJfxyvLp6elce+21LF26tEyvE83S09MZNWoUHTt2DHUoppywJGjKNfc9wLJOgG7p6en069fP7hGWkbFjx/LEE0+QkZER6lBMOWFJ0JRbqsrQoUPLrAnUnwMHDnDPPfcE9ZrRorC9eI0pLEuCptyaPXt2qXeCKax3332X2bNnh+TaxpjCsyRoyqXs7GxGjhwZ0hhGjhxJdnZ2SGMwxgRmSdCUS4sWLWLLli0hjWHz5s0sXrw4pDEU5KeffuLxxx+nU6dOVK9enaSkJFq1asW9997Ljz/+6Pe8+fPn079/fxo2bEhiYiI1atSgW7dujB07luPHj/s8Z+nSpYgIIsKOHTs4evQo//jHP2jfvj2VK1ematWqnHvuuUyYMIFTp07lOnf69OmICGPGjPHsc7/Xrbfe6tnn7rU5evRotm/fTu/evUlOTqZ69ep07dqVNWvW5HrfnTt38uCDD9KuXTsqV65McnIyLVq0YMiQIWzYsKEY36iJNDZtmimXJk2aFOoQAHjxxRfp2bNnqMPwaebMmQwaNChf0tq0aRObNm3i1Vdf5Z133uGqq67yHDt69Cg333wzH374Ya5zDh48yLJly1i2bBkTJkzgww8/pHPnzn6v/eOPP3LxxRfnS7QrVqxgxYoVvPXWWyxevJjKlSsX67P9+uuv/PGPf+Tnn38G4MSJE3z33XecccYZnjKvvPIKw4cP5+TJk7nO3bJlC1u2bGHq1KmMGTOGRx99tFgxmMhgSdCUO8ePH2fu3LmhDgOAuXPncvz48bCbdPuTTz5h4MCBAFSuXJnhw4fTo0cPABYvXsy///1vTpw4wcCBA1mzZg0tW7YkMzOTfv36sWDBAgA6duzIsGHDaN26NQcPHuTdd99l+vTp7Nmzh4suuoiVK1fSsmVLn9e/4YYb2LdvH9dffz033HADNWvW5LvvvuOf//wnu3fvZtWqVTz55JM8++yzAFx11VWsWbOGyZMn8/LLLwN4anUpKSn53v/ll19GVRkxYgRXXXUVu3btYvfu3VStWhWAN954g7vuusvz+e+//34uuugi4uPjWb58Oc899xy//PILI0eORER45JFHSuurN+FGVW2zrcgb0BZQ95aamqoFycjI0A0bNuTaMjIyCjyvqL788kv1ji3U21dffVXqn7Ek0tLS9PTTT1dAU1JSdMOGDfnKzJ8/X2NiYhTQm266SVVVp06d6vlMffv29flvN2fOHBURBbRr1665ji1ZsiTX9zJp0qR85+/atUuTk5MV0Hr16uU7/vjjj3vO96VRo0ae43/96199ljl48KBWqVJFAa1du7Zu3LgxX5m9e/dqy5YtFdDY2Nh835H7Ot26dfN5jWh34sQJXblypWc7duxYqqquyrudOnVqTWpq6vHU1NTjq3JU1yD+ltk9QVPurF69OtQh5BJu8SxcuJBdu3YB8OSTT9K6det8ZXr37s0ll1wCOPdXVZX//Oc/ANSoUYNXX32VuLj8DUnXXnstgwcPBmD58uUsW7bMZwxnnnkmd999d779DRs2pHfv3gDs3bu3RMNbhgwZ4nP/1KlTOXLkCADjxo3zWVutW7cur732GgBZWVmMGzeu2HGY8GZJ0JQ7a9euDXUIueTtjBFqn3zyCeB0LLnhhhv8lpsyZQo7duxgz5497Nu3z9NR5LrrrqNKlSp+z3M3MwKeptO83AnWlyZNmnieHzt2zG+5QKpXr06zZs18Hvvss88AqFq1Kv379/f7Hl27dqVdu3aA/89hIp8lQVPuHDx4MNQh5HLo0KFQh5CLe/WMJk2aeO6R+dKwYUMaNWqEiOSaq/Pss88O+P7t27cnISEB8D/HZ6NGjfye733/tLiD4xs0aOD3mDum9u3bEx8fH/B9zjnnHMDpReqv16uJbJYETbmTt3t9qAVryrbCck/pVqNGjUKf490sWbt27YBlY2NjPe/t7w+SQB2FRMTzXLV4azUGqqm6P0tBnyNvmXD748qUDkuCptxx10LCRWJiYqhDyKU4tSvvZOSdpPzJysoqdNmyEOi67s9SlM9R2PIm8lgSNOWOry7zoVS9evVQh5CLO56i1Gy8v9OCJgfPyMjwNAGH278F5MRUmEnOvcuE27+jKR2WBE2506FDh1CHkEu4LfvTpk0bwBmwfvToUb/lZs6cyemnn85FF12Uqza7cuXKgO+/du1azyoP/sYJhpK7s4t3nP6sWLECgNNOOy3sxnqa0mFJMIKIyAgRUddmEx34EWimklAIt3guvvhiwJlfddasWX7LzZ07l927d7Ns2TI6d+7sSZ6zZs0KmDynTJmS71qlJSam5D9Z7hl8Dh8+zJw5c/yWW7ZsmadHbGl/DhM+LAlGCBFpCowOdRyRoGPHjj7HsIVCXFxc2NVM+/Xr5+nw8eijj/qcI/SLL77g7bffBqB///5UqlSJYcOGAbB//34GDx6c636Z23vvvcfUqVMBp0bevXv3Uo3du0Za3OETgwYN8tTq7r//frZu3ZqvzC+//MLtt98OOB197r333mJdy4Q/S4KR42UgKdRBRILk5GSuuOKKUIcBwBVXXBF2zWiJiYlMnjwZcH7szz77bJ555hm+/vprFi5cyMMPP0yvXr3Izs6mWrVqPPXUUwAMHjzYM7XazJkzOeecc3j99df59ttvmT9/PoMGDaJ///6oKklJScyYMaPUO5PUqVPH83zUqFGsWrWqyBNdp6Sk8OKLLwKwb98+unTpwujRo/niiy9YtmwZ//73v+nQoQObNm0CnNVAunTpUnofwoSV8Phz2QQkIrcDPUIdRyQZOnQoH3zwQajDCNvFda+55hqmTZvG3XffzYEDB3j44YfzlalTpw7vv/8+p59+OuA0Rb7//vtcf/31fPLJJ/zvf//LtYKDW+PGjZk9e7an+bQ09ejRg6SkJNLS0hg3bhzjxo2jW7dufPXVV0V6nz//+c+kpaUxfPhwDh8+zJgxY3KtUAFODfCf//wnI0aMKM2PYMKM1QTDnIjUAZ5zvdwfylgiSY8ePWjevHlIY2jRokVY30u6/fbb2bhxI8OGDaNly5ZUrFiRpKQk2rVrx6OPPkpqaipdu3bNdU7lypWZO3cuH330Eddeey3169cnISGBunXrcsEFFzB58mTWrVtXZjWnhg0b8umnn9KtWzcqVapEcnJyscdhDhkyhI0bN3LffffRpk0bkpOTqVy5MmeeeSb3338/69ev529/+5sNjSjnpLiDUU1wiMgsoD+wGNgN/Nl1KF5VizedRunE1RZIdb9OTU2lbdu2Ac/JzMzMt8Zf8+bNy+z+3axZsxgwYECZvHdhrx9oWi5jyqu0tLRcswW1bt16fXJy8sm85TIyMmI3b97cAuDkyZM/uHZf0rlz56BNs2Q1wTAmIlfiJMCTgO/ZgI1f/fv3p2/fviG5dr9+/SwBGhMBLAmGKRGpDLhXhn1SVUO7THoEEhEmTZpEzZo1g3rdmjVrejpeGGPCm3WMCV/PAqcB64F/leWFRKQ2UKuIpzUti1hKW+3atZk9ezZ9+vQJyhyeiYmJvPvuu4Wal9IYE3qWBMOQiHTDaf5U4E5VDTytRckNBR4v42uETPfu3ZkzZw59+/Yt00RYoUIF5syZw4UXXlhm1zDGlC5LgqVIRJoDxfkF/EpVN7reIwGYAggwWVV9r0pqiuTyyy9n/vz59OvXr0QLtfpTs2ZN3n33XUuAxkQYS4KlqxtOAiuqu4GNruePAq2BvcDfSykug1Mj3LBhA0OHDg04XVZR9e3bl0mTJlkTqDERyDrGhBERaUNO4humqoeDdOlJwJlF3K4OUmylyn2PcNasWbRo0aJE79WiRQtmzZpl9wCNiWBWEyxFqjodmF6cc0UkBpgKJABzVfXd0ossMFX9FSh4XRkvkTyAWEQ8wycWL17Miy++yNy5cwu1zl5cXBxXXHEF99xzDz169Ijo78EYY0kwnNwFdAWygCki0sFHGe/F2dqLSBZwSlWLNnmiAZxpwHr27EnPnj05fvw4a9euZfXq1axZs4ZDhw6Rnp5OYmIi1atXp2PHjnTu3JkOHTqE3VygxpjisyQYPs51PcYCHxai/CrX406gcVkEFE2Sk5Pp1q0b3bp1C3UoxpggsnuCJmh8NR3atH3GRK/s7Gx3DvL+ITgVzBgsCYYJVb1VVSXQBrzudUq8a3/jEIVcZL4WRE1LSwtBJMaYcJCWllYBwGssdBZwIpgxWBI0QSMiVKxYMde+gwcPWm3QmCikqhw4cKAGQHZ29lHX7v917tw5qD8Idk/QBFWVKlU4cSLnD720tDR27txJSkoKFSpU8FlbNMZElqysrFyvMzMzYzIyMmLBaQJNS0urcODAgRonT55MAjQzM9M9HGxhkEO1JGiCq3Llyvzyyy+5an9paWns2bMnhFEZY0pTdnZ2rte7du1qEhMTk+2jqGZkZOxR1XQgA1gahPBysT+7TVDFxcVx2mmn2fg6Y6KXquqprKysA+np6T9mZWUdxUmAD3bu3PlgsIOxmmAEUdVbgVtDHEaJVapUidNOO42ffvrJ7gcaUw6lp6fzww8/eF43adJkW1JSUr5FdV3cCfDroASXhyVBExKVKlWiWbNmHD16lCNHjuS6T2iMKfeygP/h3ANcGooaoJslQRMycXFxVK9enerVq6OqZGdnW83QmHJg48aN3HPPPZ7X7du3Hzx+/Hj3IgGngBPB7gXqjyVBExZEhNjY2FCHYYwpBSLC0aNHPa+/+uqrI507dz4UwpD8so4xxhhjopYlQWOMMVHLkqAxxpioZUnQGGNM1LIkaIwxJmpZEjTGGBO1LAkaY4yJWpYEjTHGRC0bLG+KK8H7xdatW0MVhzEmzPj4PUjwVS4ciE1TZYpDRK4CPgx1HMaYiHC1qn4U6iB8seZQY4wxUcuSoDHGmKhlzaGmWESkKnCh167dOLPDB1NTcjfJXg1sC3IM4cC+B4d9D45w+B4SgIZerz9X1cNBjqFQrGOMKRbXf9AhbeP3sTr9NlVdH4pYQsm+B4d9D44w+h7WhOCaRWbNocYYY6KWJUFjjDFRy5KgMcaYqGVJ0BhjTNSyJGiMMSZqWRI0xhgTtSwJGmOMiVqWBI0xxkQtS4LGGGOiliVBY4wxUcuSoDHGmKhlc4eaSPYbMCbP62hk34PDvgeHfQ9FYKtIGGOMiVrWHGqMMSZqWRI0xhgTtSwJGmOMiVqWBI0xxkQtS4LGGGOiliVBY4wxUcuSoDHGmKhlSdAYY0zUsiRojDEmalkSNMYYE7UsCRpjjIlalgSNMcZELUuCxhhjopYlQVMuiMgIEVHXFhVLhIlIGxGZIiLbRSRdRH4VkS9E5C4RSQh1fMEkIr1EZJaI7HZ9F7+JyDwRuTLUsQWDiNzk9d9/QVuzUMcbTmwpJRPxRKQpsA5Icu2KV9XMEIZU5kRkMPAC4C/ZfQ/8SVV/DF5Uwef6g2cacEuAYq8AQ7Qc/9iJyFjgwUIWb66qW8synkgSFX8xm3LvZXISYLknIpfhfGYBDgPPAN8AVYBrgFuBs4CPReQcVT0RolCD4RVyEuAa4N/ANqAFMBJoDtwJbAGeC0WAQdLe9fglMKyAsrvKOJaIYjVBE9FE5HacmoC3clsTFBEBNuH8uB8HOqnq5jxlHgTGul7+RVVfDG6UwSEiPYDPXC8XAleqarrX8Wo4ibExcASoq6ppQQ4zKETkF6A28C9V/Vuo44kkdk/QRCwRqUPOX/f7QxlLEJ2PkwDB+cHb7KPMf4CDrud9gxJVaIxyPR4GBnonQABV/R142vWyCtAzeKEFj4jUxUmAAN+FMpZIZM2hJpI9D1QHFgO7gT+HNpygiAHm4jR/feyrgKpmi8hW4BzgtCDGFjQiUh/4o+vlOFU96KfoR0A3nD+SyusfSu29nq8NVRCRypKgiUiuXn/9gZPAEODR0EYUHKr6OfB5oDKuJtOGrpf7yjyo0LgE554owLv+CqnqPsr/H0fuJHgSp6ncFIE1h5qIIyKVgUmul0+q6pZQxhOGhgL1XM9nhzKQMtTO9ZgG/ODeKSJJItJcRBq6/hiIBh1cj6lAZxF5XUR2icgp17CZj0Tk8hDGF9YsCZpI9CxOM9964F8hjiXkxFFdRP4oIu/gNBMDfI3Te7I8au163ONq/u0gIh/jdIDZjNMDco+IjBSRxJBFGRzummBLnF7Ct+C0BMQDtYArgbki8ma0jR8tDEuCJqKISDec5k8F7lTVjBCHFA4ewekI8wUwEKeZcBpwed7OIuVIDdfj7yJyA7ACuILct3jqAU8An4lIlSDHFxQiUgFnOAhAZWAP8BBwoWt7FPjNdfwm4KVgxxjuLAmaiOH6K3YKzo/8y6q6LMQhhYuGPvb1pHzfC6vkejwdeA2nWXQYTuJLAs4DlrjKnA9MDXaAQdKWnMS/DDhLVZ9T1S9c21M4zaXbXGVuF5Fy2Uu2uGycoIkYIjIGp1v8XqC1qh72OjadnB/9cjtO0BcRuQQ45dr+gDNzSH3X4RdV9S+hiq2siMg24AzXy3Tgj6q6Mk+ZOOBTcoZGdCtvfziJSCzOOMgmwHeq+pufchcCS10vP1bVq4ISYASwJGgigoi0wRn4nAD0V9V38xyfTpQmwbxEpDbO/UD3HJF9VPX/QhhSqROR9UAb18uJqjrcT7kzcabUg3L6B0FhuYbNNAWOAlXL8zRyRWHNoSbsiUgMTnNWAjA3bwI0uanqr8A9XrsCzasZqY56Pf/IXyFVTcUZQwpwdplGFP7cfwxUxhlfa7BxgiYy3AV0BbKAKSLSwUeZFK/n7UUkCzilqhuCEF84WgScACqSM5ygPPEe//hzAWV/wrlvWrPswokI3nPIWi9RF0uCJhKc63qMBT4sRPlVrsedOPdLyg3XfJjNgRqqOt9fOVXNEpHDOEmwPP7gpQJXu54XVKtxf/7fyyyaEBGR9jjN3rVwOosFauKs5XrMImdavahnzaHGRJapOMMBPhSRZH+FXMfcP3p7ghFYkK3wen6Ov0KupnT3EIIdZRlQiDyAM2POS+R8znxEJB7o4nq5TlVPBSG2iGBJ0IQ9Vb1VVSXQBrzudUq8a3/jEIVclr5yPSYA1wcodwM5LT2LyjSi0Pg/nImzAQa5ekn6ciXOPTAIcO8wgn3h9fyGAOVuJqfGPKvswok8lgSNiSwzyOkU8oSInJ63gKuJzL2U0hHK4awxrkkA3EtEnYkzi1AuIlIPmOB6+RvwXnCiC6p3yWnafMD1b5+LiJxFzmorv+GsRWlcLAkaE0Fc48BGuF7WBb4Tkb+LSHfX9jTO8Iiq5MyqU15XT3gS594gwF9FZKGI9BWRc0XkbmAl0Mh1/C+qetTnu0Qw11jZ+1wvKwFfisgoEekmIheIyJM4g+ir49wLvCPAihtRycYJmnIh2sYJuhbOfQans5AvJ4DBqvp28KIKPhGphbOk1Ll+imQBw8vrwsJuIvIXYBz+OzsexfmD6L/BiyoyWBI05UK0JUHwDAQfDlwMNAAygO04s6RMUNW9IQwvaFydX27BmRvzLJx7gD/hLDk1QVXXBTi93BCRVjhTx/XEmWA+C6eH9CfAC6q6O8DpUcuSoDHGmKhl9wSNMcZELUuCxhhjopYlQWOMMVHLkqAxxpioZUnQGGNM1LIkaIwxJmpZEjTGGBO1LAkaY4yJWpYEjTHGRC1LgsYYY6KWJUFjjDFRy5KgMcaYqGVJ0BhjTNSyJGiMMSZqWRI0xhgTtSwJGmOMiVqWBI0xxkQtS4LGGGOiliVBY4wxUcuSoDHGmKhlSdAYY0zUsiRojDEmasWFOgBjTOGIyFnAdcAFQEugGnAK+BVYD3wK/FdVD4UqRmMijahqqGMwxgQgIo2BF4DLC1H8d2CMqo4vw5CMKTcsCRoTxkTkj8DHQFXXrj3Au8AXwD4gFmiEkyD7AvGuchNVdXhwozUm8lgSNCZMiUhLYCVQ2bXrn8CTqnrST/lWwHtAa9euP6nqh2UeqDERzJKgMWFKRJYBXV0vh6vqxEKcUx/4AagCbFbVlmUYojERz3qHGhOGRORKchLg/MIkQABV/Rl4zvWyhYicXRbxGVNeWBI0JjwN8Xo+pojnvgF8C4zCuW8IgIiMFhEVkR0iEiciT4nIPhFJE5EtIvKQ95uISKKI3Ckin4nIbyJySkT2ishcEbleRMTXxb2uoyLSzF+QIvKTq8zSPPu7e53fSkRqi8gLrrjTXTHPEpE/BPoSRKSdiEwWkU2uz3hMRLaKyGsickHBX6OJBjZEwpgwIyLJQE/Xy304Ca3QVHUnEDBBAJOBQV6vm+H0LHXH0AqYA7TJc15dnE44lwNDReRaVf2tKPEVURPgM6CB1746QH+gn4jc56uWLCKDgJdxOg55a+rabhWRl4G71e4JRTWrCRoTfroACa7nC8vgR7ohTgJchpPMeuIMwZgJnvuKC8lJgO8D1wDnAgOBJa795wMLRSSplOPz9hpOAlzkiuE84K/AQUCACSJyvfcJrg5FL+EkwK3AYNd5fwSGAjtcRe8Ccp1roo/VBI0JP96dWbaXwfvHALuAXqp63LVvkdfxZ4HTXM/vzzPmcAUwU0TGA8OB9sBjwCNlECc4tb5pwGCvPwaWi8jHwHKgBjBWRD5U1ROu49fjDBXJAi5S1Z+83u8rEfkIWAdUB24D3i6j2E0EsCRoTPhJ8Xr+a6CCItKhgPc6pqpbfex/0ysBer9fXXJqRwsDDLp/ELgEp7Z4j4j8w9/QjRLaBfwlb21YVbeIyEicGl8D4FKc5ltwEifAMeCXvG+oqntE5HGgPpBaBjGbCGJJ0Jjw432bwmfnEwARiQPWFPBeX+M0W+a1wk/5i8i5jzbV35uqaqaITAP+jTMc4w/A0gJiKY7XAyTXGTjNuLHAZeQkwU2ux6o4tdZHVHWj94mq+nwZxGoikN0TNCb8HPF6XruMrrHHz/62Xs9XFvAe3om0rd9SJbPc3wFVPUpOc3ELr0NvktMr9hrgB1cP0QkicpmIVCibUE0ksiRoTPjZ4vW8vr9CqpqpquJrA3YWcI0jfvbX8HoesCk2z/EUv6VKZm8Bx/e7Ht1NoKjqAaAX8L1XuRbAMOAT4ICI/FdEzi3NQE1ksiRoTPhZCWS7nvcMVLAE/PU4lUKUcfMeflCcHqyF+f3JLGQMGd47VXUd0AHn+3uJ3H8UVAQG4HSwGVGoSE25ZUnQmDCjqgeBL10vG4tIpyBe/qDX84KaYr2Pe5/nnRD93tPEuZdYkIJqmLVcj/vyHlDHIlUdqqqNgebAPTjjDt2xPe0aUmGilCVBY8LTi17PHw7iddd5PS9oyrVzvJ5v8nruXXvzOYZQRKoDyYWIp72/A673aOx6+b3X/goicpaI5LpPqapbVXWSql4C/N21Owan6dREKUuCxoSnd8mZKaafiNxT2BNFpB3Fv0e3lJwkNshfIVfP1NtcL9OAb7wO/+71/HQ/b1HYZt6BAY7dQk5N03u1jJ3Ad+T+QyKv+V7Py3KwvwlzlgSNCUOucXE3ktPM+IKIjBWRSv7OEZFk19i5b8hZfqlI9+pUdS9OAgboLSJ/8VN0LDlLNr2SZ8yh99i7oXnnGHWNRXyqkCGd5ysGVy1vtOvlWnKajwHmuR4vEJEr/Lyvd3JdXchYTDlkSykZE8ZEpD1Oj0b33JnHcJLUEpwaTzbONGg9gGuBal6nf4kz08om13uNBh53HWvuZxC9O0mtwZknFJzxd2/i9NRsjDO590WuYxuBzl6ztbhriVtxFvsFZ1Hgl4CjOFOvPQDUw5m+rAnwuap29zq/OzlTs4GTyN/AGRd4HOgO/A3nnmIm8EdV/cbr/FY4iTEROAVMARbgDJyvhzPv6PU4tchvVbWgeVZNOWZJ0JgwJyIpwNM4zY/xBRQHpynwOWCG90wrhU2CrrItcZJX8wDXWQzcoKr5ZmURkQtxamQVfZyXjTPjTFucJtdASfAVnJ6cVX28z1Ggn6ou8HH9fjiJO9CYwO+A3r7iN9HDmkONCXOqelBV78KpNf0V+BTYDaS7tp+Ar3BWnj9fVTuo6lslmXjbVXtsh9ObcilwAKdWtQPn/ttVwCX+Eoiqfo4zpdpLrnNO4fTgfNcV47hChrISOAt41XV+GrAe+BfQ0lcCdF3/XeBMYDxOZ5+jOMMo9gH/B9wBdLEEaKwmaIwJK3lqgoNV1e/0bcaUlNUEjTHGRC1LgsYYY6KWJUFjjDFRy5KgMcaYqGVJ0BhjTNSy3qHGGGOiltUEjTHGRC1LgsYYY6KWJUFjjDFRy5KgMcaYqGVJ0BhjTNSyJGiMMSZqWRI0xhgTtSwJGmOMiVqWBI0xxkQtS4LGGGOiliVBY4wxUcuSoDHGmKhlSdAYY0zUsiRojDEmalkSNMYYE7UsCRpjjIlalgSNMcZELUuCxhhjopYlQWOMMVHLkqAxxpioZUnQGGNM1LIkaIwxJmpZEjTGGBO1LAkaY4yJWpYEjTHGRC1LgsYYY6KWJUFjjDFRy5KgMcaYqPX/wN2qnqNu1YgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 330x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make swarm plot with error bars for the log2 ratio of counts\n",
    "\n",
    "fig_width, fig_height = 1.1, 1.4\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "ax = sns.swarmplot(x='groups', y='log2ratio_of_counts', data=df,\n",
    "                   hue='treatment', palette={'control': 'black', 'collagenase': 'red'},\n",
    "                   size=2, order=['4', '3', '6', '5'])\n",
    "ax = sns.pointplot(x='groups', y='log2ratio_of_counts', data=df, order=['4', '3', '6', '5'],\n",
    "                   ci=95, capsize=.2, errwidth=.7,\n",
    "                   markers='_', scale=.5, join=False, color=\"Gray\")\n",
    "\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"$\\mathregular{Log_2}$(bud ratio)\")\n",
    "\n",
    "plt.ylim(-5, 8)\n",
    "plt.yticks([-4, 0, 4, 8])\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + datasetPrefix + '-log2-ratio-of-counts.svg'\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "  Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     1      2     -1.0    0.9  -11.637   9.637  False\n",
      "     1      3  -4.1818 0.8387 -14.8189  6.4552  False\n",
      "     1      4  12.3636 0.0137   1.7266 23.0007   True\n",
      "     1      5     41.0  0.001   30.363  51.637   True\n",
      "     1      6  62.5455  0.001  51.9084 73.1825   True\n",
      "     2      3  -3.1818    0.9 -13.8189  7.4552  False\n",
      "     2      4  13.3636  0.006   2.7266 24.0007   True\n",
      "     2      5     42.0  0.001   31.363  52.637   True\n",
      "     2      6  63.5455  0.001  52.9084 74.1825   True\n",
      "     3      4  16.5455  0.001   5.9084 27.1825   True\n",
      "     3      5  45.1818  0.001  34.5448 55.8189   True\n",
      "     3      6  66.7273  0.001  56.0902 77.3643   True\n",
      "     4      5  28.6364  0.001  17.9993 39.2734   True\n",
      "     4      6  50.1818  0.001  39.5448 60.8189   True\n",
      "     5      6  21.5455  0.001  10.9084 32.1825   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.83867427 0.01370476 0.001      0.001      0.9\n",
      " 0.00602139 0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fea4a44a890>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group 1: time 0 of collagenase treatment and recovery group\n",
    "# Group 2: time 0 of control group\n",
    "# Group 3: time 24 h of collagenase treatment and recovery group\n",
    "# Group 4, time 24 h of control group\n",
    "# Group 5: time 48 h of collagenase treatment and recovery group\n",
    "# Group 6, time 48 h of control group\n",
    "\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "  Multiple Comparison of Means - Tukey HSD, FWER=0.05  \n",
      "======================================================\n",
      "group1 group2 meandiff p-adj   lower    upper   reject\n",
      "------------------------------------------------------\n",
      "     1      2      0.0    0.9  -1.5392   1.5392  False\n",
      "     1      3  -0.5536 0.8943  -2.0928   0.9856  False\n",
      "     1      4   2.2335  0.001   0.6943   3.7727   True\n",
      "     1      5  16.4773  0.001   14.938  18.0165   True\n",
      "     1      6   2.8974  0.001   1.3582   4.4367   True\n",
      "     2      3  -0.5536 0.8943  -2.0928   0.9856  False\n",
      "     2      4   2.2335  0.001   0.6943   3.7727   True\n",
      "     2      5  16.4773  0.001   14.938  18.0165   True\n",
      "     2      6   2.8974  0.001   1.3582   4.4367   True\n",
      "     3      4   2.7871  0.001   1.2478   4.3263   True\n",
      "     3      5  17.0309  0.001  15.4916  18.5701   True\n",
      "     3      6    3.451  0.001   1.9118   4.9903   True\n",
      "     4      5  14.2438  0.001  12.7046   15.783   True\n",
      "     4      6    0.664 0.7752  -0.8753   2.2032  False\n",
      "     5      6 -13.5798  0.001 -15.1191 -12.0406   True\n",
      "------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.89431434 0.001      0.001      0.001      0.89431434\n",
      " 0.001      0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.77515587 0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fea49ed2490>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group 1: time 0 of collagenase treatment and recovery group\n",
    "# Group 2: time 0 of control group\n",
    "# Group 3: time 24 h of collagenase treatment and recovery group\n",
    "# Group 4, time 24 h of control group\n",
    "# Group 5: time 48 h of collagenase treatment and recovery group\n",
    "# Group 6, time 48 h of control group\n",
    "\n",
    "tukeyTest(df.ratio_of_counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "====================================================\n",
      "group1 group2 meandiff p-adj   lower   upper  reject\n",
      "----------------------------------------------------\n",
      "     1      2      0.0    0.9 -0.4179  0.4179  False\n",
      "     1      3  -1.2313  0.001 -1.6492 -0.8133   True\n",
      "     1      4   1.6201  0.001  1.2022   2.038   True\n",
      "     1      5   4.1134  0.001  3.6955  4.5313   True\n",
      "     1      6   1.9095  0.001  1.4916  2.3274   True\n",
      "     2      3  -1.2313  0.001 -1.6492 -0.8133   True\n",
      "     2      4   1.6201  0.001  1.2022   2.038   True\n",
      "     2      5   4.1134  0.001  3.6955  4.5313   True\n",
      "     2      6   1.9095  0.001  1.4916  2.3274   True\n",
      "     3      4   2.8514  0.001  2.4334  3.2693   True\n",
      "     3      5   5.3447  0.001  4.9267  5.7626   True\n",
      "     3      6   3.1408  0.001  2.7229  3.5587   True\n",
      "     4      5   2.4933  0.001  2.0754  2.9112   True\n",
      "     4      6   0.2894 0.3334 -0.1285  0.7074  False\n",
      "     5      6  -2.2039  0.001 -2.6218 -1.7859   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.33338678 0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fbd5e577390>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group 1: time 0 of collagenase treatment and recovery group\n",
    "# Group 2: time 0 of control group\n",
    "# Group 3: time 24 h of collagenase treatment and recovery group\n",
    "# Group 4, time 24 h of control group\n",
    "# Group 5: time 48 h of collagenase treatment and recovery group\n",
    "# Group 6, time 48 h of control group\n",
    "\n",
    "tukeyTest(df.log2ratio_of_counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()\n",
    "df['log10counts'] = np.log10(df.counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "====================================================\n",
      "group1 group2 meandiff p-adj   lower   upper  reject\n",
      "----------------------------------------------------\n",
      "     1      2  -0.0331    0.9 -0.2181  0.1519  False\n",
      "     1      3  -0.3706  0.001 -0.5557 -0.1856   True\n",
      "     1      4   0.4546  0.001  0.2696  0.6396   True\n",
      "     1      5   0.8676  0.001  0.6826  1.0526   True\n",
      "     1      6   1.0294  0.001  0.8444  1.2144   True\n",
      "     2      3  -0.3376  0.001 -0.5226 -0.1526   True\n",
      "     2      4   0.4877  0.001  0.3027  0.6727   True\n",
      "     2      5   0.9007  0.001  0.7157  1.0857   True\n",
      "     2      6   1.0625  0.001  0.8775  1.2475   True\n",
      "     3      4   0.8253  0.001  0.6403  1.0103   True\n",
      "     3      5   1.2383  0.001  1.0532  1.4233   True\n",
      "     3      6   1.4001  0.001  1.2151  1.5851   True\n",
      "     4      5    0.413  0.001   0.228   0.598   True\n",
      "     4      6   0.5748  0.001  0.3898  0.7598   True\n",
      "     5      6   0.1618 0.1198 -0.0232  0.3468  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.001      0.001      0.001      0.001      0.001\n",
      " 0.001      0.001      0.11978812]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fea4b6dec50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.log10counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='groups', ylabel='log10counts'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "sns.swarmplot(df.groups, df.log10counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_control_end = df[df.groups=='6']\n",
    "df_collagenase_end = df[df.groups=='5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.57957711575716, pvalue=0.0018751955381959993)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "ttest_ind(df_control_end.log10counts, df_collagenase_end.log10counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.30980392156862746"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "( df_collagenase_end.counts.mean()-df_control_end.counts.mean() ) / df_control_end.counts.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groups</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>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.446406</td>\n",
       "      <td>0.142253</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.341346</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.233478</td>\n",
       "      <td>1.006063</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.422222</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.0</td>\n",
       "      <td>17.477273</td>\n",
       "      <td>2.536153</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.897437</td>\n",
       "      <td>1.248251</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3.327652</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.914336</td>\n",
       "      <td>7.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean       std        min        25%        50%  \\\n",
       "groups                                                                \n",
       "1        11.0   1.000000  0.000000   1.000000   1.000000   1.000000   \n",
       "2        11.0   1.000000  0.000000   1.000000   1.000000   1.000000   \n",
       "3        11.0   0.446406  0.142253   0.250000   0.341346   0.428571   \n",
       "4        11.0   3.233478  1.006063   1.500000   2.422222   3.500000   \n",
       "5        11.0  17.477273  2.536153  12.750000  16.375000  17.250000   \n",
       "6        11.0   3.897437  1.248251   2.666667   3.327652   3.400000   \n",
       "\n",
       "              75%        max  \n",
       "groups                        \n",
       "1        1.000000   1.000000  \n",
       "2        1.000000   1.000000  \n",
       "3        0.535714   0.666667  \n",
       "4        3.785714   4.800000  \n",
       "5       18.250000  22.000000  \n",
       "6        3.914336   7.166667  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('groups').describe()['ratio_of_counts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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