SMG-bud-count-Itgb1-time-course-Rei-Kaz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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, plotCountBar\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix, get_annotated_countDF\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",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetFolder = '../data/220509-30-for-bud-count-Kaz/sg-Itgb1-time-course/'\n",
    "\n",
    "files = glob.glob(os.path.join(datasetFolder, '*fileNameRecord.txt'))\n",
    "\n",
    "datasetPrefixes = []\n",
    "for f in files:\n",
    "    datasetPrefixes.append('-'.join(os.path.basename(f).split('-')[:3]))\n",
    "\n",
    "sgRNAs = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "dfs = []\n",
    "for datasetPrefix in datasetPrefixes:\n",
    "    dfs.append( get_annotated_countDF(datasetPrefix, datasetFolder, sgRNAs) )\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df_Kaz = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetFolder = '../data/220509-30-for-bud-count-Rei/sg-Itgb1-time-course/'\n",
    "\n",
    "files = glob.glob(os.path.join(datasetFolder, '*fileNameRecord.txt'))\n",
    "\n",
    "datasetPrefixes = []\n",
    "for f in files:\n",
    "    datasetPrefixes.append('-'.join(os.path.basename(f).split('-')[:3]))\n",
    "\n",
    "sgRNAs = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "dfs = []\n",
    "for datasetPrefix in datasetPrefixes:\n",
    "    dfs.append( get_annotated_countDF(datasetPrefix, datasetFolder, sgRNAs) )\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df_Rei = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['file_name', 'scrambled_file_name', 'counts', 'sample_id', 'sgRNA',\n",
       "       'time_d', 'groups'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>file_name</th>\n",
       "      <th>scrambled_file_name</th>\n",
       "      <th>counts_Kaz</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>sgRNA</th>\n",
       "      <th>time_d</th>\n",
       "      <th>groups</th>\n",
       "      <th>counts_Rei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plate07152021_WellD02_ChannelMono_Seq0000.nd2</td>\n",
       "      <td>7c0bdcf17473df8ac66bffb380235882.nd2</td>\n",
       "      <td>12</td>\n",
       "      <td>plate5_1_01</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plate07152021_WellD03_ChannelMono_Seq0001.nd2</td>\n",
       "      <td>a3c93853407b0271087d2a9fc3d1d9a3.nd2</td>\n",
       "      <td>2</td>\n",
       "      <td>plate5_1_02</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plate07152021_WellD04_ChannelMono_Seq0002.nd2</td>\n",
       "      <td>62df7a46ec86a9af8b3f84077cfec6b5.nd2</td>\n",
       "      <td>9</td>\n",
       "      <td>plate5_1_03</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Plate07152021_WellD05_ChannelMono_Seq0003.nd2</td>\n",
       "      <td>107cc7d771fde6944cfac121e3a55378.nd2</td>\n",
       "      <td>4</td>\n",
       "      <td>plate5_1_04</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plate07152021_WellD06_ChannelMono_Seq0004.nd2</td>\n",
       "      <td>96a4a80dd34cb85d2558bef101af734d.nd2</td>\n",
       "      <td>7</td>\n",
       "      <td>plate5_1_05</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       file_name  \\\n",
       "0  Plate07152021_WellD02_ChannelMono_Seq0000.nd2   \n",
       "1  Plate07152021_WellD03_ChannelMono_Seq0001.nd2   \n",
       "2  Plate07152021_WellD04_ChannelMono_Seq0002.nd2   \n",
       "3  Plate07152021_WellD05_ChannelMono_Seq0003.nd2   \n",
       "4  Plate07152021_WellD06_ChannelMono_Seq0004.nd2   \n",
       "\n",
       "                    scrambled_file_name  counts_Kaz    sample_id       sgRNA  \\\n",
       "0  7c0bdcf17473df8ac66bffb380235882.nd2          12  plate5_1_01  sg-Control   \n",
       "1  a3c93853407b0271087d2a9fc3d1d9a3.nd2           2  plate5_1_02  sg-Control   \n",
       "2  62df7a46ec86a9af8b3f84077cfec6b5.nd2           9  plate5_1_03  sg-Control   \n",
       "3  107cc7d771fde6944cfac121e3a55378.nd2           4  plate5_1_04  sg-Control   \n",
       "4  96a4a80dd34cb85d2558bef101af734d.nd2           7  plate5_1_05  sg-Control   \n",
       "\n",
       "   time_d        groups  counts_Rei  \n",
       "0       1  sg-Control_1          10  \n",
       "1       1  sg-Control_1           3  \n",
       "2       1  sg-Control_1           9  \n",
       "3       1  sg-Control_1           4  \n",
       "4       1  sg-Control_1           6  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two data set and compare the countings of Rei and Kaz\n",
    "df_Kaz.rename(columns={'counts': 'counts_Kaz'}, inplace=True)\n",
    "df_Rei.rename(columns={'counts': 'counts_Rei'}, inplace=True)\n",
    "df = df_Kaz.merge(df_Rei, on=['file_name',\n",
    "                              'scrambled_file_name',\n",
    "                              'sample_id',\n",
    "                              'sgRNA',\n",
    "                              'time_d',\n",
    "                              'groups'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exclude the samples where the sample is outside\n",
    "# the field of view or severely off-focus\n",
    "samples_to_exclue = ['plate5_1_09',\n",
    "                     'plate6_1_04',\n",
    "                     'plate6_1_12',\n",
    "                     # 'plate6_2_01', # partial view\n",
    "                     # 'plate6_2_02', # partial view\n",
    "                     'plate6_2_04',\n",
    "                     'plate7_3_10'\n",
    "                    ]\n",
    "df = df[~df.sample_id.isin(samples_to_exclue)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate average counting data\n",
    "df['counts'] = (df['counts_Rei'] + df['counts_Kaz']) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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KCgoGfQC8fv36Do/t27cvbhBcX1+vX/3qV1E/Z8eNG6cbb7xRksJ/VwQCAb300kvauHGjKisrw32fe+45rV69ulfrC69du7bH5wDtNmzYoK1bt4b3Dxw4oHvuuUeTJk3ST3/6U9XV1UmSjh07pgMHDuiRRx5hVB8wyL311ls6f/58eL+srEyrV6/u8OZSoD8xXAcAAPTY22+/Hb5Q0+7AgQOdTrMJAOie0tLScADcrr6+XidOnIhqM8YMZFkpp6WlJXzh1uv1qqSkJKbPokWLtHDhwpiLue0jgQAMHsOHD1dRUdGgD4C7MmXKFHk80WNOPB6PfD5fzI02p06d0sWLFyUFR1u2q6ioiAqApeCoqkOHDvVT1UB8DQ0NUdObS8H/q5s3b9Z7770X/hzRrrq6Wvv27RvIEgEkmPPnz0cFwFLwBid+h8EpjAQGAAA9Vl1d3WE707QBQO+tW7dOPp8v6mJ4u+eff15S8EK4FFxj6t577+00DM7IyOh0qrLBqK2tTa+//rp27dolv9+vUaNG6e6779batWs1ZMiQ8PfT7Xbr5ptvlsvl0kMPPaQ333xT586d08iRI7Vy5Uru5AeAOHJycnTzzTdr3759unjxooYNG6aSkhK98847cftXVlZqz549USOBO/pb48rADehvly5dirn5WQr+3RtvGYn2YwAGr45+V/E7DE4hBAYAAD02bty4mLsYXS6XioqKHKoIAFJDc3NzOOTtjpaWln6sJjW9++672r59e3i/oqJCzzzzjL761a/q5ptvVlpamlpbW+V2u8Nr3RcVFWndunVOlQwASWX48OFauXJlVFtBQYHKy8uj2owxKi0tVW1tbbjNWqszZ87Efd0RI0b0fbFAJwoKCpSRkaHm5uao9rFjx2r8+PE6cOBAzDljx44dqPIAJKDhw4f3qB3ob0wHDQAAemzVqlXKzs6OaluxYoVyc3Mdqij5FBQUKDs7W16vV5mZmcrMzFRWVpays7PDD2NM+NHelpWVFe7r9XrD7YzARqIIBAI6ePCg3nzzTR05ciRm6kd0LiMjQ+np6fJ4PHK73eGHx+ORy+WS2+2O6u92u5Wenq6MjIwOH4gWb5rGuro6nThxQjU1NeERP/FGYwMAemfu3LnKzMyMapsyZUpUANyupqZGkydPjmrLycnRrFmz+rVG4Erp6elas2ZN1KwrGRkZWrNmja655pqYwHfq1Kn8PwUGuWHDhqm4uDiqLTs7W3PmzHGoIgx2jAQGAABxFRQUxKxJGclaq8bGxvD+97///U5fz+v1smZwBJ/P1+n3N15/ING1trbqiSee0OnTp8NtU6dO1QMPPBAeUYnOtU/d3NbWptOnT+vSpUsaNmyYxowZo/Xr16u+vj7mnJUrV2r06NEDXWrKOXbsmH71q1+FQ2C/36/jx49rypQpDlcGAMkvNzdXa9euVXl5uZqamlRUVKSsrCyVlpbG7b9w4UJNnjxZ58+fl9fr1fjx41mPGY5YsGCBxo0bpw8++EDp6emaM2eOcnJyJEkPP/ywDh8+rMrKShUVFam4uLjTZToADA4LFy7U+PHjdf78eWVlZWnChAn8DoNjCIEBAEBchJT9y+v1yu/3x4ySNMbI4/HIGBP1Pc3KylJra2vM67SPEvR6vf1eM9CVPXv2RAXAklRaWqpDhw5x53MPuVwujR8/XuPHjw+3TZo0Sfv374/q5/V6VVhYONDlJbWSkhJt3Lgxqs3r9erAgQNRP5OttXrttdf0pS99aaBLBICUlJ6ermnTpkW1FRYWqrKyMqpt3Lhx8ng8Kiws5HccEkJBQUHcmZfcbrdmz56t2bNnO1AVgETG7zAkCkJgAAAQV3dCxciQsqv+hJTRqqqq9IMf/CDuFHif+MQnVF1drdtuu02tra3KyMjQpk2b9Nprr8X0LSoq0iOPPDIQJaeMy5cva+PGjTpx4oRyc3O1bNkypm3rI6dOnYrbfvr0aULgPjBr1iw1NTXpxIkTCgQCGjZsmK6//vqYKaLRuWXLlqm+vl67du2S3+/XqFGjdOONN+r555+P6VtVVaWmpqaYKUwBAD1XVlamw4cPq7m5WaNHj9a8efO0dOlSbdu2LdzHGKNFixY5WCUAAHBaS0uLNm/erIMHDyo9PV3XXnutrrvuOhljdPLkSfn9fknB2ZsqKio0atQohytOXITAAAAgru5M3ZydnS2fzyev16uGhoYBqCq1DB8+PG4IvHv3bpWXl4fXowwEAjpy5EiHr4HuCwQCeuKJJ1RdXS0puA7oc889pwceeCBm3R70XEf/H/l/2jfcbrcWLVqk+fPny+/3Kysry+mSkpLL5dLtt9+um2++Wc3NzRoyZIhaWlqUnp6ulpaWqL45OTlKT093qFIASB1lZWXaunVreP/48eOqqanRbbfdplWrVulHP/qRAoGA3G4369kDADDI/fa3v9Xhw4fD+6+99ppaW1s1Y8YMPfnkk+EZnNra2vT444/rS1/6koYMGeJUuQmNEBgYhHw+n06fPq28vLx+m5bi3Llzqqur0/jx4xk5AaSw9g9d7WElemb58uUqLy8Prz8pSZMnT9aHH34Y0/f06dMaN25c1EjL9PR0LVu2bCBKTRnHjh0LB8CRtm/fTgjcBxYuXKhdu3aprq4u3DZ8+HCVlJQ4WFXqSUtLY02pPpCenh4OeNt/nr755ptRfVasWMF61gDQB44ePRrTdvHiRVVVVUXdLMbfFb0XCARUXl4uY4wmTJjA7y8AQMJYv359t/vW19frxRdfjGk/efKkJkyYoCNHjoQ/L1hrtW/fPj322GN9NjX/2rVr++R1EgUhMDDI7NixQxs2bAhPmTBjxgzdd9998nj65sdBc3OzfvWrX6msrExS8ILanXfeqblz5/bJ6wNIDI2NjXrmmWeipl85fPiwZsyY4XBlyWXixIn6sz/7M+3YsUMNDQ2aNm2aJk6cqB/96EcxfVtbW3X//fdr//79OnHihIYOHarrrrsu7tpU6FhHa1ezpnXfyMnJ0ec//3lt375dlZWVKioq0nXXXceIHvSbnlxM6I7IGyRdLpcqKir69D1S7YICAHRXc3Nz3PaKigq9++674ZsiA4GALl++rNzc3IEsL+lVVFTomWee0eXLlyVJw4YN0wMPPMDfCgCApNPRZ4bm5mY1NTX16BxI3BIGDCI1NTV69dVXw6GNJB0+fFjbt2/vs/d46623wgGwFJy///e//z0X14EUs2nTpqgRqdZa/fa3v+VDVy+MHj1ad999tx544AEtWrRIBQUFGjFiREy/CRMmKCcnR0uWLNG6deu0du1aLur0wtSpU+OOimAUcN/Jzc3V6tWrtW7dOq1cuZL1wJFUxo8fH15j2RjjcDUAkDqKiopi2txut8rKytTY2BjVvmPHjoEqK2X87ne/CwfAUnCUdbxRVAAAJLphw4bFvY5QVFSksWPHxj0n3ucMBDESGCnh3Llz2rlzp3w+n4qLizV//nymvYnj2LFjcadWOnr0qGbPnq0dO3aourpa48aN06JFi8Kjdg4fPqz9+/fL5XJp/vz5mjJliqTgqKnt27eroqJCI0eO1OLFi+NO8eT3+3X8+HFGA8fh9/u1a9eu8Ki+9gAISHTx1qdtaWnRhx9+qOnTpztQUWq555579Mwzz4T3jTG68847HawodeTm5mrt2rV65ZVXwjdFTZ48mWm1AQAA+tHcuXNVU1OjCxcuSAoGwCUlJdqzZ09M38rKSrW2trL0QTfV1tbq/PnzMe2nTp1SXV2dDh06FPeaQ2VlpXbu3KnLly9rypQpWrBgQZ/NEgcAQG+5XC4tWbJEf/zjH8ODTXJzc3XttdcqOztbFRUVUf1nzpypUaNGOVFqUuA3O5JeWVmZnnrqqfDUQYcPH1ZZWZnuvfdehytLPFlZWXHb29ra9JOf/CQ8WvfIkSM6cOCAPv/5z+vdd9/Vpk2bwn3379+vO++8U7Nnz9bPfvYz1dTUhM/Zu3dvh+v/Mgoovl/+8pc6fvx4eP/999/X5z73OX5xIeF5vd6oO80j23H1ioqK9LWvfU3f/OY35ff75fF44o4ORu8sWLBAM2bM0MmTJzV06FCNHj3a6ZIAAABSWkZGhm699VZduHBBTU1NKiwsVFtbm95///2Ym9U9Hg839vdARkaG3G53+LpYu7S0NP36179WeXl5uK39mkMgENDjjz+u1tZWScFrOseOHdO6desGtHYAAOIZOXKk7r77blVWVsrtdquwsDA8U9N1110X/r3ndrt1zTXXOFxtYuMTFZLeW2+9FfNBd//+/aqqqnKoosQ1c+bMmHV1jDFyuVwx0zVXVFRo7969+uMf/xjzOps3b9bu3bvDAXC72trauAFQQUGBJk+e3AdfQWr58MMPowJgKTiS8p133nGoIqD7rrvuupi2oqIijRs3zoFqUpPb7Q5f/GJK0r7n9Xo1Y8YMAmAAAIABNGLECI0dO1bp6enKzMzUxIkTY/pMnTo1PDU/upaZmamSkpKY9jFjxkQFwNJH1xzeeeedcADc7tixY1FL/iCooKBA2dnZHT6MMeFHZ/2ys7OZ+Q0AesDj8aioqEgjR46MuS7Wvs/1sq4xEhhJr7q6usN2PlxFS0tL02c/+1lt3LhRH374ofLy8nTjjTdq165dcfufOnVKLS0tMe11dXVxpxqSpKFDh+qee+7Rli1bVF9frylTpuiWW27hB3Icnf3fBRLdNddcI2utvve970kKTtXCXeMAAAAAemLx4sVRs5a1L0OF7isoKFBDQ4Pa2trU1tYmSeGbSdsHTbQvgyIpPM12vOXCfvjDH+rixYv9XXJS8fl8MQMnOusLAEAiIQRG0hs7dqw++OCDqDaXy8Vi4B3Iz8/XJz/5yai28+fP69ixYzF9Z8yYoWPHjqm+vj6qfcSIEZo8ebL27t0bc87YsWNVUlIS9y5UROtoIfuO2oFEs2DBAqWlpam1tVUej0fZ2dlOlwSEFRQUdHkRJvJ4Z1OZe71eZhgBAADoB263W/Pnz5fH41EgEJDL5WIq6B7y+XxqbGzsdv8rRwBHal97ER/pasmj7v5N0Z3jAAD0NUJgJL1Vq1apvLw86kPX8uXLY6Y9RsfuvfdeXb58OeouUJfLpX/+53+WtVaBQCDqj4TMzEwZYxQIBMJ3mUaek52dPegvlldXV+uDDz5QWlqa5syZEw7H2tradPjwYVVVVWn06NFauHCh3nvvvfB5Q4cO1Y033uhU2YNeTU2NDh06JI/Hozlz5ignJ8fpkpBE1q9f36evV19fH/7Z6/f7+/z1165d26evl2h6csd+e38AqK+vD3++jfxsbK3VmTNndPHiReXl5WnMmDGEFACAhNAeLFpro0YCt1+3CQQCUSOB20de+/3+qN91brdb2dnZqq2t1cGDByVJs2fP1tChQwfqS0kIV379XV3fys7Ols/nk9frVUNDQ6d9a2pq9Mc//pFrDgCAAUMIjKRXUFCgL3/5y9q/f798Pp+Ki4s1ZswYp8tKKo2NjZ3eCXqlpqamTo8P9qmf9+zZoxdffDH8x9SmTZv06U9/WiNHjtSTTz4ZtcbOtGnT9JnPfEYffvihcnNzNXfuXGVkZDhV+qC2b98+vfDCC+E/mjdt2qQHH3xQ48ePd7gyDEYnT57Uu+++G/7/GAgEVFFRoVGjRjlcWfLozl32PRkJDCD1dfSzt6CgQG+99ZYqKirCfQsLC7Vy5UrWrAQAOK6qqkqHDx/W888/H57+2ePx6FOf+pSmTJmiDz/8UDNmzFBzc7OysrLCn4Gbmpq0f/9+1dXVacqUKZowYYKOHTumf/u3fwu/zsaNG3X//feruLjYsa9vIB09elTPPvts+OvftGmT7r//fk2bNu2qX5trDgAAJ3DrMlKC1+vVddddp5UrVxIA94LX6+3ycTX9B5OWlha9/vrrUXfTNjc36/XXX9eePXuiAmBJOnbsmBobG7Vq1SotXLiQANghfr9fr732WtTI9paWFr322msOVoXBKhAIaNeuXTFrdHW0fjviq6qqUkNDQ6eP9t9X7Xftd/QY7LNbAINBZz97T548GRUAS1JlZaXKysoGskQAAOJqa2vTK6+8Eg4upeDfuK+88oqstZo4cWL4pqXIm/YzMzO1aNEirVq1ShMmTJC1NuZ1AoGAXn311bjrB6eaeF9/5PfxanDNAQDgFEYCI2X5/X69/fbbOnDggNxut6655hotWbJk0I9Sjac7F7d7Mr3NYFZVVRV3pPSpU6c6nELp1KlTam1t1fbt29XY2Kji4mKtXLlSWVlZamho0KZNm1RaWqqcnBwtXbpUs2fP7u8vI2Ht3r1bO3bsUFNTk6ZPn65Vq1YpIyNDdXV12rRpk06cOKEhQ4Zo6dKlmjVrVoev8/7770d9v2fOnBl3KtizZ8/K7/fL40mNX5d9PZ1wWVmZWlpaJAX/gP3Nb37TpzcypPp0xR1paGiIu6ZXbW2tWlpalJ6e7kBVqaWurk4bN24Mz4IReTEGwODU2c/eKwPgdlVVVZo6dWp/lwYAKS0QCOjgwYPh6Yrb2tpkreXaTQ/U1tbq8uXLMe3V1dU6f/68tm3bFvdz76FDh7RlyxbV1dVp8uTJWrRokS5evBjzOhcvXlRdXV3KL7t2+fJlXbp0Kab94sWLOnfunHbu3Mk1BwBA0uE3DFLWiy++qH379oX3N2zYoKamJq1atcrBqpDq8vLy5HK5YgKF/Px8DR8+PO45lZWV2rZtW3h/x44dqqio0MMPP6wnn3xS58+flxT8w+7555+XMabTPzZS1c6dO/Xyyy+H97dv367Kykp9+tOf1pNPPhm+maG2tlbPPfecPvWpT2n69Okxr7N79269+OKL4f0dO3bo5MmT8ng8UeskScE1mvljLL6ysjJt3bo1fEe0tVabN2/WmjVrHK4s+WVlZcX9/5iZmcn/xz4QCAT0+OOPq7q6Ovz/NxAI6NixY30yzRuA5NTZz96ObuQbMmTIQJQGACmtPVhr19bWpr1792r+/PnOFZVkcnJylJGRoebm5qj2zMxMPfvss7p48WLM595AIKDnnnsu3HfPnj0qLy9XZmZmzI3tmZmZg2LGt+zs7A6//l//+teqqamRxDUHAEBy4bcMkorP59OZM2c0bNgwjRgxIurY2bNn5fP5NH78eLW2turAgQMx5+/YsUMrVqyQyzV4Z0Jva2vTqVOnZK3V+PHjB/X3oj9kZ2dr8eLFUaHu97///XAo7Pf7o+5qNsbIWhv1h0D7HwBf/OIXY8LktLQ0jR8/flCGwJHf03ZlZWXavn173NHs27Zt0/Tp03Xp0iVVVVVp1KhRGjJkSNzXqaio0IwZM3T48OGo9hUrVvRZ/anmyu+VFLzT/MKFCzE/n9EzaWlpmjlzpvbv3x/VPmfOHH5m94HS0lJVV1dHtVlrtX37dkJgYBBLS0vTxIkTtWfPnqgpH+fMmRNeIzFyNhyv18soYAC4Sk1NTXGn1j927Jjmzp3LuuvdlJaWpmXLlmnTpk1R7ZMmTdIHH3wQ1db+uTdyyuN2NTU1mjlzZsw5N95446AIKj0ej2644Qa98cYbUe3xvo/SR9ccIm+Mbm+/EtccAABOSf3f4EgZO3bs0IYNG8Jh2ezZs3XPPfeopaVFv/zlL3Xy5ElJwTv0VqxYEXdqx6amJgUCgUF7Eb2qqkrPPPNMeHqfvLw8fepTn9LIkSMdriy1rFmzRqNHj9ahQ4eUlpamtra2uFNEd+TKO0OvNFin4+7o674yzGlXX1+vl19+We+9956stXK5XLrhhhviTsEkSddee61mzZqlgwcPyuPxaMGCBZoyZUqf1Z9qrrzLvF1P/q+jY3PnzlVubq6ef/55SZLL5VJxcbHDVaWGjn6WDNafrQCC3nvvPR0/fjw8DakU/bP31ltv1ZEjR3Tx4kUNHTpUM2bMUGZmppMlA0DSa21tjbvWamtrq9ra2giBe2D58uUaMWKE9u3bJ5fLpXnz5qmhoSFueNnQ0BA3BJakGTNmaN68edq7d68kqaSkRDNnzuzX2hPJDTfcoOHDh4dnFpw3b56amprifh/brzm0X8Px+/3atGkT1xwAAAmFEBhJ4cKFC3r11Vej/jg4ePCgxo0bp+rq6nAALAUDiE2bNik3NzdmTZSJEycqLS1twOpONL///e+j1ne5dOmSXnjhBf35n/+5g1WlHmOM5s2bp3nz5kkKjugNBAIxf9y6XK7wdEDt66pKwbtPjTFyu90x56WlpQ3aIKi4uDhqincpOHXj0qVLtXv37pgbPzIyMrRz587wfltbm95++20VFRWpvr4+pu+ECROUnp6ukpKS/vsiUkhRUZFKS0uj2jwejwoLCx2qKPVMmDBBbrdbfr9/0N681B+mTJkSd9p+RgH3TEFBQYcXuCRFHetq+kCv1xt3RgegP6xbty7mRqa2traonwntn71aW1t13333dfp6GRkZevrpp/u+UAAYBHJycuJeuykoKBjU1256a9asWVGzhtXW1nb4uTcQCKiysjKq3e12a/LkyRoyZIhmzJgxIDUnopkzZ0YF33V1dXG/j+3XHCJHAnPNAQCQaAiBkRSOHTsW9+7QI0eOhNfkiNTS0qKlS5dqx44d4YuQw4YN09q1a/u91kTl8/l0+vTpmPZz587p4sWLOnHihEpLSzVkyBAtWrRIBQUF4T5HjhwJ39kY+aHX5/Np586dOnfunEaOHKnFixcrOztbklReXq49e/bI7/fH/CEy2JSWluoHP/hB3GNf//rXtXv3bt18880KBALyeDz61re+pU984hOaOXOm9u7dq5deein8/R83bpxuuummgSw/YaxevVrnz58Pr5GckZGhT3ziE8rPz9fatWv1yiuvhL9PEyZMCP9fvNLIkSNlrdW5c+ckSenp6fr4xz+u9PT0gflCUkRJSUnMz9/rrruO7yMS3tChQ3XHHXfotddeC7e1zxSA7vP5fJ2GwFf2BRJFc3Nzh7NZdNQfANA/jDFasmSJ3nrrraj2xYsXO1RRaon3uddaqyFDhmju3Lk6ffq0ysrKVFFRodraWi1btkyNjY2seX+FIUOGcM0BAJC0CIGRFDqaai0rK0uZmZmqra2NOTZ58mTdcMMNOnHihNxutyZNmjSoR1J5PJ7wqNNIbrdbL730kk6cOBFu27Nnjz772c9qzJgxeuedd7Rx48aoNW13796t2bNn6+c//3l4Kt7Dhw9r7969euSRR3T8+HH99re/DQf3Bw4c0PLly7Vq1aoB+moTS0ZGRty7RtPS0vSb3/xGH374YVT7unXrNGHCBEnBqYeKi4v14YcfasiQIRo7duxAlZ1whgwZoi984Qs6efKkGhsbNWnSJGVkZIRHo1lro6ZvDAQCCgQCMestu91uuVyucF9jjB599FFJjEbriczMTN1222366U9/qkAgILfbHf5/CyS6hQsXaubMmXr00Ufl9/vl8Xi4KNNDXY3u7elIYGCgZGRkxLRdORI4corMeP27ej0AQPcNHz5cd999t5566qnw3xVDhw51uqyUsXDhQmVmZupb3/qWpODfvy+//LLOnj2rhx9+WI899piqq6s1depU1dTU6Cc/+Yk++9nPDuprD/EsWLBAM2bMUHl5efjazKuvvhq377Bhw3TXXXfp1KlT8vl84WsXAIDuizeDU6T2Y83Nzcze1AVCYCSFWbNmaePGjVHTqRhjtHjxYtXU1OjFF1+M6j969GiNGzdOxphBO3XuldLT03XNNddETY8rSWPHjo0KgKXg1Hdvv/227r33Xv3xj3+Mea0333xTzc3NMWuxXrp0Sbt379auXbtiRm5v2bJFS5YsUVZWVh99RckjMzNTJSUlev/996Pax4wZEw6AjTHh9p07d0aFaVlZWYNqDZ7OGGNigsaejEbz+/1drrmMnmn/vxv5fxhIBtnZ2fy/vQpd3TCTnZ0tn88nr9fLestIKPH++K+trdVrr70WDn/bZ2e58847lZOTM9AlAkDK6epCrhS8DiEFZ3bjYm7vtX+fI0PHHTt2xPR7//33NXXqVFVWVkbNBOf3+/X222/rgQce6P9ik4zX6426NnPttddq165dUX0yMjI0f/58GWM0fvz4gS4RAFJGT2ZwYvamzhECIymMHTtWDQ0N4fVRjTFyuVz653/+Z0nBu/ebmprC/bOysvTXf/3XHb7eYB3td9tttykrK0t79+6VtVYlJSXKz89XeXl5TN8LFy6orq4u7g/Ruro6VVRUxH2P8+fPR6073M7v96u2tnZQhsCStHbtWuXk5Gjfvn0yxmj+/PnKzs6OGQUsBb/36L7ORpBd+bMhMzOz0xkBGI0GDA6nT5/W+vXrwxcbI0f9ARh8hg4dqpUrV2rXrl3av3+/WltbNWrUKO3du1eLFy9mXUoAuEpMxd//Ghoa9OKLL+ro0aMyxmjGjBm66667lJmZGfcag7VWx48fj/taXJPonsLCQj344IP6zne+Iyk4I9lDDz2k3NxchysDgOTX1QwKkZ8VmL2pc4TASAo+n0+NjY3d7t+TvoOJ2+3WqlWroqZlrq6uljEmZuTu2LFjlZeXp+zs7JgRPCNGjNCkSZO0d+/emPcYP368qqqqYkLizMxMDR8+vA+/muTi8Xh0yy236JZbbgm3dRSkjxkzZqDKSgmMRkOy6s6ICKa36XvNzc16+umnoz4rBAIB7dq1S9dee62DlQFwUmFhoTIyMqKmv2y/UXLZsmVOlQUAKaE7F1+5mHt1XnjhBR07dkxSMOA9dOiQjDH65Cc/GXdqZ7fbrZKSEu3Zsyfu9SB0z8SJE+XxeNTS0iKPx6OioiKnSwKAlMD1rb5DCIyk0J3Reaw91zvDhw/X9ddfr61btyoQCKiyslIej0czZsyQ2+3WmjVr9MILL4T7G2O0Zs0aTZo0Se+//37USNaxY8dq/vz5GjFihJ555pnwCCtjjG699VZGUVxh1KhRWrhwod57771wmzFGy5cvd7CqxNbU1KSDBw+qsbFR06dPj5q26uzZszp+/LiGDBmi2bNnd/r/LfJ1iouLVVhYGD527tw5lZaWKicnR7Nnz2adUPQbRkQ44+jRo3FvFtu7dy8hMDCI+Xy+uDfonTp1Krx2OLqnsbFRBw8eVHNzs6ZPn64RI0aEj505c0YnTpxQbm6uZs2axd8HwCDBhdz+1dDQoNLS0pj2Dz74QM3NzZo1a1ZM0Lt8+XJNnDhRS5Ys0ZYtW8LtOTk5WrFiRX+XnDLa2trU1tYWft4+eyEAAImCv2SRFLozdTOj/XpvzZo1Gj16tB577DEFAgGNGDFCzz77rFatWqXly5erqKhI3/ve98IXwKZNmyZJ+vSnP63Dhw/r3LlzGjlypGbNmiW3261Jkybpy1/+cng6vdmzZ0eFbPjI2rVrNXv2bP3jP/5j+Ps7dOhQp8tKSJWVlXr88cfDN3xs3LhRt99+uxYvXqw33ngjav3qzZs363Of+1zcaZguXLigxx9/PLzG+BtvvKE1a9ZoyZIl2rx5szZv3hz1Og8//LDy8vL69WvD4MSICGe0X6TpbjuAweHKi+NdtSO+iooKPfnkk+HPa2+88YY+9rGPaeHChdqwYUNU0PDWW2/p4Ycf1pAhQ5wqFwAS0vr163vUv7GxUUeOHIlpN8bo0UcfVWlpafj3WVtbm0aOHKm6urrw+4wfP17nzp1TZmamJk6cGPWzujvWrl3bo/6porW1VU899ZT8fr+k4DJov/rVr3T//fd3ugwVAAADiRAYgCTp2LFjGjVqVFTb5s2bwyN73W63JEXd0eh2uzV79mzNnj075vWGDh2qG264oX+LThGTJk2K+/1FtD/84Q9RI/6ttfrDH/6goqIivfvuu1F9L126pLfeekt33nlnzOu88cYb4QA4sm3cuHF66623otpra2u1efNmffzjH++7LwQIYUSEM6ZPn66MjIyYkdUlJSUOVQQgEWRnZ6uwsFCVlZVR7WPHjmUUcA/E+7y2YcMGFRYWxoQKNTU1euedd3THHXcMdJkAkFKysrI0cuRInT9/Pqo9Ly8vPEK4/VqDMUbnz5/XuHHjwv0KCwu5cb8X3n///fDSEe2OHDmiw4cPa9asWQ5VBQBANP6aheNqa2u1adMmlZeXKy8vTzfccIOmTp0qSTpw4IC2bdumhoYGTZ06VStXruyzqZx3796tnTt3qrm5WTNmzNCKFStSZtrXnt41Kkmvvvpq1AWbdk888YTq6+vV0tIiKXin40svvSRjjC5evKj9+/erpqZGeXl5mjt3bq/W/U22u0ZbWlq0efNmHT58WBkZGVq0aJEWLFjgdFmqqqrSm2++qbNnz2rEiBG66aabov6wS3YnT56MaWttbdXu3bvjjtI5efKkzpw5E3VXbmVlpU6dOhXTNxAIxF0LqaP3HSxYsxapKDMzU5/61Kf04osvhtvcbrcWLVrkYFUAEsHSpUu1ZcsWVVZWyhij0aNH87Ohh+J9bmppadHevXu73R8A0HNLlizRtm3bVFFREf4dVlBQoIsXL8b07c5se+haR7/DTp48SQgMAEgYhMBwlN/v1y9+8QtdunRJUnD0Xnl5uT772c+qrq5Ov/71r8N9d+7cqbNnz+rzn//8Vb/vzp079fLLL4f32y/2PPjgg1f92slqyJAhcUPg48ePq7KyMmrqoH379mnatGl64403wuv++nw+VVZW6o477lBOTs6A1j7Qnnvuuaj1dl588UX5/X4tXrzYsZoaGhr0i1/8IvxveOnSJX344Yd65JFHUuaO3vz8fJ07dy6qzRijCRMmaPfu3TH909LS9Pjjj0etz/OLX/xC2dnZcaeMnzBhgnbt2hX3fQcr1qxFIunNDU6dmTRpUtQ0bZGfC/pCst3gBEDyer265ZZb5PP55HK5lJmZ6XRJSSc/Pz9mJJoxRuPHj+dzFgD0I6/Xq1WrVqmxsVHGGGVmZsZd615Syl+z6Upf/V1x5MgRHT16NHy9zFqro0ePKjc3N3wz+tXibwoAwNVigQI46vDhw+EAuJ21Vtu3b9e2bdti+p85c0YnT55UW1ubysvLderUqW6t03Xp0iUdP348HPxs3bo1pk9paemgvhty9uzZMVMRjxw5Mu735OjRozp27Fg4AG7n9/ujwtFUdOHChbhfY/v/14aGBh0/fjzm/7UknT17VmVlZTF/DDQ3N3e4FmVVVZWOHz8eE675/X6VlZWFQ9F9+/bFhPh+v1/vvfdet7+2RHfTTTfF/B8tKSnRvHnzNGnSpKh2j8cjr9cb83+0sbFRw4cPj1mfZ86cOSopKQnPQtDO7Xbrxhtv7MOvIrlkZGR0+bia/oCTjDFMwQ8gLq/XSwDcS/E+r82fP18lJSWaMGFCVLvH49GyZcsGsjwASHlZWVnh32EjR46Me1M4o1Q7Z63V/7+9/46P67rv/P/XwQAsYO+9kyoURUtUtaplS3JT1vEm2Z9jbbLOOt5NnHXKbvpmN5tvymadTXOKs6m7SewkSuzEvcmyZUu2rC5RlCiKFIsoimKRWEGQKOf3x70DDoABSZBzZuYOXs/H4/MAcOfO4M4bZz64wL1z7r59+wa9IaKa1atXD9tfmDx5MsuXL0+8hZIknTvfCayGGnpdzsrlI922fft2PvnJTw4cZJs9ezbvfe97q64bY+Szn/3swHSxpVKJ2267req7ACE7gDdnzpzRP5EWMH/+fO644w62bNlCd3c3CxcuZP78+Xz+858ftm5PTw8nTpyo+jjd3d2pN7Whjh07xpYtW4Ytb29v58Mf/jBPP/00/f39hBBYtWoV11xzDSdPnuT+++/n4MGDQDYV6Y033si8efPYvn07jzzyyMDBylOnTvGpT32KGCMPPPAAe/bsAbJ3tV577bUsW7aMvXv38uCDDw4cGJ49e/aga/1U6u7ubpnrrF1yySW8733v45FHHqG7u5uLL76Yq666CoD3vve9PPzww2zbto2pU6dy3XXXVT3ZA7J3nJQfp6uri4svvpirr74agPe85z088sgjbN26lcmTJ3PdddexcOHCuj3HZuPUzZIkaTTWrl3LD/7gD/Loo48OXHZnw4YNhBC4++67efjhh3nxxReZNm0a1113HfPnz2/0JktSywohcOutt7Jlyxb+8R//EchOdD6fy3iNFYcPH+b+++8f+J/k5MmTufXWW5k2bdqwdTs7O7nzzjv527/9W/r6+mhra+OOO+5omUvNSZJagweB1VBr1qzhS1/60rAz69asWcORI0d45JFHBi1vb2/niSeeGPQuywMHDvCZz3ym6uM//fTTg6Yd6+vr495772XBggXDppWdOHEiixcvvsBnVGyzZ89m9uzZA1/HGJkyZQpHjx4dtN6cOXNYsmQJL7744rDHaPUDZosXL2bcuHED10gumzJlCk8++eTA1zFGtm7dypw5c3jllVcGDgBDdmD2wQcf5M477+Q73/nOoHcBxxh59tln6e/vHzgADNmB94ceeojZs2cPOgAM2WtgpHcSt9rPY9myZcPeRQLZQfIbb7xx0LtJVq9ezdNPPz1s3TVr1rB06VKWLl067Lb29nbe+MY38sY3vrG2Gy5JkjQGzJkzp+olZipV3t7Z2Tniep2dnWN6piZJqoWOjg4uu+wySqUSvb29zoRzFg899NCgN6UcO3aMhx56iLe+9a1V1588efLATGNtbW1MnDixLtspSdK58iCwGmrWrFnccccd3HvvvQMHsVatWsX1119PT08PL7/88sCBsPb2dt70pjdx7733Dnuc7du3D9y/t7eXL33pS1xzzTU8//zzVb/vwoUL6e3tHfinwrhx43j3u99Ne7sviUonT55kxowZPP/884MO1Jff3drX18eePXuYPn06U6ZMYeXKlSxZsqSBW5xe+SDhgw8+ODCt87Rp05g1axavv/76sPV3795d9To83d3dPPfcc1UP3u7evbvqlEN9fX0899xzVa+7euzYMS699FI2b948cN9ly5aN6WmILr/8crZt2zbwdQiB66+/npUrVzZwq1rL66+/ztatWzl58iSLFi1i+fLl/lOhhvbs2cOOHTsIIbBy5UrmzZvX6E2SJOmMjh8/PuKMQdWc7YCxzt3x48d5+OGH2b9/PwsWLOCaa65xWnNpjDt06BBbt26lu7t7xBPHdVpXV9egE/jLDh48yPHjx3nllVd45ZVX6OzsZM2aNUydOnXEx/JvZUlKp7u7my1btnD48GFmzpzJmjVrnIXhDDzipYa74YYbuPzyy9m1axfTp09n0aJFQHa24gc+8AF27drF8ePHWbFiBf39/dx3333Ddl7b29vp6+sDoL+/n29/+9s8/vjjI76zd/bs2dx1113s2LGD7u5uVq5c6bUqh+ju7ubLX/4yx44dGzRNW1tbGwcPHuQ73/kOpVKJuXPn0t3dzerVq7n22msbuMX1s2jRIt797nezd+9eOjo6mDdv3ognHIwbN46Ojo5h16YFmDRpUtX7jB8/fsQ/0EZ6t8S4ceO48sorWbNmDQcPHmTq1KnMmDHjHJ9Rawoh8O53v5uOjg56e3tpb2/nbW97W6M3q2Xs27ePr33tawO9d9euXezdu9d3UdfIs88+O2h2ge3bt3PdddexatWqxm2UJEln0N3dTVtb26ATa0MItLe3D/rH92jeCaxzc/z4cf70T/+Uw4cPA9l+xNNPP80HPvAB/yEmjVH79+/nvvvuG/S/Mp1ZR0cHbW1tw7IqlUp85zvfGXSC/7Zt27j99tuZOXPmsMfZu3cvX//61wceZ9euXezbt4/rrrsu7ROQpDGgu7ubL37xiwN/U7z00kvs2LGDO++8k46OjgZvXXPyILCawpQpU7jsssuGLQ8hDJv6de3atTzxxBOD/pmwePHiYe+cPHnyJKdOnRq2AzdhwgQuv/xyQgisWLEiwbNpDdu2bRuYAqetrW0g6xjjoCl2x48fz/jx49m5cycbNmwYM++m7ujoGPSu5+XLl7Nx48ZBB3tDCKxevXrYVNEAc+fO5eKLL+aFF14Ydo3qNWvW0N/fz759+wYtnzx5Mpdccgm7d+/mwIEDg2676KKLBtaZPHlyLZ5iyyiPXc+6ra2NGzcO/EOhbPv27axdu7bq9ZJ07np6eti0adOw5Rs3bmTlypWOZTW9U6dOVZ3RQlJre/zxx/npn/7pYcvf9ra3cf3119PX10dPTw+zZs2iq6uLzs7Ogf3g7u5u2tvbx8zfErX26KOPDhwALtu/fz9PP/00V199dYO2SlIjPfPMM8P+XgPcRzuDjo4OVqxYMWhGMchmMRw6w1tvby+bNm3i5ptvHsi0/PGZZ54ZdiD5xRdfZO3atUyZMiXhM5Ck1vfCCy8Mm03o8OHD7NixgzVr1jRoq5qbf2GpIT772c+e1/22b9/OE088wfbt2+nq6mLx4sVs2LCBAwcODNrp2rJlC5BdK3X9+vVs3LiRI0eOMHv2bFauXMnXv/71UX3fu+6667y2t8iOHDlSdXmMseoUbz09PXR1dZ1xOpxWNmHCBN7ylrfw1FNPceDAAaZOncrll1/OrFmzmDlzJiEEXnjhBXp6eli8eDFXXHEFpVKJt7zlLTzxxBMDj9PW1jZwcLk8/XNXVxcLFizgiiuuoK2tjVtuuYUnn3ySl19+mY6ODi666CIuueSSRj11jVEj9YgjR454EPgCnThxoursAV1dXfT09PiOHjWtrq4uPvOZz/D8888PXDLBfzRKY8fQkxTL9u3bx7333svDDz/MqVOnBvoDZAcqP/3pT/PSSy8xbtw4rr76am6//faB6yvq3IyU/UjLJbW+oSeGlLlvdmZXX30148aNY8eOHQCsWLGCzs7OYSfpQ/a374svvjhwsL2vr4+XXnqp6t/KMUaOHDniQWBJukBn+n+kqvMgsArj4MGDPPTQQ8QYWbBgAXD6ncLVzm6E7Gy9hQsXsnDhwnpuakuYNWsW27dvH7Y8hMCUKVM4evTooOUTJkwYcXrjsWLmzJncdtttw5aHELj00ku59NJLh902efJkbr75Zn7/93+fvr6+Qf/wWrlyZdXr106YMIHrr7++thvfRM73JJGRVP5BVuvHHosniJTNmjWL3bt3D1oWQqg6HZZGZ9KkSUyYMIHu7u5By6dMmeLUNmpq//Iv/zJwIl75H4wj7aNJaj2LFi3i8ccfH7b84MGDg5aX3x3V39/Pxz72MQ4dOgRkswh861vforOzk5tuuqku29wqFi1axMaNG4ct9+9gaewqz7owlLMKnVmpVOLKK6/kyiuvHFhW/j1Vbd3vfOc7g5Y98MADTJs2bdjfciGEMX/JLkmqhVmzZrFz585hy/1/5Mg8CKzCePHFF4edsRhjZPv27axbt27QMsiuIbV27dqB5ceOHWP37t2USiWWLl3qNYDPYuXKlWzfvp2DBw/S09MzkGsIgQ0bNvDNb35z4B84IQSuvPJKSqVSIze5cHp6enjppZfo7u72bNwLNNLrO8Y4ME5jjMQYB/7oPXHiBC+99BIxRpYsWeJ150Zp/fr17Nu3j1OnTg0sW7t27Zg/GaQWyv94KJ/4BNksARs2bPCfNkrmQk+S6e7u5nOf+9yw6fD6+vr453/+55qewDCWT8CRmtn69et54oknBp0ktmTJkmEnj5b19/dX/cf6E0884UHg3NGjR3n22WcBuOyyy0a87MuGDRt46qmneOWVVwaWrVixouoll8oOHTrE5s2bKZVKXHbZZe4LqyFijOzZs4dDhw4xc+ZM5s+f7/5ujZT/Xjt58iRdXV3+z+ECTJ8+nTVr1vDCCy8MLJswYQITJ07ktddeG7RujJGpU6dy/PjxQbM7rVu3zj4rSTWwatUqtm/fzuuvvz6wbN68eSxdurSBW9XcPAiswhhph7Wrq2vQPx0hOyPktttuG5gyc/v27YP+mf7kk0/y5je/mVmzZqXf8IJqb2/n9ttv5+GHH+ahhx4aWN7f3097ezvvfOc72bFjBzFGli5dyvTp0xu3sQV07Ngx7r333oEzc32n1Pkb6fU9ffp0vva1rw0cBO7r6+Ob3/wmN910E/v27eP+++8fyP3JJ5/kpptuYtGiRQ17HkUzffp07rrrLrZv387JkydZuHAhc+fObfRmtYwVK1YwY8YMdu3aRQiB5cuXO3WYmlr5RJuRbpPU+jo6OvihH/ohNm3axKuvvsr8+fNZu3YtH/3oR0f1OPaMzNatW/n7v//7gemz7733Xt7znvewatWqYeuOGzeO97///TzzzDPs37+fBQsWcOmll454ku4zzzzDJz/5yYH95HvvvZcf+IEfYPHixemekDREX18f999//6BrrS5cuJBbbrnFKeFH6e677+bkyZPDlscY6evrG3Ry9KlTp/ie7/meEQ+2jx8/no997GNJt7eIrrnmGpYsWcIrr7xCZ2cny5cvH3Rpr0qTJ0/mrrvuYseOHZw8eZJFixYxZ86cOm+xJLWmjo4O7rzzTnbu3Mnhw4eZOXMmixcv9s1pZ+BBYBXGsmXL2Lp166BlIQSOHDlCV1fXwA5sCIHXX3994OBOb28vjz322KB/JvT09PD4449zxx131O8JFFAIgb179zJv3rxBfyA89thjvOMd7+Dyyy9v4NYV29NPP111aib/6TU6vb29PProo1Vf38uWLRt23Z7du3eze/dunn766UEH3vv6+nj00UdZuHChZ56PwoQJE6pOc67amD59uifYqDAmTpzI3Llzh/XdEILXsZbGkFKpxPr16wctW7duHV//+teHrdvW1sbkyZM5duzYsPXHuhgjn/vc5wZdP7mnp4c3vOEN9Pf3j7i/Wvn3xUjvOIsx0tvbS3t7Oz/7sz8LwMmTJ/nCF77ABz7wgRo+C+nMtm/fPugAMMCePXt46aWXWLZsWYO2qphOnjxZ9SDwSCpnc9K5mz9/PvPnzx/4evny5bz44ouD1ilftm7ixIn+rSxJiZRKpaqXUFR1HgRWU9u1axebN2+mu7ubhQsXsn79ejZv3sypU6cYP348b3jDG6pe+6i/v58DBw6wZMkSDh8+XHUHd//+/YOmhtVwx44d48SJE8OWHzp0iFOnTvlP3Qsw9J/kZeWDmQcPHmTjxo0cOXKEWbNmsW7dOqZNm1bPTSyEw4cPD5piqWz//v1MnDix6n1efvlljhw5Mmz58ePHOX78+IjT7EmSzuyGG27gW9/6Fjt27Bg0Ff/hw4f9HSaNYTfffDNHjx7l4YcfZuvWrQP9AeC9730v//zP/8z+/fsHDiDfeuutDdza5nD06NFBU9yVnTx5ctCB4TOpdsJppaEnn7788ssDB4eleti/f3/V5fv27fMg8CiNdLmz/v7+QTNjlbW3t4/4jikvnXbu5s+fz4YNG7jnnnsGll133XVe+1eS1FTcu1fT2rlzJw8++ODA11u2bGHWrFl893d/NydOnKCzs5NSqcT27durHqgsH8jp7OwkhDDsj9xJkyZ5ADh3tqmDgEEf77nnHj7xiU84fdAFmDJlStV/zIQQOHz4MPfee+9A5seOHWPv3r28853vZMKECfXe1KY20ut78uTJI06dO336dMaNGzfs5JCOjg7zlaQL0NnZybXXXsvevXsH9eavfOUrvPOd7xzx5BxJzetCrxdeFmNk79699PT0DPwN0dPTw+c+9zmWLVvGrFmz6OjooFQq8YUvfOG8v0+rXDN84sSJjB8/ftjfaNVOwg0hUCqVaGtrG/ZO4P7+/mEHjcv5Dz3YO3XqVA8Aq65GOvnWS6CM3kj/f9m7dy/33XffsOVLlizh5ptvTr1ZY8Ill1xCqVSir6/Pd6ZJkpqSe/hqWps3bx627ODBg7z22mvMnj2bgwcP0tbWxtq1a/nGN74xaL3FixcPnHk3ceJElixZwrPPPsuECRMG/rB1mrHTRjt1UF9fn9ewHYXyQfahByrLGQ69Ps/73ve+qtNCf+xjH2PixIkeYK8wceJEVq9ezQsvvDBo+bp165g/fz7btm0btHzSpEmsWrWKGCNPPvnkoNsuueQS//EltaAYIwcOHCCEwKxZszwBLLGtW7fS1tY2KOdTp07x4osvctlllzVwyyTVS1dXF0eOHGHatGkDJ3/s2bOH48ePDzuIuXnzZhYsWOBMLEN0dHRw4403Djt48+d//uc8//zzw9ZfuXIlP/iDP0hnZycnTpxg4sSJHD9+nI9+9KO8+uqrw9a/+OKLhz2O78BWvZX/juvu7ibGSFdXF5MmTWL58uWN3rSWMW/ePObMmTPoXdelUom1a9c2cKtaT+Xl6SRJajb+t1tNq7u7u+ryffv28dBDDw1cO2ratGm88Y1vHJh+pa2tjRtvvBHI/vH7yCOPsGvXLk6dOsXBgwdZvnw5t956KwsXLqzPEymAs03309/fP2jK3bOt7/RBg3V3d4/qmjsjTfHW19dHW1tbrTarZVx99dXMmDGDXbt20dHRwapVqwZe33feeSd/8zd/A2S94Y477mDcuHGsXbuWyZMnD1y/Z/ny5f6zQWpBhw4d4hvf+MbAPsPUqVO59dZbfYdJQiPtv43mZDNJxfXkk0/y3HPPDVx2Z+3atbzhDW8YsTeMtFxwyy23MHPmTJ5++mkA1q9fT29vb9WDwMeOHeOLX/ziwN8Rvb293H///cOut1x21VVXcckll7Bp0yba29vZsGEDF110UbonI1UxceJE3vrWt/LII4/w6KOPEkJg3LhxfPWrX+XWW2/15JAaCCHwpje9iS1btrB37146Ozu5+OKLmTlzZqM3TZIk1YkHgdW0FixYwNatWwctK5VKbNu2jePHjw8sO3z4MC+++CKlUone3l7a2toGrm2yY8eOgceYOXMmM2fOJMboQcohfGdpWqVSqer1dtra2ka8Pk+1dwKXSiXHbhUhBFavXs3q1auH3TZ58uRBvaGzs3PgtqVLl7J06dJ6bqqkOrr77rvp6uoa1k//4i/+YqAnlw9Mnjx5ku/93u8d8bG8zMG5W7BgATt27Ki6XFJr27NnD88+++zA1zFGNm3axNy5c5k/f37VS3h4Yu6ZrVu3btAMVkeOHBn0N0RZR0cHDz300EC+MUa+9rWvsWjRokF/O0M2pfSyZcsYP348V155ZfonIZ3BpEmT6OrqGtQLDh8+zMMPP8yb3/zmBm5Z6+jo6OCyyy5zRhZJksYoDwKraa1fv56DBw/y+uuvA9kBsHXr1vHUU08NW3fv3r2DDqY98cQTrFmzht27d1d97N27dzNr1qx0G19Qhw8fZuvWrXR3d7Nw4UKWLVvmO09r4Pu+7/sGvZO67KKLLmLLli3Dls+YMYOpU6eyc+dOIDvIefHFF7Nhw4bk21pUfX19bN++nVdffZWJEyeyZs2a83qnX39/Pzt27OCVV15hwoQJrF69mmnTpiXYYkmpdXd3jzizQrXlvlO1NpYtW8Yrr7wyaNlFF13kQWBpDDjT314XXXQRnZ2dPP/88wMHKkMIXHrppWzfvp09e/Ywfvx4Vq9ezfTp0+u41cUydepU3v72t/PFL35x4CTSxYsXM2XKFF5++eVh68+ZM4eenh727dsHZAeD3vWud3liqZrGsWPHOHTo0LDl5WuId3R01H+jWkyt/lYey8qX+BrJuZ5YCp5cKkmqPw8Cq2lNmDCBt73tbezbt4+f+ImfoKenh3vuuWfEa9GWl/f29vIrv/IrAFXPNodsp+sf/uEf0m18AR04cICvfvWrAznu3LmTV155hRtuuKHBW1Z8HR0dVQ8CV74rtdL48eO58cYbWbt2LYcPH2bmzJlMnTo19WYW2oMPPjjoH49bt27ljjvuGLg2+Ln69re/PXDwvfw4t99+uyeNSAU0fvz4s16/vvL2cePGjXgdL/9Zfu7a2tq44YYbKJVK9PX1USqVuPrqqxu9WZLqYOj1fstOnDgxcNBy3rx5A8tLpRKPPfYY27dvH1i2bds2brvtNubOnZt8e4vqmmuu4dJLL2XHjh1MmTKFZcuW8YUvfKHqujNnzuRd73oXO3bsoKuri5UrVw5cp1lqBu3t7VX/b1MqlTwhvUa++c1vsmfPnoGvt23bxh133OEJN6Nw8uTJcz5h1BNLJUnNxoPAamohBObNm0dvb++orql6tn/6jvTOoLFs48aNw3LbsWMHa9euZfr06fT29hJCqDqtsc5szZo1w97BPnfuXC6++GK2bNlCV1fXsPUhe0fwaA9ijkUHDhwY9s6T3t5ennnmGW6++eZB0+IN1dfXR4yR9vZ2Xn/99UEHgMu3P/PMM9x6663pnoCkJD7+8Y/zrW99a9jUxHPmzGH//v3D1l+2bBk33nhjnbaudbh/IKls5cqVPP/884P+piiVShw7dmxgWUdHx8AJN/39/YMOAMPpfa+xMg3sZz/72Qu6f19fH48//jjHjh1j69atg/Z7X3zxRS699FI+97nPDaw/NO/RuOuuuy5oW6VqJkyYwNKlS4f9HbZq1Sr3LWpg3759gw4AA/T09LBp0yb3e0fhbCeEVh74Pdu6nlwqSao3DwKrEMo7Sf39/QP/QIgxDvzTMcY46MBu+Y+FEMLAWaXl9dva2pgwYUL9n0STO3z4cNXl+/fvZ+PGjezevZsQAsuWLePqq692WqZRWLt2LSEEXnjhBU6dOsWSJUu44oorKJVKvOUtb+HJJ59k7969dHZ2cumll7JkyZJGb3KhjDR2jxw5wvbt2wd6Rn9/P7t372bx4sX09PTwyCOPsGvXLmKMLFmyhDlz5ozq8SU1v2uvvZYJEyYMHAheuXIlnZ2dVQ8CHzlypM5bV2xdXV08/PDDvPLKK7S1tbFixQquuuoqSqXSiL1XUmubOnUq//Iv/0J3d/egv72Gnmha/ro801M1f/zHf+yUmWcQY+Spp57ihRdeoKenh7lz53LVVVcNzHYVQuC2225j0qRJDd5S6eyuu+46xo8fz86dO2lra2PlypWDroWt8zfS/q37vaPj7yJJUpF5EFiF8LGPfYyDBw/y5S9/edC7+UIIvP3tb6e3t5cvf/nLw+63evVqrr322npuamHNmjVr2DtSywcuy9foiTEOnD3+xje+sd6bWFghBNauXcvatWuH3TZlyhRuvvnmBmxV65g5c2bV5aVSiYceemjg6xgjDzzwAG9/+9t55plnBp1tvmvXLo4ePVr1cZwKWiqu9vZ2NmzYMOia6q+99lrVdUfqJarum9/8JgcPHgSyAzpbt26lra2NZcuWjdh7vca61Pp6enpGNevS2S71o+qeffZZnn322YGv9+3bR1dX18D0uaVSacQTHKVm097eztVXX+3lIxIYaf/W/V5JksYOL7Chwti+ffuw6VxjjOzYsYNp06bR2dnJgQMH6O7uBrLrrV522WWN2NRCuvzyy4ddx2v58uUDB4Ar7dy50ym11TRmzJjB6tWrBy2bMGECEydOHNYz+vv72bp1K7t27Rr2OIcOHWLFihXDHsez0KXWMnPmTFauXDlo2cSJE91nGIXDhw8PHACu9OKLL1bdX+vv7x82Lbek1jR+/PhhNW7cOEqlUtVrfHZ0dAzcVq5x48YN3FfVvfjii8OWHTt2bODz/v5+XnjhhYG/jSWNTe731k9XVxfPP/+8vVeS1HR8J7AKo7+/v+ry48eP87nPfY7jx48zfvx4Dh06xLp167j11lv9x8EoTJ8+nXe+851s376d7u5uFi5cyJQpU6peN6o8vbbUDO6++25Onjw5aFyGEOjv7yfGOPBOkr6+Pu65556BKeKr6ezs5Hd+53fYs2cPEydOZMWKFU4fL7Wg6667jqVLlw5Mxb9ixQr3GUZhpH2yyp5b7TZJrW+kKTNPnDjBV77yFV544QWmTJnC5MmTmTJlCrfffjtHjhzh5ZdfZsKECaxYsYKJEyfWeauLZ6SeWu7P/f39PPLIIzz55JO86U1v8l3B0hjmfm96L730Eg8++OBAD7b3SpKaiQeBVRjLli1j69atg5aFEDh69ChdXV2EEJgyZQpTpkzhtddeG/EflBrZxIkTh01ZPHv2bA4cODBo2aJFi7wmsJrGyZMnOXny5Dmte7apBU+dOsX8+fOZP39+LTZNUpMKIbBw4UIWLlzY6E0ppBkzZjBt2rRh10xfunQpy5cvH3YCWQiBpUuX1nMTJTWZU6dOcezYMRYsWDCw7OjRo2zatImrr76aefPmNXDrimfZsmVs2rRp0LLx48cP+xu4p6eHxx57jLe97W313DxJTcT93rT6+vp45JFHBvVfe68kqZl4EFiFMW/ePDZs2MDGjRvp6elh3LhxXHHFFWzcuHHYuv39/Rw4cIAlS5Y0YEtby4033siDDz44cCB4/vz5XmdZTaXaWczldwX39/cPOvDb0dFBW1vbwG2V7xxua2vzjGhJOkc33XQT3/rWt3j99dcJIbBo0SKuuuoqxo0bx5VXXsk999wzsO61117rteekMW7//v2jWq7q7r77brq7u+nr6xv0buC2traBmXDg9Aw4ZX/2Z39GCGHQY40fP37Ed25Lkgbr7u7mmWee4ZVXXmHixIlceumlLFq0iKNHj1ad/vm1116jp6fHN1BIkhrOg8AqlEsuuYTVq1dz/PhxJk+eTKlUYvv27Zw4cWLYupMnT27AFraeSZMmceedd3Ls2DHa2tro7Oxs9CZJg1T759Wjjz7Kli1bgOyfYL29vaxfv56rrrpq0HpdXV3EGJk0aVJdtlWSWsUHP/jBgan4ITuZ5iMf+cjA7ZVT8f/cz/3cGR/LAxFS6xvpbzP3wUanu7ubU6dODVtebRasyhMhzzYbjiRpZDFG7rvvPg4dOgRkM1ns37+fW2+9lVmzZlEqlYb12QkTJtDe7r/dJUmN19boDZBGq729nWnTplEqlQC47LLLhp3VvGjRImbMmNGIzWtZkydP9gCwCqGnp4dt27YNfF0qlRg/fjzbtm0b9odZZ2en/3yUpPNQnor/1KlTnDp1auDralP0D72tWklqbfPmzWP27NmDlrW1tQ27FI3ObNy4cZRKpWHV3t4+bEab8m0dHR2MHz++akkaG3p6enj11Vc5evRoozelkPbu3TtwALgsxsjmzZsZP348S5cu5dixY/T29g7cvm7dumH/q5QkqRE8JamBQggTgZ8A/g1wERCB7cA/AR+JMR5q3NYVx8KFC3nLW97Cli1b6O7uZtGiRVx00UWN3ixJDXLy5Mmq73bo7e2lp6dn4AQSSdL5O9vBg8oDu2db1wMRUusLIXDbbbexefNmXn31VTo7O7n44ouZNWtWozetUH7913+dhx56aNjyadOmsWDBAjZv3sxrr73G0aNHWbp0KbfccouXSJLGuJ07d/Lwww/T09MDwJIlS7jhhhv8u3gUqs0+WF7++OOPs337dk6dOsXBgwftvZKkpuNB4AYJISwEvgIMPfX58rx+KITwzhjjs3XfuAKaO3cuc+fObfRmSGoCkydPZurUqRw5cmTQ8pkzZzJhwoQGbZUktRanb5Y0Wh0dHVx++eVcfvnljd6Uwpo/fz4hhEHXA4ZstqzNmzcD2T5v+TrsnmQjjW0nTpzg29/+9qAp41966SWee+451q1b18AtKxZ7rySpyJwOugFCCO3Ap8gOAEfgo8CbgTcBHwH6geXAp0MIUxuzldJpe/fu5aGHHuLhhx9m//79jd6cltLX18e2bdt48MEHefLJJzl27FijN6mQXn75Zb797W/zyCOPcPDgQa677jrGjx9Pb28v+/btY+/evcyYMWPg7GdJkiSpaDo7O7nqqqsGTTE6a9asES/bs3v37nptmqQmtGfPnqrXDLc3jI69V5JUZL4TuDE+AFydf/4TMcY/qLjt/hDCt4G/A1YB/wX45TpvnzRg8+bNPP744wNfb9u2jeuvv54VK1Y0cKtaxwMPPMDLL7888PULL7zAHXfcwfTp0xu3UQXz9NNP88wzzwx8vXXrVm666Sbe+ta38olPfIIJEyYwefJktm3bxsGDB7nzzjtpb/fXnyRJkornoosuYvHixQPTas+dO5fHHnus6rodHR113jpJzWSkHmBvGD17rySpqHwncGP8eP7xOeCPht4YY/x74LP5lx/K3zks1V1vby8bN24ctCzGyFNPPTVsGhyN3r59+wYdAAbo6elh06ZNDdqi4jl58iTPPffcoGUxRp5++ml27NhBR0cHU6dOpa0t+3V36NAhduzY0YAtlSRJkmqjs7OTFStWMG/ePEIIrFq1amB/t6y9vd0Td6UxbtGiRVXfrbpmzZoGbE3x2XslSUXkQeA6CyFcAlySf/nxGOPweVky/y//OAO4LfmGSVV0dXVVnT53pOUanaHXrD3bcg137Ngx+vr6hi0/fPgwhw4dqnqfw4cPJ94qSZIkqX5mzJjBLbfcwsyZMymVSsyZM4c3velNTJ48udGbJqmBSqUSb3nLW1i0aBGlUokpU6Zw3XXXsXTp0kZvWkuw90qSisB3mNbfDRWff+MM6z1Q8fmtwFfSbI40skmTJjF+/HhOnjw5aPnkyZOd3qYGZsyYMarlGm7q1Km0t7fT29s7aPmMGTOYNWsWu3btGnafmTNn1mvzJEmSpLpYuHAhCxcubPRmSGoyU6ZM4dZbb230ZrQse68kqdn5TuD6u7Ti860jrRRj3AucqHIfqW5KpRJXXHEFIYSBZW1tbVx55ZWDlun8zJo1a9g0QRMmTGDdunUN2qLi6ejoYP369YOWlUolrrzySlavXj3s2spz5szxrGdJkiRJkiRJUsvzncD1Vz49rBd45Szr7gFWVdxHqrtVq1Yxc+ZMdu7cSQiB5cuXM23atEZvVsu4/vrrWbp0KXv37h24vsyECRMavVmFcskllzB79mxeeumlgevvlKdfuvPOO9mxYweHDx9m5syZLF26lFKp1OAtliRJkiRJkiQpLQ8C1195ntfjMcZ4lnWP5x8v6IhbCGEuMGeUd7uk8outW0d80/J52blzZ00fL7VNmzY1ehNGJUW+5SmKDx06NOK1Vs+X+cLs2bMBePXVV2v+2GMl3/I0zwcPHuTgwYMDyzs6Ogby3b1794VvYIWxkm2jmG9a5puW+aZjtmmZb1rmm5b5pmO2aZlvWuablvmmY7ZpmW9a5puW+aZT62yrHFsbV9NvcBYeBK6/8fnH7nNYtzwd9PgzrnV2HwR++UIe4Lu/+7svcBMkSZIkSZIkSZKkMWsJ8ES9vpnXBK6//vzj2d4FDFC+6Gr/GdeSJEmSJEmSJEmSpJwHgevvWP7xXC76WV7nZKJtkSRJkiRJkiRJktRinA66/o7mHzvPYd1J+cfXL/B7/jHwj6O8z2TgauAIcBh4CTh1gduR0irgUxVfvwvY1qBtaUXmm5b5pmO2aZlvWuablvmmY7ZpmW9a5puW+aZjtmmZb1rmm5b5pmO2aZlvWuablvmmU7Rsx5FNAV12fz2/uQeB6698BexxIYQ5Mcb9Z1h3Yf5xz4V8wxjjPmDfedz1OxfyfesphDB00bYYY7Gujt7EzDct803HbNMy37TMNy3zTcds0zLftMw3LfNNx2zTMt+0zDct803HbNMy37TMNy3zTaeg2dbtGsBDOR10/T1X8fmqkVYKISwAJla5jyRJkiRJkiRJkiSNyIPA9fdwxec3nmG9myo+/1aibZEkSZIkSZIkSZLUYjwIXGcxxq3AxvzLHwxV3rue+3f5x9ep8xzhkiRJkiRJkiRJkorLg8CN8dH843rgZ4beGEL4/wHvzL/80xjjyXptmCRJkiRJkiRJkqRia2/0BoxRfwp8ALgS+F8hhMuAvwFOAe8GPpSvtwP4n43YQEmSJEmSJEmSJEnF5EHgBogx9oUQ7gK+ClwC/GBelV4G7ooxHq739kmSJEmSJEmSJEkqLqeDbpAY4x6ydwL/HPA4cBQ4CTxH9u7f9THGTY3bQkmSJEmSJEmSJElF5DuBGyjG2A18OC9JkiRJkiRJkiRJumC+E1iSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiNcEVqvYD/zKkK9VO+ablvmmY7ZpmW9a5puW+aZjtmmZb1rmm5b5pmO2aZlvWuablvmmY7ZpmW9a5puW+aZjtqMQYoyN3gZJkiRJkiRJkiRJUo04HbQkSZIkSZIkSZIktRAPAkuSJEmSJEmSJElSC/EgsCRJkiRJkiRJkiS1EA8CS5IkSZIkSZIkSVIL8SCwJEmSJEmSJEmSJLUQDwJLkiRJkiRJkiRJUgvxILAkSZIkSZIkSZIktRAPAkuSJEmSJEmSJElSC/EgsCRJkiRJkiRJkiS1EA8CS5IkSZIkSZIkSVIL8SCwJEmSJEmSJEmSJLUQDwJLkiRJkiRJkiRJUgvxILAkSZIkSZIkSZIktRAPAkuSJEmSJEmSJElSC/EgsBoihPBTIYRrG70dkjSW2Hslqf7svZJUf/ZeSao/e68kNR8PAqvuQgj/Dfht4B9DCFc0eHMkaUyw90pS/dl7Jan+7L2SVH/2XklqTh4EVl2FEKYAtwEngSXAJ0MIVzZ2q1pHCGFNo7ehVZltWuablr03LcdvOmablvmmZe9Ny/GbjtmmZb5p2XvTcvymY7ZpmW9a9t60HL/pmG1a5tscPAisuooxHgV+APgs0AUsBz4RQtjQyO1qBSGEXwW+EEL4V43ellZjtmmZb3r23nQcv+mYbVrmm569Nx3Hbzpmm5b5pmfvTcfxm47ZpmW+6dl703H8pmO2aZlv8/AgsOoqhNAWY3wZ+EngS5zeMfgndwzOXwhhMvAmYCXwn0MI39XYLWodZpuW+daHvTcNx286ZpuW+daHvTcNx286ZpuW+daHvTcNx286ZpuW+daHvTcNx286ZpuW+TYXDwKrrmKM/RU7Bj+OOwY1EWM8BmzOv7wJ+Dmba22YbVrmWx/23jQcv+mYbVrmWx/23jQcv+mYbVrmWx/23jQcv+mYbVrmWx/23jQcv+mYbVrm21w8CKy6c8egtkII5dfx4fxjG3A9NtcLZrZpmW992Xtry/GbjtmmZb71Ze+tLcdvOmablvnWl723thy/6ZhtWuZbX/be2nL8pmO2aZlv8/EgsBrCHYPaiTH255++XrG4DbgW+Bmb6/kz27TMt/7svbXj+E3HbNMy3/qz99aO4zcds03LfOvP3ls7jt90zDYt860/e2/tOH7TMdu0zLf5eBBYDeOOQW2EEEL+6YL84+eBTwLtZGfZ2FzPk9mmZb6NYe+tDcdvOmablvk2hr23Nhy/6ZhtWubbGPbe2nD8pmO2aZlvY9h7a8Pxm47ZpmW+zceDwKqriiZACKGU7xiU3DE4fzHGmE+zsCpf1BVj/F7g77G5XhCzTct868feW3uO33TMNi3zrR97b+05ftMx27TMt37svbXn+E3HbNMy3/qx99ae4zcds03LfJuPB4FVF+WdgRhjLC+LMfYN+eiOwfnrACYD/cAWgBjje7G51oLZpmW+Cdl7k3P8pmO2aZlvQvbe5By/6ZhtWuabkL03OcdvOmablvkmZO9NzvGbjtmmZb5NpL3RG6DWl5/51RdCmAZcBLwDmAdMBB4CNsUYH4BsxyCE8OPAR4C3cnrH4HtjjI835AkUw/XAjfnnT5UXxhjfm++PvYfTzZUY42fqv4mFZbZpmW8i9t66cPymY7ZpmW8i9t66cPymY7ZpmW8i9t66cPymY7ZpmW8i9t66cPymY7ZpmW8ziTFaVrICSvnHxcCngR1kZ4BU1l7gL4CpQFvF+p8AjuXrvAhsaPTzadYCpgNfBw4Dy/JlHRW3fzzP8RTwDeC7Gr3NRSmzNd8ilr23bjk7fs22kGW+yXK199YnZ8ev2RayzDdZrvbe+uTs+DXbQpb5JsvV3lufnB2/ZlvIMt/mqoZvgNW6VfELfinwQv7CPgjsBr4DPJ4v68k/fgG4otwQgEXuGJxTziH/uBz4pSG3tVd8bnM126Yq802Wq723Pjk7fs22kGW+yXK199YnZ8ev2RayzDdZrvbe+uTs+DXbQpb5JsvV3lufnB2/ZlvIMt/mq4ZvgNXaBcwFHslf0A8D7wbmV9z+PrIzxk7m63wVWFdx+9AdgxfcMThj3uUm21axzOZqtk1f5lvzPO299c3b8Wu2hSzzrXme9t765u34NdtClvnWPE97b33zdvyabSHLfGuep723vnk7fs22kGW+zVEN3wCrNQtoAwLwX/MX8nPAGyte+KWKda8Afhvoztf97JBmUN4xOJzffhxY0ujnWKSyuZptUct8R52XvbeJyvFrtkUt8x11XvbeJirHr9kWtcx31HnZe5uoHL9mW9Qy31HnZe9tonL8mm1Ry3zrW21ICcQY+2P2Kr4lX/Qg8FS+jBhjX8ivAh5jfBL4U+DPyaYKeQfwexWP9TLwIbKzxgD+Nsb4Uh2eRlMq5zbS19XEGHtDCO355+8F/h5o5/QF2O9Ksa1FY7ZpmW969t50HL/pmG1a5puevTcdx286ZpuW+aZn703H8ZuO2aZlvunZe9Nx/KZjtmmZbwE0+ii01ZpFdlbYCuB1sjM5vj9f3naG+1wPPAD0kp1JtjZfXso/LgF+uGL9Uoptb3SRnz1XZfmI2Z3pfkPWqXaWzQmys2y+r9HP3WyLXebb+LL3Xlh2Iyx3/JptU5f5Nr7svReW3QjLHb9m29Rlvo0ve++FZTfCcsev2TZ1mW/jy957YdmNsNzxa7ZNXeZb/GpHqoEQQilmZ3uNizGeijHGEEIHMDVfZT5kZ4yN9BgxxodCCJ8EbgAuBtYAz+aP2xazs8H+vPL7JX1SDRBCmB5jPDT0+VXkOxt4E3AnWbYB+BLwYIzx+bM9fszPsokx9sYY3xtC6AfeC9wEfDLFc2oWZpuW+TaGvbc2HL/pmG1a5tsY9t7acPymY7ZpmW9j2Htrw/GbjtmmZb6NYe+tDcdvOmablvm2iNgER6KtYhenz9xaCPwSsDT/egGwAzgJ/F7lulUeo3ztiGXAq2RnffxU5W2tXmRTonQBK4fkWv64lOzMuYN5PuXqIjsD79+Xsz9bbgw+y+YLwMca/fzNtrhlvg3L3d5bmxwdv2ZbyDLfhuVu761Njo5fsy1kmW/Dcrf31iZHx6/ZFrLMt2G523trk6Pj12wLWebbOtXwDbCKXeRv+89f9JvyF/p/r7j9Xzj9Nv4rK+8zwuNNBXbl9/nhfFnL7xQA/7qiUW6vaK4d+cdFwPP57XuAZ/OGuAs4nC8/CXwUuOYcv2d7lWUtN+WK2ZpvK5a9t2Y5On7NtpBlvg3L3d5bmxwdv2ZbyDLfhuVu761Njo5fsy1kmW/Dcrf31iZHx6/ZFrLMt7Wq4RtgFb+AGcDj+Yt7M/BhYHp+2/cAL+a3PQGsyZdX3TEA7qpoMDc0+rnVMcNLgL8GXsmf+0sVzXUi8H/y5Q8Bbwdm57fNA/4T8PX89h7gL4H15/h9Q8XnZ5zHv6hltubbqmXvrUmGjl+zLWSZb0Ozt/deeIaOX7MtZJlvQ7O39154ho5fsy1kmW9Ds7f3XniGjl+zLWSZb2tVwzfAKmZx+m3/bWTXf3id7IyPa4BJFevNIbuodw/QC9wLXFS+75DHnAz8z3y9z5d3LMZKAauB/wvsq2iuy/PbHgcO5E21fDZee/5xHHAp8Kn8fqeA3wQmN/o5NUuZrfm2Stl7k2Tq+DXbQpb51jVre2/tM3X8mm0hy3zrmrW9t/aZOn7NtpBlvnXN2t5b+0wdv2ZbyDLf1qmGb4DVvDX0l3bF8vL1HJYDfwv81/wX+V9R5WwNYCXwHU6f8fUccAMwsWLdVcB/rljnRxr9/BuU+dDmugt4C/AC2dQLC89w30XAZyua69sb/XyaqczWfItS9t6GZO74NdtClvnWNEt7b/0zd/yabSHLfGuapb23/pk7fs22kGW+Nc3S3lv/zB2/ZlvIMt/WqIZvgNW8BSw5w20zgW35i/i+/ONfVFmvvGNwEfBtsmtF9APdwKeBPwD+HPhWxQ7Bb1bcv+WvD1Els6HN9XWyM+u+fA73vbpiB+xJYG6jn08zldmabxHK3tuw3B2/ZlvIMt+a5WjvbUzujl+zLWSZb81ytPc2JnfHr9kWssy3ZjnaexuTu+PXbAtZ5lv8akOqIoRwMfCNEMJdFctKFascBr4KHAHelC9bGUKYn68bAGKM/SGEthjjFuC9ZHPJbyabFuAdwI8B/55sWpHXgF+JMf58+fvFvFuMJTHGrcCvkU2R8jowDSiR5bviLHd/jqyxQnbtjqmptrOIzDYt871w9t7GcfymY7Zpme+Fs/c2juM3HbNNy3wvnL23cRy/6ZhtWuZ74ey9jeP4Tcds0zLf4vMgsEbSCSwD/iqE8FaAGGNfCGFBCOHGGGMf8KNkF/Z+Pb/PrcAP5uvGKjsG24GfI9sJ+EvgfuB54GngvwM/GGP8FRjYIeir03NtuBDCoNdiRXP9DNmOF2TX21iRr1+iihjjcbIdr+PAEuD6RJtcGGablvnWnL23jhy/6ZhtWuZbc/beOnL8pmO2aZlvzdl768jxm47ZpmW+NWfvrSPHbzpmm5b5tphavaXYaq0CruP09B+vk525tRDYQXYG19X5eiXgtzk9HUA/8O8qHidU+7zivpOAziHLq16bolULKOUf5wBvGHJbebqFA3m2m4EF1XIq50s2FcuefP13N/r5mW3rlvkmydTeW7+sHb9mW8gy3ySZ2nvrl7Xj12wLWeabJFN7b/2ydvyabSHLfJNkau+tX9aOX7MtZJlv61XDN8BqzgImA98PPJG/QA8Dr+afP5C/eMsNYeiOQRfw3orHGrozMKZ+6Z9j3iuBnWTXzbhqyG1Dm+sXgHnl7IdmDLyTbF7+U8AbG/3cGl1ma75FKntv3fN2/JptIct8a56nvbe+eTt+zbaQZb41z9PeW9+8Hb9mW8gy35rnae+tb96OX7MtZJlva1XDN8Bq3gKmA28HNnH6rK9NwGUV65Rf2G3A/6548XcBd1es547A8Hwrm+Ev5LkdB/4GuGbIuuXmeihf70vAwiqPOQn4daAP+DbZNAsh5fNoxjJb8y1y2XuT5+v4NdtClvkmz9femzZfx6/ZFrLMN3m+9t60+Tp+zbaQZb7J87X3ps3X8Wu2hSzzbd1q+AZYzVnlFyOwDjhYsVNwBHjrkHXdMRh9vuXMJubNcAPZWTP9wMmzNNfX8/W2AW8FLslvvw74xYqf1c80+nmabeuV+SbP196bNl/Hr9kWssw3eb723rT5On7NtpBlvsnztfemzdfxa7aFLPNNnq+9N22+jl+zLWSZb2tXwzfAat4COoA/y1+kL5DN8d6fv7DfNmRddwzOPddyVouAj+XZ9g+pk8D/O0Nz3c/pnbRDwLNkZ9SU7/+/Ku4zZs6uMVvzbYWy9ybL1fFrtoUs861bzvbeNLk6fs22kGW+dcvZ3psmV8ev2RayzLduOdt70+Tq+DXbQpb5tn6Vz/6RqgohvAF4D/B5skbwc8AbyK4Z8Z4Y45cq1i3FGPtCCG3Ah4H3ATOBbuCHY4wfr/PmN50QQluMsT+EsBS4j2x+/VfJdqKeAS4lm5ZlKdk8+f8A/GGM8ZGKx1gN/BLwXcCMfPGLwA6yi6zfH2P8i3zdUoyxL/0zazyzTct868veW1uO33TMNi3zrS97b205ftMx27TMt77svbXl+E3HbNMy3/qy99aW4zcds03LfMeIRh+FtpqryM/c4vT0IAGYmH/eCXw/8ASnzxAbzVQhP9zo59cMBcwGHsxzeYjsOhxz89tmkk2V8ElOn2Xz15x53v1TwNeAi6v9LMZSma35FrXsvXXJ2PFrtoUs802arb03fcaOX7MtZJlv0mztvekzdvyabSHLfJNma+9Nn7Hj12wLWebb+tXwDbCaoxgybUe+M1AaehvZnPDvGcWOwW9x+hoTv9Po59kMBfwb4DXgJeCOih2x0pD1/ijPrZuRp1v4S+Bovt6PVP78Gv08zbb1ynyTZGrvrV/Wjl+zLWSZb5JM7b31y9rxa7aFLPNNkqm9t35ZO37NtpBlvkkytffWL2vHr9kWssy39avhG2A1vip+ic8Dfhr4Z7KzPj4G3Fxl/XPZMZhQfmzgz6mY973Va+gOVpXbP5rn9iwwb6SfR/75/zmH5vop4Lca/bzNtvhlvnXP295b2zwdv2ZbyDLfuudt761tno5fsy1kmW/d87b31jZPx6/ZFrLMt+5523trm6fj12wLWeZrNXwDrAYPgNM7BEvzX/InGH7h7w8C04bcr9qOwR35be3AjwDfm38dhn6/VipOT6VyF7Aq/3xYc61Y7//lmX38bD+X/PPPcHq6hb+p0lxnt2q+Zmu+rVr2XsdvM5fZmm+rlr3X8dvMZbbm26pl73X8NnOZrfm2atl7Hb/NXGZrvladx0SjN8Bq4A//9At9IbApf/HuI3vr/07gcL6sl+yMsZlD7j90x+AQ8DucnhqgH9gw9Pu1YgHjgAeALcDyfNn4/OPQqRP+JM9mIzD/DI9Z3mF7G7A3v88psvn1r6jyc2zJfM3WfFut7L2O3yKU2Zpvq5W91/FbhDJb8221svc6fotQZmu+rVb2XsdvEcpszdeqX7WhMSvGGEMI44FfBi4FHgXeB1wFrAf+P+Axsms9/CbwwyGEWRX3P0525sf/BB4HpgI/TnZWGMBvxxgfr/x+iZ9SI/UDkWzKg0+EENbEGE+GEFYAvxBCuLhi3Z35x3lkO2SEEEpDHzDG2Jd/+kz+2JCddfce4P0hhNX5erHyYwsy27TMt87svTXl+E3HbNMy3zqz99aU4zcds03LfOvM3ltTjt90zDYt860ze29NOX7TMdu0zFenne0osdV6xeC373eQ/eJ/BbiVwVN5dAL/GniQ02eI/Swwa8jjTQTuJDu7pJ/szLJfq/b9WrWAGcDHgf15Bg8BNwOv5l9/CBiXr7sY2Jwvf4T8jDvOMD9/vt6LFRl3kZ2tF2jxs2rM1nxbpey9jt8ildmab6uUvdfxW6QyW/NtlbL3On6LVGZrvq1S9l7Hb5HKbM3XquN4aPQGWHX+gecvXmAZ2Vkat+Yv1K8CkyrWK79tfzzwrype0FV3DPJ128nOKltTsawldwjKTXJIVkuBPyObYqWf7ALp/cB3gDeU1yXbifoNsulU+oF/AGZU5sXgnbPryK7d8SxwEafn3T8G3NDoLMy2WGW+Dcvd3lubHB2/ZlvIMt+G5W7vrU2Ojl+zLWSZb8Nyt/fWJkfHr9kWssy3Ybnbe2uTo+PXbAtZ5mudcXw0egOsBvzQYX7+wuwH/htwHPiD/LZhv8TPZcdghPuNeLZIkQv4T8D7gcn51wNnuAATgL8jmw+/BzgKvHNoHsDlwLfzPE8A/0LFRdMr1usEfgnoA/4iX7aebDqWfuDvgVJlIy5yma35tnJh73X8NmmZrfm2cmHvdfw2aZmt+bZyYe91/DZpma35tnJh73X8NmmZrflaDR4jjd4AqwE/9Gxu9ycZfAbI31bcPuxFyijOEGvlyptcP9mZLt9f2Vzzj1OA14dk+xiwIr+9vWLdG8mmTSjvoD0HvB24LL/9ZrKdtvLt78+XTwL+KV/2LVrk7DuzNd9WL+y9jt8mLLM131Yv7L2O3yYsszXfVi/svY7fJiyzNd9WL+y9jt8mLLM1X6vx1fANsBr0g4clwDcrXrQngbvOcp+hOwYnyXYM5jT6+dQps2nAh8nmuO8HNgJ3VzTXNuAH8lw2AZ/g9Lz7jwEr8/Uqm+vNZHPyv5av9xpwJF//eMXP538N2ZafJNsx+zYV07oUtczWfMdKYe91/DZRma35jpXC3uv4baIyW/MdK4W91/HbRGW25jtWCnuv47eJymzN12qOavgGWA384Q/eMTgJfAq45iz3Ke8YfKPiRX97o59LHTNbSnbGy5ERmutk4N3AonzdPx3SXKudZbMW+IX8sfqH1PPAfx2yDdPyn1U/8E+NzsRsi1Hm2zxl73X8NlOZrfmOlbL3On6bqczWfMdK2Xsdv81UZmu+Y6XsvY7fZiqzNV+r8dXwDbAS/4CHXKeh4sVcvqj3kopf8MeAvwCuOMtjlncMNgN/3ejn2IBMlwK/XKW5TitnW5HzsnNsrm1kUyd8L9mZM7+Qf76+8mdJNqf/7XnDfR34/sqfa9HLbM23VQp7r+O3QGW25tsqhb3X8VugMlvzbZXC3uv4LVCZrfm2SmHvdfwWqMzWfK0Gj5FGb4CV6Ac7fGdgFjAfmFJl3SXA/fkL/yjntmMwAbi84usxNdd7ntl/r2iuz+TNdUp+eyj/DPJ1/w8jTLcwws9rpOt0bOD0Ttw3gfmNzsJsi1Xmmzxfe2/afB2/ZlvIMt/k+dp70+br+DXbQpb5Js/X3ps2X8ev2RayzDd5vvbetPk6fs22kGW+1hnHR6M3wErwQz191tdc4KeBzwK7yM78eg74M+AdVMzPPsKOwZXn+P3aav0cilBnaK4DF2AvN8gqzfVxYHU5v7xpzi3fL/84Dfj3ZBd1/1fA/wAeze//Uvn+rVhma75FLHuv47foZbbmW8Sy9zp+i15ma75FLHuv47foZbbmW8Sy9zp+i15ma75Wg8ZGozfAqvEPdPC0H98GevIXYrl684/lnYOZFfcd9RliY71q0FyXA1OB/0g2d/7N+brjgF/L1zs15Gf4PHBJo5+72Ra7zLfmedp765u349dsC1nmW/M87b31zdvxa7aFLPOteZ723vrm7fg120KW+dY8T3tvffN2/JptIct8rarjotEbYNXwh3n6BbwY2FbxIvw74APAbwB/NWQH4SvA7IrHcMdg9LmfT3Pdl6/7KvC5ip/Jj+brTQbeBTzF6R25R4A/AJY1+jmbbWuU+dYsR3tvY3J3/JptIct8a5ajvbcxuTt+zbaQZb41y9He25jcHb9mW8gy35rlaO9tTO6OX7MtZJmvNWxMNHoDrBr/QGE68Pn8hfgd4KbyC7xinbfmOwvls8Y+D8youL1yx+AQ8JfAtY1+bs1c59Fcfx/Yka9b/jl8uMrjjgOuAW4BJgITGv1czba1ynxrlqO9tzG5O37NtpBlvjXL0d7bmNwdv2ZbyDLfmuVo721M7o5fsy1kmW/NcrT3NiZ3x6/ZFrLM1xr0c2v0Blg1+kGeftHeCuwmO2vjnRXL24asdx3wcP6C7gJ+D5hY8XhLgPs4fdbH3Y1+js1eo2yuC4D/QHbB9M8AP1vxOKXKj9V+zmOtzNZ8m7XsvY0vx6/ZFrXM94Kys/c2/mfg+DXbQpb5XlB29t7G/wwcv2ZbyDLfC8rO3tv4n4Hj12wLWeZrDfycGr0BVo1/oPBb+Yt6GzDnLOveAOzJ138SuC5fXt6BWJI3h481+nkVpUbTXPOvO4DOiq+HNVPLbM23+cve2/D8Hb9mW8gy3wvOz97b2Pwdv2ZbyDLfC87P3tvY/B2/ZlvIMt8Lzs/e29j8Hb9mW8gyXyvGSBtqaiGEEX9GIYRQ5fNl+cetMcb9Z7p/jPFbwIfyL9cD35Mv788/vgTcFGO8O/8epfN9HmNFntlfAf8bOAasBX4BeFcIYXLMVazfE2PsguxnGGPsa8R2p3Km8TdaZpuW+Q5m7y0Wx+9g9t7iMN/B7L3F4vgdzN5bHOY7mL23WBy/g9l7i8N8B7P3FovjdzB7b3GYrwDaG70BOrMYY38IYQ4wFZhNNif7JqA3xtiXvxgj0Ab0AePzu06ouH+ofDHDoJ2IrwLfILuWxJoq3/9Qvn5bq77o8+fWX6vHizG+FEL4q/zLn+Z0cyWE8KkY47ER7herLS+yoblWG4ujfDyzZeQxa761Y+9Nz96bjr03DXtvevbe9Oy96dh707D3pmfvTc/em469Nw17b3r23vTsvenYe9Ow9yoVDwI3sRDC7WQX2f4A2S/76flNDwPHQwj/Psa4E6DiF/bDwHcBN4cQvivG+JlqL9iKZYdCCK+S7VQsCCF0xBh7qqxfs1+azSKEcHWM8dF8x+mCdgxCCBcDs2J2tt1IzfVns1XDp2OMRy74CTSxEMJqYDXZFDTHgMPAPwAngJOj+eVlttWFEBaQ9YUJwPMxF0IojWYH3nyHs/emZe9Nx96bnr03HXtvWvbedOy96dl707H3pmXvTcfem569Nx17b1r23nTsvenZe5VEbII5qa3hBfwEsJfsTLB+4Hj+sbLeWrF++SLedwKvAqeAe4CLz+F7/V3+eF8C2hv93OuU76/nz/mDFcvazvOx1pCdXdcPvHPIbeV591/Pb38VuKbRzz9xtj8KPFFlvD4G/DywIF/vrBeON9uqmfwb4C+A/cDBvEf8A/ChinXO6XoN5ls1E3tv2nztvemytfemzdfemzZfe2/afO296bK196bN196bNl97b9p87b3psrX3ps3X3ps2X3tv2nztvemytfemzdfeayWrhm+AVeWHAr9Y0UgfBH4b+G7gx4BfA3YBfwYsrHLfAHwqv+/rwP8HLK28fcj6FwNPAb3Af2z0c69jxn9UsbN13jsGwEX5zlR/nuEbqqxTbq79wF83+rknzrVy7J7Ix9Z+Tu/U7gI+Aswz2/PK92fIpgEqZ3y04vPjwO+M4rHMd/jztfemz9jemyZXe2/afO29afO196bP2N6bJld7b9p87b1p87X3ps/Y3psmV3tv2nztvWnztfemz9jemyZXe2/afO29VtJq+AZYQ34g8J8qXuT/A7ikyjoXASurLC/lH+eSTRPST3bmyK8D66qsP4ns7f/9wHbgjY1+/nXM+b9V5HzqfHYMhjTVQ+WMqXJWDrAcePtov0eRimyntZzpbwJvzpdfTDZlzaGKsfb+M+VgtlUz+amKfD+W94rvAn4DOJLvLBwE3nsOj2W+w5+nvbc+Odt7a5+pvTdtvvbetPnae+uTs7239pnae9Pma+9Nm6+9tz4523trn6m9N22+9t60+dp765Ozvbf2mdp70+Zr77WSV8M3wKr4YcBtZGfO9JOd0TWl4rZ2zm06hfI0IdcDj+ePdThvAN8PzCY7e+yO/HuUm8wvNPr51ynjtvzjj+ZNtDz9Sg/wI0PXO8PjDG2ql+fLzzotw7msU7QCrgZeyPP4VWBqlcyv4/SZTPePlIPZVn1eNwC780x+DZg/5Pb3ke3c9gO/dZbHMt/hz8vemz5je2+aXO29afO196bN196bPmN7b5pc7b1p87X3ps3X3ps+Y3tvmlztvWnztfemzdfemz5je2+aXO29afO191p1qYZvgDWoaf5y/sL+InDRBT5mCbiW02eIlacU2AFsHrLsw0O3pdUL+PcVO0QHRrNjACwF7jufptqqBdydj90XgQ0Vy8s7qeUx/nsVO2LvNttzzveH8ty2AusrlndUfP5/8tweAyaM8DjLzHdQHvbe+mdu761tnvbetPnae9Pkau+tf+b23trmae9Nm6+9N02u9t76Z27vrW2e9t60+dp70+Rq761/5vbe2uZp702br73Xqku1oYaLMfaHEBYBHyI7A+zrMcYto3mMEEIYsmhCjPFh4M1kUwk8ny9fSnZmCMBDwE/HGH82f4xSjLH/PJ9GIYQQymN+dv5xI/B3ZNcxKAF/EEL4ERj4uVR7jXQBa4GTwM0xxo15dn1pt7455Rm9lWzsbokxPl6+LcYY84/lcfUYWc6QTTsxlNkOkef7JrLcdsQYny7fFmPsCSG051++kn98AJgdQlgXQrgthDA3hDAxv60buAzzBey99WTvrT17b1r23nTsvfVj7609e29a9t507L31Y++tPXtvWvbedOy99WPvrT17b1r2XtVT+9lXUZ2sAcaTzfH+95D9oi831bMprxdCuJNsapAQQvj1GOMx4AdCCJcB15DtFASyM8aejTHuzO83VhpDOc+1+cfXgf9CdnbYB4E5ZDsGxBj/pLxjUP6lln9+IIRwFTAxxrh1DGVXVZ5R+Rf97BDChBhjd+U6FWP5IWAPsJDsl9PA2DPb6obkuzCEMCnGeLzi9t78l/4V+aJ3kE3/s5jsOjDPA/eHEH4/xrg5hHANMD7G+IL5AvbeerH31pi9Ny17b3L23vqw99aYvTcte29y9t76sPfWmL03LXtvcvbe+rD31pi9Ny17r+rJg8DN4w1kL+BTZNN2cK47BGUhhCXAj5M1hf3AHwCv5Y+1Cdg0wv3CWGkMFZlOzz9ujzGeAn4lZGfg/Ahn2DGo+PxlGNhJGBPZVVPxy35nvmgc2RQUz1fu1FbkfgToyD+fm99WHu9mO0RFhi/ki9rJfvk/OOT2JcCqfJ1VFQ/RA1xC9jO5LoTw72KMG/P7jvl8c/beOrD31pa9Ny17b13Ye+vA3ltb9t607L11Ye+tA3tvbdl707L31oW9tw7svbVl703L3qt6czro5nEi/zgBmB/CsOk+zsVhYBvZ2U8zge+FwVOHhMygn/todz6KLoSwHrg9//Lb5eUxxl8G/oSzTBUSK6ZQqfx8LKoYO1/NP64jm+ZmpHHVS3Z9Aji9c1D5eGZboSLDb+QfVwNvq7w9H5d/SHam3QGya0W8D/i3wC+Snf1YPnPsd0MIi/P7jvl8c/beOrH31o69Ny17b13Ye+vE3ls79t607L11Ye+tE3tv7dh707L31oW9t07svbVj703L3qt68yBw8yi/QCcAi/MX+6h2DGKMR4BPkk3/USJr0IOac8yM9WbwDPApsp2oJwBCCB1w7jsGGuabwF+R7dy+IYSwfIT1ushyh+wsMsLpaxxoZA8C5bH5+0NuWw1MI5ta6IPAz8YY/zrG+PEY428DNwHla85cBtwGg66XMtbZe+vH3lt79t607L3p2Hvrx95be/betOy96dh768feW3v23rTsvenYe+vH3lt79t607L2qizDGTgpqWiGEi4D7yObOvw/4/hjj/lE+RhvZFCNfB64E7okxvqfGm1po5Wk+Qjbn/k/mTbN828B8+SGEX+H0VCF9wIdijH9S+RgN2PymFkJ4E3Ax8EDMpqOpts5M4ClgEfD5GONd1fLMdxQ6Y4xHzDsTQpgPvB5jPBmGXNshhPAOsulW7okxduXLAtAWs+tvvAF4gKw/fC7G+F0NeApNyd5bH/bedOy9adl707D31oe9Nx17b1r23jTsvfVh703H3puWvTcNe2992HvTsfemZe9VPXhmQPPYR349B2AN8KbRPkDeOE+RndEEWQMQp6dIKe8QxBj7yjsE5TNk8uZZ/twzxM5RvoNFjPHrwN+UdwhGOLNxEtlZTAAn8/sN3SEYB7wfuCeEcOlY3yGoGJN7Y4zlzMo7r+Vx/XngE+UdgnxZzMd0Kcb4FNlZo/3AkhDC1Ho/jyZm703I3puOvTcte29y9t6E7L3p2HvTsvcmZ+9NyN6bjr03LXtvcvbehOy96dh707L3qp5sbE0ghBBijIeAXyX7pb4E+LEQwrz89tIZ7j5UX14AL5Ufv3ZbWyzh9NQTpfzrQWfUwPDrElT8knPH4Awqsg3516Whv5Sq3K2N09dD6S7fr+IxxwE/DHwUuBO4ufZbXgwV+bblXw/rA5UZxxiPjvBQ5fG9J3+sKdj7AXtvSvbedOy9adl707P3pmPvTcfem5a9Nz17bzr23nTsvWnZe9Oz96Zj703H3puWvVeN4MBoAhUv7EeBz5NdTP0W4M/y2/tG8Yv93WRTg/TnjzVScx4TYoy9IYQ1wNMhhPXlM2XOcp++s+wY/Mf8tv6xvMN1PtkCM8mugwLZ2ZAAEQbtEPxhvvwjMcY/rfV2F8V55lvtccqv/8n5x1Oc3lEY0+y96dh707H3pmXvTc/em469Nx17b1r23vTsvenYe9Ox96Zl703P3puOvTcde29a9l41ggeBm0iMcTvwN8DLZC/au0II/xxCmHIuv9hDCLOB28ia7P3AMym3t9mFzETgj4BLgK+EEC6rwY7BH4YQ/kt+25jc4bqAbNs5vVMQYWDnaugOwe/FGH8y/16j/kVYdBcydvP7t1WeuRhCWAasIztr9O9ijEdSbXsR2Xtry96bjr03LXtvfdl7a8vem469Ny17b33Ze2vL3puOvTcte2992Xtry96bjr03LXuvGsWDwE0ihIG53v8Z+DDQRdY03wX8UwhhfQhhUr5uebqAjor7TwHeS3Zx+wB8Msa4o57PoVmE03PqxxjjCeD/Ak8Ac4BvXsCOwR8DB8h2DJYmfApNqwbZlsh3BsjOUCKEMIHBOwS/H2P8z/ltw6ZzaWUXkm8IYWIIYX1+//6YT3sTQpgJfA9wK3AceLIez6Uo7L21Y+9Nx96blr23/uy9tWPvTcfem5a9t/7svbVj703H3puWvbf+7L21Y+9Nx96blr1XDRdjtJqkgLaKz/8T8CrQQ3aW2BPArwGrqtzvZuA38vX6gf9VcVto9POqc4al/OM84BeAjwPPkV2XoDwtwuvAusr1R/Fz+W3gfzb6eRY1W+Aa4CjZGUq/nS/78Yqx+7tDv99YqQvNF3gTcAj4R+ADwMXA+4A/qMj3Vxr9PJux7L01ydDe28TZ2nvT5WvvvaDs7b0XnqG9t4mztfemy9fee0HZ23svPEN7bxNna+9Nl6+994Kyt/deeIb23ibO1t6bLl97r1WLCjGWT9JQMwghtMXTZ3T8W+A/kDXS8WRndXQBnySbX38isJhsSpB5+UP8Vozx5/L7j7WzakoxO2tmKfBZsukQAE6SnS3Xzul3vx8Cbo4xbjqXnKqtM5byrVW2IYSFnD7T6X7gE8BH8pt/P8b4U5XfL/HTahoXmm8IoRP4e+CufJ1+sp2vafnnbcD/jjH+bP79BvqMMvbe82fvTcfem5a9t/HsvefP3puOvTcte2/j2XvPn703HXtvWvbexrP3nj97bzr23rTsvWoWHgRuQkN2DK4ne1v/L5Jd6DuQTa8w9AL1m4C/jDH+bn6/MdVUy0IIC4D7yM6KeRz4a+BeoAOYDfwMcCmwhNHvGISYv2AqPx8rapFtCGFWft8lwGGyX1owhncIyi4g37aYXWfjB4CrgQ/mD1meQuRzwBdjjH+Uf58xme+5sPeeP3tvOvbetOy9jWfvPX/23nTsvWnZexvP3nv+7L3p2HvTsvc2nr33/Nl707H3pmXvVVOITfB2ZGt4AbOGfH0p8N+BLwA7yc4Q2wo8TTaVyPUV646paRXy5xzImudvk50J8zTZGXUd+e1t+cf5wIeAp/L1XgMuG6u51TNbTp908mGy6S768vU+UvG9xtzPoBb5Dnm8S8n+kHgbcAUwaSznex4/D3vv6PKy9zZ5tvbedPkOeTx774X9POy9o8vL3tvk2dp70+U75PHsvRf287D3ji4ve2+TZ2vvTZfvkMez917Yz8PeO7q87L1Nnq29N12+Qx7P3muddzV8A8ZClZvhKNa/iGx++F8d4fZ5wApgEjBjyG1t57ONrVB5c/163jD/HJhY7ecATAF+ANhY0VzP+ZoRY7FqlG17/vHfcvqaBWN6h6DW+Z6p14y2D7VC2Xvrl7O9t6mztfcmztfee2HP2d57/jnbe5s6W3tv4nztvRf2nO2955+zvbeps7X3Js7X3nthz9nee/4523ubOlt7b+J87b3WhVZ5znElEEJozz8t5V+XzrB6+T4XAX8IvAf4vnw6hfJt5Z/XPmBHjPE42TQBA+IYnfc9z2YV2Rk1AA/HGE9UrhNjjPnHo8Cngb8BXgGmA98MIayL2Xz7Z/05jSU1zLY3X+dvgS8CfxNj/PH8e4zZKStqnO+IPb38GGOBvbd+7L3p2HvTsvfWnr23fuy96dh707L31p69t37svenYe9Oy99aevbd+7L3p2HvTsveqmXgQOKEYY28IYQ3wdAhh/dl+4eQ7BH8A3E52ke/vizEerHi8/vxjrGgSvtAZyKYrr16y6RZG3BGLMR4mO/tuT75oGvCAOwbD1TLbitXeFWP8d+XHGas7BODYTcHeWz+O33TsvWk5dmvP3ls/jt907L1pOXZrz95bP47fdOy9aTl2a8/eWz+O33TsvWk5dtVMPAicSMhMBP4IuAT4SgjhspFetBU7BHcAR4CbYowbfYGPyn7gZbJpEt4VQhg/0i+bEEKIMe4GfpdsmoU+YCrZz+mS/Ofk6+O0mmWbr9aXr9s2lncIKjh2a8Te2xCO33TsvWk5dmvE3tsQjt907L1pOXZrxN7bEI7fdOy9aTl2a8Te2xCO33TsvWk5dtUUHDg1Vn4x5idvnQD+L/AEMIfsbfzDdgxCCGuBj3B6h+Dm8g6BDXOwEEI4yypfIWuUK4F3nqE5lh/n9fzzA8B2sutv/GMIYUWMsf8cvl/LaES2cQxNZ+PYTcvem5bjNx17b1qO3bTsvWk5ftOx96bl2E3L3puW4zcde29ajt207L1pOX7Tsfem5dhVEXgQuIbyX+L9IYR5IYRfCCF8HPhvwFqyt/1PZ8jb+PMX/vcBdwKHcYdgRHkmMYTQGUJYGUJ4fwjh34YQrgshdMQYe4DPko3rlcAPAZdXa44Vv4wWkjXWJ8iuW/A6cBnwyyGEaTGOjelXzDYt803L3puW4zcds03LfNOy96bl+E3HbNMy37TsvWk5ftMx27TMNy17b1qO33TMNi3zVWHEGK0aFFDKPy4FniY7w6MfOAF0k+0UlJe9BlxWcd85wMeADZWPZVXNdyHwV8Cmijz3Af+jYp2fqLjtn8guwN6e3za+4jGnAX+er/c9wGTgX/KvtwBXNPp5m23xy3zrlq+9N22+jl+zLVSZb93ytfemzdfxa7aFKvOtW7723rT5On7NtlBlvnXL196bNl/Hr9kWqszXKlI1fANaqYAFwHP5C/NR4MfJzgp7A/AWsrM3do6wY1Cq/GgNyrVyh+uZPL9TwNH8823Afyg3TWA+8DsVzfXLwPuBjorHXFrRgI8Ab86XX55/3Q/8XKOfu9kWu8y3bjnbe9Pk6vg120KW+dYtZ3tvmlwdv2ZbyDLfuuVs702Tq+PXbAtZ5lu3nO29aXJ1/JptIct8raJVwzegFYrsLfodwG/nL8inyc7o6Mhvb8s/zgc+BDxVsWOwLr/NnYERss0/LgSez3N7DPiPwDqy62q8H5g25H6rgT+saK4H8/v9MfBnwIMVt/1axc9xIvBQvvxnG/38zba4Zb71ydje6/gtWpmt+Ra97L2O3yKW2Zpv0cve6/gtYpmt+Ra97L2O3yKW2ZqvZQ2thm9Aq1T+ovx6/oL8c2Di0Nvzj1OAHwA2umNwztl2An+b5/UIcC35lAn57ePyj+OBScCa/OOcvOl2kU3R0g/0VTTUk8CvD/lec4EX89t/ptHP3WyLXeZbl4ztvemydfyabSHLfOuSsb03XbaOX7MtZJlvXTK296bL1vFrtoUs861LxvbedNk6fs22kGW+VtGqHV2wEEL54t7X5IsejjGeqFwnxhjzj0dDCJ8mm0rkJ/KP3wwh3BxjfCa/oHhfHTe/CGYDVwHHgN8im3ollm+MMZ4KIawlO+PmFmA5sBf4CPD/gO+QnYVzF9lc+rPI5tN/OMZ4D0B+QfZSvt4ssub6ueTPrPHMNi3zTcjem5zjNx2zTct8E7L3Juf4Tcds0zLfhOy9yTl+0zHbtMw3IXtvco7fdMw2LfNVsTT6KHSrFNkUAPvJ5n//sXzZiGd6AYvJzhQpn+lxCM8QGymr78kz2gesGnLbNcBPc3rO/f78Z9APvAz8Ivn0LBX3mVLle5SADZyeeuHvgamNfu5mW+wy37pkbO9Nl63j12wLWeZbl4ztvemydfyabSHLfOuSsb03XbaOX7MtZJlvXTK296bL1vFrtoUs87WKVg3fgFYpsmtEPJm/KL9MfuHvEdYtTxXyXqAX6Mnv9wpwSX5bW+ptLkpVNNbjFfm05w31cU5Pn7CH7PobHwN25cu2AUvLuXP6eh2deVMeR3Y2zb+uaKpbgBWNft5mW/wy37pkbO9Nl63j12wLWeZbl4ztvemydfyabSHLfOuSsb03XbaOX7MtZJlvXTK296bL1vFrtoUs87WKVg3fgCJV+Zf5CLd1kL39vxfYmr9Qq/5ir3hxv51s3vdX8gbQT3btiBVn+35jqcjOuns6z2cv2fQI5SZYrnvJrr0xPb/PnWRn6vUDPzrk8aYBv0t28fatZNMplNd9Cbi40c/ZbFujzLdmOdp7G5O749dsC1nmW7Mc7b2Nyd3xa7aFLPOtWY723sbk7vg120KW+dYsR3tvY3J3/JptIct8raKV1wQ+R+VrN4QQOoH5wG1kF+t+AXg8xtgTQvgs8F/IrhfxQ8C2EMLTMcZY+Vgxxv7804VkZ3w8AWwH3gNcBvxyCOEnYoyH6/HcCuAQ8GmyhrgE+FcVtz1B1lR/KcbYU7H8OeAI2Zkzg/LPH+MWsp9TKV+2G/gi8KEY47Yab38zO4TZpnQI870g9t6GOoTjN5VDmG1KhzDfC2LvbahDOH5TOYTZpnQI870g9t6GOoTjN5VDmG1KhzDfC2LvbahDOH5TOYTZpnQI81WBeBD4HFTsECwEfh24Frg0v/kA8MchhF+NMd4fQvgpsjM33km20/C/QghPxBh7QwjjY4wn88ecBrwxf4y/AL4ELCJrGjcAK8imGxnzYoxdIYTfI5tn/18Da4ATwJ8AX48xPgqnf0753VaSNdV+sikTKh/vmRDC+8gu4L6e7CLunwdeiDEeTP6EmojZpmW+F8be21iO33TMNi3zvTD23sZy/KZjtmmZ74Wx9zaW4zcds03LfC+MvbexHL/pmG1a5qvCiU3wduRmLqCUf1wKPEP2Qj3F6Yt7bwP+A/k1IcjOGvsdTr/1/8vA+4GOisdcCvxEfvsR4M358svzr/uBn2v0c2/WAqaTT6VQsWx8xedTgN/Ic/wGsKzR21yUMlvzbZay9zZfOX7NtqhlvqPKyt7bZOX4NduilvmOKit7b5OV49dsi1rmO6qs7L1NVo5fsy1qma/V7OU7gc8ghBDi6TPCvkJ2VscTwJ+SzfO+gOwX/D/F/IyvGOPeEMIfk13E+4PA7WRncXwwhPAdsmtJrOX0WWEfiTHeF0IIZHO+P0t25tnQaQHGvPznEWOMh0IIbfmyuUBvjPG1/OvlwPcCP5/f7Z4Y484zPd7Qz8cis03LfEfH3ttcHL/pmG1a5js69t7m4vhNx2zTMt/Rsfc2F8dvOmablvmOjr23uTh+0zHbtMxXReFB4DOIMcaQXRPiw2Q7BI8BP0Z2TYhe4JkQwrgY46kQwniyPBcCe4BfIduB+ANgEnAlcAXZNSEAeoD/HWP8pYrvNQWYW/72dXiKhVLZ+GKM/SGEEvDfgBtCCH9JNn/+OuAd+WofjjH+IVRvnEMeb0znbbZpme/o2Hubi+M3HbNNy3xHx97bXBy/6ZhtWuY7Ovbe5uL4Tcds0zLf0bH3NhfHbzpmm5b5qjBiE7wduZmL7Myv8oW7/w3QBoQh66wFfp9sJ+D1fP0fBTrJXug/BXwV+A7Z2V//G/g3FfcPZDsUdwOH83XWNvq5N3sBP8npaVhO5R97gF7g1yrWKzV6W4tWZmu+jS57b/OW49dsi1rme04Z2XubtBy/ZlvUMt9zysje26Tl+DXbopb5nlNG9t4mLcev2Ra1zNdq1vKdwGd3DXAxcAB4LMbYX74hhHANcCvwy2Rnf0H2or4Y+CVgBvCbMcZngN/N7zMlxnh0yPdoI7vo9wfJ5oj/ArA71RNqIV8E3gBcRpZ5B/APwNdijH8Nwy7ArnNntmmZ79nZe5uX4zcds03LfM/O3tu8HL/pmG1a5nt29t7m5fhNx2zTMt+zs/c2L8dvOmablvmqKYUYfWf5mYQQvgf4R+AEcFWMcXMIoZ3szI73kp0VNg7YC+wHngFuBhYD24HbYoy7QgiBLO/+fMqRy4CnyHYCbgX+C9l1I7YCb40xbq/fsyyu/GdRnpalJ8b4UsVtbZU7cRods03LfM/M3tvcHL/pmG1a5ntm9t7m5vhNx2zTMt8zs/c2N8dvOmablvmemb23uTl+0zHbtMxXzciDwGcRQlhIdhbHOmAf8BAwh+wXeNl9wP8DPhOzC4HfCXwMmAX8WIzxoxWPNw34H2RzwZfIzgqbkq/7MnB7jPH5xE+r5VWbV1+1YbZpmW/G3ltMjt90zDYt883Ye4vJ8ZuO2aZlvhl7bzE5ftMx27TMN2PvLSbHbzpmm5b5qpGcDvrsDgGfBqaRXcz7X1Xc9gRwL/BLMcaeiuXla0rMAoa+uJcAtwAryXYKIJsK5IvAh2KM22q8/WOSTTUds03LfAccwt5bOI7fdMw2LfMdcAh7b+E4ftMx27TMd8Ah7L2F4/hNx2zTMt8Bh7D3Fo7jNx2zTct81UgeBD6LGGNXCOH3yM4K+9fAGrKpQv4E+HqM8VEYNp/7SrIdgn5gy5DHeyaE8D7gKrLrQhwDPg+8EGM8mPwJSVIB2Hslqf7svZJUf/ZeSao/e68kaaxwOuhRCiFMB4gxHqpYNj7GeDL/fArwC8DPAw8APxBj3Fn/LZWk1mHvlaT6s/dKUv3ZeyWp/uy9kqRW5TuBz1F53vb8GhBt+bK5QG+M8bX86+XA95LtEADcM9IOQeU88M4JL0nV2Xslqf7svZJUf/ZeSao/e68kqdX5TuDzFEIoAb8H3AD8Jdm1H9YB78hX+XCM8efzdf2lL0k1YO+VpPqz90pS/dl7Jan+7L2SpFbjQeDzFEL4SeB38i97yd5V3QcE4DdjjL+Ur1d57QhJ0gWw90pS/dl7Jan+7L2SVH/2XklSq3E66PP3ReANwGXAxUAH8A/A12KMfw3uEEhSAvZeSao/e68k1Z+9V5Lqz94rSWopvhP4AoQQ2skOpC8EemKML1Xc1hZj7G/YxklSi7L3SlL92Xslqf7svZJUf/ZeSVIr8SBwAl4TQpLqz94rSfVn75Wk+rP3SlL92XslSUXkQWBJkiRJkiRJkiRJaiFtjd4ASZIkSZIkSZIkSVLteBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiAeBJUmSJEmSJEmSJKmFeBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiAeBJUmSJEmSJEmSJKmFeBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiAeBJUmSJEmSJEmSJKmFeBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiAeBJUmSJEmSJEmSJKmFeBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWogHgSVJkiRJkiRJkiSphXgQWJIkSZIkSZIkSZJaiAeBJUmSJEmSJEmSJKmFeBBYkiRJkiRJkiRJklqIB4ElSZIkSZIkSZIkqYV4EFiSJEmSJEmSJEmSWsj/H0tSntXNkJUbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1950x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 6.5, 1.8 # inches\n",
    "line_width = .8\n",
    "output_prefix = '220516-22-plates-5-8-time-course-Itgb1'\n",
    "outputFigPath = '../jupyter_figures/' + output_prefix + '.svg'\n",
    "# outputFigPath = '../jupyter_figures/' + output_prefix + '.tif'\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.10, 0.15, 0.9, 0.8])\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'groups', y = 'counts',\n",
    "              size = 1.5,\n",
    "              color = 'k',\n",
    "              alpha = .5\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'groups', y = 'counts',\n",
    "            ci = 'sd',\n",
    "            color=\".7\", # This a nicer looking lighter gray to my own taste\n",
    "            errwidth=.7,\n",
    "            errcolor=\"k\",\n",
    "            capsize=.2)\n",
    "\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "plt.ylim(0, 83)\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = ax.get_xticklabels()\n",
    "x_labels_short = [i.get_text()[:-2] for i in x_labels]\n",
    "ax.set_xticklabels(labels=x_labels_short, rotation=45, ha=\"right\")\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Bud count\")\n",
    "\n",
    "# ax.get_legend().remove()\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",
    "plt.savefig(outputFigPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x840 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 2.8, 2.8 # inches\n",
    "line_width = .8\n",
    "output_prefix = '220516-22-plates-5-8-time-course-Itgb1-Kaz-vs-Rei'\n",
    "outputFigPath = '../jupyter_figures/' + output_prefix + '.svg'\n",
    "# outputFigPath = '../jupyter_figures/' + output_prefix + '.tif'\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.15, 0.15, 0.8, 0.8])\n",
    "\n",
    "sns.scatterplot(data = df,\n",
    "                x = 'counts_Kaz', y = 'counts_Rei',\n",
    "                # size = 1.5,\n",
    "                color = 'k',\n",
    "                alpha = .5\n",
    "               )\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "x = df.counts_Kaz\n",
    "y = df.counts_Rei\n",
    "res = stats.linregress(x, y)\n",
    "\n",
    "plt.plot(x, res.intercept + res.slope*x, 'r', label='fitted line')\n",
    "\n",
    "# plt.xlim(0, 45)\n",
    "# plt.ylim(0, 45)\n",
    "\n",
    "# ax.set_xticks([0, 10, 20, 30, 40])\n",
    "# ax.set_yticks([0, 10, 20, 30, 40])           \n",
    "\n",
    "plt.xlabel(\"Count by observer 1\")\n",
    "plt.ylabel(\"Count by observer 2\")\n",
    "\n",
    "# ax.get_legend().remove()\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",
    "plt.savefig(outputFigPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.5877391635433826, intercept=2.3240703681715598, rvalue=0.8892705094017354, pvalue=2.6732652127713575e-74, stderr=0.020712840571824823)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7908020388916219"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.rvalue**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stats for day 1\n",
      "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",
      "sg-Control_1 sg1-Itgb1_1   1.5526 0.3107  -0.793 3.8982  False\n",
      "sg-Control_1 sg2-Itgb1_1   0.8947 0.7235 -1.4509 3.2404  False\n",
      "sg-Control_1 sg3-Itgb1_1  -0.2539    0.9 -2.5701 2.0622  False\n",
      " sg1-Itgb1_1 sg2-Itgb1_1  -0.6579  0.871 -3.0035 1.6877  False\n",
      " sg1-Itgb1_1 sg3-Itgb1_1  -1.8066 0.1792 -4.1227 0.5095  False\n",
      " sg2-Itgb1_1 sg3-Itgb1_1  -1.1487 0.5563 -3.4648 1.1674  False\n",
      "--------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.31073869 0.72353663 0.9        0.87100081 0.17918108 0.55630895]\n",
      "Stats for day 2\n",
      "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",
      "sg-Control_2 sg1-Itgb1_2    0.475    0.9  -6.1554 7.1054  False\n",
      "sg-Control_2 sg2-Itgb1_2   -2.725 0.6329  -9.3554 3.9054  False\n",
      "sg-Control_2 sg3-Itgb1_2   -4.425   0.26 -11.0554 2.2054  False\n",
      " sg1-Itgb1_2 sg2-Itgb1_2     -3.2 0.4761  -9.4512 3.0512  False\n",
      " sg1-Itgb1_2 sg3-Itgb1_2     -4.9 0.1523 -11.1512 1.3512  False\n",
      " sg2-Itgb1_2 sg3-Itgb1_2     -1.7 0.8485  -7.9512 4.5512  False\n",
      "---------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.63289198 0.25996487 0.47610148 0.1523438  0.84848422]\n",
      "Stats for day 3\n",
      "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",
      "sg-Control_3 sg1-Itgb1_3  -5.9556  0.001  -9.6476  -2.2635   True\n",
      "sg-Control_3 sg2-Itgb1_3    -12.9  0.001 -16.4936  -9.3064   True\n",
      "sg-Control_3 sg3-Itgb1_3    -14.1  0.001 -17.6936 -10.5064   True\n",
      " sg1-Itgb1_3 sg2-Itgb1_3  -6.9444  0.001 -10.6365  -3.2524   True\n",
      " sg1-Itgb1_3 sg3-Itgb1_3  -8.1444  0.001 -11.8365  -4.4524   True\n",
      " sg2-Itgb1_3 sg3-Itgb1_3     -1.2 0.7827  -4.7936   2.3936  False\n",
      "-----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.001      0.001      0.78270507]\n",
      "Stats for day 4\n",
      "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",
      "sg-Control_4 sg1-Itgb1_4  -9.1667  0.001  -13.587  -4.7463   True\n",
      "sg-Control_4 sg2-Itgb1_4 -14.9667  0.001  -19.387 -10.5463   True\n",
      "sg-Control_4 sg3-Itgb1_4 -15.5333  0.001 -19.9537  -11.113   True\n",
      " sg1-Itgb1_4 sg2-Itgb1_4     -5.8 0.0054 -10.2204  -1.3796   True\n",
      " sg1-Itgb1_4 sg3-Itgb1_4  -6.3667 0.0019  -10.787  -1.9463   True\n",
      " sg2-Itgb1_4 sg3-Itgb1_4  -0.5667    0.9   -4.987   3.8537  False\n",
      "-----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.0053522  0.00189881 0.9       ]\n",
      "Stats for day 7\n",
      "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",
      "sg-Control_7 sg1-Itgb1_7    -13.6 0.0273 -25.8717  -1.3283   True\n",
      "sg-Control_7 sg2-Itgb1_7    -26.8  0.001 -39.0717 -14.5283   True\n",
      "sg-Control_7 sg3-Itgb1_7    -32.9  0.001 -45.1717 -20.6283   True\n",
      " sg1-Itgb1_7 sg2-Itgb1_7    -13.2 0.0329 -25.4717  -0.9283   True\n",
      " sg1-Itgb1_7 sg3-Itgb1_7    -19.3 0.0019 -31.5717  -7.0283   True\n",
      " sg2-Itgb1_7 sg3-Itgb1_7     -6.1  0.504 -18.3717   6.1717  False\n",
      "-----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.02733728 0.001      0.001      0.03285618 0.00185879 0.50400403]\n"
     ]
    }
   ],
   "source": [
    "for day in df.time_d.unique():\n",
    "    df_temp = df[df.time_d == day]\n",
    "    print('Stats for day', day)\n",
    "    tukeyTest(df_temp.counts, df_temp.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sg-Control_1  n = 19\n",
      "sg1-Itgb1_1  n = 19\n",
      "sg2-Itgb1_1  n = 19\n",
      "sg3-Itgb1_1  n = 20\n",
      "sg-Control_2  n = 4\n",
      "sg1-Itgb1_2  n = 5\n",
      "sg2-Itgb1_2  n = 5\n",
      "sg3-Itgb1_2  n = 5\n",
      "sg-Control_3  n = 10\n",
      "sg1-Itgb1_3  n = 9\n",
      "sg2-Itgb1_3  n = 10\n",
      "sg3-Itgb1_3  n = 10\n",
      "sg-Control_4  n = 15\n",
      "sg1-Itgb1_4  n = 15\n",
      "sg2-Itgb1_4  n = 15\n",
      "sg3-Itgb1_4  n = 15\n",
      "sg-Control_7  n = 5\n",
      "sg1-Itgb1_7  n = 5\n",
      "sg2-Itgb1_7  n = 5\n",
      "sg3-Itgb1_7  n = 5\n"
     ]
    }
   ],
   "source": [
    "for i in df.groups.unique():\n",
    "    print(i, ' n =', len(df[df.groups==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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