SMG-bud-count-Ecad-integrin-blocking-antibody.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob\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 scipy import stats\n",
    "from sw_plotting import change_bar_width, plotCountBar\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Treatment</th>\n",
       "      <th>Measurement</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ECCD-1</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Treatment   Measurement  Value\n",
       "0  untreated  bud_count_2h    1.0\n",
       "1  untreated  bud_count_2h    1.0\n",
       "2  untreated  bud_count_2h    1.0\n",
       "3  untreated  bud_count_2h    4.0\n",
       "4     ECCD-1  bud_count_2h    5.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the raw data sets to plot\n",
    "# These are the E-cadherin and integrin blocking antibody treatment that was done on 9/1-9/3/2020\n",
    "\n",
    "df = pd.read_csv('../data/SMG-bud-count-Ecad-integrin-blocking-antibody/20200901-03-bud-count-summary.txt', sep='\\t')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_to_plot = df[(df.Measurement == 'bud_count_ratio_24h_to_2h') & (df.Treatment.isin(['alpha6-beta1', 'IgG-IgM']))]\n",
    "\n",
    "# outputFigPath = '../jupyter_figures/' + '20200901-03-bud-count-summary-integrin-set-24-to-2h' + '.svg'\n",
    "# plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "#              yLabel='Bud ratio (24h/2h)',\n",
    "#              yMax=None, yTicks=None, fig_width=0.3, fig_height=0.9)\n",
    "\n",
    "# df_to_plot = df[(df.Measurement == 'bud_count_ratio_48h_to_2h') & (df.Treatment.isin(['alpha6-beta1', 'IgG-IgM']))]\n",
    "\n",
    "# outputFigPath = '../jupyter_figures/' + '20200901-03-bud-count-summary-integrin-set-48-to-2h' + '.svg'\n",
    "# plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "#              yMax=None, yTicks=None, fig_width=0.3, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# by_treatment = df_to_plot.groupby('Treatment')\n",
    "# by_treatment.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stats.ttest_rel(df_to_plot[df_to_plot.Treatment=='alpha6-beta1']['Value'],\n",
    "#                 df_to_plot[df_to_plot.Treatment=='IgG-IgM']['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Treatment</th>\n",
       "      <th>Measurement</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>untreated</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alpha6-beta1</td>\n",
       "      <td>bud_count_2h</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>IgG-IgM</td>\n",
       "      <td>bud_count_ratio_48h_to_2h</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>IgG-IgM</td>\n",
       "      <td>bud_count_ratio_48h_to_2h</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>IgG-IgM</td>\n",
       "      <td>bud_count_ratio_48h_to_2h</td>\n",
       "      <td>10.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>IgG-IgM</td>\n",
       "      <td>bud_count_ratio_48h_to_2h</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>IgG-IgM</td>\n",
       "      <td>bud_count_ratio_48h_to_2h</td>\n",
       "      <td>14.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Treatment                Measurement      Value\n",
       "0      untreated               bud_count_2h   4.000000\n",
       "1      untreated               bud_count_2h   7.000000\n",
       "2      untreated               bud_count_2h   8.000000\n",
       "3      untreated               bud_count_2h   5.000000\n",
       "4   alpha6-beta1               bud_count_2h   6.000000\n",
       "..           ...                        ...        ...\n",
       "65       IgG-IgM  bud_count_ratio_48h_to_2h  15.500000\n",
       "66       IgG-IgM  bud_count_ratio_48h_to_2h   8.800000\n",
       "67       IgG-IgM  bud_count_ratio_48h_to_2h  10.833333\n",
       "68       IgG-IgM  bud_count_ratio_48h_to_2h   9.000000\n",
       "69       IgG-IgM  bud_count_ratio_48h_to_2h  14.250000\n",
       "\n",
       "[70 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the raw data sets to plot\n",
    "# These are the E-cadherin and integrin blocking antibody treatment that was done on 9/4-9/6/2020\n",
    "# This experiment has only integrin set but not the E-cadherin set\n",
    "\n",
    "df = pd.read_csv('../data/SMG-bud-count-Ecad-integrin-blocking-antibody/20200904-06-bud-count-summary.txt', sep='\\t')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      "     IgG-IgM alpha6-beta1  -2.1333 0.0101 -3.7199 -0.5468   True\n",
      "     IgG-IgM    untreated  -0.0994    0.9 -1.7822  1.5834  False\n",
      "alpha6-beta1    untreated   2.0339  0.019  0.3512  3.7167   True\n",
      "----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.01012012 0.9        0.01899837]\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",
      "     IgG-IgM alpha6-beta1  -6.3233 0.0012 -9.8009 -2.8458   True\n",
      "     IgG-IgM    untreated  -0.1883    0.9 -3.8768  3.5003  False\n",
      "alpha6-beta1    untreated   6.1351 0.0024  2.4465  9.8236   True\n",
      "----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00123451 0.9        0.00240462]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fa644875850>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 135x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 135x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = df[df.Measurement == 'bud_count_ratio_24h_to_2h']\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + '20200904-06-bud-count-summary-all-24-to-2h' + '.svg'\n",
    "plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "             yLabel='Bud ratio (24h/2h)', xLabel_off=True, xticklabels_angle=45,\n",
    "             yMax=None, yTicks=None, fig_width=0.45, fig_height=0.9)\n",
    "\n",
    "tukeyTest(df_to_plot.Value, df_to_plot.Treatment, alpha=0.05)\n",
    "\n",
    "\n",
    "df_to_plot = df[df.Measurement == 'bud_count_ratio_48h_to_2h']\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + '20200904-06-bud-count-summary-all-48-to-2h' + '.svg'\n",
    "plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "             yLabel='Bud ratio (48h/2h)', xLabel_off=True, xticklabels_angle=45,\n",
    "             yMax=None, yTicks=None, fig_width=0.45, fig_height=0.9)\n",
    "\n",
    "tukeyTest(df_to_plot.Value, df_to_plot.Treatment, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "     IgG-IgM alpha6-beta1  -6.3233 0.0012 -9.8009 -2.8458   True\n",
      "     IgG-IgM    untreated  -0.1883    0.9 -3.8768  3.5003  False\n",
      "alpha6-beta1    untreated   6.1351 0.0024  2.4465  9.8236   True\n",
      "----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00123451 0.9        0.00240462]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fa644a6db90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_to_plot.Value, df_to_plot.Treatment, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fa644ada6d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = df[(df.Measurement == 'bud_count_ratio_24h_to_2h') & (df.Treatment.isin(['alpha6-beta1', 'IgG-IgM']))]\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + '20200904-06-bud-count-summary-integrin-set-24-to-2h' + '.svg'\n",
    "plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "             yLabel='Bud ratio (24h/2h)', xLabel_off=True, xticklabels_angle=45,\n",
    "             yMax=None, yTicks=None, fig_width=0.3, fig_height=0.9)\n",
    "\n",
    "df_to_plot = df[(df.Measurement == 'bud_count_ratio_48h_to_2h') & (df.Treatment.isin(['alpha6-beta1', 'IgG-IgM']))]\n",
    "\n",
    "outputFigPath = '../jupyter_figures/' + '20200904-06-bud-count-summary-integrin-set-48-to-2h' + '.svg'\n",
    "plotCountBar(df_to_plot.Treatment, df_to_plot.Value, outputFigPath,\n",
    "             yLabel='Bud ratio (48h/2h)', xLabel_off=True, xticklabels_angle=45,\n",
    "             yMax=None, yTicks=None, fig_width=0.3, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-4.506014511483958, pvalue=0.001986241348307924)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Not using the paired t-test because for this set I did not keep track of paired glands from the same embryo,\n",
    "# although surely the two glands of the same embryo were separated to different groups\n",
    "stats.ttest_ind(df_to_plot[df_to_plot.Treatment=='alpha6-beta1']['Value'],\n",
    "                df_to_plot[df_to_plot.Treatment=='IgG-IgM']['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cQ+xG9XczO9gsOrPMbJyZnUbM0hxHhMh3GvkLZJlZK7F2RF+7TM8BKh/8q4FXp2s/TKx32Qb82sz2qG1JRfqmqNmVz5nZUcDlxCjGDYBJdPZE3AF8HtgVOADYkbgjWGFmL2TOrZz/Xnfvy+IvNWdmGwC/BQ7u4/k7Ae9N3/4BeH2m1+UWM7scuIUY5fkN4vcXaajCOonc/W5ge+BzwK3Af4mRjZ8ADnL3Je5+MzGeYTkRAusAk4lyG7AIONHdf9L43wDM7EjgLjpDoaMPl72HKP8q4Mwuxmf8E/hi+vYVumuQIhS64Yy7LySC4XM9nPMHM7sVOAl4KdGT8SzRpfmb9B4NZ2YXAW+oFBP4EjGWore/8K9Ojze5+6xuzvklnVWq1xLhI9IwZduJqkvuPhs4u+hyVNk7PT4OvN3drzWzG3q6wMy2pHO8xU3dnefu883sUWBbVJWQAmi82cDNAT4KbOfu1/bxmu0zz2d2e1b4bxfXiDTEkLhjqDCzGcCPiRWh+9TYV0f7uXtf2hSyNsk8762xdHZ6XN/MRrv7yn7+rBeZ2RSibaY/Zgz058nQN6SCgeiaPJASLCQ7gFCAGK9Q8UIv5y7NPJ9ATDcfqDOAzwziemkyqko0Vnbw04pezs0uTNDroCmRWirzhjNdGdnDewyFDWeydxm93fVkVyYYyN2JyIAVueGMs/Y//v4w8pvWFF696INs9WFML+dmjw+4fSE5D/hdP6+ZQSypJ02oqGD4AbGwSuXDvAB4qA/XjQP2SNd1291XYksyz7ubWVp93Ilh0gPm7vPoZxuFaSmlplbUkOgzzOwCoodhO2KI82PAh3sasGRmuwL3pPd4ZQOKWmtPZJ5P7eXcyvH57q4p5dJQRQ6J/jsxF+KLxPDgtwGPmNmbiipTAzySed5bd+CWXVwj0hCF9kq4+yp3P4uoHtxOTKi6wMz+kl1Cfrhw9zlAZa2Gfbs7z8wmA5U1GbSOpTRcKbor3f0hYtrxB4j++yOAB83sw2ZWijLW0J/S46Fmtkk355ycef7n+hZHJK80HzoP3yGmWP+VWJPgq8CdZrZnoYWrrR8RS9uNBn5QvVmOmW1L57L5t7n77Q0un0h5gqHC3We5+5HACcQsyt2IdQq+BXS5ktNQ4u4PEt2HAEcDN5nZa81sn7R6VWUthlXEFG2RhivtkGh3v9DMrgC+CZxILG7ytmJLVTMfIuZNvI6oQlWv/LQSONnd72x0wUSghHcMWe7+nLufDBxOdPUN+TsGeLHR9TjgTcTydQuIO4SngPOB3d39ouJKKM2utHcMWe5+tZntCHwW2Kvg4nQrbZ3Xn/MvItaKFCmVIREMAO6+HPhY0eUQaQZDJhhk6Dv++ONZubJz2kfl+cqVKznuuLVXyx89ejQXXHBBQ8snnRQM0jArV65cKxiqj0l5KBikYUaPXntZiWwYVB+r/l4aS8EgdbVyZQuzZ49j4cJRfPzjf2WTTZYyaZLuDspOwSB1s3DhKO68cwqrV3f2is+ePY6pU5ey884LCiyZ9KbU4xhk6HKHe+/dYK1QqJg9eyyzZ/e2HIUUScEgdTF//hhWrOj+hvSpp8Y1sDTSX6pKSF30FAoAS5eOYO7cNtrbWxg/fhUTJ6rdoUxKHQxmNpXYF3JHYH1imbPngEeB69398eJKJz1pa+t+0anFi0eyePFY2ts7//lNmNDObrvNZ5111jSieNKLUgaDmU0Cvk3sDTmih/P+DJzu7vMbVDTpo0mTVtDWtpply9b+J7ZixQhmzRrHtGlL13p90aJR3H33FPbd9+lGFlO6Ubo2BjPbgljX8c1EcFkPX8cC96VrpETM4CUvmc+oUWvfATz33BgmTVrJhAntuWuWLBnJs8/2tni2NEKp7hjMrBW4lM6l4a8Cfkgs+zafCLIpxESqdwCHEpvE/t7M9taiqeUyfvwqXvGKOTz99FgWLRpFa2sHra1rWL2625tAFi0axQYb9LYXj9RbqYIBOAXYgWhLODOt6FTtyfT1ezP7APANYjGX16GZiqXT2upMn/4C06fH94sXj+K557oPhpEjtbdOGZStKvEGIhT+1E0orMXdv0WsoWjAW+taMqmJjTde2u2xlhZno42WNbA00p2yBcPO6fGX/bjm/PS4Q22LIvUwdepSJk3quqqw7bYLGTVKdwxlULaqxHrpsT9N03PT45TaFkXqoaUF9thjHrNmjWf27LEvjmPYfPMl3QaGNF7ZgmEBsbfEFsAdfbym0iPxfF1KJDXX0gKbbbaEzTZb0vvJUoiyVSXuJNoLTuvHNacR7RJ316VEIk2obMFQWbLnlWb29Z42m7HwNeCg9NKFdS+dSJMoW1Xi/4hl4iu7Uh1uZucT1Yp5xJ3BFOClRC9EpcHxdndXMIjUSKmCwd3dzI4DrgB2AbYndqPqjgEPEyMgRaRGylaVwN3nEhu+fglYTPfDoRcCXwFe6u7PFFJYkWGqVHcMFe6+FPi0mX2WqDbsCEwiAuE54AHgLndfVVghRYaxUgZDhbuvAW5NXyLSIKWrSohI8Qq5YzCzz1eeu/tZXb0+ENn3EpGBK6oq8Smi6xHgrG5eHwgFg0gNFNnGYHQdAjbA9xtMoIhIRlHB0N2KS1qJSaQECgkGd3+iP6+LSGOVqrvSzE5KTy9x94V9vGYT4HRgXXf/QJ2KJtJUShUMxKIrDryEGNnYF5sQjZbPEfMrRGSQhsM4hh3T4zqFlkJkGClqHMOOwHfovgfiJ2b2Qh/eahywK3GX8Z8aFU+k6RXV+PiQmT1HrOzsrB0QBuw5gLf9bi3KJiLFtjG8B5jI2jtNHUDnaky93TF0ACuBZ4BL3f2P9SikSDMqLBjSVOlDsq+ZWWWJ4FPd/f7Gl0pEoHy9Er+kc+NaESlIqYLB3d9adBlEZHh0V2JmM8zsE0WXQ2S4KNUdA4CZjSA2rH0tsCkwhq4DrBUYBYyn8/c4uxFlFBnuShUMabn4y4ldrKF/My01u1KkRspWlXgzcFh67sBM4Ob0/RrgRuAuoouSzHnfBPZoUBlFhr2yBcMb0+NS4GXuvo27vwJ4jCjru939pe6+MXAw8Hg6/6XAfY0urMhwVbZg2J24A/iuu9+Zeb1y1/DiuAd3vx44ggiRfYHjGlVIkeGubMGwfnr8R9Xr9xLtDXtnX3T3fwG/SMdOrHfhRJpF2YKhMvJxXtXrj6bHHcm7Ij3uUpcSiTShsgXDs+lxStXrM9Pjtqk7M2t+N9eIyACVLRgqW9kfUfX648BqYtzC7lXHpqfHgS4iKyJVyhYMlxEf8HeY2WlpXAPuvprOXoczKien4+9O385uZEFFhrOyBcOvgP8CI4HvA09UHTPgJDO70sy+CtwOHEj0ZFzV2KKKDF+lCoa0Se2RwFPk9534AXHXYES35YeJtSEBFhE7X4tIDZQqGADc/Z/AdsD7gYsyr7cTQ6X/ROeqT0bsfH2Iuz/Z+NKKDE9lmysxxt1XuPtyuliqzd2fBV5nZlOIzWkWuPvM6vNEZHBKFQzAH81sPHCuu1/c3UnuPo/8WAcRqZGyBcPuwGQ6uyBFpABla2OYkB7vKrQUIk2ubMHwz/So4c0iBSpbMHyMmC/xZTN7VdGFEenJkiUwbx60txddktorWxvDCuALwFnAZWb2FHAHMapxMb2s0uTuZ9W9hNL0nnsObr4Z5s6N71tbYeutYZ994vlwULZf4wY6P/wGTEtffaVgkLpauhQuuwxWrOh8bfVqeOQRWLYMDj+8uLLVUtmqEtA5cCn7vC9fInX30ENrh0LWE0/As892fWyoKdsdwxZFF0CkJ0891fvxDTZoTFnqqVTB4O5P9H6WSHFaernH7u34UDFMfg2Rxth888EdHyoUDCL9sP32sO66/T821CgYRPph9Gg45hjYZhsYkRYZHDsW9t4b9tuv2LLVUqnaGESGgrY2OPBA2H9/WLUqwsKGWb+YgkFkgEaM6LxrGG5UlRCRHAWDiOQoGEQkR8EgIjlqfBTpo8mTJ7Ns2bIXv88+b2trW+vctrY25s+fz1BVSDCY2XV1eFt394Pr8L4iQARBNgyqjw0nRd0xHEjnEvBdqV53ofq8ro73uFaDyGBV3xUsW7YGGA28QFvbmB7PHWqKCoab6P6D3ALsS2f7xxPEGpDPAKuAicCudC7/thT4NbG3pUjdVKoGixfDLbfAMce8m/b2VYwaBddf/yP23HP4TKIqJBjc/cDujpnZBUQoPAq8y91v6Oa8nYCfAnsCmxE7WInU1fLlcOmlsWBL543sKO69F154AQ46qLiy1VKp8s3MTgTeDMwC9u0uFADc/UHgIOBfwOHAextRRmluDz9cCYW8mTPh+ecbW556KVUwAKcTVYyz3f253k5296XAV4nofludyybCk71shNjb8aGibMGwU3q8ux/XPJQet6pxWUSaVtmCYWV67M/iWJumxyU1LotIzqbpX9uaNdDRMQWYRkfHernjQ13ZguFf6fGUflzzPqL68WDti1M/ZnaCmXkfv3Q3VBI77BD7Sdx6K7hvDWyB+47cdRdsvDFMnFh0CWujbMFwPtFe8Boz+6KZdVs+MxtjZj8H9k8vfb8B5aulXYsugPTf0qUwciSstx509rivZv3145gPk9E0ZRsS/UvgDGA34BPAm8zsD8B9QKUxcjLRRfl6YMP02uXu/qfGFnXQKsHwN+KupyfDpElr6HvwQRgzBnbcEVpabmfNGqelpYMttjiRxYth1qzhUZ0oVTC4e7uZHQ5cTQxg2hL4cDenVzqRLwPe2IDi1VolGG5x93uLLIjAZZdd1qfz/v73jViyZBQA7quAVbiP5P777wdg+fKFzJixuE/vddRRRw2orI1QtqoE7j4f2B14D3Bverl6cxkHbgbe6O7HuPvyAoo6YGa2ETAlfXtfkWWR/hk1qmNQx4eKUt0xVLh7B3AecJ6ZbQhsS+cHaS7wT3cfynv+ZNsX7i2qENJ/m2yylAULxnR5bMQIZ6ONhsdkqlIGQ5a7P0PMkxhOKsGwghj6LUPExhsv5Zln2pg3b521XjeD7bZ7jpEjh8cdQ+mqEk1it/T4ILCHmf3CzJ40s3Yzm2dml5iZ5n6UUEsL7LbbfHbaaQHwNPA8Zv9hr73mMn16N2Olh6BS3TGY2aB2q3b3z9eqLHVWuWPYFriVtaeVTwaOBo42s18Dp7p7e4PLJz1oaYFp05YyYsRfWL16JS0to5k48d1FF6umShUMwGcZ3LoKpQ8GMxsDbJO+HQ88BXwbuD29th/wASIgTgDagVMH+TOnpPfrjxmD+ZkytJUtGKB/W9o7sJDOMQ5DwY50/nf/B3CUu2fn5N1kZucTa1bMAE4xs9+4+zWD+JlnAJ8ZxPVN79lnxzBr1jhWrGilrW0V06e/UHSR6qpswbB1L8dbgHWBacChwNuJcDjF3f9W57LVyr3EhK8tgPuqQgEAd59jZqcCN6SX3gcMJhhkEB59dD3++9/OTSkXLRrF00+PpaNjZ+DO4gpWR6UKBnd/rI+n3gVcbGYXEh+YP5nZLu4+p36lqw13XwM8lr56Ou9GM3uMuGs40MzMfbgMuB06nn9+1FqhkOW+FzG9Z2WXx4eyUgVDf7n7P8zsPOCDwMeA9xdcpFp7gAiG8cSSdgOtMp0H/K6f18wALh7gzxs25swZ9+Lzn/70p6xe3bmC4OrVq4BtWLnyTo477ri1rhs9ejQXXHBBo4pZc0M6GJK/EsFwJMMvGLKjZUYN9E3cfR4wrz/X2HDbpXWAVq7s7NFfvXp1CoOsddJ5w+uuYTgEQyXCpxZaij4ys12JNobJwA97qR5UehLWMLQaWIeN8eNXMS9FamtrfFzi/9hk1qyZBLQBLYwaNROzzsG4o0ePbnRRa2o4BENlJsrCIgvRDx8ETkrPr6ebkY9mNpKYRQrwgMYyFGPatBf473/XpaPDOPXUU3GHWbPGsWTJKFpanG22WciIEY4Z7LTTAqZOHR6DnIbsyEczG2dmHyX6/J2YvjwU3JR5/pYezjuRaFcA+L/6FUd6ss46a9htt/m0tsZQ50WLRrFkyShGjOhg002XMGJE3PC5w8MPr8+qVcOjClaqOwYz68u6A0ZU7NZj7dmW36pbwWrr98DXgPWBD5rZH919rRmWZrYLcG76dj7ww8YWUbKmTFnBgQfO5umn27j77ilMnfoC667bnttDYs0aY+7ctmExNLpUwUCMT+ivDuDj7v6PWhemHtx9kZl9gFiUZhzwNzM7F7gWGAEcRtwFjSXaFt7elxWzpb5aW53p05fy5JPLX1yPoSvt7SMaWKr6KVsw9LRDVYUTDY4LiUlIF7n7kJqh6O6/MrMJwDeJrsjPpa+sJcBp7n5Jo8sn3Vt33VU9BsP48cOjKahUwdDTDlXDjbv/r5ldQ4xqPIS4W1pDbMl3OfC/7j6rwCJKFzbddAlz5oztcm3HsWNXMXnyisYXqg5KFQzNxt3/ScxjkCFiwoR2dtppAQ8/vD5r1nQ2NI4du4rdd5/PcBn+oWAQ6aepU5cyZcpy5s5to729hfHj25k8ecWwCQUYQsFgZm8gpiFvRexw/SDwK3e/rtCCSVMaObJjWM+wLDQYLMbdvoboz9+aGAJ8J/B9d384ndMC/BZ4XdXluwMnmdklwPHuPjwW2xMpgcIGOKWVkm8iJve8hti3ci+izn2Pmb0rnfpV4DjyK0VXvo4hgkNEaqSQYDCzEcTMvZfT+QFfRMwHMGAk8L9mdgLwLjpHNr6SWI9hInEH8Ug6/0gzO7jBv4bIsFXUHcMJwEvT818BW7v7+u4+GZhODP4x4BfELJXbgYPd/UZ3f8HdF6Wdp/ajc5emNzf0NxAZxooKhtemxyvd/eTsAi3uPtvd30pUMyrDnc9199XVb5JWP/peOm+PupdapEkUFQw7Ex/4nlay+Enm+e3dngW3pcfpgy2UiISigqGyzsDjPZyTXfqsp12nlqTHcT2cIyL9UFQwjE2PPU1De7H7sZe9KStb/4wcbKFEJBS9HsPw2M9LZJgpOhhEpIQUDCKSU3QwaJ8EkRIqehLVX82su5UtXlwNw8z+08N7DHhZdRHpWtHBsEkvxyt3FJv34RwRqZGiguFJ9IEWKa1CgsHdNy/i54pI3xTd+CgiJaRgEJEcBYOI5CgYRCRHwSAiOQoGEclRMIhIjoJBRHIUDCKSo2AQkRwFg4jkKBhEJEfBICI5CgYRyVEwiEiOgkFEchQMIpKjYBCRHAWDiOQoGEQkR8EgIjkKBhHJUTCISI6CQURyFAwikqNgEJEcBYOI5CgYRCRHwSAiOQoGEclRMIhIjoJBRHIUDCKSo2AQkRwFg4jkKBhEJEfBICI5CgYRyVEwiEiOgkFEchQMIpKjYBCRHAWDiOQoGEQkR8EgIjkKBhHJUTCISI6CQURyFAwikqNgEJEcBYOI5CgYRCRHwSAiOQoGEclRMIhIjoJBRHIUDCKSo2AQkRwFg4jkKBhEJEfBICI5CgYRyVEwiEiOgkFEchQMIpKjYBCRHAVDgcxsHTP7uJndbWYvmNkSM7vfzM4ys/WKLp80r9aiC9CszGwT4Gpgh6pDO6evt5nZke7+cMMLJ01PdwwFMLNW4GIiFBz4PnAQcCDwHaAD2By4xMzWLaaU0swUDMV4B7Bnev5+dz/D3a939xvd/f3A8enYDOBDhZRQmpqCoRjvS4+PAN+rPujuvwUuS9++N91hiDSMgqHBzGw7YLv07YXu3tHNqb9IjxOBV9a9YCIZCobGe3nm+U09nHdz5vkBdSqLSJcUDI23feb5zO5Ocve5wPIurhGpO9VdG2+T9LgaeLqXc+cQDZCb9HJej8xsCjC5n5dtl/1m5sxuMwyAJ554op9vP3gPPfRQzd6rbOXv4r/3qLoWpoqCofEmpsel7u69nLs0PU4Y5M88A/jMYN7g2GOPHWQRZJCmA/c06oepKtF4o9Pjij6cW6lKjO7xLJEaUzA0XqUXore7BQCrukakIVSVaLwX0uOYPpxbOWflIH/mecDv+nnNOGIQ1mJgETALaB9kObJmEKM/K14NPFbD96+3epd/FFF9qLixhu/dKwVD4y1Jj219OHdsenx+MD/Q3ecB8wZw6W2D+bk9MbPqlx5z99q1JtZZg8rfsDaFaqpKNF6l+XuUmfXWU1DpjZhTx/KI5CgYGu+RzPMZ3Z1kZhsD63RxjUjdKRga7/bM8317OG+/zPN/1KksIl1SMDSYu88EHkjfnmRdVFaTk9Pj8zS44UlEwVCM76fHXYCPVB80szcCR6Zvf+Tug+2VEOkX9UoU40fEmgwvAb5qZjsCvyK6A18DvDed9zhwdhEFlOamYCiAu68xs6OAa4k5CSelr6zZwFHuvqjR5RNRVaIg7j6HuGP4GHA3Mb5hJdEDcTawy1Dq15fhRXcMBXL3FcDX0pdIaeiOQURyFAwikqOqhBRlPvC5qu+HkqFe/h5Z72uFiEizUVVCRHIUDCKSo2AQkRwFg4jkKBhEJEfBICI5CgYRyVEwiEiOgkFEchQMIpKjYBCRHAWDiOQoGEQkR8EgIjkKBhHJUTCISI6CQURyFAwikqNgkG6Z2bZmNrrockjjKRikS2b2KWIz3beZ2aZmNmT+rZjZ/kOpvGWk/3iSY2aTgE8BU4DzgB8Cx6TXS83MPkIE2k/MbOeiyzNUKRikKx8CRhHb5gEcDlwIfMnMXm5mYwsrWe860uNbgb+b2WfNbMMCyzMkKRhkLekW/ClgDTAeWA0sAMYApxEBcaaZbWdmIwsraPd+BjyQno8DzgJuMLMTzWyd4oo1tCgYZC3u3uHu5xF/cZ8FRgB/B74CzAI2BT4P/Bw4wcymmpkVVNwcd38euD7z0jJgW+AXwJ/M7IBCCjbEKBhkLZlGu98D3wEMOAYYCfxPen0VsDfwU+DbwBFmtl7DC1vFzEakp7elx3biDuJvRBXjMOB6MzvPzLYpoIhDhnaikm6lrsqvAu8j7hY+DtwKvBp4A/CydOpi4HzgAuBBd1/e8MJmmNnuwBXAJOAU4FHgTcChwPbptGeBc4CfufuCIspZZgoG6ZKZmbt7akf4KvAB4J/ACe5+t5ntCRwLvB7YOl02E/gR8GfgcXdf3ehyA5jZdOA+YD3gOHf/o5lNAQ4EjgZeCWySTr8P+DJwsbu3N7605aRgkG5lwmFb4PvEB+tJ4GR3vzG19u8BvCZ9rZ8uvQn4MXAt8Iw38B9Zau8YTXzgtwY+5u7nZI5vCbwKOBLYj2igBLgY+Kq739qospaZgkH6xMwmEB/21wGXAye5+8J0bDPgAOLu4f8R7RIQPRg/A+6unNugsm5CVCW2B97p7j8zs9bsHUy64zma6IrdK728hmhX+a67P96o8paRgkF6ZWYj3H2Nme0P/IT4S3wL8aF7MJ3TCmxHfNDeCOyZLn+e6BH4hLuvbFB5RxJ3MO8Efu3uP88cs8odjJm1AfsTjauH0Fklmg2cDVzYyEArEwVDk6t0Nfb1dj/9pT0f2IGoXnzA3Vdljo8DdiM+bMcCWwG/cveTa1nuPpZ1C+Bpd1+RDYR0LBsQGxINk0cR1aUp6bQHgP3dfXFjS148BUMTq9wJdPVaVx8kiAAxsyOJUJgG/C/wcXdfVvU+k4m/xq8iwmOpmbW4ewd1Vl32vp6X2lL+H9GteTjwZ3d/bf1KWl4KhiaVbre/AzxIDAhyd3+kh/PX+lCb2TuJcIAY8HS2u6/soi6/rrsvblQoDETV3UMLsA9wBHBOM94tgIKhaZnZN4guSIghz7OApcQAprnAv9z9HjMb1V03npmdBHydaGx8v7tfkDnWp7/a9ZDpTelXGaoCYmS2itRsFAxNyMxmAP9O364kJkwtpbPrbgEwgRjMdBuwCLgUeAZYkemNGAd8GvhIuu7dxGjIVY0Ohd5CoKtqk3RPwdCEzOz7RIt9xQLgD8RowFOAdYFWIjCy/k4EyV3A1cBzwD3A+4lRhA8Ap7n7nfUsf1bVX/kxRLvG1sBE4GFgBXBN5a9/dVVHuqZgaEJm9iaix2BfYGp6eRnwMaLdYA+iN2G/9HxEesxaSQTDMqLr8ghgAyJkTnD3K+v6S1Qxs8OA9xADlyrjKJanMj4LfMvdf5E5v7CqzlCgYGhSZrYBMd/hNcBL6By1eDfwQXe/KZ23IfHXdywxR2IS0R25D1HFmJCuc2Ki0giiF+I7dShzdS9Cq7uvNrODiSrMpunQamIswmZVb3Ez8CF3v6PWZRtuFAxNpnrcgpltD7yZ6J7bgQgAiDaFj7j7v6qub3P3ZWk8w/ZEULwOaCMGOK0DjHH39lr+VTazMe6+opvf4cFU9ruA3wLXEXcKOwInALsCWxBrSswm2kV+DXSUtaekaAqGJtVF9+OBREBU6uiVKcznAJ/JfCi7rKOb2Q7EX+ox7n5/LevyZvYaYuLTryp/7TM9DycTa0P8CzieGH7tVdcfQbSdHEa0n9wKvMPdH6pF+YYjBUOTywZEWuHo1cSchz2B6em0Z4nJSD+vXAOMcPdV9W7tTw2KlcFTVwBXApe7+8x0/Fzgg8BnibEUqzKhkf3dJhC9J59M73UncFizDnnujRZqaVKZ2/EX7xrcfbm7/xZ4L/At4AbilnwD4KdmdpuZ7euxytMqi4VR6n0rvg0wJz0/Avgo8HkzOz4F1LPpWEel56Fyx5AJhRZ3X+Tun0rXLwd2AbRYbDcUDE2oqovvkLRWwYvcfY67fxM4k2jUu4f4ML0U+JuZ/drMprr7mvSXubVeZXX3+4GdgA8TPR4bE4uufIoIr63SqZPS75PbB8PdO6xzdafLgIVEV+wr6lXuIc/d9dVkX3RWIT9E/MX/GfEhGdvN+YcR9fhH0/kdxBTlTwEtDSz3lsD3MmVYRSwe00FUDVr7+D5XpWt+UPT/i7J+6Y6hCXn6dBCt9RALv/4B+KSZ7WhVqz+7+1XE8m5fJv7iziHGCnweWJ4aHhtR7v+4+7uJELuCaCDdhviQ705Udzbt4S0ws3WJ6scaoD1zJyEZCobm9hHgG8SHZBLwCWJxlVPMbJpldnNy9yUeA4TeB3yX2NSlA1jj7g83stDufjMxQOskYmh3pZyHAu9K4fZi9abyPP0+k4huzBHAo65h0l1Sr0QTyUypnkh0TX6O6L6r3CGsJoZCA/yFWL/xbx5Lsle/157A6cDv3P3KooYap4FaZxBtEJW5HlcSC9PeCTzmncOhdyDmc7yLmDS2pYKhawqGJmRm3yM+1E785b+OmEQ1iRi0VFmDoJ1YlOUXwH2eX3OhNMOKzWw74g7obemlFUSvyrPEnInNiLaSLYhVpU5x94sbX9KhQcHQJDJ3CwcB1xBzHT4K/MTTcu+pRX8MsQbix4lBRRCjBX8EXAT8x2MYcinXVzCzQ4j9Lyoby3TQWdVYSNxN/NDdb2h44YYQBUOTMbOrgYOJlZc+4bGyUlcrOU0mbrnfS+oKJLotvwlc4iVefCUN1HoL0WtSmS/xPLGb1uWNbhMZitT42ETMbGtiHMAK4F5ibAJd1bPdfT4xhuGvlZeIyVa/BL5nZpuWKRTMbIyZbQQvDtT6KfByYk+MdmIi2FeJ2ZfSCwVDc1lCjGIcQyym0lEZAdkVd59NVDf+SXRPVtZZOB6408zKtB7iG4AnzezTmVGdT7v7J4jt9P6YzruguzeQTgqG5lPpYTg0PbZ0Fw5pebO5QGWy0a+BLwL/IQLm8xYrMRfKYh+JdxA9Khu7x7JulePufh8x/2Mbd5/TzdtIhoJhmOjpL39F+pBfl759mZlt553DmnPXe8yHaCGqHhALx34N+GF6bQc6140cTNm7HGTUl98ple/NxKIz84kJVdC5WAsQg7o8TbyS3ikYho/3m9kRaR3GnMyH7FJiQNNWxLbwR1XNnaj+N7E+cXcAMM3dX/DY8u2X6bWXmNmovnyIu1Np4zCzrcxsdBqdmB2h2ZNsOH3CYw+J1jK1fwxFCoZhIHVBfoOoP3/azHYxs7XWa6x8yNz9CqLdYAEwgxjt+KE0DoDU7lAZKTiB2ITlcGJewk2Zt6xsNb8l0bs1oO4tM9vMzD5uZrcT60jOAn5gZof3Nlw5da++g1ie7m53/1n6HbSm4yDVbVacNNQhxK39RGKQz9HA983sEmCWd04/rnyAf0P8pT2VWKJtS+BAM7uLaKSbn7orP0B0bQJ8090fz7zHOGKk5EPE6Mn5/S10Gon4rVT+rEOB3/RhVOLLiXUeSWXVYq81onEMw4CZbUN8uI4iVmCqLM92HbG46w3uvqCL604kuvA2Si9VhkT/l+jWHEMEzm3Aq9JtuhHLuH2FGF78R3c/boDlvpIIgaXECMyniTuZSUS14NnuPuhpAZczgS8Bv3X3t5RpJOZQp2AYRqxzB+cjiLUTINoTfkVMrb7H3ZdWXTOVWP79HcRYhfUyhxcTu1Vd5GkZtNQGsR/wJ+IO5Uh3/yv9ZGavJ0ZSzic2q/lt5lj1snPjiTEUi4CRnpanT1WeU4A/ufv8rgZqycAoGIaBqsbD7A7Oh9K5kMkzxDb2vwX+7VW7LKXFWg4mBgNtCTwCzHH3u6vOO4iYsHQEcK27H0o/pbuOK4m7nC8BX/LONSWzy7EdSoyZOIy4q3meWFHqaWL7uEsz71nKUZhDlYJhGKkKiMoOzkcTOzhPTqfdR1Qv/kJ88Dt6uwW3WEz1CKKa8UbiVv9Z4JU+gAVV07iD3xF3Ne8m5mtUL+B6JNGYum56aQVRtcn6P+Cj7v5kf8sgPVMwDDPVH3Lr3MH5SKKxrvLhupwYj/D37LTqyl/ezOOUdN6rMz/mH8CPPbOBSz/LOJEYTTkZONndf1UVapsRE71mEKs/30QsDd9CtKPMIO5qWokh2+9x9/+qjaGGvATLSOmr9l+k0E/PKzs4f5n4gFWWRltO3D3sDazTzftUeg5uAO4nJl9Nzb7/AMq2PnBHKsPFlfIS7QcQsyM7iOnSO1VdO5oYxXht5nf4ctH/vYfbl+4Yhrmqv8TjiWrF0UT9fvN02iziruD3pGnVVe/RRnwA24Bl7v3fSbqLcp1DrDk5D3i1u9+WXl+H6ALdnNSwmcYzVK/8PA04j7iDWAYc6u63DLQ8sjYFQ5OoCoipRIPe0cT6iZXt6W4hFn29xN3nZa+r1W165v32JtaPnESs33imuz9qZhsTXZfLU9kWZ39uarg0j2rONsS2cxsA/+PuZ6sRsjY08rFJZD9c7j7bY/OYs4idpv5GjGzch1iQZYfq62oRClXvdxvRFQrRsPlJM1vf3Z8mBk9tQKxa7dXXp1AY4bF93g3p0B7puEKhBhQMTcLMWs1sl+xr7v4g8HViL8dvESs13euNW93oG8BP0vMTgYfM7A3AJUQjaVt3F3qsRtVCdK8CPGtJPQvcLDQkunm8DLjCzB4g5j4sgRfnFdxkZvcTO12/OHiouq2hllKVYqmZvQ94Cng7MI0YZ/EsMXjqU2Z2pnexGG0yltT2QKwvoXpxjeiOoQmk2YrvJv4CP+/ui9MtebZ6sdDdL3L3x9L3dZ1vkGm3WEEMvPoBMa17NZ1tHocTS9lPrZ71mRpEDyd2pYLO2Z5SA2p8bALp9rwy5Hgbd59ZPXy4DGMAzOxoYvXqfekc2OTEQKariTkcdxJ7Tp5IjM2YBvzC3d+We0MZMAXDENLF4KVeP8xplOEfiLEK57r7R4ueU9BTudPgp1OJDWW2Air7ai5OjyuIsQwTiLuLm4E3u/sz9Sxzs1EwlFhVF+MYYDwxyWkNUaee1cv1LcQEqa8T8wymesyQLEWXXpp2vSnwuLv/s+rYDGK05W7EpK3NWXsp+CXAt4ELq6+VwVMwlFxqZX87cDJx2zyNuKV+Ejjd3f/dw7UvIeZEbAic6u4/L3K9gsww672JqsAZxAd8PDEv4lyP9Rmz12xEdF++DNgaeIHYH+LG1F0pdaBgKDEz25lYgKSr+vNdwNtSl2Pl/OqqxtHAxcCT7r55fUvbM+vc8GYjOidQjao6bSUxtuG77v5UH9+38LaR4UjBUFJp8tN36FzN+UpiKvSDwC7APHf/cub8MURj3GPAw+7enl7fkpgH8VCRdwuZcv6cuPupLADzT+IuaDtichTAE8S8jt+6+5J0XWXEZCmqQcNeVxMo9FX8FzFvoYP4oP9PF8dHp8dKuB+Tzn8IeCdRJx9d9O+RyjYiPe6XythOrL40Ib0+nhhg1UEERmWS1w3EqMjRVe/XUvTvNNy/NI6hhMzszcTGsguIUYlfT6+PSKsW4e4r06Nb7PhcWftwe2LG5HeBI8xsEgXzzh6QT6XHnwG/dvdF6fgSYJN07FJiyvVqYq7EX4Cfm9kBZrahaQXohlBVomRST8K/iPUGPgN83at2me7imtOIAUJriIlQ+6VDy4mdqi8E7vK0eW0RzOzlxKSpVcCxnpkJaWa7EuWcTFSHNiDuel6XeYslRNVjEXC8p6qS1IfuGMrnbUQoPEqsVdBbKMygc1+Fs4ATiN2iHgbWIQYMXUIs0lKkycQQ/KeJIdDAi70u2xFjFv4FPOPu1xDdrDen09YQ1Y1DiIBTKNSZgqFE0loElb/2N5P5AHVzfivwVuKD9R93P9tjmbNvA+8jFltdSox9KEMwjCPCaiG8eHdkxNDmNuBOj9mVeGwlV9ln8iHgk8RO1V9pbLGbk4KhJNKHZDWdI/0WuPtzvVy2F7FNPaSt2VIdfIG7X0estlSpj1cGShU1+3Ae0ei4IWl+Q2or2JQY0wBR5akEHsR0cIit7M9x96MbVtomp2AoifQhWRc4IL1UmeXY5f8jMxsJvCZdc6O7X5LeZ3Xmw/8Cnb0AlZ9TVKPSXcAcorxnpbsjiEbTEUS16Z7ULVnpUt2GqEY8CkzQlOrGUTCURPpHP4bow4cYCkwvLfD3EO0Hp6f3qJ5GP58IhlGkaklRHy53n03c1cwk/vovN7P1gW1TGe9OZRuRytkKTE/fL0vvoZbyBtF6DCWRuh2fp/P/yS7Q/boIHvtCXGhmV7r7guxf2swHaG9iXYM5pK3sC/5wXUoMzPpH+v55os2hBdggla3yu25K55Tq6/pQrZIa0h1DSaQhw8uI9Q4B9rLYpr7LkYqVv/yetp6r/sCnv7ibE9WIuUQjZKHcfU0lFFIVqZXovgQ4MY3fwMz2Ifbg3IdoqPxWwwvb5BQM5VGpMlSCYQqxMe02XZ3ch7/8E4jGyVHAP9394ZqUskbcvSPd9fyUmFI9gfh9/0NUj96RTv2ku79QUDGbloKhJDIf9AuJbkaA3YHT02pFlZGPvbYRpLuFlwGVzWa/U7m+poWujUuIBWiNaJjcnFg5+jngfHf/QXFFa14a+VgimYlCryRWXKpsK3ch8F5Pax/2NhnKzPYlJiHtT2wnf3zZZyFmJo21EY2wZwM3e1rGXhpLwVBSZnYMseHrjumlZ4DPuvsPuzg3uxHswcRQ6v2IRse93H1O0as29ZWZjfHODW5LHWbDmYKhpFI//9uJEX8bZg79G/g88ACxGcvjqaoxjhgO/ZF0/iPAF939N0MlFKQ8FAwll6oF3yJtqJLxGJ2rGU0k6uVT07EHgE9XBj3pL6/0l4KhBDKrG61HVB1urf4Lb2anE3cD6xP18JHEMOdsY2Q70R5xjrs/kq5TKEi/KRhKxMz+j9ge7qfAFZUPd+b4OkRPw1bEugu7ELMVVxNbxd/s7tencxUIMmAKhpJI4xUqqx0/T6xedDFwdWXGYebc1jQnYhwxXHhEGhMgUhMKhhIxs5OBTxCThyBWg76KGEp8o7svTee1EP/v1mS6OCsjIfU/VAZNwVCgTNvCVsQGK2+ks5Exu4fCXcRW8Ze4+x2Z61VdkLpQMBQks8fCGOCvxPqGRtwdzCKqCJXAgFim7WbgcuAv7j4z814KCKkpBUNBMsHwVaK34SHgHOCCdBdROb4zsSBsZXjzXKL94VLgGnefX0DxZZhTMBQg0y6wGdHgOBo4Hvi9u6/KVDGyW9S9llj5eeP0No8RKyj/geje1DqIUjOaRFWAzG3/EcT/g/uBeyo9C5UxDCk8RqTnfyT2jribGL8wg1j96HPASZVl4ks6UUqGGAVDnfUyG3IpMVBpUzoXSF3r/EpIpNfvIbowjVhO3Yil4L4MvCt7vshgKBjqLNuV2IXK0vDtpA1XempETBOlfkTsrXAzcAox/HkD4PNm9mMzG6m1EWWwFAx1YmYvNbNDzWxkpp2g+gN7P9HbMIXYYKVyba46kAmMScQCqbsQy6u/Oj22A68CNlMPhQyWgqEO0grOlxK3+Gea2U7Q+eHODEaaCfw6XfZWM/twer1SfWjJnp9MIIZALwY2dPfHiV2cVhN3Ha+s5+8mzUHBUB+nEXcBewAfA75gZqeY2TTIVS/OJraVGwl8zcx+b2Z7pfM6qnomphCDoKYAsz1tFe+xc9MN6f12bsQvKMObVomuj8uJqdCnEEuVvRrYFdjHzC4BbvC0vXtaT+ELxFiFvYgBTS81s8uI7smnU9ViE2L59bekn3F25YeZ2USiIXIN8HidfzdpAhrHUCdp3cVdgVOJbeTGpEP3AlcSVY1bMysvHUHMk9i/6q3+QXRNdhBjGFYAP3f3d2d+1nZElWR34HR3/1FdfilpGgqGOktTpQ8iNoU5Mr28Evg7MUDpisoKzmY2FjgTeAOx3+S0qrd7gRi3cH5l2XgzG02s9PRdYim3nStrQ4oMlIKhQcxsA2JLuXeRdpkidoq6gah6XOuxWxNmtimxyvMEYm+F/wCzgTvc/cGq9z0O+AqxQ/aH3f0b9f5dZPhTMDRQ6mXYktjE9TQ613KcCVxDBMRNlfaHHt7nyPQ+ewCHElWM29x9nzoVXZqMgqEAqTtzDyIcTiTt1wjcQbQ/XO7ut2XOH5HpwjwE+Dmd6zsCXAt8KnuNyGAoGAqUVmA6jGh/OCS9vJRof7gM+Ku7P5Y534gejtPTdQ8A1wPfcPcnG1h0GeYUDCVgZhsR06rfRazlCLGW43VEQFxXmV6dGhsBNgLmuvvKBhdXmoCCoSRS+8O2wMnEvo0T06EFRKPiLzLnZgc9aZEWqTkFQ8mY2SiiJ+LtxBoNANPdfbZCQBpFwVBSZrYu8HpiBPXPetuvUqSWFAwlpiqDFEXBICI5ml0pIjkKBhHJUTCISI6CQURyFAwikqNgEJEcBYOI5CgYRCRHwSAiOQoGEclRMIhIjoJBRHIUDCKSo2AQkRwFg4jkKBhEJEfBICI5CgYRyVEwiEiOgkFEchQMIpKjYBCRHAWDiOQoGEQkR8EgIjkKBhHJUTCISI6CQURyFAwikqNgEJGc/w8lgUJcskBJUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 120x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm and bar plot of different collagenase concentrations\n",
    "\n",
    "outputPrefix = '20200904-06-bud-count-summary-integrin-set-48-to-2h'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width=0.4\n",
    "fig_height=1.6\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='Treatment', y='Value', data = df_to_plot,\n",
    "                   order=['IgG-IgM', 'alpha6-beta1'],\n",
    "                   color=\"blue\",\n",
    "                   size=2.0,\n",
    "                   alpha=.4)\n",
    "\n",
    "ax = sns.barplot(x='Treatment', y='Value', data = df_to_plot,\n",
    "                 order=['IgG-IgM', 'alpha6-beta1'],\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.7, errcolor=\"k\", capsize=.2, ci=95)\n",
    "\n",
    "plt.ylim(0, 18)\n",
    "# plt.yticks([0, 10, 20])\n",
    "\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(\"Bud ratio 48h / 2h\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = ['IgG-IgM', 'a6-b1']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=60, ha=\"right\")\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .6)\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}