plot_polarity_index_inhibitors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 scipy import stats\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\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.DEBUG)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 8}\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": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read in data from 3 datasets\n",
    "parent_folder = '../data/Fig4-polarization-cytoskeletal-drugs/_selected-smFISH-for-polarity-quantification/'\n",
    "folders = glob.glob( os.path.join(parent_folder, '*-output'+os.path.sep) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# first dataset: 181022-polarity-smFISH-only-ctrl-bleb-noc-Y27632\n",
    "\n",
    "folder = folders[0]\n",
    "files = glob.glob( os.path.join(folder, '*polarization_summary.txt') )\n",
    "assert len(files) == 1\n",
    "df = pd.read_csv(files[0])\n",
    "\n",
    "# get date\n",
    "folder_basename = folder.split(os.path.sep)[-2]\n",
    "date = folder_basename.split('-')[0]\n",
    "df['date'] = [date]*len(df)\n",
    "\n",
    "# get treatment\n",
    "treatments = []\n",
    "for i in df.file_name.tolist():\n",
    "    treatment = i.split('-')[0]\n",
    "    if treatment == 'Ctrl' or treatment == 'ctrl':\n",
    "        treatment = 'Control'\n",
    "    if treatment == 'Bleb' or treatment == 'bleb':\n",
    "        treatment = 'Blebbistatin'\n",
    "    if treatment == 'Noc' or treatment == 'noc':\n",
    "        treatment = 'Nocodazole'\n",
    "    treatments.append(treatment)\n",
    "\n",
    "df['treatment'] = treatments\n",
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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>apical_dot_number</th>\n",
       "      <th>basal_dot_number</th>\n",
       "      <th>apical_area</th>\n",
       "      <th>basal_area</th>\n",
       "      <th>polarization_index</th>\n",
       "      <th>date</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...</td>\n",
       "      <td>314</td>\n",
       "      <td>33</td>\n",
       "      <td>154073</td>\n",
       "      <td>155148</td>\n",
       "      <td>0.81100</td>\n",
       "      <td>181022</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...</td>\n",
       "      <td>337</td>\n",
       "      <td>85</td>\n",
       "      <td>167934</td>\n",
       "      <td>200189</td>\n",
       "      <td>0.65070</td>\n",
       "      <td>181022</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...</td>\n",
       "      <td>332</td>\n",
       "      <td>60</td>\n",
       "      <td>161094</td>\n",
       "      <td>162363</td>\n",
       "      <td>0.69590</td>\n",
       "      <td>181022</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...</td>\n",
       "      <td>247</td>\n",
       "      <td>61</td>\n",
       "      <td>158778</td>\n",
       "      <td>160197</td>\n",
       "      <td>0.60670</td>\n",
       "      <td>181022</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...</td>\n",
       "      <td>212</td>\n",
       "      <td>283</td>\n",
       "      <td>179946</td>\n",
       "      <td>214538</td>\n",
       "      <td>-0.05646</td>\n",
       "      <td>181022</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...</td>\n",
       "      <td>216</td>\n",
       "      <td>226</td>\n",
       "      <td>152042</td>\n",
       "      <td>153384</td>\n",
       "      <td>-0.01823</td>\n",
       "      <td>181022</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...</td>\n",
       "      <td>145</td>\n",
       "      <td>48</td>\n",
       "      <td>156526</td>\n",
       "      <td>157622</td>\n",
       "      <td>0.50520</td>\n",
       "      <td>181022</td>\n",
       "      <td>Y27632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...</td>\n",
       "      <td>179</td>\n",
       "      <td>46</td>\n",
       "      <td>162825</td>\n",
       "      <td>163871</td>\n",
       "      <td>0.59320</td>\n",
       "      <td>181022</td>\n",
       "      <td>Y27632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...</td>\n",
       "      <td>108</td>\n",
       "      <td>87</td>\n",
       "      <td>156134</td>\n",
       "      <td>191808</td>\n",
       "      <td>0.20790</td>\n",
       "      <td>181022</td>\n",
       "      <td>Y27632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                           file_name  apical_dot_number  \\\n",
       "0  Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...                314   \n",
       "1  Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...                337   \n",
       "2  Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...                332   \n",
       "3  Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...                247   \n",
       "4  Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...                212   \n",
       "5  Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...                216   \n",
       "6  Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...                145   \n",
       "7  Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...                179   \n",
       "8  Y27632-17h-Col4a1-TMR-Net1-655_2018_10_22__20_...                108   \n",
       "\n",
       "   basal_dot_number  apical_area  basal_area  polarization_index    date  \\\n",
       "0                33       154073      155148             0.81100  181022   \n",
       "1                85       167934      200189             0.65070  181022   \n",
       "2                60       161094      162363             0.69590  181022   \n",
       "3                61       158778      160197             0.60670  181022   \n",
       "4               283       179946      214538            -0.05646  181022   \n",
       "5               226       152042      153384            -0.01823  181022   \n",
       "6                48       156526      157622             0.50520  181022   \n",
       "7                46       162825      163871             0.59320  181022   \n",
       "8                87       156134      191808             0.20790  181022   \n",
       "\n",
       "      treatment  \n",
       "0  Blebbistatin  \n",
       "1  Blebbistatin  \n",
       "2       Control  \n",
       "3       Control  \n",
       "4    Nocodazole  \n",
       "5    Nocodazole  \n",
       "6        Y27632  \n",
       "7        Y27632  \n",
       "8        Y27632  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# second dataset: 181024-polarity-Golgi-ER-costaining-ctrl-bleb-noc-Y27632\n",
    "\n",
    "folder = folders[1]\n",
    "files = glob.glob( os.path.join(folder, '*polarization_summary.txt') )\n",
    "assert len(files) == 1\n",
    "df = pd.read_csv(files[0])\n",
    "\n",
    "# get date\n",
    "folder_basename = folder.split(os.path.sep)[-2]\n",
    "date = folder_basename.split('-')[0]\n",
    "df['date'] = [date]*len(df)\n",
    "\n",
    "# get treatment\n",
    "treatments = []\n",
    "for i in df.file_name.tolist():\n",
    "    treatment = i.split('-')[0]\n",
    "    if treatment == 'Ctrl' or treatment == 'ctrl':\n",
    "        treatment = 'Control'\n",
    "    if treatment == 'Bleb' or treatment == 'bleb':\n",
    "        treatment = 'Blebbistatin'\n",
    "    if treatment == 'Noc' or treatment == 'noc':\n",
    "        treatment = 'Nocodazole'\n",
    "    treatments.append(treatment)\n",
    "\n",
    "df['treatment'] = treatments\n",
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>apical_dot_number</th>\n",
       "      <th>basal_dot_number</th>\n",
       "      <th>apical_area</th>\n",
       "      <th>basal_area</th>\n",
       "      <th>polarization_index</th>\n",
       "      <th>date</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y27632--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4...</td>\n",
       "      <td>173</td>\n",
       "      <td>64</td>\n",
       "      <td>167593</td>\n",
       "      <td>169534</td>\n",
       "      <td>0.464400</td>\n",
       "      <td>181024</td>\n",
       "      <td>Y27632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y27632--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4...</td>\n",
       "      <td>203</td>\n",
       "      <td>111</td>\n",
       "      <td>157235</td>\n",
       "      <td>158387</td>\n",
       "      <td>0.296300</td>\n",
       "      <td>181024</td>\n",
       "      <td>Y27632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bleb--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4a1...</td>\n",
       "      <td>276</td>\n",
       "      <td>84</td>\n",
       "      <td>163184</td>\n",
       "      <td>164354</td>\n",
       "      <td>0.535900</td>\n",
       "      <td>181024</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bleb--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4a1...</td>\n",
       "      <td>159</td>\n",
       "      <td>60</td>\n",
       "      <td>153797</td>\n",
       "      <td>155194</td>\n",
       "      <td>0.455600</td>\n",
       "      <td>181024</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ctrl--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net...</td>\n",
       "      <td>125</td>\n",
       "      <td>35</td>\n",
       "      <td>162976</td>\n",
       "      <td>164040</td>\n",
       "      <td>0.564700</td>\n",
       "      <td>181024</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ctrl--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net...</td>\n",
       "      <td>338</td>\n",
       "      <td>104</td>\n",
       "      <td>165244</td>\n",
       "      <td>200084</td>\n",
       "      <td>0.594800</td>\n",
       "      <td>181024</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ctrl--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-N...</td>\n",
       "      <td>171</td>\n",
       "      <td>105</td>\n",
       "      <td>157376</td>\n",
       "      <td>158532</td>\n",
       "      <td>0.242600</td>\n",
       "      <td>181024</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ctrl--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-N...</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>159451</td>\n",
       "      <td>160557</td>\n",
       "      <td>0.766100</td>\n",
       "      <td>181024</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...</td>\n",
       "      <td>44</td>\n",
       "      <td>70</td>\n",
       "      <td>173329</td>\n",
       "      <td>175007</td>\n",
       "      <td>-0.223500</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>154296</td>\n",
       "      <td>156086</td>\n",
       "      <td>0.230300</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...</td>\n",
       "      <td>77</td>\n",
       "      <td>64</td>\n",
       "      <td>165194</td>\n",
       "      <td>167280</td>\n",
       "      <td>0.098420</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...</td>\n",
       "      <td>141</td>\n",
       "      <td>144</td>\n",
       "      <td>165598</td>\n",
       "      <td>171087</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...</td>\n",
       "      <td>168</td>\n",
       "      <td>138</td>\n",
       "      <td>188091</td>\n",
       "      <td>189546</td>\n",
       "      <td>0.101900</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...</td>\n",
       "      <td>159</td>\n",
       "      <td>155</td>\n",
       "      <td>152429</td>\n",
       "      <td>153607</td>\n",
       "      <td>0.016590</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...</td>\n",
       "      <td>123</td>\n",
       "      <td>113</td>\n",
       "      <td>154518</td>\n",
       "      <td>155761</td>\n",
       "      <td>0.046370</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...</td>\n",
       "      <td>142</td>\n",
       "      <td>132</td>\n",
       "      <td>151302</td>\n",
       "      <td>152329</td>\n",
       "      <td>0.039870</td>\n",
       "      <td>181024</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            file_name  apical_dot_number  \\\n",
       "0   Y27632--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4...                173   \n",
       "1   Y27632--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4...                203   \n",
       "2   bleb--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4a1...                276   \n",
       "3   bleb--Bleb-2-Y27632-2-17h-antiRCAS1-488-Col4a1...                159   \n",
       "4   ctrl--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net...                125   \n",
       "5   ctrl--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net...                338   \n",
       "6   ctrl--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-N...                171   \n",
       "7   ctrl--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-N...                 45   \n",
       "8   noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...                 44   \n",
       "9   noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...                 79   \n",
       "10  noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...                 77   \n",
       "11  noc--Ctrl2-Noc4-4h-antiPDI-488-Col4a1-TMR-Net1...                141   \n",
       "12  noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...                168   \n",
       "13  noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...                159   \n",
       "14  noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...                123   \n",
       "15  noc--Ctrl2-Noc4-4h-antiRCAS1-488-Col4a1-TMR-Ne...                142   \n",
       "\n",
       "    basal_dot_number  apical_area  basal_area  polarization_index    date  \\\n",
       "0                 64       167593      169534            0.464400  181024   \n",
       "1                111       157235      158387            0.296300  181024   \n",
       "2                 84       163184      164354            0.535900  181024   \n",
       "3                 60       153797      155194            0.455600  181024   \n",
       "4                 35       162976      164040            0.564700  181024   \n",
       "5                104       165244      200084            0.594800  181024   \n",
       "6                105       157376      158532            0.242600  181024   \n",
       "7                  6       159451      160557            0.766100  181024   \n",
       "8                 70       173329      175007           -0.223500  181024   \n",
       "9                 50       154296      156086            0.230300  181024   \n",
       "10                64       165194      167280            0.098420  181024   \n",
       "11               144       165598      171087            0.005778  181024   \n",
       "12               138       188091      189546            0.101900  181024   \n",
       "13               155       152429      153607            0.016590  181024   \n",
       "14               113       154518      155761            0.046370  181024   \n",
       "15               132       151302      152329            0.039870  181024   \n",
       "\n",
       "       treatment  \n",
       "0         Y27632  \n",
       "1         Y27632  \n",
       "2   Blebbistatin  \n",
       "3   Blebbistatin  \n",
       "4        Control  \n",
       "5        Control  \n",
       "6        Control  \n",
       "7        Control  \n",
       "8     Nocodazole  \n",
       "9     Nocodazole  \n",
       "10    Nocodazole  \n",
       "11    Nocodazole  \n",
       "12    Nocodazole  \n",
       "13    Nocodazole  \n",
       "14    Nocodazole  \n",
       "15    Nocodazole  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# third dataset: 181024-polarity-Golgi-ER-costaining-ctrl-bleb-noc-Y27632\n",
    "\n",
    "folder = folders[2]\n",
    "files = glob.glob( os.path.join(folder, '*polarization_summary.txt') )\n",
    "assert len(files) == 1\n",
    "df = pd.read_csv(files[0])\n",
    "\n",
    "# get date\n",
    "dates = []\n",
    "for i in df.file_name.tolist():\n",
    "    date0 = i.split('-')[0][2:]\n",
    "    dates.append(date0)\n",
    "df['date'] = dates\n",
    "\n",
    "# get treatment\n",
    "treatments = []\n",
    "for i in df.file_name.tolist():\n",
    "    treatment = i.split('-')[5]\n",
    "    if treatment == 'Ctrl' or treatment == 'ctrl':\n",
    "        treatment = 'Control'\n",
    "    if treatment == 'Bleb' or treatment == 'bleb':\n",
    "        treatment = 'Blebbistatin'\n",
    "    if treatment == 'Noc' or treatment == 'noc':\n",
    "        treatment = 'Nocodazole'\n",
    "    if treatment == 'colch':\n",
    "        treatment = 'Colchicine'\n",
    "    if treatment == 'collagenase':\n",
    "        treatment = 'Collagenase'\n",
    "    if treatment == 'cytochalasinD':\n",
    "        treatment = 'Cytochalasin D'\n",
    "    if treatment == 'latrunculinA':\n",
    "        treatment = 'Latrunculin A'\n",
    "    treatments.append(treatment)\n",
    "\n",
    "df['treatment'] = treatments\n",
    "df3 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>apical_dot_number</th>\n",
       "      <th>basal_dot_number</th>\n",
       "      <th>apical_area</th>\n",
       "      <th>basal_area</th>\n",
       "      <th>polarization_index</th>\n",
       "      <th>date</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-1-ctrl-2h-1-z-1-pp-pp</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>162372</td>\n",
       "      <td>178096</td>\n",
       "      <td>0.61540</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-1-ctrl-2h-2-z-1-pp-pp</td>\n",
       "      <td>225</td>\n",
       "      <td>88</td>\n",
       "      <td>149357</td>\n",
       "      <td>164840</td>\n",
       "      <td>0.47670</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-1-ctrl-2h-3-z-1-pp-pp</td>\n",
       "      <td>296</td>\n",
       "      <td>161</td>\n",
       "      <td>206676</td>\n",
       "      <td>237654</td>\n",
       "      <td>0.35780</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-1-ctrl-2h-4-z-1-pp-pp</td>\n",
       "      <td>315</td>\n",
       "      <td>127</td>\n",
       "      <td>231389</td>\n",
       "      <td>262740</td>\n",
       "      <td>0.47590</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-2-noc-2h-1-pp-pp</td>\n",
       "      <td>212</td>\n",
       "      <td>110</td>\n",
       "      <td>147321</td>\n",
       "      <td>148097</td>\n",
       "      <td>0.31910</td>\n",
       "      <td>201117</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-2-noc-2h-2-pp-pp</td>\n",
       "      <td>281</td>\n",
       "      <td>214</td>\n",
       "      <td>167222</td>\n",
       "      <td>182957</td>\n",
       "      <td>0.17920</td>\n",
       "      <td>201117</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-2-noc-2h-3-pp-pp</td>\n",
       "      <td>763</td>\n",
       "      <td>484</td>\n",
       "      <td>468977</td>\n",
       "      <td>531281</td>\n",
       "      <td>0.28210</td>\n",
       "      <td>201117</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-2-noc-2h-4-pp-pp</td>\n",
       "      <td>219</td>\n",
       "      <td>249</td>\n",
       "      <td>204624</td>\n",
       "      <td>221138</td>\n",
       "      <td>-0.02538</td>\n",
       "      <td>201117</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-3-colch-2h-1-pp-pp</td>\n",
       "      <td>194</td>\n",
       "      <td>341</td>\n",
       "      <td>149513</td>\n",
       "      <td>165071</td>\n",
       "      <td>-0.22840</td>\n",
       "      <td>201117</td>\n",
       "      <td>Colchicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-3-colch-2h-2-pp-pp</td>\n",
       "      <td>140</td>\n",
       "      <td>313</td>\n",
       "      <td>240399</td>\n",
       "      <td>271425</td>\n",
       "      <td>-0.32890</td>\n",
       "      <td>201117</td>\n",
       "      <td>Colchicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-3-colch-2h-3-pp-pp</td>\n",
       "      <td>286</td>\n",
       "      <td>301</td>\n",
       "      <td>236890</td>\n",
       "      <td>268127</td>\n",
       "      <td>0.03636</td>\n",
       "      <td>201117</td>\n",
       "      <td>Colchicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-4-collagenase-24h-2...</td>\n",
       "      <td>313</td>\n",
       "      <td>90</td>\n",
       "      <td>104719</td>\n",
       "      <td>105204</td>\n",
       "      <td>0.55500</td>\n",
       "      <td>201117</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-4-collagenase-24h-3...</td>\n",
       "      <td>349</td>\n",
       "      <td>171</td>\n",
       "      <td>166076</td>\n",
       "      <td>181829</td>\n",
       "      <td>0.38170</td>\n",
       "      <td>201117</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-4-collagenase-24h-4...</td>\n",
       "      <td>338</td>\n",
       "      <td>215</td>\n",
       "      <td>125229</td>\n",
       "      <td>140879</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>201117</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-4-collagenase-24h-5...</td>\n",
       "      <td>566</td>\n",
       "      <td>126</td>\n",
       "      <td>151928</td>\n",
       "      <td>167302</td>\n",
       "      <td>0.66370</td>\n",
       "      <td>201117</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-5-ctrl-24h-1-pp-pp</td>\n",
       "      <td>430</td>\n",
       "      <td>162</td>\n",
       "      <td>200446</td>\n",
       "      <td>216547</td>\n",
       "      <td>0.48290</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-5-ctrl-24h-2-pp-pp</td>\n",
       "      <td>1148</td>\n",
       "      <td>623</td>\n",
       "      <td>434521</td>\n",
       "      <td>496739</td>\n",
       "      <td>0.35620</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-5-ctrl-24h-3-pp-pp</td>\n",
       "      <td>750</td>\n",
       "      <td>411</td>\n",
       "      <td>326949</td>\n",
       "      <td>388704</td>\n",
       "      <td>0.36900</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-5-ctrl-24h-4-pp-pp</td>\n",
       "      <td>575</td>\n",
       "      <td>208</td>\n",
       "      <td>222311</td>\n",
       "      <td>238542</td>\n",
       "      <td>0.49570</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20201117-smFISH-Col4a1-TMR-5-ctrl-24h-5-pp-pp</td>\n",
       "      <td>622</td>\n",
       "      <td>271</td>\n",
       "      <td>150596</td>\n",
       "      <td>167068</td>\n",
       "      <td>0.43600</td>\n",
       "      <td>201117</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-1-ctrl-4h-1-pp-pp</td>\n",
       "      <td>401</td>\n",
       "      <td>145</td>\n",
       "      <td>176015</td>\n",
       "      <td>191703</td>\n",
       "      <td>0.50150</td>\n",
       "      <td>201129</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-1-ctrl-4h-2-pp-pp</td>\n",
       "      <td>185</td>\n",
       "      <td>92</td>\n",
       "      <td>84632</td>\n",
       "      <td>100048</td>\n",
       "      <td>0.40780</td>\n",
       "      <td>201129</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-1-ctrl-4h-3-pp-pp</td>\n",
       "      <td>479</td>\n",
       "      <td>231</td>\n",
       "      <td>209366</td>\n",
       "      <td>228689</td>\n",
       "      <td>0.38740</td>\n",
       "      <td>201129</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...</td>\n",
       "      <td>356</td>\n",
       "      <td>138</td>\n",
       "      <td>186707</td>\n",
       "      <td>202472</td>\n",
       "      <td>0.47330</td>\n",
       "      <td>201129</td>\n",
       "      <td>Cytochalasin D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...</td>\n",
       "      <td>622</td>\n",
       "      <td>271</td>\n",
       "      <td>234584</td>\n",
       "      <td>250968</td>\n",
       "      <td>0.42120</td>\n",
       "      <td>201129</td>\n",
       "      <td>Cytochalasin D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...</td>\n",
       "      <td>563</td>\n",
       "      <td>153</td>\n",
       "      <td>217857</td>\n",
       "      <td>234108</td>\n",
       "      <td>0.59630</td>\n",
       "      <td>201129</td>\n",
       "      <td>Cytochalasin D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...</td>\n",
       "      <td>764</td>\n",
       "      <td>193</td>\n",
       "      <td>295136</td>\n",
       "      <td>311203</td>\n",
       "      <td>0.61350</td>\n",
       "      <td>201129</td>\n",
       "      <td>Cytochalasin D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-1...</td>\n",
       "      <td>642</td>\n",
       "      <td>177</td>\n",
       "      <td>263053</td>\n",
       "      <td>279697</td>\n",
       "      <td>0.58820</td>\n",
       "      <td>201129</td>\n",
       "      <td>Latrunculin A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-2...</td>\n",
       "      <td>532</td>\n",
       "      <td>171</td>\n",
       "      <td>216230</td>\n",
       "      <td>231857</td>\n",
       "      <td>0.53870</td>\n",
       "      <td>201129</td>\n",
       "      <td>Latrunculin A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-3...</td>\n",
       "      <td>610</td>\n",
       "      <td>241</td>\n",
       "      <td>218483</td>\n",
       "      <td>234993</td>\n",
       "      <td>0.46270</td>\n",
       "      <td>201129</td>\n",
       "      <td>Latrunculin A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-4...</td>\n",
       "      <td>789</td>\n",
       "      <td>260</td>\n",
       "      <td>248466</td>\n",
       "      <td>264609</td>\n",
       "      <td>0.52740</td>\n",
       "      <td>201129</td>\n",
       "      <td>Latrunculin A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-4-collagenase-24h-1...</td>\n",
       "      <td>404</td>\n",
       "      <td>231</td>\n",
       "      <td>182270</td>\n",
       "      <td>199020</td>\n",
       "      <td>0.31260</td>\n",
       "      <td>201129</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-4-collagenase-24h-2...</td>\n",
       "      <td>499</td>\n",
       "      <td>266</td>\n",
       "      <td>229011</td>\n",
       "      <td>244769</td>\n",
       "      <td>0.33440</td>\n",
       "      <td>201129</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>20201129-SMG-Col4a1-smFISH-4-collagenase-24h-3...</td>\n",
       "      <td>486</td>\n",
       "      <td>222</td>\n",
       "      <td>188212</td>\n",
       "      <td>189210</td>\n",
       "      <td>0.37520</td>\n",
       "      <td>201129</td>\n",
       "      <td>Collagenase</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            file_name  apical_dot_number  \\\n",
       "0    20201117-smFISH-Col4a1-TMR-1-ctrl-2h-1-z-1-pp-pp                157   \n",
       "1    20201117-smFISH-Col4a1-TMR-1-ctrl-2h-2-z-1-pp-pp                225   \n",
       "2    20201117-smFISH-Col4a1-TMR-1-ctrl-2h-3-z-1-pp-pp                296   \n",
       "3    20201117-smFISH-Col4a1-TMR-1-ctrl-2h-4-z-1-pp-pp                315   \n",
       "4         20201117-smFISH-Col4a1-TMR-2-noc-2h-1-pp-pp                212   \n",
       "5         20201117-smFISH-Col4a1-TMR-2-noc-2h-2-pp-pp                281   \n",
       "6         20201117-smFISH-Col4a1-TMR-2-noc-2h-3-pp-pp                763   \n",
       "7         20201117-smFISH-Col4a1-TMR-2-noc-2h-4-pp-pp                219   \n",
       "8       20201117-smFISH-Col4a1-TMR-3-colch-2h-1-pp-pp                194   \n",
       "9       20201117-smFISH-Col4a1-TMR-3-colch-2h-2-pp-pp                140   \n",
       "10      20201117-smFISH-Col4a1-TMR-3-colch-2h-3-pp-pp                286   \n",
       "11  20201117-smFISH-Col4a1-TMR-4-collagenase-24h-2...                313   \n",
       "12  20201117-smFISH-Col4a1-TMR-4-collagenase-24h-3...                349   \n",
       "13  20201117-smFISH-Col4a1-TMR-4-collagenase-24h-4...                338   \n",
       "14  20201117-smFISH-Col4a1-TMR-4-collagenase-24h-5...                566   \n",
       "15      20201117-smFISH-Col4a1-TMR-5-ctrl-24h-1-pp-pp                430   \n",
       "16      20201117-smFISH-Col4a1-TMR-5-ctrl-24h-2-pp-pp               1148   \n",
       "17      20201117-smFISH-Col4a1-TMR-5-ctrl-24h-3-pp-pp                750   \n",
       "18      20201117-smFISH-Col4a1-TMR-5-ctrl-24h-4-pp-pp                575   \n",
       "19      20201117-smFISH-Col4a1-TMR-5-ctrl-24h-5-pp-pp                622   \n",
       "20       20201129-SMG-Col4a1-smFISH-1-ctrl-4h-1-pp-pp                401   \n",
       "21       20201129-SMG-Col4a1-smFISH-1-ctrl-4h-2-pp-pp                185   \n",
       "22       20201129-SMG-Col4a1-smFISH-1-ctrl-4h-3-pp-pp                479   \n",
       "23  20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...                356   \n",
       "24  20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...                622   \n",
       "25  20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...                563   \n",
       "26  20201129-SMG-Col4a1-smFISH-2-cytochalasinD-4h-...                764   \n",
       "27  20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-1...                642   \n",
       "28  20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-2...                532   \n",
       "29  20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-3...                610   \n",
       "30  20201129-SMG-Col4a1-smFISH-3-latrunculinA-4h-4...                789   \n",
       "31  20201129-SMG-Col4a1-smFISH-4-collagenase-24h-1...                404   \n",
       "32  20201129-SMG-Col4a1-smFISH-4-collagenase-24h-2...                499   \n",
       "33  20201129-SMG-Col4a1-smFISH-4-collagenase-24h-3...                486   \n",
       "\n",
       "    basal_dot_number  apical_area  basal_area  polarization_index    date  \\\n",
       "0                 41       162372      178096             0.61540  201117   \n",
       "1                 88       149357      164840             0.47670  201117   \n",
       "2                161       206676      237654             0.35780  201117   \n",
       "3                127       231389      262740             0.47590  201117   \n",
       "4                110       147321      148097             0.31910  201117   \n",
       "5                214       167222      182957             0.17920  201117   \n",
       "6                484       468977      531281             0.28210  201117   \n",
       "7                249       204624      221138            -0.02538  201117   \n",
       "8                341       149513      165071            -0.22840  201117   \n",
       "9                313       240399      271425            -0.32890  201117   \n",
       "10               301       236890      268127             0.03636  201117   \n",
       "11                90       104719      105204             0.55500  201117   \n",
       "12               171       166076      181829             0.38170  201117   \n",
       "13               215       125229      140879             0.27760  201117   \n",
       "14               126       151928      167302             0.66370  201117   \n",
       "15               162       200446      216547             0.48290  201117   \n",
       "16               623       434521      496739             0.35620  201117   \n",
       "17               411       326949      388704             0.36900  201117   \n",
       "18               208       222311      238542             0.49570  201117   \n",
       "19               271       150596      167068             0.43600  201117   \n",
       "20               145       176015      191703             0.50150  201129   \n",
       "21                92        84632      100048             0.40780  201129   \n",
       "22               231       209366      228689             0.38740  201129   \n",
       "23               138       186707      202472             0.47330  201129   \n",
       "24               271       234584      250968             0.42120  201129   \n",
       "25               153       217857      234108             0.59630  201129   \n",
       "26               193       295136      311203             0.61350  201129   \n",
       "27               177       263053      279697             0.58820  201129   \n",
       "28               171       216230      231857             0.53870  201129   \n",
       "29               241       218483      234993             0.46270  201129   \n",
       "30               260       248466      264609             0.52740  201129   \n",
       "31               231       182270      199020             0.31260  201129   \n",
       "32               266       229011      244769             0.33440  201129   \n",
       "33               222       188212      189210             0.37520  201129   \n",
       "\n",
       "         treatment  \n",
       "0          Control  \n",
       "1          Control  \n",
       "2          Control  \n",
       "3          Control  \n",
       "4       Nocodazole  \n",
       "5       Nocodazole  \n",
       "6       Nocodazole  \n",
       "7       Nocodazole  \n",
       "8       Colchicine  \n",
       "9       Colchicine  \n",
       "10      Colchicine  \n",
       "11     Collagenase  \n",
       "12     Collagenase  \n",
       "13     Collagenase  \n",
       "14     Collagenase  \n",
       "15         Control  \n",
       "16         Control  \n",
       "17         Control  \n",
       "18         Control  \n",
       "19         Control  \n",
       "20         Control  \n",
       "21         Control  \n",
       "22         Control  \n",
       "23  Cytochalasin D  \n",
       "24  Cytochalasin D  \n",
       "25  Cytochalasin D  \n",
       "26  Cytochalasin D  \n",
       "27   Latrunculin A  \n",
       "28   Latrunculin A  \n",
       "29   Latrunculin A  \n",
       "30   Latrunculin A  \n",
       "31     Collagenase  \n",
       "32     Collagenase  \n",
       "33     Collagenase  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dfs = [df1, df2, df3]\n",
    "df = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>apical_dot_number</th>\n",
       "      <th>basal_dot_number</th>\n",
       "      <th>apical_area</th>\n",
       "      <th>basal_area</th>\n",
       "      <th>polarization_index</th>\n",
       "      <th>date</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...</td>\n",
       "      <td>314</td>\n",
       "      <td>33</td>\n",
       "      <td>154073</td>\n",
       "      <td>155148</td>\n",
       "      <td>0.81100</td>\n",
       "      <td>181022</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...</td>\n",
       "      <td>337</td>\n",
       "      <td>85</td>\n",
       "      <td>167934</td>\n",
       "      <td>200189</td>\n",
       "      <td>0.65070</td>\n",
       "      <td>181022</td>\n",
       "      <td>Blebbistatin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...</td>\n",
       "      <td>332</td>\n",
       "      <td>60</td>\n",
       "      <td>161094</td>\n",
       "      <td>162363</td>\n",
       "      <td>0.69590</td>\n",
       "      <td>181022</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...</td>\n",
       "      <td>247</td>\n",
       "      <td>61</td>\n",
       "      <td>158778</td>\n",
       "      <td>160197</td>\n",
       "      <td>0.60670</td>\n",
       "      <td>181022</td>\n",
       "      <td>Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...</td>\n",
       "      <td>212</td>\n",
       "      <td>283</td>\n",
       "      <td>179946</td>\n",
       "      <td>214538</td>\n",
       "      <td>-0.05646</td>\n",
       "      <td>181022</td>\n",
       "      <td>Nocodazole</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           file_name  apical_dot_number  \\\n",
       "0  Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...                314   \n",
       "1  Bleb-17h-Col4a1-TMR-Net1-655_2018_10_22__21_17...                337   \n",
       "2  Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...                332   \n",
       "3  Ctrl-4h-Col4a1-TMR-Net1-655_2018_10_22__23_02_...                247   \n",
       "4  Nocodazole-4h-Col4a1-TMR-Net1-655_2018_10_22__...                212   \n",
       "\n",
       "   basal_dot_number  apical_area  basal_area  polarization_index    date  \\\n",
       "0                33       154073      155148             0.81100  181022   \n",
       "1                85       167934      200189             0.65070  181022   \n",
       "2                60       161094      162363             0.69590  181022   \n",
       "3                61       158778      160197             0.60670  181022   \n",
       "4               283       179946      214538            -0.05646  181022   \n",
       "\n",
       "      treatment  \n",
       "0  Blebbistatin  \n",
       "1  Blebbistatin  \n",
       "2       Control  \n",
       "3       Control  \n",
       "4    Nocodazole  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='treatment', ylabel='polarization_index'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hQwdt2bIl37G8vb3l7e1dJuXGDTntyzkjnnLkjHiaOyKK9mfAIpiGAVZlM27Qw/bkyZPq2LGjtm3bpmrVqql9+/Z677331LZt23z7Hj16VF27dtXRo0eLdey0tDT5+voqNTVVPj4+pVxy5BY7O6HA+Roigvy0fHxHF5QIgCNu/lIi3WhC5ksJXMHR+7dbNDnVqVNH06dPV0xMjFq2bKkRI0aobdu26tOnj7Zv3+7q4qGYGPEEWNutpmFgkUa4M4draA4dOqRGjfJ2+NywYYO6du1amuUqNdTQlB1qaABrazp1la5mZOXbXrWyl/7xYCS1NyhTTq+hiY6O1gcffCBJunr1qp588knFxcU5XFCUP0WNeOKbHeD+ipqGgUk04e4cDjRbtmzRxx9/rP79+ysqKkpVq1bVjz/+6IyywWIKm1HTSCyPAFhAUV9KaFKGu3N4lFNAQIDatWunjz76SDabTVFRUbrjjjucUTZYUEEzasbOTsi3X843O6qqAfeR86VkzoZDOnD6okL9a2h810a6LyyARRrh9hwONM2bN1enTp20Z88enT59WiNHjtSCBQv0xRdfOKN8cEOODuvkmx1gHYVN8/9ETIjGfbxDuXtdMokm3InDTU6vvfaa3n//ffn4+Cg0NFQJCQnq0qWLM8oGN3Sr1bUL6ivD8giA9bFII9ydwzU0ffr00TvvvKOffvpJM2bM0Pz58/XMM884o2xwQ7fqGFjQxHrjujTSjydT+WYHWByLNMKdOVxD8/TTTysxMVFr166VMUZff/21nnzySWeUDW6oqOajwsLOlsNn+WYHAHAqh2toNm3apF27dqlly5aqUaOGvvjiC4WHhzujbHBDRXUM3J9ScJ+YA6cv8s0OAOBUDtfQeHl5yRhjX0zy6tWreRaSRPlW1LBO+soAAFzF4STy0EMPaciQITp79qz+8Y9/qHPnznrooYecUTa4oaI6BhYVdgAAcKbbWpxy7dq1WrVqlbKystStWzfdf//9zihbqWDpg7K1ek9KgXNYALAmVt+Gqzh6/y52oNm0aVORj7vr0G0CDQDcHlbfhis5ev8udqfgadOmSZLOnj2rQ4cOqUOHDvLy8tKWLVsUHh6ub7/99vZLDQBwO0VN00CggbspdqBZv369JKlv375aunSpfcXtY8eOaezYsc4pHQDAZZjlG1bicKfgI0eO2MOMJNWrV0/Hjh0r1UIBAFyPkYuwEofnoWndurVGjBihoUOHyhijBQsWqHPnzs4oG2AZdJxEecT6TbASh0c5Xb9+XW+99ZY2bNggm82mHj166IknnlDFig5nozJBp2A4Gx0nUZ4xchGu4rRRTjc/SWpqqnL/13r16jl6mDJBoIGzxc5OKHD25IggPy0f39EFJQKcg5pIlCWnjXLKMWPGDL388suqVauWbDabfdbgw4cP31aBAauj4yQ8wc01kTmLz1ITCXfhcKCJj4/XwYMHFRDAGxiQil7fCigvGMINd+fwKKegoCDCDJALSz7AE1ATCXfncA1Nq1atNHDgQPXq1UtVqlSxb3/kkUdKtWCAVeSsb0XHSZRn1ETC3TkcaC5cuCBfX19999139m02m41AA4/WMyzAI6vd6STqORjCDXd3W6OcrIRRToBzMFzd8zCEG2XJaaOc7r//fn355Zdq0KCBbDd3GJAY5QR4GDqJeh5PrYmENRQ70MTHx0uSNmzYUOg+GzduVHR0dIkLBcD90UkUgDspdqAJDAyUJAUHBxe6z9NPP62dO3eWvFSAxXhiXxI6iQJwJw4P2y5KOe+OAxQopy9J0olUXc3Isk84tnpPiquL5lQMV/c8q/ekKHZ2gppOXaXY2Qnl/j0OaynVQFNQ3xqgvCuqL0l5ljNcPSLIT1UreykiyE/vjoiik2g55anBHdbhnitKAhbiyX1J6CTqOegEDndXqjU0gCdq7F+9wO30JUF54snBHdZAHxqghOhLAk9AcIe7u60mpz179ujcuXN5AkyXLl30r3/9q9QKBlgFSx/AEzBTMNydwzMFT5gwQcuWLVNISIi9E7DNZtO6deucUsCSYqZgACgdzBSMsuTo/dvhQBMSEqKkpCRVq1bttgtZlgg0AABYj6P3b4f70AQHB8vb2/u2CgcAAOAMDvehadSokTp27Kju3burSpUq9u1/+tOfSrVgAAAAxeVwoKlTp47q1KkjiVFNyM8TlwAAALiew31oJOnMmTPaunWrMjIy1L59ewUEuO8Niz40ZSdnJtHcbDZp7ogoQg0AwCFO70OzevVqRUZGav78+froo48UHh6u5cuX31ZhUb546hIAAADXc7jJ6f/+7/+UkJCgBg0aSJIOHz6sAQMGKDY2ttQLB2thJlEAgKs4HGgyMjLsYUaSGjZsSF8aSLoxk2jSidR82z1hJlH6DgGAaznc5FS/fn3NmjVLaWlpSktL06uvvqrg4GBnlA0W46lLALAKMQC4nsOB5r333tMPP/ygkJAQNWjQQNu2bdO8efOcUTZYTM4SABFBfqpa2UsRQX56d0RUuZ9JlL5DAOB6Djc51apVS4sWLXJGWVAO9AwL8LimFvoOAYDrFTvQ3H///fryyy/VoEED+xpOuR0+fLhUCwZYhSf3HQIAd1HsQBMfHy9J2rBhg7PKAlgSqxADgOsVuw9NYGCgJGnixIkKDg7O8/PII484rYCAu/PUvkMA4E6KXUMzcOBAJSYmKjk5WQ0bNrRvz8zMZJQTPJ4n9h2SGK4OwH0Ue+mDtLQ0nTt3ThMmTNDbb79t316xYkUFBgaqQgWHB0yVCZY+AJyDpS4AOJPTlj7w8fFR/fr19eWXX+rcuXP65ZdfdOzYMe3fv1/z588vUaElaeHChWrWrJlCQ0M1e/bsfI9/8803ioqKUmRkpLp3765jx46V+DkB3D6GqwNwJw4P246Li9OmTZv022+/qWnTpkpMTFSnTp00evTo2y7EyZMnNWXKFO3cuVNVqlRRhw4dFB0drfDwcEnS9evX9fDDD2vTpk1q3Lix4uPj9dRTT7GGFOBCDFcH4E4cbidat26dfvrpJw0ePFjz5s3Txo0bdeXKlRIVYs2aNerevbtq1qypatWqafDgwVqyZIn98fT0dL3xxhtq3LixJKlly5b65ZdfCjxWenq6fRbjnB8Apa+xf/UCtzNcHYArOBxoAgIC5OXlpWbNmmn37t2KiorSuXPnSlSI5ORk1a5d2/57YGCgTp06Zf+9Ro0aevDBByVJWVlZevHFF9W/f/8CjzVjxgz5+vraf4KCgkpUNgAF89SlLgC4J4cDTZ06dTR9+nS1bdtW77zzjj799FNdu3atRIXIzs7OM1mfMabATsZXr17VkCFDlJ2drRdeeKHAY02ZMkWpqan2n+PHj5eobAAKxnB1AO7E4T40//znP7Vy5Uq1b99eAwYM0KJFi/Tee++VqBB169bV5s2b7b+npKTkqbGRpPPnz6tv375q2LChFi1apEqVKhV4LG9vb3l7e5eoPACKx1OHqwNwPw7X0CxevFhDhgyRdGOSveXLlyshIaFEhejRo4fWrFmjX3/9VZcvX9bixYvVq1evPPsMHDhQ7dq104IFCwoNM4CrrN6TotjZCWo6dZViZyew0jYAlDGHA824ceN033335ek38/nnn5eoEDnNWDExMWrZsqVGjBihtm3bqk+fPtq+fbu++eYbbdiwQWvXrlXLli0VGRmpnj17lug5gdKSMx9L0olUXc3IUtKJVI37eAehBgDKkMNNTmFhYXrwwQfVvn17LVmyROHh4Srm3HxFGj58uIYPH55n21dffWX/d2k8B+AMRc3HQnMMAJQNhwONzWZTXFycQkND1a9fP7366quqXLmyM8oGWALzsQCA6zkcaHJqSrp27ap169YpNjaWWXvh0Rr7V1fSidR825mPBQDKjsN9aObMmWP/d8OGDbVlyxZNmTKlVAsFWAnzsQCA6xW7hmbevHkaM2aM/v3vf+ubb75xZpkAS8mZj2XOhkM6cPqiQv1raHzXRszHAgBlqNiBJnenXDroAnkxHwsAuFaxA83YsWMlSfv379cnn3zitAIBAAA4yuE+NAcOHGDBRwAA4FYcHuUkSfXq1VOTJk1UtWpV+7Z169aVWqEAAAAc4XCgmTlzpjPKAQAAcNscbnKKjo6Wj4+PKlSoIJvNpuzsbB08mH+mVAAAgLLicA1NXFycNm3apN9++01NmzZVYmKiOnXqpNGjRzujfAAAALfkcA3NunXr9NNPP2nw4MGaN2+eNm7cqCtXrjijbAAAAMXicKAJCAiQl5eXmjVrpt27dysqKirPytsAAABlzeEmpzp16mj69OmKiYnRlClTlJ2drWvXrjmjbAAAAMXicA3NP//5TzVs2FDt27fXgAEDtGjRIsXHxzujbAAAAMViM8Vcx+CXX34p8vF69eqVSoFKW1pamnx9fZWamiofHx9XFwcAABSDo/fvYjc5RUdHy2azFbiOk81m0+HDhx0rKQAAQCkpdqA5cuSIM8sBAABw2xzuQ3PmzBk9+OCDuuuuu+Tr66vY2FglJyc7o2wAAADF4nCgGTt2rNq0aaPDhw/r2LFj6tixo+Li4pxRNgAAgGIpdqfgHJGRkUpMTMyzLTw8XLt37y7NcpUaOgWjtKzek6I56w9q/+lLauxfXU/EhKhnWICriwUA5ZKj92+Ha2gqVKigY8eO2X8/evSoKla8rUW7ActYvSdFYxfsUNKJVF3NyFLSiVSN+3iHVu9JsT8eOztBTaeuUuzsBPt2AEDZcDiJvPzyy+rYsaPatm0rm82m77//XvPmzXNG2QC3MWd9/gVYjZHmbDgkSRq7YId9e07YmTsiihocACgjDtfQdO/eXRMnTtSyZcu0bNkyjRs3Tr1793ZG2QC3sf/0pQK3Hzh98ZZhBwDgfA4HmlGjRmn79u1avny5Pv/8cyUmJmrixInOKBvgNhr7Vy9we6h/jSLDDgCgbDjc5LRjxw7t3bvX/nu/fv0UFhZWqoUC3M0TMSEa9/EO5e5Cb7NJ47s20tvrDyrpRGq+/xPqX6MMSwgAns3hGpqGDRvq4MH/VrGfPn3abZc9AEpLz7AAzR0RpYggP1Wt7KWIID+9OyJK94UF6ImYENlseffPCTsAgLLh8LDtmJgYbdu2TV26dFHFihWVkJCgwMBABQTc6Py4bt06pxT0djFsG2Vh9Z4UzdlwSAdOX1Sofw2N79pI99EhGABum6P3b4cDzcaNG4t8PDo62pHDOR2BBgAA63Ha4pQ53C2wAAAAONyHBgAAwN0QaAAAgOURaAAAgOURaAAAgOURaAAAgOURaAAAgOU5PGwb8FSr96RozvqD2n/6khr7V9cTMSGspg0AboIaGqAYVu9J0dgFO5R0IlVXM7KUdCJV4z7eodV7UlxdNACAqKEBimXO+oP5thkjzdlwSD3DAqi9AQAXI9AAxbD/9KUCtx84fdFee5Mjp/Zm7ogoQk05RYAF3A9NTkAxNPavXuD2UP8aRdbeoPyh+RFwTwQaoBieiAmRzZZ3m80mje/aqMjaG5Q/BFjAPRFogGLoGRaguSOiFBHkp6qVvRQR5Kd3R0TpvrCAImtvUP4QYAH3RB8aoJh6hgUU2E/iiZgQjft4h4z577ac2huUP439qyvpRGq+7QRYwLWooQFKqKjaG5Q/RTU/AnAdmzG5v1eWP2lpafL19VVqaqp8fHxcXRxYHKNbIP3/74MNh3Tg9EWF+tfQ+K6NCLBAKXP0/k2gAYrp5uHZ0o1v5gzPBoDS5+j9myYnoJgY3QIA7otOwUAx3WpyPZqiAMB1qKEBiqmw4dm/r+HNRGsA4GJuE2gWLlyoZs2aKTQ0VLNnz873eGJiolq3bq3GjRtr9OjRysjIcEEp4ckKG91SEJqiAKBsuUWgOXnypKZMmaLNmzcrMTFR8fHx2r17d559RowYoTfeeEP79++XJM2dO9cVRYUHK2x49um09AL3Z6I1ACg7btGHZs2aNerevbtq1qwpSRo8eLCWLFmi8PBwSdKxY8d05coVdezYUZI0cuRIvfDCC3ryySfzHSs9PV3p6f+9waSlpZXBGcBTFDS5HhOtAYDruUUNTXJysmrXrm3/PTAwUKdOnSr247nNmDFDvr6+9p+goCDnFRwQE60BgDtwi0CTnZ0tW647gjFGFSpUKPbjuU2ZMkWpqan2n+PHjzuv4ICYKRgA3IFbNDnVrVtXmzdvtv+ekpKSp0ambt26eWpkbn48N29vb3l7ezuvsPBYRQ3NLmydJwBA2XCLGpoePXpozZo1+vXXX3X58mUtXrxYvXr1sj8eHBysKlWq2EPPBx98oN69e7uquPBAObMEMzQbANyTWwSaOnXqaPr06YqJiVHLli01YsQItW3bVn369NH27dslSZ988okmT56sJk2a6OrVq3rqqadcXGp4klvNErx6T4piZyeo6dRVip2dQNABgDLGWk5AMTSdukpXM7Lyba9a2Uv/eDCSNZ4AoJSxlhPgBIXNEhzqX4M1ngDADRBogGIoamh2UWs8AQDKBoEGKIaihmYXVXsDACgbbjFsG7CCwoZmPxETonEf71Du3mhMrAcAZYsaGqCEmFgPAFyPGhqgFDCxHgC4FjU0AADA8gg0AADA8mhyAkpBUes8AQCcjxoaoIRY5wkAXI9AA5QQMwUDgOsRaIASYqZgAHA9Ag1QQswUDACuR6ABSqiodZ4AAGWDQAOUEDMFA4DrMWwbKAXMFAwArkUNDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDwCDQAAsDyXB5oLFy6ob9++atq0qbp06aJTp07l2yc9PV0jR45UeHi4wsLC9Prrr5d9QQEAgNtyeaB54YUX1KFDB+3du1dxcXGaMGFCvn3efPNNeXl56ccff9TWrVv17rvvaufOnS4oLQAAcEcVXV2AlStXauPGjZKkYcOG6fHHH9f169dVuXJl+z7t2rXTAw88IJvNpurVq6tRo0Y6fvy4WrVqle946enpSk9Pt/+elpbm/JMAAAAu5fIamuTkZNWuXVuSVLFiRfn5+em3337Ls0+XLl0UGhoqSUpISNDWrVvVuXPnAo83Y8YM+fr62n+CgoKcewIAAMDlyizQfPbZZwoICMjz06ZNGxlj8uxnjFGFCgUXa82aNRo8eLA+/fRT3XnnnQXuM2XKFKWmptp/jh8/XurnAgAA3IvN3JwoyliDBg20efNm1a1bV5mZmbrzzjt19uxZVapUKc9+CxYs0P/+7/9qyZIl6tSpU7GPn5aWJl9fX6WmpsrHx6e0iw8AAJzA0fu3y5uc+vTpow8++ECS9Omnn6pz5875wszatWv1/PPPa8OGDQ6FGQAA4BlcXkNz7tw5jRw5UocOHZKfn58++eQT1a9fXytWrNCKFSv03nvvqWPHjjpw4IC9r40kTZs2TQMGDLjl8amhQVlYvSdFc9Yf1P7Tl9TYv7qeiAlRz7AAVxcLACzL0fu3ywONsxFo4Gyr96Ro7IIdebbZbNLcEVGEmnKKAAs4n+WanACrm7P+YL5txkhzNhxyQWngbDkBNulEqq5mZCnpRKrGfbxDq/ekuLpogEcj0AAltP/0pQK3Hzh9sYxLgrJAgAXcE4EGKKHG/tUL3B7qX6OMS4KyQIAF3BOBBiihJ2JCZLPl3WazSeO7NnJNgeBUBFjAPRFogBLqGRaguSOiFBHkp6qVvRQR5Kd3R0TpPjqJlksEWMA9McoJABy0ek+K5mw4pAOnLyrUv4bGd21EgAVKmaP3b5cvTgkAVtMzLIBh2oCbockJAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYHoEGAABYXkVXFwAArGb1nhTNWX9Q+09fUmP/6noiJkQ9wwJcXSzAo1FDAwAOWL0nRWMX7FDSiVRdzchS0olUjft4h1bvSXF10QCPRqABAAfMWX8w3zZjpDkbDrmgNAByEGgAwAH7T18qcPuB0xfLuCQAciPQAIADGvtXL3B7qH+NMi4JgNwINADggCdiQmSz5d1ms0njuzZyTYEASCLQAIBDeoYFaO6IKEUE+alqZS9FBPnp3RFRuo9RToBLMWwbABzUMyyAYdqAm6GGBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWB6BBgAAWF65X8vJGCNJSktLc3FJAABAceXct3Pu47dS7gPNxYsXJUlBQUEuLgkAAHDUxYsX5evre8v9bKa40ceisrOzlZycrBo1ashms5Xpc6elpSkoKEjHjx+Xj49PmT63K3HenLcn4Lw5b0/gyvM2xujixYuqXbu2KlS4dQ+Zcl9DU6FCBdWtW9elZfDx8fGoD0AOztuzcN6ehfP2LK467+LUzOSgUzAAALA8Ag0AALA8Ao0TeXt7a9q0afL29nZ1UcoU5815ewLOm/P2BFY673LfKRgAAJR/1NAAAADLI9AAAADLI9AAAADL87hAc/ToUVWuXFmRkZGKiIhQ06ZNFRcXp2vXrqlr167asGFDkf+3fv36BT5Wv359HT16NN/2P/3pT1qxYkWhx/zyyy/12muvFVnmI0eOaPTo0ZKk7du367HHHity/9KQlpamJ598UuHh4YqMjFSnTp307bff3taxpk2bps2bNzv0fwp7PR119OhR2Ww2ff311045fkFK+9gvvviiXnzxxVI7Xo64uDg9+OCDebatX79e9erV086dO9WhQwdFRESoffv2SkpKkiRFRkbaf5o2baoKFSrowIEDun79up566im1bNlSYWFh+ve//y1JSk1NVWxsrP19tGbNGkk3Jsz64x//qLCwMIWFhem5554r9fOTHH8fF/UZv9Xjffr0UXJycqH/91aP366SnOPIkSP1wQcflHqZSsvtXIfi4+P16aef3tbzOTr56u2+fnPnztXcuXOLvf+kSZPk5eVlv281adJEf/7zn5WVlVXo/7md625pyXkdHT1PSdq/f78qVKigDz/80OHnLfcT6xWkdu3aSkxMlHTjwvrAAw9o/vz5TnmuP//5z0U+vn379lse49ixYzp06JAkqXXr1nrvvfdKpWyFyc7OVu/evdWtWzft2rVLFStW1LZt29S3b1/t3LnT4WUkNm7cqJiYGCeV9tYqVaqksWPHavfu3Q5N0lTezZo1S82bN9eKFSvUv39/XblyRWPGjNH777+vhx9+WO+88466dOmib775Ro888oiSkpLsnxtJGjNmjAYMGKDQ0FD99a9/1W+//aadO3dq37596t69u06cOKGXX35ZYWFhWr58uX766Sf16NFDycnJWrJkiZKSkpSUlCRjjO677z4tXbpUAwYMKLXzK+338a189dVXJXr8dpT1OZal2z23b7/9Vl27di3bwjpo3LhxDv+fGjVq2D9/ly5dUmxsrLKysvTSSy8VuL+rr7vS7Z3n+++/r8GDB2vu3Ll69NFHHfvPxsMcOXLEBAcH23+/evWquffee83nn39uoqOjzfr1640xxsyaNcu0atXKtGjRwkyYMMFkZGSYI0eOGH9/fzNkyBDTokUL88ADD5hz584ZY4wJDg42I0eONC1btjTt2rUzSUlJxhhjHn30UTN//nxz5coVM2jQIBMZGWmaNm1q5s6da3788Ufj7+9v/P39TXx8vDlx4oTp1auXadeunQkODjb/93//Z4wxplmzZqZatWpm7NixZv369SY6OtoYY0x0dLR57rnnTIcOHUxwcLCZP39+qbxG33zzjQkKCjJZWVl5tq9atcqcOHHC/O1vfzNNmzY1zZs3N5MnTzaZmZnmyJEjpkWLFmbkyJGmRYsWpm3btubw4cPm/fffN9WqVTONGjUyu3btMtHR0eaBBx4wd999t/nuu+/MV199ZSIjI014eLiJjY01KSkp9tfzyJEjJT6XnL/3Y489ZkaNGmXfnnP8gs7FGGPefvtt06RJE9O0aVMzadIkk52dbU6fPm369etnwsPDTcuWLc3XX39tjDHm7Nmzpnfv3qZ58+ZmyJAhxt/f3xw5csSkpqaawYMHm3vuucfUr1/fjBo1ymRnZ5s33njDREREmIiICNOkSRMjyezfv7/Q40+bNs1MmzbN/rdp166dadmypbn//vtNcnJyiV6fVatWmbp165qLFy+aZ5991kyYMMFkZWWZf/7zn/Z9zpw5Y/z8/PL8v82bN5smTZqY9PR0Y4wxzZs3t7/njTFmz549JjMz02RnZ5uMjAxjjDFfffWVadasmTHGmB07dpgdO3bY9x8/frx5/fXXS3QuN7vd93HO9eH48ePmvvvuM2FhYaZ169Zm+/bt9mvAsGHD7H+nQ4cOGWP++55KT08348aNM40bNzZhYWHmww8/zPP4/PnzzZAhQ0yvXr1MaGioGTx4sP11XLhwoYmKijIRERFm+PDhJi0tzannmHN9MsaY//f//p9p166dufvuu010dLQ5deqUMcaYzz77zNx9992mZcuWZvTo0ebRRx81xhizc+dO06VLF9OyZUsTExNj9u7da4wp/LpU2PXtwIEDpkOHDiYiIsK0atXKfPvtt8YYY9555x3j7e1tIiMj8xw/59weffRR8/bbb9vPuW/fvmbVqlXmd7/7nQkKCjIrV64s9DN14cIFM2TIENOkSRPTokULs2rVKmOMMZLM448/bv9sbtu2zRhjzIYNG0zHjh1Nq1atTKNGjcznn39erNcvKyvLjBs3zrRo0cI0bdrU/jnO/ZmuVauWmTRpkomKijJhYWFm+/bt+f7OEydONL6+vnm2ff/99+auu+4yb775pmnXrp1p3ry5adWqlfnpp59ued3NfeufP3++/W8aHBxspk6datq1a2dCQkLsr1dhn4Xc99Lc55RzfEfPMzMz09SuXdv85z//Mf7+/iYxMTHfPkXxyEBTqVIlExERYVq0aGH8/PxMZGSkuXTpkj3QfPPNN2bQoEH2C3JcXJyZPXu2OXLkiLHZbGbTpk3GGGOeffZZM2nSJGPMjTfCX/7yF2OMMStXrjSRkZHGmP++4ZcvX24GDBhgjLnxwX744YeNMXn/4DNnzjTvv/++McaYixcvGh8fH3PmzJk8IebmQDNhwgRjzI2Ly5133lkqr9HMmTNNv379Cnzs66+/Nm3atDGXL182GRkZpn///nlemx9++MEYY8xTTz1lJk+ebC9nTlCMjo62X8hOnz5tateubb8hvPLKK2bw4MHGmNIPNKmpqaZevXr2C1dwcLB55513CjyXnTt3mpCQEHP+/HmTlZVl+vbtazZu3GiGDBliXnnlFWOMMYcOHTKBgYEmJSXFjB8/3jz//PPGmBs3eknmyJEjZuHChfb3REZGhgkNDc33IR4+fLh54YUXjDGm0OPnvEd+++03ExkZaQ/RH3/8sf31KolRo0aZwYMHm7CwMHP58uV8j48dO9Y88sgjebZ17drVLFmyxP57lSpVzOzZs02nTp1Mu3btzLp16/Ls361bN+Pl5ZUnKOXYt2+fueuuu8y+fftKfC653e77OOciHRsba9566y1jzI0bWp8+fezv8y1bthhjjHn66afNs88+a4z573v2H//4hxkyZIjJysoyv/32m2nWrJm5dOlSnkBTt25dc+HCBZOZmWlatWplVqxYYfbu3Ws6depkrl69aowx5q9//av92M46x5zr04EDB8wDDzxgD0YjR440r776qvn1119NQECAOX78uMnMzDSxsbHm0UcfNdevXzeRkZHm6NGjxhhjEhISTOvWrY0xhV+XCru+Pf300+aNN94wxtwIva+88oq5fv26qV27tunRo0e+4+fYuHGjueeee4wxxqSkpJhGjRqZ7OzsPCGjsM/UxIkTzTPPPGOMuRGocq7XksyiRYuMMca88cYb9s/XoEGDzE8//WR/LzRv3rxYr19SUpJp2bKlMcaYS5cumT/84Q/m6tWr+W7+OZ+l1157zQwcODDf37KgQHP58mUjyXTs2NFcuXLFGGPMiy++aH/tC7vu5jxnjpsDzauvvmqMMebzzz83rVq1MsYU/llwJNAU5zy/+OILEx4eboy58SVn3Lhx+fYpisc3OWVkZGj48OGaMGGC/fFVq1Zp69atioqKkiRdu3ZNlSpVUt++fRUSEqLOnTtLkh5++OE8VWKjRo2SdKOtfMSIEbpw4YL9sdatW+vJJ59U79691bt3b82cOTNfuZ555hmtWbNGM2fO1H/+8x+lp6fr8uXLRZ5L3759Jd3o23Du3DnHX4wCVKhQQVWqVCnwsbVr12r48OGqWrWqpBvn/OGHH6pv3776/e9/r9atW9vLs2nTpgKP0aFDB0nSDz/8oDZt2qhhw4aSbjRhzJgxo1TO4WY+Pj6Kj49XXFycdu/eXeS5XLt2Tf369ZOfn5+kG/2cJGnQoEF69913JUkNGzZUu3bttHXrVm3YsEELFy6UJHXq1Ml+PsOGDdOWLVv0+uuv66efftKZM2d06dIle5lefvllpaWl2Zsl161bV+Dxc3z33Xc6evSovRo5OztbXl5eJX5tXnvtNQUHB2vZsmX210KSsrKyNGnSJG3fvl1r1661b9+3b5+OHTumgQMH2rdlZmbqyJEj2rRpk3bv3q2ePXtq37599ia+tWvX6tixY+rQoYPat2+vpk2bSpJ27dql2NhYzZo1S3fffXeJzyW3230f597no48+kiRFR0crOjpaR48eVWBgoNq3by9JCg8Pz/c+X7t2reLi4lShQgXVrFlTe/bsyff8HTt2tL82zZs317lz57RmzRrt379f99xzj6Qb16ZGjRo59RxzhISE6NVXX1V8fLx+/vlnJSQkqH79+tq8ebPat29vXw9v5MiRWrZsmX7++Wft379fsbGx9mOcOXNG169fl1Twdamw61vv3r01fPhwbdu2Tb1799aTTz6pn3/+WWfOnNEPP/ygyMjIPMevXLmyJKlLly46e/asDhw4oBUrVuihhx7K1wemsM/U2rVr9cknn9jPfdeuXfb/k/O+Dg8P19KlSyVJCxYs0BdffKHFixfr+++/z/M5Lur1CwkJUUZGhrp06aI+ffrolVdeKfDvlfv1KqrPZW455zp//nx9+umn2r9/v1avXm1/vW6Wc929lYL+doV9Fhx1q/N8//33NWzYMEnS8OHD1atXL82cOVPVq1cv1vE9rlPwzSpVqqQRI0Zo27Zt9m05F/LExEQlJiZq69at9htt7htIdna2Klb8bybM/ZgxRpUqVbL/Xrt2be3bt0+PP/649u3bp1atWuUJPJI0efJkzZkzRw0aNNDUqVN11113ydxi3sOcD0dpriTepk0bbd++Pd9zT5s2TWvWrMnzXMYYZWRk5ClLTnkKK3vOBTYrK6vQYznDfffdp/vuu0/PPPNMkc9fsWLFPNuTk5N17ty5fB3wcva/+Vxz3hNvvPGGnn/+ef3+97/XU089pWbNmtn3W7p0qRYuXKhPPvnE/lyFHT9HVlaWOnXqZH9fbt++XatWrSrx6+Lr6ys/P788nV3T09M1aNAg7d27V+vWrcvT92jp0qUaOnRontcoICBADz74oGw2m1q0aKGgoCD9/PPPWrdunX799VdJUnBwsDp06GC/wa9Zs0Y9e/bUP/7xDz3yyCMlPo+b3e77OEfuz7YkexDO/bku6H1+8/vnwIEDunbtWp59CvqsZGVlaciQIfa/77Zt227Z4bSk55jjhx9+UM+ePSVJf/jDHzRgwAAZY/IF5tzv1YYNG9rLumvXLm3dutUeNgq6LhV2fbv33nvt/as+++wz9evXT1lZWapbt67uvPNO7dq1K8/xp02bpi+++ELSjYC1cOFCLVy4sMD+FoV9pm7+G+3du9e+b87fN/fftmPHjtqxY4dat26tF154Id/rXdjrV7VqVe3atUtTp07Vb7/9pvbt22v//v35ypn79brVNT/Hrl27VLt2bd177726ePGi+vTpo5EjR97yupv7tZCU7z1RUFkK+izcXNbiXLuLOs8zZ85o5cqVevvtt1W/fn0NHz5cV69etQfP4vD4QCPdSPE5tTGS1K1bN3300Ue6ePGisrKyNHToUHun4f3792vHjh2SbiTje++91/7/cr6lL126VE2bNlW1atXyPDZu3Dj1799fb775pqpXr67jx4+rYsWKyszMlHQjBT/77LMaPHiwDhw4oOTkZGVlZeXZpyx06tRJgYGBmjp1qv15N27cqLlz52r06NH65JNPdOXKFWVmZmr+/PmKjo4u8niFlb9du3b6/vvvdfjwYUnSvHnzbnmskpo1a5b+/e9/Kzk5Wd26dSvwXLp06aKvvvpKFy9eVHZ2tkaOHKmEhAR169ZN8fHxkqTDhw/bv7326NHD3iP/hx9+0MGDByXd+HuOHTtWw4cPV3p6uhITE5WVlaWkpCRNnDhRS5cuzbN6bWHHz/16fffdd9q3b58kaebMmbfV6a44xowZo8qVK+vrr7/Ot8Luli1b8nW67Nevn31kydGjR/XLL7/o7rvv1ueff65XXnlFknTy5Elt27ZNbdq00d69ezV06FAtXbpUgwYNcso5lPR93LVrVy1atEiStHnz5mJ3UOzatas+++wzGWN09uxZdevWTRcvXizW/1u6dKlSUlIkSU8//bT+8pe/OPUcc2zevFkxMTEaO3asGjdurJUrVyorK0sdOnTQtm3bdOrUKRljtGjRItlsNjVp0kTnz5+3jwpduHCh+vXrV2RZC7u+Pf7441q6dKlGjhyp2bNna+fOnWrSpInS09NVtWpVTZ06VQsWLFC/fv3s5xYeHi5JevTRR/XBBx/Ix8fHXjOa+3pT2Geqa9eu9vfroUOHdO+99xYaBM6dO6dDhw7pxRdfVO/evfXFF1/kC0qFvX4JCQnq1auXunXrpldffVXNmjXTzz//XOTrVBznz5/X1KlTde+996px48aaOHGi2rRpo2XLltnLVtR9o2bNmvrxxx+VnZ2t5cuX3/L5Cvos+Pn56ezZszp9+rSuXbtW4i9XCxYsUJcuXXTixAkdPXpUR48e1d/+9jeHRkl5ZJNTcnKyIiMjZbPZlJWVpeDgYMXHx2vo0KGSblycf/zxR91zzz3KyspS165dNX78eJ04cUINGzbU9OnTtX//fjVv3tz+YZGkgwcPKjIyUtWrV8/3zWrgwIFavny5wsLCVLlyZQ0aNEjh4eFKS0vTiBEjVLNmTU2ZMkVDhw5VjRo1VK9ePbVu3VqHDx9WVFSUUlNTNWzYMI0dO9bpr4/NZtOKFSs0efJkhYeHq1KlSvLz89OKFSvUrl07XbhwQW3atFFmZqZ69Oihp556qsjhqH379tXYsWPzjSTz9/fXvHnzNHDgQGVkZKhevXp5Xk9nyGl66tmzp+6///4Cz6Vy5cqaOHGiOnTooOzsbPXv31/9+/dX69atNWbMGHvVa3x8vGrXrq2XXnpJI0eOVFhYmJo0aWK/sE6aNElxcXF6+eWXdeedd6pjx446fPiw/v73vyszM1PDhg2zX3ymT5+uN998s8Dj5wgICND8+fM1dOhQZWVlyd/fXwsWLCj11+jAgQP66KOPdPfdd6tNmzb27Tt27JCXl5cOHTqkevXq5fk/f/vb3zRhwgSFhYVJuhFOfX199de//lWjRo2yv4/eeustBQcH66GHHlJmZqbGjx9vP0ZcXFye30uqpO/jt956S3FxcZozZ47uuOOOYg8jzamFjYiIkHSjSa9WrVq3/H8RERF66aWX1KNHDxlj1LRp01tO6VBan9UHH3zQPmKtSpUq9mtPrVq19Oabb+ree+9VlSpVFBwcrN/97nfy9vbW4sWLNWnSJF29elXVqlW75Tfpwq5vzzzzjB555BHNmTNHFStW1Ny5c+Xt7a0lS5Zo/Pjxevvtt3Xt2jXVq1dPU6dO1YoVK+y1iXXq1FH9+vX1P//zP/bn6dmzp/74xz+qRo0ahX6mXnzxRY0dO1YRERHy8vLSxx9/nK8WIsedd96p0aNHKyQkRDVq1FBMTIyuXr2ap9mpsNevY8eOatq0qZo3b6477rhDrVq1Uu/eve1fih2Rmpqap8asXr16WrZsmQYOHKgGDRqoSpUqio6OttckFnbdlaS///3vio2NVUBAgDp37qwzZ84U+dwFfRZ8fX313HPPqV27dqpbt67atWvn8DnlNn/+/HyjgseMGaO//OUv2rp1a7GOz1pOAIACnT9/Xq+//rqmTZumChUqaPz48fYaAXdw5swZtW/fXj/++GO+JhV4HpqcAAAF8vPz04ULF9S8eXOFh4crNTVVY8aMcXWxJEmLFy9WWFiYXnrpJcIMJFFDAwAAygFqaAAAgOURaAAAgOURaAAAgOURaAAAgOURaAAAgOURaAA4XWpqqgYMGFAqx8pZy8pZjhw5otGjRzv1OQCUPgINAKc7f/58ngUASyJnun1nOXbsmA4dOuTU5wBQ+gg0AJxu/PjxSk5OVv/+/dW4cWN17txZXbp0UXZ2tp577jm1atVKLVq0sK9dlJmZqbi4OLVv316NGjXSAw88oCtXrtiXRshZ1b1WrVoaP368WrRooW7dumnx4sXq3LmzgoODtX79ekk3alx69eqlVq1aqX379tqyZYukGwsbTpo0SV26dFH9+vU1ffp0e1m3b9/utHWyADiJAQAnO3LkiAkODjZHjhwxksyBAweMMcbEx8ebSZMmGWOMuX79uundu7f58ssvzcaNG824ceOMMcZkZ2ebbt26mSVLlhhjjMl92ZJk/vWvfxljjImOjjbDhg0zxhjz/vvvmwceeMAYY0znzp3Njh07jDHGHDx40DRo0MBkZGSYRx991MTGxpqsrCyTnJxsqlatas6fP2/Wr19voqOjnf+iAChVHrk4JQDXqVWrlkJCQiRJq1at0q5du+y1KZcvX9bu3bv1/PPPy8/PT2+//bb27t2rffv25VkMMLf7779fklS/fn116tRJktSgQQOdP39ely5d0vfff69Ro0bZ979+/bqOHz8uSerVq5cqVKigwMBA3XXXXUpNTXXaeQNwLgINgDKVe92drKwsvfLKKxo0aJAk6ezZs7rjjju0bNky/elPf9LkyZM1atQonT17VqaQVVoqV65s//fNKyZnZWWpSpUqSkxMtG87efKkAgMDJUlVqlSxb7fZbIU+BwD3Rx8aAE5XsWJFZWZm5tverVs3zZs3TxkZGbpy5Yq6d++ub775RuvWrdODDz6okSNH6ne/+502bNigrKwsSZKXl1eBxyqIr6+vQkND9eGHH0qSEhISFBUVVeT/L6ysANwbNTQAnC4wMFD169dX586d5eXlZd8+btw4HTx4UJGRkcrMzNTQoUMVGxurhg0baujQoVqwYIGqVaumjh076vDhw5KkgQMHqkWLFvrhhx+K9dyffPKJHn/8cc2aNUteXl5asmRJnlqdmzVr1kypqakaNmyYPv3005KdOIAyw2rbAADA8mhyAgAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlkegAQAAlvf/Ae5232xqcgsXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(data = df, x='treatment', y='polarization_index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm plot of 4 categories\n",
    "\n",
    "outputPrefix = 'Fig4-polarity-index-inhibitors-summary-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width = 3.2\n",
    "fig_height = 1.0\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.85, 0.8])\n",
    "\n",
    "plotting_order = ['Control', 'Nocodazole', 'Colchicine', # microtubule drugs\n",
    "                  'Collagenase', # basement membrane removal\n",
    "                  'Latrunculin A', 'Cytochalasin D', # actin drugs\n",
    "                  'Blebbistatin', 'Y27632'] # myosin contractility inhibitors\n",
    "\n",
    "ax = sns.swarmplot(x='treatment', y='polarization_index', data=df,\n",
    "                   order=plotting_order,\n",
    "                   color='k',\n",
    "                   alpha=.4,\n",
    "                   size=2.5)\n",
    "\n",
    "# ax = sns.barplot(x='gene', y='polarization_index', data=df,\n",
    "#                  color=\".7\", alpha=1.0,\n",
    "#                  errwidth=.7, errcolor=\".7\", capsize=.2, ci='sd')\n",
    "# # make the bar width narrower\n",
    "# change_bar_width(ax, .5)\n",
    "\n",
    "ax = sns.pointplot(x='treatment', y='polarization_index', data=df,\n",
    "                   ci='sd', capsize=.2, errwidth=.7,\n",
    "                   order=plotting_order,\n",
    "                   markers='_', scale=.5, join=False, color=\"Blue\")\n",
    "\n",
    "plt.ylim(-0.4, 1.0)\n",
    "# plt.yticks([0, 0.3, 0.6])\n",
    "\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(\"Polarity index\")\n",
    "\n",
    "# rotate x tick labels\n",
    "x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "# # Remove legend (symbols representing orgnoids)\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=21.44318850371561, pvalue=3.705130365347921e-13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = [df[df['treatment'] == i]['polarization_index'] for i in df['treatment'].unique()]\n",
    "stats.f_oneway(*groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "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",
      "  Blebbistatin     Colchicine  -0.7869    0.0 -1.1192 -0.4547   True\n",
      "  Blebbistatin    Collagenase   -0.199 0.3105 -0.4717  0.0737  False\n",
      "  Blebbistatin        Control  -0.1226 0.7418 -0.3631  0.1179  False\n",
      "  Blebbistatin Cytochalasin D  -0.0872 0.9851 -0.3949  0.2204  False\n",
      "  Blebbistatin  Latrunculin A   -0.084  0.988 -0.3917  0.2236  False\n",
      "  Blebbistatin     Nocodazole  -0.5422    0.0 -0.7888 -0.2955   True\n",
      "  Blebbistatin         Y27632  -0.1999 0.3897 -0.4917  0.0919  False\n",
      "    Colchicine    Collagenase    0.588    0.0  0.2877  0.8882   True\n",
      "    Colchicine        Control   0.6644    0.0  0.3931  0.9357   True\n",
      "    Colchicine Cytochalasin D   0.6997    0.0  0.3674   1.032   True\n",
      "    Colchicine  Latrunculin A   0.7029    0.0  0.3706  1.0352   True\n",
      "    Colchicine     Nocodazole   0.2448  0.119  -0.032  0.5216  False\n",
      "    Colchicine         Y27632    0.587    0.0  0.2693  0.9048   True\n",
      "   Collagenase        Control   0.0764 0.9138 -0.1174  0.2702  False\n",
      "   Collagenase Cytochalasin D   0.1118 0.8965 -0.1609  0.3844  False\n",
      "   Collagenase  Latrunculin A   0.1149 0.8822 -0.1578  0.3876  False\n",
      "   Collagenase     Nocodazole  -0.3432    0.0 -0.5446 -0.1418   True\n",
      "   Collagenase         Y27632  -0.0009    1.0 -0.2557  0.2538  False\n",
      "       Control Cytochalasin D   0.0353 0.9998 -0.2051  0.2758  False\n",
      "       Control  Latrunculin A   0.0385 0.9996  -0.202   0.279  False\n",
      "       Control     Nocodazole  -0.4196    0.0 -0.5746 -0.2645   True\n",
      "       Control         Y27632  -0.0773 0.9514 -0.2973  0.1426  False\n",
      "Cytochalasin D  Latrunculin A   0.0032    1.0 -0.3045  0.3108  False\n",
      "Cytochalasin D     Nocodazole  -0.4549    0.0 -0.7016 -0.2083   True\n",
      "Cytochalasin D         Y27632  -0.1127 0.9222 -0.4045  0.1792  False\n",
      " Latrunculin A     Nocodazole  -0.4581    0.0 -0.7048 -0.2114   True\n",
      " Latrunculin A         Y27632  -0.1159  0.911 -0.4077   0.176  False\n",
      "    Nocodazole         Y27632   0.3423 0.0004  0.1156  0.5689   True\n",
      "--------------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.31054917 0.71601265 0.9        0.9        0.001\n",
      " 0.39163473 0.001      0.001      0.001      0.001      0.11936412\n",
      " 0.001      0.9        0.89710423 0.87598329 0.001      0.9\n",
      " 0.9        0.9        0.001      0.9        0.9        0.001\n",
      " 0.9        0.001      0.9        0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x14ca25f90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.polarization_index, df.treatment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Control, 18; Nocodazole, 14; Colchicine, 3; Collagenase, 7; Latrunculin A, 4; Cytochalasin D, 4; Blebbistatin, 4; Y27632, 5\n"
     ]
    }
   ],
   "source": [
    "sample_numbers = []\n",
    "for i in plotting_order:\n",
    "    sample_numbers.append(i + ', ' + str(len(df[df.treatment==i])))\n",
    "print('; '.join(sample_numbers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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