{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "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>488</th>\n",
       "      <th>640</th>\n",
       "      <th>ratio</th>\n",
       "      <th>Slice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>1979.312</td>\n",
       "      <td>741.975</td>\n",
       "      <td>0.374865</td>\n",
       "      <td>1.WT-DMSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>398.188</td>\n",
       "      <td>225.613</td>\n",
       "      <td>0.566599</td>\n",
       "      <td>1.WT-DMSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>696.062</td>\n",
       "      <td>393.575</td>\n",
       "      <td>0.565431</td>\n",
       "      <td>1.WT-DMSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>624.775</td>\n",
       "      <td>372.887</td>\n",
       "      <td>0.596834</td>\n",
       "      <td>1.WT-DMSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1273.675</td>\n",
       "      <td>597.900</td>\n",
       "      <td>0.469429</td>\n",
       "      <td>1.WT-DMSO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>61.050</td>\n",
       "      <td>169.613</td>\n",
       "      <td>2.778264</td>\n",
       "      <td>6.KO-PCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>68.213</td>\n",
       "      <td>188.637</td>\n",
       "      <td>2.765411</td>\n",
       "      <td>6.KO-PCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>86.763</td>\n",
       "      <td>208.863</td>\n",
       "      <td>2.407282</td>\n",
       "      <td>6.KO-PCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>888.825</td>\n",
       "      <td>1761.562</td>\n",
       "      <td>1.981900</td>\n",
       "      <td>6.KO-PCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>701.600</td>\n",
       "      <td>2169.850</td>\n",
       "      <td>3.092717</td>\n",
       "      <td>6.KO-PCB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1210 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           488       640     ratio      Slice\n",
       "                                             \n",
       "1017  1979.312   741.975  0.374865  1.WT-DMSO\n",
       "869    398.188   225.613  0.566599  1.WT-DMSO\n",
       "868    696.062   393.575  0.565431  1.WT-DMSO\n",
       "867    624.775   372.887  0.596834  1.WT-DMSO\n",
       "866   1273.675   597.900  0.469429  1.WT-DMSO\n",
       "...        ...       ...       ...        ...\n",
       "500     61.050   169.613  2.778264   6.KO-PCB\n",
       "501     68.213   188.637  2.765411   6.KO-PCB\n",
       "502     86.763   208.863  2.407282   6.KO-PCB\n",
       "492    888.825  1761.562  1.981900   6.KO-PCB\n",
       "605    701.600  2169.850  3.092717   6.KO-PCB\n",
       "\n",
       "[1210 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Green = pd.read_csv('quantification/488.csv',index_col=0)\n",
    "iRFP = pd.read_csv('quantification/640.csv',index_col=0)\n",
    "\n",
    "df = Green\n",
    "df.columns = ['488', 'Slice']\n",
    "df['640'] = iRFP['Mean'].values \n",
    "df['ratio'] = df['640'].values/ df['488'].values\n",
    "df = df.reindex(columns=['488','640','ratio','Slice'])\n",
    "\n",
    "samples = ['5.KO-BV', '4.KO-DMSO','6.KO-PCB','2.WT-BV', '1.WT-DMSO','3.WT-PCB',]\n",
    "\n",
    "for i in range(6):\n",
    "    df.loc[df['Slice']==2*i+1, 'Slice'] = samples[i]\n",
    "    df.loc[df['Slice']==2*i+2, 'Slice'] = samples[i]\n",
    "df = df.sort_values('Slice')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.KO-BV\n",
      "228\n",
      "4.KO-DMSO\n",
      "199\n",
      "6.KO-PCB\n",
      "207\n",
      "2.WT-BV\n",
      "208\n",
      "1.WT-DMSO\n",
      "176\n",
      "3.WT-PCB\n",
      "192\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.boxplot(data=df, x='Slice', y='ratio', color ='coral',fliersize=0)\n",
    "sns.swarmplot(data=df, x='Slice', y='ratio', color ='gray', alpha=0.8, size=1.5)\n",
    "plt.savefig('Hela.pdf')\n",
    "plt.show()\n",
    "\n",
    "for i in samples:\n",
    "    print(i)\n",
    "    print(len(df[df['Slice']==i]))"
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}