{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob"
   ]
  },
  {
   "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>Mean</th>\n",
       "      <th>sample</th>\n",
       "      <th>genotype</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>37.391</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>48.565</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>42.594</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>61.681</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>61.754</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</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>269</th>\n",
       "      <td>15112.101</td>\n",
       "      <td>SK282-1</td>\n",
       "      <td>synPCB2.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>13334.159</td>\n",
       "      <td>SK282-1</td>\n",
       "      <td>synPCB2.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>13791.696</td>\n",
       "      <td>SK282-1</td>\n",
       "      <td>synPCB2.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>13998.739</td>\n",
       "      <td>SK282-1</td>\n",
       "      <td>synPCB2.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>16156.899</td>\n",
       "      <td>SK282-1</td>\n",
       "      <td>synPCB2.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mean        sample   genotype  order\n",
       "99      37.391  SK276-DMSO-1       DMSO      1\n",
       "71      48.565  SK276-DMSO-1       DMSO      1\n",
       "70      42.594  SK276-DMSO-1       DMSO      1\n",
       "69      61.681  SK276-DMSO-1       DMSO      1\n",
       "68      61.754  SK276-DMSO-1       DMSO      1\n",
       "..         ...           ...        ...    ...\n",
       "269  15112.101       SK282-1  synPCB2.1      6\n",
       "270  13334.159       SK282-1  synPCB2.1      6\n",
       "271  13791.696       SK282-1  synPCB2.1      6\n",
       "261  13998.739       SK282-1  synPCB2.1      6\n",
       "299  16156.899       SK282-1  synPCB2.1      6\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genotypes = [\"PCB\",\"DMSO\",\"BV\",\"HO1 tFnr-Fd PcyA\",\"HO1\",\"synPCB2.1\"]\n",
    "orders = [5,1,2,4,3,6]\n",
    "flist = glob.glob(\"quantification/*.csv\")\n",
    "df = pd.DataFrame()\n",
    "for file, genotype, order in zip(flist, genotypes, orders):\n",
    "    file_tmp = pd.read_csv(file,index_col=0)\n",
    "    file_tmp[\"sample\"] = file[15:-4]\n",
    "    file_tmp[\"genotype\"] = genotype\n",
    "    file_tmp[\"order\"] = order\n",
    "    df = pd.concat([df,file_tmp])\n",
    "df = df.reset_index(drop = True)\n",
    "df2 = df.sort_values('order')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa7d1a9b760>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,9))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.boxplot(data=df2, x='genotype', y='Mean', color ='lightgrey',fliersize=0 , width=0.5)\n",
    "sns.swarmplot(x=\"genotype\", y=\"Mean\", data=df2, color=\"cornflowerblue\", size=5, alpha =0.5)\n",
    "#plt.savefig(\"synPCB2.1-1.pdf\")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}