{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt \n",
    "import glob "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['quantification/SK296-new-1-7.csv',\n",
       " 'quantification/SK295-1-8.csv',\n",
       " 'quantification/SK276-BV-1-2.csv',\n",
       " 'quantification/SK287-1-5.csv',\n",
       " 'quantification/SK276-DMSO-1-1.csv',\n",
       " 'quantification/SK285-1-4.csv',\n",
       " 'quantification/SK294-new-1-6.csv',\n",
       " 'quantification/SK305-new-1-9.csv',\n",
       " 'quantification/SK284-new-1-3.csv']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flist = glob.glob(\"quantification/*.csv\")\n",
    "flist"
   ]
  },
  {
   "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>Mean</th>\n",
       "      <th>sample</th>\n",
       "      <th>genotype</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>54.246</td>\n",
       "      <td>SK276-DMSO-1-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>54.739</td>\n",
       "      <td>SK276-DMSO-1-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>59.014</td>\n",
       "      <td>SK276-DMSO-1-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>64.754</td>\n",
       "      <td>SK276-DMSO-1-1</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>57.623</td>\n",
       "      <td>SK276-DMSO-1-1</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>377</th>\n",
       "      <td>8089.623</td>\n",
       "      <td>SK305-new-1-9</td>\n",
       "      <td>HO1 tFnr-Fd PcyA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>5056.667</td>\n",
       "      <td>SK305-new-1-9</td>\n",
       "      <td>HO1 tFnr-Fd PcyA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>5912.797</td>\n",
       "      <td>SK305-new-1-9</td>\n",
       "      <td>HO1 tFnr-Fd PcyA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>6749.391</td>\n",
       "      <td>SK305-new-1-9</td>\n",
       "      <td>HO1 tFnr-Fd PcyA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>6208.435</td>\n",
       "      <td>SK305-new-1-9</td>\n",
       "      <td>HO1 tFnr-Fd PcyA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean          sample          genotype  order\n",
       "224    54.246  SK276-DMSO-1-1              DMSO      1\n",
       "228    54.739  SK276-DMSO-1-1              DMSO      1\n",
       "229    59.014  SK276-DMSO-1-1              DMSO      1\n",
       "230    64.754  SK276-DMSO-1-1              DMSO      1\n",
       "231    57.623  SK276-DMSO-1-1              DMSO      1\n",
       "..        ...             ...               ...    ...\n",
       "377  8089.623   SK305-new-1-9  HO1 tFnr-Fd PcyA      9\n",
       "376  5056.667   SK305-new-1-9  HO1 tFnr-Fd PcyA      9\n",
       "375  5912.797   SK305-new-1-9  HO1 tFnr-Fd PcyA      9\n",
       "385  6749.391   SK305-new-1-9  HO1 tFnr-Fd PcyA      9\n",
       "399  6208.435   SK305-new-1-9  HO1 tFnr-Fd PcyA      9\n",
       "\n",
       "[450 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genotypes = [\"HO1 PcyA\",\"tFnr-Fd PcyA\",\"BV\",\"PcyA\",\"DMSO\",\"tFnr-Fd\",\"HO1 tFnr-fd\",\"HO1 tFnr-Fd PcyA\",\"HO1\"]\n",
    "orders = [7,8,2,5,1,4,6,9,3]\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc1f1e41040>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.boxplot(data=df2[df2[\"genotype\"]!=\"synPCB2.1\"], x='genotype', y='Mean', color ='lightgrey',fliersize=0 , width=0.5)\n",
    "sns.swarmplot(x=\"genotype\", y=\"Mean\", data=df2[df2[\"genotype\"]!=\"synPCB2.1\"], color=\"cornflowerblue\", size=5, alpha =0.5)\n",
    "#plt.savefig(\"HO1-Fnr-PcyA-2.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
}