{
 "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>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>34.696</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>45.957</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>66.029</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>50.145</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>41.797</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2714.188</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2559.957</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>3905.899</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2612.058</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>2117.739</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean         sample  order\n",
       "294    34.696   SK276-DMSO-1      1\n",
       "273    45.957   SK276-DMSO-1      1\n",
       "274    66.029   SK276-DMSO-1      1\n",
       "275    50.145   SK276-DMSO-1      1\n",
       "276    41.797   SK276-DMSO-1      1\n",
       "..        ...            ...    ...\n",
       "371  2714.188  SK284-625uM-1     18\n",
       "372  2559.957  SK284-625uM-1     18\n",
       "373  3905.899  SK284-625uM-1     18\n",
       "363  2612.058  SK284-625uM-1     18\n",
       "399  2117.739  SK284-625uM-1     18\n",
       "\n",
       "[900 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = [17,11,10,13,9,1,8,18,15,5,4,6,14,12,2,3,7,16]\n",
    "flist = glob.glob(\"quantification/*.csv\")\n",
    "df = pd.DataFrame()\n",
    "for file, order, in zip(flist, orders):\n",
    "    file_tmp = pd.read_csv(file,index_col=0)\n",
    "    file_tmp[\"sample\"] = file[15:-4]\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/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>order</th>\n",
       "      <th>strain</th>\n",
       "      <th>concentration (uM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>34.696</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>45.957</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>66.029</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>50.145</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>41.797</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2714.188</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "      <td>HO1</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2559.957</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "      <td>HO1</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>3905.899</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "      <td>HO1</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2612.058</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "      <td>HO1</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>2117.739</td>\n",
       "      <td>SK284-625uM-1</td>\n",
       "      <td>18</td>\n",
       "      <td>HO1</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean         sample  order   strain  concentration (uM)\n",
       "294    34.696   SK276-DMSO-1      1  control                 0.0\n",
       "273    45.957   SK276-DMSO-1      1  control                 0.0\n",
       "274    66.029   SK276-DMSO-1      1  control                 0.0\n",
       "275    50.145   SK276-DMSO-1      1  control                 0.0\n",
       "276    41.797   SK276-DMSO-1      1  control                 0.0\n",
       "..        ...            ...    ...      ...                 ...\n",
       "371  2714.188  SK284-625uM-1     18      HO1               625.0\n",
       "372  2559.957  SK284-625uM-1     18      HO1               625.0\n",
       "373  3905.899  SK284-625uM-1     18      HO1               625.0\n",
       "363  2612.058  SK284-625uM-1     18      HO1               625.0\n",
       "399  2117.739  SK284-625uM-1     18      HO1               625.0\n",
       "\n",
       "[900 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concs = [0,0.008,0.04,0.2,1,5,25,125,625]\n",
    "adds = ['control', 'HO1']\n",
    "\n",
    "strain =[]\n",
    "concentration = []\n",
    "for add in adds:\n",
    "    for conc in concs:\n",
    "        strain = np.hstack([strain, np.full(50, (add))])\n",
    "        concentration = np.hstack([concentration, np.full(50, (conc))])\n",
    "df2['strain'] = strain \n",
    "df2['concentration (uM)'] = concentration\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    }
   ],
   "source": [
    "sns.lineplot(data=df2[df2[\"concentration (uM)\"]!=0], x='concentration (uM)', y='Mean', hue='strain', ci='sd',palette=['mediumblue','skyblue'])\n",
    "plt.xscale('log')\n",
    "#plt.savefig(\"HO1-PCB-3.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
}