{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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>Label</th>\n",
       "      <th>Area</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Slice</th>\n",
       "      <th>sample</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK484-DMSO-2_w1CSU-BF-1-1.TIF:0225-0122</td>\n",
       "      <td>2.417</td>\n",
       "      <td>638.462</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-DMSO-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>SK484-DMSO-2_w1CSU-BF-1-1.TIF:0362-0139</td>\n",
       "      <td>2.231</td>\n",
       "      <td>217.523</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-DMSO-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SK484-DMSO-2_w1CSU-BF-1-1.TIF:0523-0353</td>\n",
       "      <td>2.383</td>\n",
       "      <td>198.837</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-DMSO-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SK484-DMSO-2_w1CSU-BF-1-1.TIF:0396-0341</td>\n",
       "      <td>2.366</td>\n",
       "      <td>235.407</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-DMSO-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SK484-DMSO-2_w1CSU-BF-1-1.TIF:0192-0403</td>\n",
       "      <td>2.366</td>\n",
       "      <td>160.071</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-DMSO-2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>SK484-PCB-1_w1CSU-BF-1-1.TIF:0259-0060</td>\n",
       "      <td>1.927</td>\n",
       "      <td>4035.614</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-PCB-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>SK484-PCB-1_w1CSU-BF-1-1.TIF:0411-0520</td>\n",
       "      <td>1.876</td>\n",
       "      <td>6272.423</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-PCB-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>SK484-PCB-1_w1CSU-BF-1-1.TIF:0345-0057</td>\n",
       "      <td>2.096</td>\n",
       "      <td>10088.484</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-PCB-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>SK484-PCB-1_w1CSU-BF-1-1.TIF:0338-0258</td>\n",
       "      <td>1.538</td>\n",
       "      <td>6709.615</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-PCB-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>SK484-PCB-1_w1CSU-BF-1-1.TIF:0564-0159</td>\n",
       "      <td>1.318</td>\n",
       "      <td>2643.064</td>\n",
       "      <td>1</td>\n",
       "      <td>SK484-PCB-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Label   Area       Mean  Slice  \\\n",
       "0    SK484-DMSO-2_w1CSU-BF-1-1.TIF:0225-0122  2.417    638.462      1   \n",
       "35   SK484-DMSO-2_w1CSU-BF-1-1.TIF:0362-0139  2.231    217.523      1   \n",
       "36   SK484-DMSO-2_w1CSU-BF-1-1.TIF:0523-0353  2.383    198.837      1   \n",
       "37   SK484-DMSO-2_w1CSU-BF-1-1.TIF:0396-0341  2.366    235.407      1   \n",
       "38   SK484-DMSO-2_w1CSU-BF-1-1.TIF:0192-0403  2.366    160.071      1   \n",
       "..                                       ...    ...        ...    ...   \n",
       "217   SK484-PCB-1_w1CSU-BF-1-1.TIF:0259-0060  1.927   4035.614      1   \n",
       "216   SK484-PCB-1_w1CSU-BF-1-1.TIF:0411-0520  1.876   6272.423      1   \n",
       "215   SK484-PCB-1_w1CSU-BF-1-1.TIF:0345-0057  2.096  10088.484      1   \n",
       "227   SK484-PCB-1_w1CSU-BF-1-1.TIF:0338-0258  1.538   6709.615      1   \n",
       "301   SK484-PCB-1_w1CSU-BF-1-1.TIF:0564-0159  1.318   2643.064      1   \n",
       "\n",
       "           sample  order  \n",
       "0    SK484-DMSO-2      1  \n",
       "35   SK484-DMSO-2      1  \n",
       "36   SK484-DMSO-2      1  \n",
       "37   SK484-DMSO-2      1  \n",
       "38   SK484-DMSO-2      1  \n",
       "..            ...    ...  \n",
       "217   SK484-PCB-1      3  \n",
       "216   SK484-PCB-1      3  \n",
       "215   SK484-PCB-1      3  \n",
       "227   SK484-PCB-1      3  \n",
       "301   SK484-PCB-1      3  \n",
       "\n",
       "[302 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label     SK484-DMSO-2_w1CSU-BF-1-1.TIF:0225-0122SK484-D...\n",
      "Area                                                136.652\n",
      "Mean                                                17974.2\n",
      "Slice                                                    66\n",
      "sample    SK484-DMSO-2SK484-DMSO-2SK484-DMSO-2SK484-DMSO...\n",
      "order                                                    66\n",
      "dtype: object\n",
      "Label     SK484-BV-1_w1CSU-BF-1-1.TIF:0312-0562SK484-BV-...\n",
      "Area                                                232.766\n",
      "Mean                                                48966.3\n",
      "Slice                                                   120\n",
      "sample    SK484-BV-1SK484-BV-1SK484-BV-1SK484-BV-1SK484-...\n",
      "order                                                   240\n",
      "dtype: object\n",
      "Label     SK484-PCB-1_w1CSU-BF-1-1.TIF:0269-0327SK484-PC...\n",
      "Area                                                221.936\n",
      "Mean                                                 656572\n",
      "Slice                                                   116\n",
      "sample    SK484-PCB-1SK484-PCB-1SK484-PCB-1SK484-PCB-1SK...\n",
      "order                                                   348\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "orders = [1,2,3]\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",
    "\n",
    "display(df2)\n",
    "\n",
    "print(df2[df2[\"sample\"]==\"SK484-DMSO-2\"].sum())\n",
    "print(df2[df2[\"sample\"]==\"SK484-BV-1\"].sum())\n",
    "print(df2[df2[\"sample\"]==\"SK484-PCB-1\"].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff608622700>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "\n",
    "sns.boxplot(data=df2, x='sample', y='Mean', color ='lightgrey',fliersize=0 , width=0.5)\n",
    "sns.swarmplot(x=\"sample\", y=\"Mean\", data=df2, color=\"cornflowerblue\", size=3, alpha =0.5)\n",
    "#plt.savefig(\"20211027-SK484-1.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
}