{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>strain</th>\n",
       "      <th>FRET/CFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK014</td>\n",
       "      <td>1.341125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SK014</td>\n",
       "      <td>1.269065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK014</td>\n",
       "      <td>1.405432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK014</td>\n",
       "      <td>1.422342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK014</td>\n",
       "      <td>1.529240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>SK097</td>\n",
       "      <td>1.028781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>SK097</td>\n",
       "      <td>1.085095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>SK097</td>\n",
       "      <td>1.021537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>SK097</td>\n",
       "      <td>1.114809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>SK097</td>\n",
       "      <td>0.987708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    strain  FRET/CFP\n",
       "0    SK014  1.341125\n",
       "1    SK014  1.269065\n",
       "2    SK014  1.405432\n",
       "3    SK014  1.422342\n",
       "4    SK014  1.529240\n",
       "..     ...       ...\n",
       "115  SK097  1.028781\n",
       "116  SK097  1.085095\n",
       "117  SK097  1.021537\n",
       "118  SK097  1.114809\n",
       "119  SK097  0.987708\n",
       "\n",
       "[120 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"all_data.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.boxplot(data=data, x='strain', y='FRET/CFP', color ='lightgrey',fliersize=0 , width=0.5)\n",
    "sns.swarmplot(x=\"strain\", y=\"FRET/CFP\", data=data, color=\"cornflowerblue\", size=5, alpha =0.5)\n",
    "plt.ylim(0.7,1.7)\n",
    "plt.savefig(\"20231130-AKAR3EV.pdf\")"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}