{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e3dd675-5eb9-4aff-80cd-4156a76d7797",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "from scipy import signal\n",
    "plt.rcParams['pdf.fonttype'] = 42\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7f8f3b7e-fc26-467b-9254-a51f2cda2619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>promoter</th>\n",
       "      <th>rel-value</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T31</td>\n",
       "      <td>0.519410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L972</td>\n",
       "      <td>0.478680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T31</td>\n",
       "      <td>0.445728</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L972</td>\n",
       "      <td>1.171911</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T31</td>\n",
       "      <td>0.446411</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L972</td>\n",
       "      <td>0.362604</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  promoter  rel-value  exp\n",
       "0      T21   1.000000    1\n",
       "1      T31   0.519410    1\n",
       "2     L972   0.478680    1\n",
       "3      T21   1.000000    2\n",
       "4      T31   0.445728    2\n",
       "5     L972   1.171911    2\n",
       "6      T21   1.000000    3\n",
       "7      T31   0.446411    3\n",
       "8     L972   0.362604    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>rel-value</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promoter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T21</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T31</th>\n",
       "      <td>0.470516</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L972</th>\n",
       "      <td>0.671065</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rel-value  exp\n",
       "promoter                \n",
       "T21        1.000000  2.0\n",
       "T31        0.470516  2.0\n",
       "L972       0.671065  2.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df = pd.read_csv(\"./_results_plot.csv\")\n",
    "display(data_df)\n",
    "display(data_df.groupby(\"promoter\", sort=False).mean())\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4, 6))\n",
    "x = np.array([\"T21\", \"T31\", \"L972\"])\n",
    "x_pos = np.arange(len(x))\n",
    "y = data_df.groupby(\"promoter\", sort=False).mean()[\"rel-value\"]\n",
    "e = data_df.groupby(\"promoter\", sort=False).sem()[\"rel-value\"]\n",
    "\n",
    "sns.swarmplot(x=\"promoter\", y=\"rel-value\", data=data_df, hue=\"exp\", s=16, ax=ax)\n",
    "ax.bar(x_pos, y, yerr=e, tick_label=x, color=\"black\", alpha=0.3)\n",
    "plt.legend([], [], frameon=False)\n",
    "fig.savefig(\"./__results_cdc25.png\")\n",
    "fig.savefig(\"./__results_cdc25.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d303e7e-d140-479c-bd43-64d276b570a0",
   "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>promoter</th>\n",
       "      <th>rel-value</th>\n",
       "      <th>exp</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>0.963179</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L972</td>\n",
       "      <td>0.090469</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A31</td>\n",
       "      <td>0.919453</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L972</td>\n",
       "      <td>0.290478</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A21</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A31</td>\n",
       "      <td>0.873767</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L972</td>\n",
       "      <td>0.077555</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  promoter  rel-value  exp  Unnamed: 3  Unnamed: 4\n",
       "0      A21   1.000000    1         NaN         NaN\n",
       "1      A31   0.963179    1         NaN         NaN\n",
       "2     L972   0.090469    1         NaN         NaN\n",
       "3      A21   1.000000    2         NaN    0.152834\n",
       "4      A31   0.919453    2         NaN         NaN\n",
       "5     L972   0.290478    2         NaN         NaN\n",
       "6      A21   1.000000    3         NaN         NaN\n",
       "7      A31   0.873767    3         NaN         NaN\n",
       "8     L972   0.077555    3         NaN         NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>rel-value</th>\n",
       "      <th>exp</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promoter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A21</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A31</th>\n",
       "      <td>0.918800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L972</th>\n",
       "      <td>0.152834</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rel-value  exp  Unnamed: 3  Unnamed: 4\n",
       "promoter                                        \n",
       "A21        1.000000  2.0         NaN    0.152834\n",
       "A31        0.918800  2.0         NaN         NaN\n",
       "L972       0.152834  2.0         NaN         NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df = pd.read_csv(\"./_results_plot_wee1.csv\")\n",
    "display(data_df)\n",
    "display(data_df.groupby(\"promoter\", sort=False).mean())\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4, 6))\n",
    "x = np.array([\"A21\", \"A31\", \"L972\"])\n",
    "x_pos = np.arange(len(x))\n",
    "y = data_df.groupby(\"promoter\", sort=False).mean()[\"rel-value\"]\n",
    "e = data_df.groupby(\"promoter\", sort=False).sem()[\"rel-value\"]\n",
    "\n",
    "sns.swarmplot(x=\"promoter\", y=\"rel-value\", data=data_df, hue=\"exp\", s=16, ax=ax)\n",
    "ax.bar(x_pos, y, yerr=e, tick_label=x, color=\"black\", alpha=0.3)\n",
    "plt.legend([], [], frameon=False)\n",
    "fig.savefig(\"./__results_wee1.png\")\n",
    "fig.savefig(\"./__results_wee1.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d8492ee-fbf9-47df-ae0c-935587c635d3",
   "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>rel-value</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promoter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L972</th>\n",
       "      <td>0.671065</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T21</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T31</th>\n",
       "      <td>0.470516</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rel-value  exp\n",
       "promoter                \n",
       "L972       0.671065  2.0\n",
       "T21        1.000000  2.0\n",
       "T31        0.470516  2.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.groupby(\"promoter\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76cc2e1-a2e6-45b1-8164-9577690938f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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