{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>strain</th>\n",
       "      <th>condition</th>\n",
       "      <th>time</th>\n",
       "      <th>chromophore</th>\n",
       "      <th>number</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>640:1052-0863:SK482-dose-0-PCB-1_w4CSU-640</td>\n",
       "      <td>987</td>\n",
       "      <td>51.598</td>\n",
       "      <td>1</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>640:0751-0491:SK482-dose-0-PCB-1_w4CSU-640</td>\n",
       "      <td>975</td>\n",
       "      <td>43.459</td>\n",
       "      <td>1</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.015242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>640:0534-0302:SK482-dose-0-PCB-1_w4CSU-640</td>\n",
       "      <td>872</td>\n",
       "      <td>51.338</td>\n",
       "      <td>1</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>640:0455-0528:SK482-dose-0-PCB-1_w4CSU-640</td>\n",
       "      <td>656</td>\n",
       "      <td>80.636</td>\n",
       "      <td>1</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.024155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>640:0239-0895:SK482-dose-0-PCB-1_w4CSU-640</td>\n",
       "      <td>530</td>\n",
       "      <td>34.908</td>\n",
       "      <td>1</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>28185</th>\n",
       "      <td>640:0783-0290:SK483-time-5-PCB-1_w4CSU-640</td>\n",
       "      <td>1587</td>\n",
       "      <td>637.147</td>\n",
       "      <td>60</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28186</th>\n",
       "      <td>640:0139-0886:SK483-time-5-PCB-1_w4CSU-640</td>\n",
       "      <td>971</td>\n",
       "      <td>611.525</td>\n",
       "      <td>60</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.653037</td>\n",
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       "    <tr>\n",
       "      <th>28187</th>\n",
       "      <td>640:0280-0632:SK483-time-5-PCB-1_w4CSU-640</td>\n",
       "      <td>409</td>\n",
       "      <td>670.660</td>\n",
       "      <td>60</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28188</th>\n",
       "      <td>640:0904-1039:SK483-time-5-PCB-1_w4CSU-640</td>\n",
       "      <td>1114</td>\n",
       "      <td>387.452</td>\n",
       "      <td>60</td>\n",
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       "      <th>28189</th>\n",
       "      <td>640:0387-0003:SK483-time-5-PCB-1_w4CSU-640</td>\n",
       "      <td>95</td>\n",
       "      <td>556.779</td>\n",
       "      <td>60</td>\n",
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       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.461694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28189 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Label  Area     Mean  Slice  \\\n",
       "                                                                          \n",
       "1      640:1052-0863:SK482-dose-0-PCB-1_w4CSU-640   987   51.598      1   \n",
       "2      640:0751-0491:SK482-dose-0-PCB-1_w4CSU-640   975   43.459      1   \n",
       "3      640:0534-0302:SK482-dose-0-PCB-1_w4CSU-640   872   51.338      1   \n",
       "4      640:0455-0528:SK482-dose-0-PCB-1_w4CSU-640   656   80.636      1   \n",
       "5      640:0239-0895:SK482-dose-0-PCB-1_w4CSU-640   530   34.908      1   \n",
       "...                                           ...   ...      ...    ...   \n",
       "28185  640:0783-0290:SK483-time-5-PCB-1_w4CSU-640  1587  637.147     60   \n",
       "28186  640:0139-0886:SK483-time-5-PCB-1_w4CSU-640   971  611.525     60   \n",
       "28187  640:0280-0632:SK483-time-5-PCB-1_w4CSU-640   409  670.660     60   \n",
       "28188  640:0904-1039:SK483-time-5-PCB-1_w4CSU-640  1114  387.452     60   \n",
       "28189  640:0387-0003:SK483-time-5-PCB-1_w4CSU-640    95  556.779     60   \n",
       "\n",
       "      strain condition time chromophore number     ratio  \n",
       "                                                          \n",
       "1      SK482      dose    0         PCB      1  0.021445  \n",
       "2      SK482      dose    0         PCB      1  0.015242  \n",
       "3      SK482      dose    0         PCB      1  0.021573  \n",
       "4      SK482      dose    0         PCB      1  0.024155  \n",
       "5      SK482      dose    0         PCB      1  0.018356  \n",
       "...      ...       ...  ...         ...    ...       ...  \n",
       "28185  SK483      time    5         PCB      1  0.500248  \n",
       "28186  SK483      time    5         PCB      1  0.653037  \n",
       "28187  SK483      time    5         PCB      1  0.870531  \n",
       "28188  SK483      time    5         PCB      1  0.200544  \n",
       "28189  SK483      time    5         PCB      1  0.461694  \n",
       "\n",
       "[28189 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mNG = pd.read_csv(\"quantification/488.csv\", index_col=0)\n",
    "iRFP = pd.read_csv(\"quantification/640.csv\", index_col=0)\n",
    "iRFP_label = iRFP[\"Label\"].values\n",
    "strain = []\n",
    "condition = []\n",
    "time = []\n",
    "chromophore = []\n",
    "number = []\n",
    "for i in iRFP_label:\n",
    "    strain.append(i[14:19])\n",
    "    condition.append(i[20:24])\n",
    "    time.append(i[25:27])\n",
    "    chromophore.append(i[-15:-12])\n",
    "    number.append(i[-11:-10])\n",
    "\n",
    "iRFP[\"strain\"] = strain\n",
    "iRFP[\"condition\"] = condition\n",
    "iRFP[\"time\"] = time\n",
    "iRFP[\"chromophore\"] = chromophore\n",
    "iRFP[\"number\"] = number\n",
    "\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"0-\"),'time'] = 0\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"1-\"),'time'] = 0.008\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"2-\"),'time'] = 0.04\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"3-\"),'time'] = 0.2\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"4-\"),'time'] = 1\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"5-\"),'time'] = 5\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"6-\"),'time'] = 25\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"7-\"),'time'] = 125\n",
    "iRFP.loc[(iRFP['condition'] == \"dose\")& (iRFP[\"time\"] == \"8-\"),'time'] = 625\n",
    "\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"0-\"),'time'] = 0\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"0.\"),'time'] = 0.5\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"1-\"),'time'] = 1\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"2-\"),'time'] = 2\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"3-\"),'time'] = 3\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"time\"] == \"5-\"),'time'] = 5\n",
    "\n",
    "\n",
    "iRFP[\"ratio\"] = iRFP[\"Mean\"]/mNG[\"Mean\"]\n",
    "\n",
    "iRFP.loc[(iRFP['condition'] == \"time\")& (iRFP[\"chromophore\"] == \"e-0\"),'chromophore'] = \"PCB\"\n",
    "\n",
    "\n",
    "iRFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Area</th>\n",
       "      <th>Mean</th>\n",
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       "      <th>1604</th>\n",
       "      <td>640:0784-0411:SK482-dose-1-PCB-1_w4CSU-640</td>\n",
       "      <td>1089</td>\n",
       "      <td>54.392</td>\n",
       "      <td>3</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0.008</td>\n",
       "      <td>PCB</td>\n",
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       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>640:0945-0980:SK482-dose-1-PCB-1_w4CSU-640</td>\n",
       "      <td>975</td>\n",
       "      <td>36.925</td>\n",
       "      <td>3</td>\n",
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       "      <td>dose</td>\n",
       "      <td>0.008</td>\n",
       "      <td>PCB</td>\n",
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       "      <td>0.017851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>640:0215-1027:SK482-dose-1-PCB-1_w4CSU-640</td>\n",
       "      <td>1140</td>\n",
       "      <td>43.437</td>\n",
       "      <td>3</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0.008</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>640:0630-0446:SK482-dose-1-PCB-1_w4CSU-640</td>\n",
       "      <td>628</td>\n",
       "      <td>37.820</td>\n",
       "      <td>3</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0.008</td>\n",
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       "      <td>0.013626</td>\n",
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       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>640:1098-0599:SK482-dose-1-PCB-1_w4CSU-640</td>\n",
       "      <td>793</td>\n",
       "      <td>34.763</td>\n",
       "      <td>3</td>\n",
       "      <td>SK482</td>\n",
       "      <td>dose</td>\n",
       "      <td>0.008</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011467</td>\n",
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       "      <td>640:1138-0534:SK483-dose-7-BV-1_w4CSU-640</td>\n",
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       "      <td>45</td>\n",
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       "      <td>dose</td>\n",
       "      <td>125</td>\n",
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       "    <tr>\n",
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       "      <td>640:0868-0264:SK483-dose-7-BV-1_w4CSU-640</td>\n",
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       "      <td>125</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21742</th>\n",
       "      <td>640:0136-0857:SK483-dose-7-BV-1_w4CSU-640</td>\n",
       "      <td>975</td>\n",
       "      <td>52.925</td>\n",
       "      <td>45</td>\n",
       "      <td>SK483</td>\n",
       "      <td>dose</td>\n",
       "      <td>125</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21743</th>\n",
       "      <td>640:0418-0165:SK483-dose-7-BV-1_w4CSU-640</td>\n",
       "      <td>68</td>\n",
       "      <td>39.397</td>\n",
       "      <td>45</td>\n",
       "      <td>SK483</td>\n",
       "      <td>dose</td>\n",
       "      <td>125</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21744</th>\n",
       "      <td>640:1145-1073:SK483-dose-7-BV-1_w4CSU-640</td>\n",
       "      <td>307</td>\n",
       "      <td>26.707</td>\n",
       "      <td>45</td>\n",
       "      <td>SK483</td>\n",
       "      <td>dose</td>\n",
       "      <td>125</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14525 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Label  Area    Mean  Slice strain  \\\n",
       "                                                                                \n",
       "1604   640:0784-0411:SK482-dose-1-PCB-1_w4CSU-640  1089  54.392      3  SK482   \n",
       "1605   640:0945-0980:SK482-dose-1-PCB-1_w4CSU-640   975  36.925      3  SK482   \n",
       "1606   640:0215-1027:SK482-dose-1-PCB-1_w4CSU-640  1140  43.437      3  SK482   \n",
       "1607   640:0630-0446:SK482-dose-1-PCB-1_w4CSU-640   628  37.820      3  SK482   \n",
       "1608   640:1098-0599:SK482-dose-1-PCB-1_w4CSU-640   793  34.763      3  SK482   \n",
       "...                                           ...   ...     ...    ...    ...   \n",
       "21740   640:1138-0534:SK483-dose-7-BV-1_w4CSU-640   838  41.848     45  SK483   \n",
       "21741   640:0868-0264:SK483-dose-7-BV-1_w4CSU-640   755  60.089     45  SK483   \n",
       "21742   640:0136-0857:SK483-dose-7-BV-1_w4CSU-640   975  52.925     45  SK483   \n",
       "21743   640:0418-0165:SK483-dose-7-BV-1_w4CSU-640    68  39.397     45  SK483   \n",
       "21744   640:1145-1073:SK483-dose-7-BV-1_w4CSU-640   307  26.707     45  SK483   \n",
       "\n",
       "      condition   time chromophore number     ratio  \n",
       "                                                     \n",
       "1604       dose  0.008         PCB      1  0.015402  \n",
       "1605       dose  0.008         PCB      1  0.017851  \n",
       "1606       dose  0.008         PCB      1  0.020107  \n",
       "1607       dose  0.008         PCB      1  0.013626  \n",
       "1608       dose  0.008         PCB      1  0.011467  \n",
       "...         ...    ...         ...    ...       ...  \n",
       "21740      dose    125         -BV      1  0.028133  \n",
       "21741      dose    125         -BV      1  0.029899  \n",
       "21742      dose    125         -BV      1  0.032449  \n",
       "21743      dose    125         -BV      1  0.028779  \n",
       "21744      dose    125         -BV      1  0.022829  \n",
       "\n",
       "[14525 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#dose-response\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for i in (iRFP[\"strain\"]==\"SK482\",iRFP[\"strain\"]==\"SK483\"):\n",
    "    for j in (iRFP[\"chromophore\"]==\"PCB\",iRFP[\"chromophore\"]==\"-BV\"):\n",
    "        for k in (0.008,0.04,0.2,1,5,25,125):\n",
    "            data_tmp = iRFP[(i)&(iRFP[\"condition\"]==\"dose\")&(j)&(iRFP[\"time\"]==k)]\n",
    "            data_IQR = iRFP.loc[(i)&(iRFP[\"condition\"]==\"dose\")&(j)&(iRFP[\"time\"]==k),\"ratio\"].values\n",
    "            Q1 = np.quantile(data_IQR,0.25)\n",
    "            print(Q1)\n",
    "            Q3 = np.quantile(data_IQR,0.75)\n",
    "            print(Q3)\n",
    "            IQR = (np.quantile(data_IQR,0.75)-np.quantile(data_IQR,0.25))*1.5\n",
    "            IQR_low = Q1-IQR\n",
    "            IQR_high = Q3+IQR\n",
    "            data_tmp = data_tmp[(data_tmp[\"ratio\"]>IQR_low)&(data_tmp[\"ratio\"]<IQR_high)]\n",
    "            plt.hist(data_tmp[\"ratio\"], bins=200)\n",
    "            df = pd.concat([df,data_tmp])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "860\n",
      "365\n",
      "1225\n",
      "469\n",
      "285\n",
      "1979\n",
      "388\n",
      "340\n",
      "2707\n",
      "499\n",
      "510\n",
      "3716\n",
      "576\n",
      "501\n",
      "4793\n",
      "533\n",
      "474\n",
      "5800\n",
      "379\n",
      "555\n",
      "6734\n",
      "1026\n",
      "439\n",
      "8199\n",
      "460\n",
      "544\n",
      "9203\n",
      "519\n",
      "372\n",
      "10094\n",
      "421\n",
      "365\n",
      "10880\n",
      "811\n",
      "731\n",
      "12422\n",
      "579\n",
      "872\n",
      "13873\n",
      "294\n",
      "358\n",
      "14525\n"
     ]
    }
   ],
   "source": [
    "#dose-response 要素のカウント\n",
    "Label_dose = [\"dose-1-PCB\",\"dose-2-PCB\",\"dose-3-PCB\",\"dose-4-PCB\",\"dose-5-PCB\",\"dose-6-PCB\",\"dose-7-PCB\",\"dose-1-BV\",\"dose-2-BV\",\"dose-3-BV\",\"dose-4-BV\",\"dose-5-BV\",\"dose-6-BV\",\"dose-7-BV\"]\n",
    "result_dose = 0\n",
    "for i in range(14):\n",
    "    dose_482 = ((df[\"Label\"].str.contains(Label_dose[i])) & (df[\"strain\"]==\"SK482\")).sum()\n",
    "    dose_483 = ((df[\"Label\"].str.contains(Label_dose[i])) & (df[\"strain\"]==\"SK483\")).sum()\n",
    "    print(dose_482)\n",
    "    print(dose_483)\n",
    "    result_dose = result_dose + dose_482 + dose_483\n",
    "    print(result_dose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01642652672694143\n",
      "0.02292655544499913\n",
      "0.36251035579138574\n",
      "0.4350470862196999\n",
      "0.43107738114581307\n",
      "0.5294407722262676\n",
      "0.5516709715302756\n",
      "0.736639189281495\n",
      "0.518617620781136\n",
      "0.6666688179438646\n",
      "0.40648517709279797\n",
      "0.5359162463766939\n",
      "0.01642652672694143\n",
      "0.02292655544499913\n",
      "0.02271609040670177\n",
      "0.02965093624310459\n",
      "0.026377078575659536\n",
      "0.03313533895341529\n",
      "0.04011737526080994\n",
      "0.05270973883165145\n",
      "0.04576608331603791\n",
      "0.06126055658083398\n",
      "0.041409177405708625\n",
      "0.05572411799678095\n",
      "0.01572672359881185\n",
      "0.02110883431473282\n",
      "0.3458895784513215\n",
      "0.47801244617270344\n",
      "0.4022431662188889\n",
      "0.5053563944908548\n",
      "0.4861611355605116\n",
      "0.6529582327540607\n",
      "0.47977976575390163\n",
      "0.7418596247128689\n",
      "0.42469669152049183\n",
      "0.596984783758731\n",
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       "  <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>strain</th>\n",
       "      <th>condition</th>\n",
       "      <th>time</th>\n",
       "      <th>chromophore</th>\n",
       "      <th>number</th>\n",
       "      <th>ratio</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>640:0514-0674:SK482-time-0-1_w4CSU-640</td>\n",
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       "      <td>0.018840</td>\n",
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       "    <tr>\n",
       "      <th>9665</th>\n",
       "      <td>640:0220-0182:SK482-time-0-1_w4CSU-640</td>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>9666</th>\n",
       "      <td>640:0136-1094:SK482-time-0-1_w4CSU-640</td>\n",
       "      <td>781</td>\n",
       "      <td>35.447</td>\n",
       "      <td>20</td>\n",
       "      <td>SK482</td>\n",
       "      <td>time</td>\n",
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       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018892</td>\n",
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       "      <th>9667</th>\n",
       "      <td>640:0282-1037:SK482-time-0-1_w4CSU-640</td>\n",
       "      <td>889</td>\n",
       "      <td>49.620</td>\n",
       "      <td>20</td>\n",
       "      <td>SK482</td>\n",
       "      <td>time</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020212</td>\n",
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       "    <tr>\n",
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       "      <td>640:0869-0931:SK482-time-0-1_w4CSU-640</td>\n",
       "      <td>756</td>\n",
       "      <td>41.341</td>\n",
       "      <td>20</td>\n",
       "      <td>SK482</td>\n",
       "      <td>time</td>\n",
       "      <td>0</td>\n",
       "      <td>PCB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009347</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27903</th>\n",
       "      <td>640:0769-0722:SK483-time-5-BV-1_w4CSU-640</td>\n",
       "      <td>470</td>\n",
       "      <td>48.621</td>\n",
       "      <td>59</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.054253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27904</th>\n",
       "      <td>640:0935-0991:SK483-time-5-BV-1_w4CSU-640</td>\n",
       "      <td>74</td>\n",
       "      <td>27.757</td>\n",
       "      <td>59</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27905</th>\n",
       "      <td>640:0736-0772:SK483-time-5-BV-1_w4CSU-640</td>\n",
       "      <td>169</td>\n",
       "      <td>50.456</td>\n",
       "      <td>59</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.033664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27906</th>\n",
       "      <td>640:0307-0085:SK483-time-5-BV-1_w4CSU-640</td>\n",
       "      <td>734</td>\n",
       "      <td>46.711</td>\n",
       "      <td>59</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.037414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27907</th>\n",
       "      <td>640:0933-0614:SK483-time-5-BV-1_w4CSU-640</td>\n",
       "      <td>61</td>\n",
       "      <td>34.803</td>\n",
       "      <td>59</td>\n",
       "      <td>SK483</td>\n",
       "      <td>time</td>\n",
       "      <td>5</td>\n",
       "      <td>-BV</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9854 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Label  Area    Mean  Slice strain  \\\n",
       "                                                                               \n",
       "9664      640:0514-0674:SK482-time-0-1_w4CSU-640   972  54.351     20  SK482   \n",
       "9665      640:0220-0182:SK482-time-0-1_w4CSU-640   959  50.772     20  SK482   \n",
       "9666      640:0136-1094:SK482-time-0-1_w4CSU-640   781  35.447     20  SK482   \n",
       "9667      640:0282-1037:SK482-time-0-1_w4CSU-640   889  49.620     20  SK482   \n",
       "9668      640:0869-0931:SK482-time-0-1_w4CSU-640   756  41.341     20  SK482   \n",
       "...                                          ...   ...     ...    ...    ...   \n",
       "27903  640:0769-0722:SK483-time-5-BV-1_w4CSU-640   470  48.621     59  SK483   \n",
       "27904  640:0935-0991:SK483-time-5-BV-1_w4CSU-640    74  27.757     59  SK483   \n",
       "27905  640:0736-0772:SK483-time-5-BV-1_w4CSU-640   169  50.456     59  SK483   \n",
       "27906  640:0307-0085:SK483-time-5-BV-1_w4CSU-640   734  46.711     59  SK483   \n",
       "27907  640:0933-0614:SK483-time-5-BV-1_w4CSU-640    61  34.803     59  SK483   \n",
       "\n",
       "      condition time chromophore number     ratio  \n",
       "                                                   \n",
       "9664       time    0         PCB      1  0.018840  \n",
       "9665       time    0         PCB      1  0.017002  \n",
       "9666       time    0         PCB      1  0.018892  \n",
       "9667       time    0         PCB      1  0.020212  \n",
       "9668       time    0         PCB      1  0.009347  \n",
       "...         ...  ...         ...    ...       ...  \n",
       "27903      time    5         -BV      1  0.054253  \n",
       "27904      time    5         -BV      1  0.042801  \n",
       "27905      time    5         -BV      1  0.033664  \n",
       "27906      time    5         -BV      1  0.037414  \n",
       "27907      time    5         -BV      1  0.025519  \n",
       "\n",
       "[9854 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#time-course\n",
    "df2 = pd.DataFrame()\n",
    "\n",
    "for i in (iRFP[\"strain\"]==\"SK482\",iRFP[\"strain\"]==\"SK483\"):\n",
    "    for j,l in zip((iRFP[\"chromophore\"]==\"PCB\",iRFP[\"chromophore\"]==\"-BV\"),[\"PCB\",\"-BV\"]):\n",
    "        for k in (0,0.5,1,2,3,5):\n",
    "            if k == 0:\n",
    "                data_tmp = iRFP[(i)&(iRFP[\"condition\"]==\"time\")&(iRFP[\"chromophore\"]==\"PCB\")&(iRFP[\"time\"]==k)]\n",
    "                data_IQR = iRFP.loc[(i)&(iRFP[\"condition\"]==\"time\")&(iRFP[\"chromophore\"]==\"PCB\")&(iRFP[\"time\"]==k),\"ratio\"].values\n",
    "            else:\n",
    "                data_tmp = iRFP[(i)&(iRFP[\"condition\"]==\"time\")&(j)&(iRFP[\"time\"]==k)]\n",
    "                data_IQR = iRFP.loc[(i)&(iRFP[\"condition\"]==\"time\")&(j)&(iRFP[\"time\"]==k),\"ratio\"].values\n",
    "            Q1 = np.quantile(data_IQR,0.25)\n",
    "            print(Q1)\n",
    "            Q3 = np.quantile(data_IQR,0.75)\n",
    "            print(Q3)\n",
    "            IQR = (np.quantile(data_IQR,0.75)-np.quantile(data_IQR,0.25))*1.5\n",
    "            IQR_low = Q1-IQR\n",
    "            IQR_high = Q3+IQR\n",
    "            data_tmp = data_tmp[(data_tmp[\"ratio\"]>IQR_low)&(data_tmp[\"ratio\"]<IQR_high)]\n",
    "            plt.hist(data_tmp[\"ratio\"], bins=200)\n",
    "            data_tmp[\"chromophore\"]=l\n",
    "            df2 = pd.concat([df2,data_tmp])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "428\n",
      "730\n",
      "1158\n",
      "387\n",
      "241\n",
      "1786\n",
      "248\n",
      "289\n",
      "2323\n",
      "535\n",
      "828\n",
      "3686\n",
      "652\n",
      "434\n",
      "4772\n",
      "400\n",
      "274\n",
      "5446\n",
      "287\n",
      "264\n",
      "5997\n",
      "381\n",
      "527\n",
      "6905\n",
      "297\n",
      "310\n",
      "7512\n",
      "467\n",
      "839\n",
      "8818\n",
      "560\n",
      "476\n",
      "9854\n"
     ]
    }
   ],
   "source": [
    "#time-course 要素のカウント\n",
    "Label_time = [\"time-0-\",\"time-0.5-PCB\",\"time-1-PCB\",\"time-2-PCB\",\"time-3-PCB\",\"time-5-PCB\",\"time-0.5-BV\",\"time-1-BV\",\"time-2-BV\",\"time-3-BV\",\"time-5-BV\"]\n",
    "result_time = 0\n",
    "for i in range(11):\n",
    "    time_482 = ((df2[\"Label\"].str.contains(Label_time[i])) & (df2[\"strain\"]==\"SK482\")).sum()\n",
    "    time_483 = ((df2[\"Label\"].str.contains(Label_time[i])) & (df2[\"strain\"]==\"SK483\")).sum()\n",
    "    print(time_482)\n",
    "    print(time_483)\n",
    "    result_time = result_time + time_482 + time_483\n",
    "    print(result_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SK482_dose_PCB  & SK482_dose_BV\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.lineplot(data=df[(df[\"strain\"]==\"SK482\")&(df[\"condition\"]==\"dose\")&(df[\"chromophore\"]==\"PCB\")], x=\"time\", y='ratio', ci=\"sd\")\n",
    "sns.lineplot(data=df[(df[\"strain\"]==\"SK482\")&(df[\"condition\"]==\"dose\")&(df[\"chromophore\"]==\"-BV\")], x=\"time\", y='ratio', ci=\"sd\")\n",
    "plt.xscale(\"log\")\n",
    "#plt.savefig(\"20211005-SK482-dose-response-1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SK483_dose_PCB & SK483_dose_BV\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.lineplot(data=df[(df[\"strain\"]==\"SK483\")&(df[\"condition\"]==\"dose\")&(df[\"chromophore\"]==\"PCB\")], x=\"time\", y='ratio', ci=\"sd\")\n",
    "sns.lineplot(data=df[(df[\"strain\"]==\"SK483\")&(df[\"condition\"]==\"dose\")&(df[\"chromophore\"]==\"-BV\")], x=\"time\", y='ratio', ci=\"sd\")\n",
    "plt.xscale(\"log\")\n",
    "#plt.savefig(\"20211005-SK483-dose-response-1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9963b65d60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SK482_time_PCB & SK482_time_BV\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.lineplot(data=df2[(df2[\"strain\"]==\"SK482\")&(df2[\"condition\"]==\"time\")&(df2[\"chromophore\"]==\"PCB\")], x=\"time\", y='ratio',ci=\"sd\")\n",
    "sns.lineplot(data=df2[(df2[\"strain\"]==\"SK482\")&(df2[\"condition\"]==\"time\")&(df2[\"chromophore\"]==\"-BV\")], x=\"time\", y='ratio',ci=\"sd\")\n",
    "#plt.savefig(\"20211005-SK482-time-course-1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f993007c430>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SK483_time_PCB & SK483_time_BV\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.lineplot(data=df2[(df2[\"strain\"]==\"SK483\")&(df2[\"condition\"]==\"time\")&(df2[\"chromophore\"]==\"PCB\")], x=\"time\", y='ratio',ci=\"sd\")\n",
    "sns.lineplot(data=df2[(df2[\"strain\"]==\"SK483\")&(df2[\"condition\"]==\"time\")&(df2[\"chromophore\"]==\"-BV\")], x=\"time\", y='ratio',ci=\"sd\")\n",
    "#plt.savefig(\"20211005-SK483-time-course-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
}