Reference_dataprep_DeepProfiler.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/share/data/analyses/benjamin/Single_cell_project'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Matplotlib requires numpy>=1.20; you have 1.19.5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[48], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n",
      "File \u001b[0;32m~/share/data/analyses/benjamin/.venv/lib/python3.8/site-packages/matplotlib/__init__.py:227\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[39mif\u001b[39;00m parse_version(module\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m parse_version(minver):\n\u001b[1;32m    223\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mImportError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mMatplotlib requires \u001b[39m\u001b[39m{\u001b[39;00mmodname\u001b[39m}\u001b[39;00m\u001b[39m>=\u001b[39m\u001b[39m{\u001b[39;00mminver\u001b[39m}\u001b[39;00m\u001b[39m; \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    224\u001b[0m                               \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39myou have \u001b[39m\u001b[39m{\u001b[39;00mmodule\u001b[39m.\u001b[39m__version__\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 227\u001b[0m _check_versions()\n\u001b[1;32m    230\u001b[0m \u001b[39m# The decorator ensures this always returns the same handler (and it is only\u001b[39;00m\n\u001b[1;32m    231\u001b[0m \u001b[39m# attached once).\u001b[39;00m\n\u001b[1;32m    232\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mlru_cache()\n\u001b[1;32m    233\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_ensure_handler\u001b[39m():\n",
      "File \u001b[0;32m~/share/data/analyses/benjamin/.venv/lib/python3.8/site-packages/matplotlib/__init__.py:223\u001b[0m, in \u001b[0;36m_check_versions\u001b[0;34m()\u001b[0m\n\u001b[1;32m    221\u001b[0m module \u001b[39m=\u001b[39m importlib\u001b[39m.\u001b[39mimport_module(modname)\n\u001b[1;32m    222\u001b[0m \u001b[39mif\u001b[39;00m parse_version(module\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m parse_version(minver):\n\u001b[0;32m--> 223\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mImportError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mMatplotlib requires \u001b[39m\u001b[39m{\u001b[39;00mmodname\u001b[39m}\u001b[39;00m\u001b[39m>=\u001b[39m\u001b[39m{\u001b[39;00mminver\u001b[39m}\u001b[39;00m\u001b[39m; \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    224\u001b[0m                       \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39myou have \u001b[39m\u001b[39m{\u001b[39;00mmodule\u001b[39m.\u001b[39m__version__\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mImportError\u001b[0m: Matplotlib requires numpy>=1.20; you have 1.19.5"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and merge metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "beactica_plates = [\"PB000046\",\n",
    "\"PB000047\",\n",
    "\"PB000048\",\n",
    "\"PB000049\",\n",
    "\"PB000050\",\n",
    "\"PB000051\",\n",
    "\"PB000052\",\n",
    "\"PB000053\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_metadata = pd.read_csv(\"bea_v1_plate_layout_ALL.csv\", sep = \";\")\n",
    "ref_metadata = ref_metadata.rename(columns = {\"cmpd_id\": \"compound_id\", \"well_id\" : \"well\"})\n",
    "#ref_metadata = ref_metadata.dropna(subset=['barcode'])\n",
    "beactica_compinfo = pd.read_csv(\"bea_v1_labdesign_compounds.csv\", sep = \",\")\n",
    "beactica_plate_info = ref_metadata.merge(beactica_compinfo, on = [\"compound_id\", \"batch_id\", \"cmpd_name\"], how =\"left\")\n",
    "\n",
    "# Create plate column\n",
    "regex_pattern = r'-(PB[^-]*)-'\n",
    "beactica_plate_info['barcode'] = beactica_plate_info['layout_id'].str.extract(regex_pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>layout_id</th>\n",
       "      <th>well</th>\n",
       "      <th>batch_id</th>\n",
       "      <th>compound_id</th>\n",
       "      <th>cmpd_name</th>\n",
       "      <th>moa</th>\n",
       "      <th>solvent</th>\n",
       "      <th>stock_conc</th>\n",
       "      <th>stock_conc_unit</th>\n",
       "      <th>cmpd_vol</th>\n",
       "      <th>...</th>\n",
       "      <th>pertType</th>\n",
       "      <th>cmpd_conc</th>\n",
       "      <th>cmpd_conc_unit</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>cells_per_well</th>\n",
       "      <th>treatment_h</th>\n",
       "      <th>cat</th>\n",
       "      <th>libtxt</th>\n",
       "      <th>target</th>\n",
       "      <th>barcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>B02</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>PB000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>B03</td>\n",
       "      <td>PHB000184</td>\n",
       "      <td>BEA-0010234-AH-001</td>\n",
       "      <td>Anatoxin A fumarate</td>\n",
       "      <td>apoptosis</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>apoptosis</td>\n",
       "      <td>PB000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>B04</td>\n",
       "      <td>PHB000222</td>\n",
       "      <td>BEA-0010271-BA-001</td>\n",
       "      <td>Bleomycin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>PB000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>B05</td>\n",
       "      <td>PHB000214</td>\n",
       "      <td>BEA-0010263-AA-001</td>\n",
       "      <td>L-Buthionine sulfoximine</td>\n",
       "      <td>ferroptosis inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>ferroptosis inducer</td>\n",
       "      <td>PB000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>B06</td>\n",
       "      <td>PHB000246</td>\n",
       "      <td>BEA-0005443-AQ-003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>40.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>1.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Test</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PB000046</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>O19</td>\n",
       "      <td>PHB000218</td>\n",
       "      <td>BEA-0010267-AA-001</td>\n",
       "      <td>Simvastatin</td>\n",
       "      <td>ferroptosis inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.30</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>ferroptosis inducer</td>\n",
       "      <td>PB000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>O20</td>\n",
       "      <td>PHB000207</td>\n",
       "      <td>BEA-0010256-AA-001</td>\n",
       "      <td>Rifaximin</td>\n",
       "      <td>apoptosis</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>apoptosis</td>\n",
       "      <td>PB000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>O21</td>\n",
       "      <td>PHB000235</td>\n",
       "      <td>BEA-0010273-AA-001</td>\n",
       "      <td>Carboplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>40.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>PB000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2462</th>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>O22</td>\n",
       "      <td>PHB000256</td>\n",
       "      <td>BEA-0005926-AA-001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mM</td>\n",
       "      <td>40.00</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>1.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Test</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PB000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463</th>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>O23</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>0.3</td>\n",
       "      <td>mM</td>\n",
       "      <td>13.33</td>\n",
       "      <td>...</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>PB000053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2464 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           layout_id well   batch_id         compound_id  \\\n",
       "0     bea-v1-PB000046-MCF7-48h-P1-L1  B02  PHB000238  BEA-0009743-AA-001   \n",
       "1     bea-v1-PB000046-MCF7-48h-P1-L1  B03  PHB000184  BEA-0010234-AH-001   \n",
       "2     bea-v1-PB000046-MCF7-48h-P1-L1  B04  PHB000222  BEA-0010271-BA-001   \n",
       "3     bea-v1-PB000046-MCF7-48h-P1-L1  B05  PHB000214  BEA-0010263-AA-001   \n",
       "4     bea-v1-PB000046-MCF7-48h-P1-L1  B06  PHB000246  BEA-0005443-AQ-003   \n",
       "...                              ...  ...        ...                 ...   \n",
       "2459  bea-v1-PB000053-MCF7-48h-P4-L2  O19  PHB000218  BEA-0010267-AA-001   \n",
       "2460  bea-v1-PB000053-MCF7-48h-P4-L2  O20  PHB000207  BEA-0010256-AA-001   \n",
       "2461  bea-v1-PB000053-MCF7-48h-P4-L2  O21  PHB000235  BEA-0010273-AA-001   \n",
       "2462  bea-v1-PB000053-MCF7-48h-P4-L2  O22  PHB000256  BEA-0005926-AA-001   \n",
       "2463  bea-v1-PB000053-MCF7-48h-P4-L2  O23  PHB000236  BEA-0010287-AA-001   \n",
       "\n",
       "                     cmpd_name                     moa solvent  stock_conc  \\\n",
       "0                  Domperidone       autophagy inducer    DMSO        10.0   \n",
       "1          Anatoxin A fumarate               apoptosis    DMSO        10.0   \n",
       "2                    Bleomycin  immunogenic cell death    DMSO        10.0   \n",
       "3     L-Buthionine sulfoximine     ferroptosis inducer    DMSO        10.0   \n",
       "4                          NaN                     NaN    DMSO         1.0   \n",
       "...                        ...                     ...     ...         ...   \n",
       "2459               Simvastatin     ferroptosis inducer    DMSO         1.0   \n",
       "2460                 Rifaximin               apoptosis    DMSO        10.0   \n",
       "2461               Carboplatin  immunogenic cell death     H2O         3.0   \n",
       "2462                       NaN                     NaN    DMSO         1.0   \n",
       "2463                 Cisplatin  immunogenic cell death     H2O         0.3   \n",
       "\n",
       "     stock_conc_unit  cmpd_vol  ... pertType  cmpd_conc cmpd_conc_unit  \\\n",
       "0                 mM     12.00  ...      trt       3.00             uM   \n",
       "1                 mM     12.00  ...      trt       3.00             uM   \n",
       "2                 mM     12.00  ...      trt       3.00             uM   \n",
       "3                 mM     12.00  ...      trt       3.00             uM   \n",
       "4                 mM     40.00  ...      trt       1.00             uM   \n",
       "...              ...       ...  ...      ...        ...            ...   \n",
       "2459              mM     12.00  ...      trt       0.30             uM   \n",
       "2460              mM     12.00  ...      trt       3.00             uM   \n",
       "2461              mM     40.00  ...      trt       3.00             uM   \n",
       "2462              mM     40.00  ...      trt       1.00             uM   \n",
       "2463              mM     13.33  ...      trt       0.09             uM   \n",
       "\n",
       "     cell_line  cells_per_well treatment_h      cat         libtxt  \\\n",
       "0         MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "1         MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "2         MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "3         MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "4         MCF7            1100          48     Test  pharmb_io_BEA   \n",
       "...        ...             ...         ...      ...            ...   \n",
       "2459      MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "2460      MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "2461      MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "2462      MCF7            1100          48     Test  pharmb_io_BEA   \n",
       "2463      MCF7            1100          48  Control  pharmb_io_BEA   \n",
       "\n",
       "                      target   barcode  \n",
       "0          autophagy inducer  PB000046  \n",
       "1                  apoptosis  PB000046  \n",
       "2     immunogenic cell death  PB000046  \n",
       "3        ferroptosis inducer  PB000046  \n",
       "4                        NaN  PB000046  \n",
       "...                      ...       ...  \n",
       "2459     ferroptosis inducer  PB000053  \n",
       "2460               apoptosis  PB000053  \n",
       "2461  immunogenic cell death  PB000053  \n",
       "2462                     NaN  PB000053  \n",
       "2463  immunogenic cell death  PB000053  \n",
       "\n",
       "[2464 rows x 23 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beactica_plate_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barcode\n",
       "PB000046    307\n",
       "PB000047    315\n",
       "PB000048    302\n",
       "PB000049    308\n",
       "PB000050    308\n",
       "PB000051    314\n",
       "PB000052    302\n",
       "PB000053    308\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beactica_plate_info.groupby('barcode').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show distribution of MoA classes before subsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of columns you want to plot histograms for\n",
    "columns_to_plot = ['cbkid']  # Replace with your column names\n",
    "\n",
    "for column in columns_to_plot:\n",
    "    plt.figure(figsize=(20, 12))  # Adjust the size as needed\n",
    "    specs_meta_full[column].hist(bins=len(set(specs_meta_full[\"cbkid\"])))  # You can adjust the number of bins as needed\n",
    "    plt.title(f'Histogram of {column}')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid(False)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batch_id\n",
       "BJ1894913    8\n",
       "BJ1836055    6\n",
       "BJ1834231    6\n",
       "BJ1895005    6\n",
       "BJ1894396    6\n",
       "            ..\n",
       "BJ1897153    4\n",
       "BJ1897260    4\n",
       "BJ1898899    4\n",
       "BJ1898931    2\n",
       "BJ1898915    2\n",
       "Name: count, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_metadata[\"batch_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PB000046',\n",
       " 'PB000047',\n",
       " 'PB000048',\n",
       " 'PB000049',\n",
       " 'PB000050',\n",
       " 'PB000051',\n",
       " 'PB000052',\n",
       " 'PB000053']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc_plates = list(beactica_plate_info[\"barcode\"].unique()) \n",
    "qc_plates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "print(len(qc_plates))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality control on sampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc_flags = pd.read_csv(\"beactica_5StD_Flags.csv\", sep = \";\")\n",
    "qc_good = qc_flags[qc_flags[\"Total\"] == 0]\n",
    "qc_good = qc_good[[\"Metadata_Barcode\", \"Metadata_Well\", \"Metadata_Site\"]]\n",
    "qc_good = qc_good.rename(columns = {\"Metadata_Well\" : \"well\"})\n",
    "regex_pattern2 = r'(PB[^-]*)-'\n",
    "qc_good[\"barcode\"] = qc_good['Metadata_Barcode'].str.extract(regex_pattern2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "beactica_plate_info_flag = qc_good.merge(beactica_plate_info, on = [\"barcode\", \"well\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# Set the environment variable\n",
    "os.environ[\"DB_URI\"] = \"postgresql://pharmbio_readonly:readonly@imagedb-pg-postgresql.services.svc.cluster.local/imagedb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pharmbio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Quering the db for beactica found 1 study: ['beactica']\n",
      "__________________________________________________\n",
      "INFO: 1\n",
      "INFO: \tbeactica\n",
      "INFO: \t['PB000045', 'PB000046', 'PB000047', 'PB000048', 'PB000049', 'PB000050', 'PB000051', 'PB000052', 'PB000053']\n",
      "INFO: \n",
      "__________________________________________________\n",
      "INFO: No replicated analysis has been found!\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000048-P3-L1/3030/3179/qcRAW_images_PB000048', '/share/data/cellprofiler/automation/results/PB000048-P3-L1/3030/3179/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000049-P4-L1/3038/3181/qcRAW_images_PB000049', '/share/data/cellprofiler/automation/results/PB000049-P4-L1/3038/3181/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000050-P1-L2/3039/3182/qcRAW_images_PB000050', '/share/data/cellprofiler/automation/results/PB000050-P1-L2/3039/3182/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000052-P3-L2-continued/3042/3189/qcRAW_images_PB000052', '/share/data/cellprofiler/automation/results/PB000052-P3-L2-continued/3042/3189/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000053-P4-L2-continue/3046/3193/qcRAW_images_PB000053', '/share/data/cellprofiler/automation/results/PB000053-P4-L2-continue/3046/3193/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000046-P1-L1-v2-continued/3061/3195/qcRAW_images_PB000046', '/share/data/cellprofiler/automation/results/PB000046-P1-L1-v2-continued/3061/3195/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000051-P2-L2-continued/3041/3202/qcRAW_images_PB000051', '/share/data/cellprofiler/automation/results/PB000051-P2-L2-continued/3041/3202/qcRAW_images']\n",
      "WARNING: No image quality file was found in: ['/share/data/cellprofiler/automation/results/PB000047-P2-L1/3029/3203/qcRAW_images_PB000047', '/share/data/cellprofiler/automation/results/PB000047-P2-L1/3029/3203/qcRAW_images']\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[58], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpharmbio\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdataset\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mimage_quality\u001b[39;00m \u001b[39mimport\u001b[39;00m get_image_quality_ref, get_image_quality_data\n\u001b[1;32m      2\u001b[0m qc_ref_df \u001b[39m=\u001b[39m get_image_quality_ref(\u001b[39m\"\u001b[39m\u001b[39mbeactica\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mfilter\u001b[39m\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mplate_barcode\u001b[39m\u001b[39m\"\u001b[39m: qc_plates})\n\u001b[0;32m----> 3\u001b[0m qc_df \u001b[39m=\u001b[39m get_image_quality_data(qc_ref_df, force_merging_columns\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mdrop\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/share/data/analyses/benjamin/.venv/lib/python3.8/site-packages/pharmbio/dataset/image_quality.py:254\u001b[0m, in \u001b[0;36mget_image_quality_data\u001b[0;34m(filtered_image_quality_info, force_merging_columns)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[39melif\u001b[39;00m force_merging_columns \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mdrop\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    251\u001b[0m     concat_method \u001b[39m=\u001b[39m (\n\u001b[1;32m    252\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mvertical\u001b[39m\u001b[39m\"\u001b[39m  \u001b[39m# merge dfs horizontally, only keeps matching columns\u001b[39;00m\n\u001b[1;32m    253\u001b[0m     )\n\u001b[0;32m--> 254\u001b[0m     common_columns \u001b[39m=\u001b[39m \u001b[39mset\u001b[39m(dfs[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39mcolumns)\n\u001b[1;32m    255\u001b[0m     \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m dfs[\u001b[39m1\u001b[39m:]:\n\u001b[1;32m    256\u001b[0m         common_columns\u001b[39m.\u001b[39mintersection_update(df\u001b[39m.\u001b[39mcolumns)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "from pharmbio.dataset.image_quality import get_image_quality_ref, get_image_quality_data\n",
    "qc_ref_df = get_image_quality_ref(\"beactica\", filter={\"plate_barcode\": qc_plates})\n",
    "qc_df = get_image_quality_data(qc_ref_df, force_merging_columns=\"drop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/share/data/cellprofiler/automation/results/PB000048-P3-L1/3030/3179/',\n",
       " '/share/data/cellprofiler/automation/results/PB000049-P4-L1/3038/3181/',\n",
       " '/share/data/cellprofiler/automation/results/PB000050-P1-L2/3039/3182/',\n",
       " '/share/data/cellprofiler/automation/results/PB000052-P3-L2-continued/3042/3189/',\n",
       " '/share/data/cellprofiler/automation/results/PB000053-P4-L2-continue/3046/3193/',\n",
       " '/share/data/cellprofiler/automation/results/PB000046-P1-L1-v2-continued/3061/3195/',\n",
       " '/share/data/cellprofiler/automation/results/PB000051-P2-L2-continued/3041/3202/',\n",
       " '/share/data/cellprofiler/automation/results/PB000047-P2-L1/3029/3203/']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(qc_ref_df[\"results\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr > th,\n",
       ".dataframe > tbody > tr > td {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>OutlierSD_FocusScore_-3_3</th><th>OutlierSD_MaxIntensity_-3_3</th><th>OutlierSD_MeanIntensity_-3_3</th><th>OutlierSD_PowerLogLogSlope_-3_3</th><th>OutlierSD_StdIntensity_-3_3</th><th>outlier_flag</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>491</td><td>408</td><td>86</td><td>280</td><td>292</td><td>970</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 6)\n",
       "┌────────────────┬────────────────┬────────────────┬────────────────┬───────────────┬──────────────┐\n",
       "│ OutlierSD_Focu ┆ OutlierSD_MaxI ┆ OutlierSD_Mean ┆ OutlierSD_Powe ┆ OutlierSD_Std ┆ outlier_flag │\n",
       "│ sScore_-3_3    ┆ ntensity_-3_3  ┆ Intensity_-3_3 ┆ rLogLogSlope_- ┆ Intensity_-3_ ┆ ---          │\n",
       "│ ---            ┆ ---            ┆ ---            ┆ 3_3            ┆ 3             ┆ i64          │\n",
       "│ i64            ┆ i64            ┆ i64            ┆ ---            ┆ ---           ┆              │\n",
       "│                ┆                ┆                ┆ i64            ┆ i64           ┆              │\n",
       "╞════════════════╪════════════════╪════════════════╪════════════════╪═══════════════╪══════════════╡\n",
       "│ 491            ┆ 408            ┆ 86             ┆ 280            ┆ 292           ┆ 970          │\n",
       "└────────────────┴────────────────┴────────────────┴────────────────┴───────────────┴──────────────┘"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import polars as pl\n",
    "from pharmbio.data_processing.quality_control import get_qc_module, get_channels, flag_outlier_images\n",
    "#get_channels(qc_df)\n",
    "flagged_images = flag_outlier_images(qc_df, default_sd_step=(-3, 3)).select(['ImageID','Metadata_AcqID','Metadata_Barcode','Metadata_Well','Metadata_Site','ImageNumber','outlier_flag']).filter(pl.col('outlier_flag') == 0).to_pandas()\n",
    "flagged_images.rename(columns={'Metadata_Barcode': 'barcode', 'Metadata_Well': 'well'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_metadata_full_flags = pd.merge(flagged_images, ref_metadata, on = [\"barcode\", \"well\"], how = \"left\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create metadata file DeepProfiler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Metadata_Barcode</th>\n",
       "      <th>well</th>\n",
       "      <th>Metadata_Site</th>\n",
       "      <th>barcode</th>\n",
       "      <th>layout_id</th>\n",
       "      <th>batch_id</th>\n",
       "      <th>compound_id</th>\n",
       "      <th>cmpd_name</th>\n",
       "      <th>moa</th>\n",
       "      <th>solvent</th>\n",
       "      <th>...</th>\n",
       "      <th>well_vol_unit</th>\n",
       "      <th>pertType</th>\n",
       "      <th>cmpd_conc</th>\n",
       "      <th>cmpd_conc_unit</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>cells_per_well</th>\n",
       "      <th>treatment_h</th>\n",
       "      <th>cat</th>\n",
       "      <th>libtxt</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PB000046-P1-L1-v2-continued</td>\n",
       "      <td>B02</td>\n",
       "      <td>1</td>\n",
       "      <td>PB000046</td>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PB000046-P1-L1-v2-continued</td>\n",
       "      <td>B02</td>\n",
       "      <td>2</td>\n",
       "      <td>PB000046</td>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PB000046-P1-L1-v2-continued</td>\n",
       "      <td>B02</td>\n",
       "      <td>3</td>\n",
       "      <td>PB000046</td>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PB000046-P1-L1-v2-continued</td>\n",
       "      <td>B02</td>\n",
       "      <td>4</td>\n",
       "      <td>PB000046</td>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PB000046-P1-L1-v2-continued</td>\n",
       "      <td>B02</td>\n",
       "      <td>5</td>\n",
       "      <td>PB000046</td>\n",
       "      <td>bea-v1-PB000046-MCF7-48h-P1-L1</td>\n",
       "      <td>PHB000238</td>\n",
       "      <td>BEA-0009743-AA-001</td>\n",
       "      <td>Domperidone</td>\n",
       "      <td>autophagy inducer</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>3.00</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>autophagy inducer</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21477</th>\n",
       "      <td>PB000053-P4-L2-continue</td>\n",
       "      <td>O23</td>\n",
       "      <td>5</td>\n",
       "      <td>PB000053</td>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21478</th>\n",
       "      <td>PB000053-P4-L2-continue</td>\n",
       "      <td>O23</td>\n",
       "      <td>6</td>\n",
       "      <td>PB000053</td>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21479</th>\n",
       "      <td>PB000053-P4-L2-continue</td>\n",
       "      <td>O23</td>\n",
       "      <td>7</td>\n",
       "      <td>PB000053</td>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21480</th>\n",
       "      <td>PB000053-P4-L2-continue</td>\n",
       "      <td>O23</td>\n",
       "      <td>8</td>\n",
       "      <td>PB000053</td>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21481</th>\n",
       "      <td>PB000053-P4-L2-continue</td>\n",
       "      <td>O23</td>\n",
       "      <td>9</td>\n",
       "      <td>PB000053</td>\n",
       "      <td>bea-v1-PB000053-MCF7-48h-P4-L2</td>\n",
       "      <td>PHB000236</td>\n",
       "      <td>BEA-0010287-AA-001</td>\n",
       "      <td>Cisplatin</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "      <td>H2O</td>\n",
       "      <td>...</td>\n",
       "      <td>uL</td>\n",
       "      <td>trt</td>\n",
       "      <td>0.09</td>\n",
       "      <td>uM</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>1100</td>\n",
       "      <td>48</td>\n",
       "      <td>Control</td>\n",
       "      <td>pharmb_io_BEA</td>\n",
       "      <td>immunogenic cell death</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21482 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Metadata_Barcode well  Metadata_Site   barcode  \\\n",
       "0      PB000046-P1-L1-v2-continued  B02              1  PB000046   \n",
       "1      PB000046-P1-L1-v2-continued  B02              2  PB000046   \n",
       "2      PB000046-P1-L1-v2-continued  B02              3  PB000046   \n",
       "3      PB000046-P1-L1-v2-continued  B02              4  PB000046   \n",
       "4      PB000046-P1-L1-v2-continued  B02              5  PB000046   \n",
       "...                            ...  ...            ...       ...   \n",
       "21477      PB000053-P4-L2-continue  O23              5  PB000053   \n",
       "21478      PB000053-P4-L2-continue  O23              6  PB000053   \n",
       "21479      PB000053-P4-L2-continue  O23              7  PB000053   \n",
       "21480      PB000053-P4-L2-continue  O23              8  PB000053   \n",
       "21481      PB000053-P4-L2-continue  O23              9  PB000053   \n",
       "\n",
       "                            layout_id   batch_id         compound_id  \\\n",
       "0      bea-v1-PB000046-MCF7-48h-P1-L1  PHB000238  BEA-0009743-AA-001   \n",
       "1      bea-v1-PB000046-MCF7-48h-P1-L1  PHB000238  BEA-0009743-AA-001   \n",
       "2      bea-v1-PB000046-MCF7-48h-P1-L1  PHB000238  BEA-0009743-AA-001   \n",
       "3      bea-v1-PB000046-MCF7-48h-P1-L1  PHB000238  BEA-0009743-AA-001   \n",
       "4      bea-v1-PB000046-MCF7-48h-P1-L1  PHB000238  BEA-0009743-AA-001   \n",
       "...                               ...        ...                 ...   \n",
       "21477  bea-v1-PB000053-MCF7-48h-P4-L2  PHB000236  BEA-0010287-AA-001   \n",
       "21478  bea-v1-PB000053-MCF7-48h-P4-L2  PHB000236  BEA-0010287-AA-001   \n",
       "21479  bea-v1-PB000053-MCF7-48h-P4-L2  PHB000236  BEA-0010287-AA-001   \n",
       "21480  bea-v1-PB000053-MCF7-48h-P4-L2  PHB000236  BEA-0010287-AA-001   \n",
       "21481  bea-v1-PB000053-MCF7-48h-P4-L2  PHB000236  BEA-0010287-AA-001   \n",
       "\n",
       "         cmpd_name                     moa solvent  ...  well_vol_unit  \\\n",
       "0      Domperidone       autophagy inducer    DMSO  ...             uL   \n",
       "1      Domperidone       autophagy inducer    DMSO  ...             uL   \n",
       "2      Domperidone       autophagy inducer    DMSO  ...             uL   \n",
       "3      Domperidone       autophagy inducer    DMSO  ...             uL   \n",
       "4      Domperidone       autophagy inducer    DMSO  ...             uL   \n",
       "...            ...                     ...     ...  ...            ...   \n",
       "21477    Cisplatin  immunogenic cell death     H2O  ...             uL   \n",
       "21478    Cisplatin  immunogenic cell death     H2O  ...             uL   \n",
       "21479    Cisplatin  immunogenic cell death     H2O  ...             uL   \n",
       "21480    Cisplatin  immunogenic cell death     H2O  ...             uL   \n",
       "21481    Cisplatin  immunogenic cell death     H2O  ...             uL   \n",
       "\n",
       "      pertType  cmpd_conc cmpd_conc_unit  cell_line cells_per_well  \\\n",
       "0          trt       3.00             uM       MCF7           1100   \n",
       "1          trt       3.00             uM       MCF7           1100   \n",
       "2          trt       3.00             uM       MCF7           1100   \n",
       "3          trt       3.00             uM       MCF7           1100   \n",
       "4          trt       3.00             uM       MCF7           1100   \n",
       "...        ...        ...            ...        ...            ...   \n",
       "21477      trt       0.09             uM       MCF7           1100   \n",
       "21478      trt       0.09             uM       MCF7           1100   \n",
       "21479      trt       0.09             uM       MCF7           1100   \n",
       "21480      trt       0.09             uM       MCF7           1100   \n",
       "21481      trt       0.09             uM       MCF7           1100   \n",
       "\n",
       "      treatment_h      cat         libtxt                  target  \n",
       "0              48  Control  pharmb_io_BEA       autophagy inducer  \n",
       "1              48  Control  pharmb_io_BEA       autophagy inducer  \n",
       "2              48  Control  pharmb_io_BEA       autophagy inducer  \n",
       "3              48  Control  pharmb_io_BEA       autophagy inducer  \n",
       "4              48  Control  pharmb_io_BEA       autophagy inducer  \n",
       "...           ...      ...            ...                     ...  \n",
       "21477          48  Control  pharmb_io_BEA  immunogenic cell death  \n",
       "21478          48  Control  pharmb_io_BEA  immunogenic cell death  \n",
       "21479          48  Control  pharmb_io_BEA  immunogenic cell death  \n",
       "21480          48  Control  pharmb_io_BEA  immunogenic cell death  \n",
       "21481          48  Control  pharmb_io_BEA  immunogenic cell death  \n",
       "\n",
       "[21482 rows x 25 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beactica_plate_info_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['P101334_specs3k_U2OS_P02_L1_2023-04-03_16.15.49', 'P101335_specs3k_U2OS_P02_L2_2023-04-07_06.11.53', 'P101336_specs3k_U2OS_P03_L1_2023-04-03_19.17.18', 'P101337_specs3k_U2OS_P03_L2_2023-04-07_09.15.40', 'P101338_specs3k_U2OS_P04_L1_2023-04-03_22.17.27', 'P101339_specs3k_U2OS_P04_L2_2023-04-07_12.17.19', 'P101340_specs3k_U2OS_P05_L1_2023-04-04_01.17.38', 'P101341_specs3k_U2OS_P05_L2_2023-04-07_15.19.47', 'P101342_specs3k_U2OS_P06_L1_2023-04-04_04.19.55', 'P101343_specs3k_U2OS_P06_L2_2023-04-07_18.21.25', 'P101344_specs3k_U2OS_P07_L1_2023-04-04_07.23.25', 'P101345_specs3k_U2OS_P07_L2_2023-04-07_21.24.14', 'P101346_specs3k_U2OS_P08_L1_2023-04-04_10.24.50', 'P101347_specs3k_U2OS_P08_L2_2023-04-08_00.25.04', 'P101348_specs3k_U2OS_P09_L1_2023-04-04_13.26.40', 'P101349_specs3k_U2OS_P09_L2_2023-04-08_03.26.53', 'P101350_specs3k_U2OS_P10_L1_2023-04-04_16.28.59', 'P101351_specs3k_U2OS_P10_L2_2023-04-08_06.29.46', 'P101352_specs3k_U2OS_P01_L1_2023-04-04_19.28.51', 'P101353_specs3k_U2OS_P01_L2_2023-04-08_09.32.55', 'P101354_specs3k_U2OS_P11_L1_2023-04-04_22.30.18', 'P101355_specs3k_U2OS_P11_L2_2023-04-08_12.36.32', 'P101356_specs3k_U2OS_P12_L1_2023-04-05_01.28.28', 'P101357_specs3k_U2OS_P12_L2_2023-04-08_15.38.28', 'P101358_specs3k_U2OS_P13_L1_2023-04-05_04.29.40', 'P101359_specs3k_U2OS_P13_L2_2023-04-08_18.38.41', 'P101360_specs3k_U2OS_P14_L1_2023-04-05_14.29.01', 'P101361_specs3k_U2OS_P14_L2_2023-04-09_00.43.45', 'P101362_specs3k_U2OS_P15_L1_2023-04-05_17.33.56', 'P101363_specs3k_U2OS_P15_L2_2023-04-09_03.48.37', 'P101364_specs3k_U2OS_P16_L1_2023-04-05_20.36.51', 'P101365_specs3k_U2OS_P16_L2_2023-04-09_06.49.54', 'P101366_specs3k_U2OS_P17_L1_2023-04-05_23.41.26', 'P101367_specs3k_U2OS_P17_L2_2023-04-09_09.52.47', 'P101368_specs3k_U2OS_P18_L1_2023-04-06_02.45.16', 'P101369_specs3k_U2OS_P18_L2_2023-04-09_12.55.05', 'P101370_specs3k_U2OS_P19_L1_2023-04-06_05.48.43', 'P101371_specs3k_U2OS_P19_L2_2023-04-09_15.59.27', 'P101372_specs3k_U2OS_P20_L1_2023-04-06_08.53.11', 'P101373_specs3k_U2OS_P20_L2_2023-04-09_19.03.06', 'P101374_specs3k_U2OS_P21_L1_2023-04-06_11.57.06', 'P101375_specs3k_U2OS_P21_L2_2023-04-09_22.03.52', 'P101376_specs3k_U2OS_P22_L1_2023-04-06_15.01.47', 'P101377_specs3k_U2OS_P22_L2_2023-04-10_01.06.29', 'P101378_specs3k_U2OS_P23_L1_2023-04-06_18.04.23', 'P101379_specs3k_U2OS_P23_L2_2023-04-10_04.08.28', 'P101380_specs3k_U2OS_P24_L1_2023-04-06_21.06.19', 'P101381_specs3k_U2OS_P24_L2_2023-04-10_07.11.12', 'P101382_specs3k_U2OS_P25_L1_2023-04-07_00.06.55', 'P101383_specs3k_U2OS_P25_L2_2023-04-10_10.13.56', 'P101384_specs3k_U2OS_DMSO_L1_2023-04-05_07.29.34', 'P101385_specs3k_U2OS_DMSO_L2_2023-04-07_03.09.07', 'P101386_specs3k_U2OS_DMSO_L3_2023-04-08_21.41.12', 'P101387_specs3k_U2OS_DMSO_L4_2023-04-10_13.16.58']\n"
     ]
    }
   ],
   "source": [
    "import glob, os\n",
    "#os.chdir('/home/jovyan/share/mikro/RMS/RMS-e02-v01-P014561-RH30-48h/2021-10-29/826/')\n",
    "root_path = '/home/jovyan/share/mikro2/squid/specs3k'\n",
    "plate_paths = os.listdir(root_path)\n",
    "plate_paths.sort()\n",
    "print(plate_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def find_latest_parquet(folders, filename=\"featICF_nuclei.parquet\"):\n",
    "    paths = []\n",
    "\n",
    "    for folder in folders:\n",
    "        # Initialize the highest subfolder number and path to the Parquet file\n",
    "        highest_num = -1\n",
    "        path_to_file = \"\"\n",
    "        \n",
    "        # Check if folder path exists\n",
    "        if not os.path.exists(folder):\n",
    "            print(f\"Folder {folder} does not exist.\")\n",
    "            continue\n",
    "        \n",
    "        # List all subfolders in the current folder\n",
    "        for subfolder in os.listdir(folder):\n",
    "            subfolder_path = os.path.join(folder, subfolder)\n",
    "            \n",
    "            # Check if it's a directory and the name is an integer\n",
    "            if os.path.isdir(subfolder_path) and subfolder.isdigit():\n",
    "                subfolder_num = int(subfolder)\n",
    "                \n",
    "                # Check if the subfolder number is greater than the current highest\n",
    "                if subfolder_num > highest_num:\n",
    "                    # Check if the specified file exists in this subfolder\n",
    "                    potential_file_path = os.path.join(subfolder_path, filename)\n",
    "                    if os.path.isfile(potential_file_path):\n",
    "                        highest_num = subfolder_num\n",
    "                        path_to_file = potential_file_path\n",
    "        \n",
    "        # If a valid path was found, add it to the lis\n",
    "        if path_to_file:\n",
    "            paths.append(path_to_file)\n",
    "        else:\n",
    "            print(f\"No '{filename}' found in the folder {folder}.\")\n",
    "    \n",
    "    return paths\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "def read_combine_parquets(file_paths: list):\n",
    "    combined_df = pd.DataFrame()\n",
    "    columns = [\"Metadata_Barcode\", \"Metadata_Site\", \"Metadata_AcqID\", \"Metadata_Well\", \"FileName_CONC\", \"FileName_HOECHST\", \"FileName_PHAandWGA\", \"FileName_SYTO\", \"FileName_MITO\", \"PathName_MITO\", \"PathName_HOECHST\", \"PathName_PHAandWGA\", \"PathName_SYTO\", \"Location_Center_X\", \"Location_Center_Y\", \"AreaShape_MajorAxisLength\"]\n",
    "    parquet_paths = find_latest_parquet(file_paths)\n",
    "    for file_path in tqdm.tqdm(parquet_paths):\n",
    "        try:\n",
    "            # Read the Parquet file with selected columns only\n",
    "            df = pd.read_parquet(file_path, columns=columns)\n",
    "            combined_df = pd.concat([combined_df, df], ignore_index=True)\n",
    "        except Exception as e:\n",
    "            print(f\"Error reading {file_path}: {e}\")\n",
    "\n",
    "    return combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_paths = [\"/share/data/cellprofiler/automation/results/P013725/3072/\",\n",
    "               \"/share/data/cellprofiler/automation/results/P013726/3073/\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:01<00:00,  1.07it/s]\n"
     ]
    }
   ],
   "source": [
    "check = read_combine_parquets(plate_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Metadata_Barcode</th>\n",
       "      <th>Metadata_Site</th>\n",
       "      <th>Metadata_AcqID</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>FileName_CONC</th>\n",
       "      <th>FileName_HOECHST</th>\n",
       "      <th>FileName_PHAandWGA</th>\n",
       "      <th>FileName_SYTO</th>\n",
       "      <th>FileName_MITO</th>\n",
       "      <th>PathName_MITO</th>\n",
       "      <th>PathName_HOECHST</th>\n",
       "      <th>PathName_PHAandWGA</th>\n",
       "      <th>PathName_SYTO</th>\n",
       "      <th>Location_Center_X</th>\n",
       "      <th>Location_Center_Y</th>\n",
       "      <th>AreaShape_MajorAxisLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P013725</td>\n",
       "      <td>6</td>\n",
       "      <td>3072</td>\n",
       "      <td>A03</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>27.141176</td>\n",
       "      <td>1.658823</td>\n",
       "      <td>21.362976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P013725</td>\n",
       "      <td>6</td>\n",
       "      <td>3072</td>\n",
       "      <td>A03</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>557.783142</td>\n",
       "      <td>2.975904</td>\n",
       "      <td>25.859978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P013725</td>\n",
       "      <td>6</td>\n",
       "      <td>3072</td>\n",
       "      <td>A03</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>2473.148682</td>\n",
       "      <td>12.694836</td>\n",
       "      <td>55.841057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P013725</td>\n",
       "      <td>6</td>\n",
       "      <td>3072</td>\n",
       "      <td>A03</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>2632.066895</td>\n",
       "      <td>57.518616</td>\n",
       "      <td>88.254044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P013725</td>\n",
       "      <td>6</td>\n",
       "      <td>3072</td>\n",
       "      <td>A03</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>2551.355225</td>\n",
       "      <td>77.630051</td>\n",
       "      <td>63.285812</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503772</th>\n",
       "      <td>P013726</td>\n",
       "      <td>2</td>\n",
       "      <td>3073</td>\n",
       "      <td>L12</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>138.535400</td>\n",
       "      <td>2987.422607</td>\n",
       "      <td>41.186794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503773</th>\n",
       "      <td>P013726</td>\n",
       "      <td>2</td>\n",
       "      <td>3073</td>\n",
       "      <td>L12</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>967.298523</td>\n",
       "      <td>2987.430908</td>\n",
       "      <td>59.916874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503774</th>\n",
       "      <td>P013726</td>\n",
       "      <td>2</td>\n",
       "      <td>3073</td>\n",
       "      <td>L12</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>2806.061523</td>\n",
       "      <td>2987.622314</td>\n",
       "      <td>81.681999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503775</th>\n",
       "      <td>P013726</td>\n",
       "      <td>2</td>\n",
       "      <td>3073</td>\n",
       "      <td>L12</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>2950.103516</td>\n",
       "      <td>2987.438721</td>\n",
       "      <td>39.091148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503776</th>\n",
       "      <td>P013726</td>\n",
       "      <td>2</td>\n",
       "      <td>3073</td>\n",
       "      <td>L12</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>/share/mikro/squid/AROS-Reproducibility-MoA-Fu...</td>\n",
       "      <td>1416.403931</td>\n",
       "      <td>2989.416748</td>\n",
       "      <td>50.504890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1503777 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Metadata_Barcode  Metadata_Site  Metadata_AcqID Metadata_Well  \\\n",
       "0                P013725              6            3072           A03   \n",
       "1                P013725              6            3072           A03   \n",
       "2                P013725              6            3072           A03   \n",
       "3                P013725              6            3072           A03   \n",
       "4                P013725              6            3072           A03   \n",
       "...                  ...            ...             ...           ...   \n",
       "1503772          P013726              2            3073           L12   \n",
       "1503773          P013726              2            3073           L12   \n",
       "1503774          P013726              2            3073           L12   \n",
       "1503775          P013726              2            3073           L12   \n",
       "1503776          P013726              2            3073           L12   \n",
       "\n",
       "                                    FileName_CONC  \\\n",
       "0        A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "1        A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "2        A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "3        A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "4        A03_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "...                                           ...   \n",
       "1503772  L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "1503773  L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "1503774  L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "1503775  L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "1503776  L12_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "\n",
       "                                 FileName_HOECHST  \\\n",
       "0        A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "1        A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "2        A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "3        A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "4        A03_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "...                                           ...   \n",
       "1503772  L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "1503773  L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "1503774  L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "1503775  L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "1503776  L12_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "\n",
       "                               FileName_PHAandWGA  \\\n",
       "0        A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "1        A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "2        A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "3        A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "4        A03_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "...                                           ...   \n",
       "1503772  L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "1503773  L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "1503774  L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "1503775  L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "1503776  L12_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "\n",
       "                                    FileName_SYTO  \\\n",
       "0        A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "1        A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "2        A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "3        A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "4        A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "...                                           ...   \n",
       "1503772  L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "1503773  L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "1503774  L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "1503775  L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "1503776  L12_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "\n",
       "                                    FileName_MITO  \\\n",
       "0        A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "1        A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "2        A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "3        A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "4        A03_s6_x2_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "...                                           ...   \n",
       "1503772  L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "1503773  L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "1503774  L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "1503775  L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "1503776  L12_s2_x1_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "\n",
       "                                             PathName_MITO  \\\n",
       "0        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "2        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "3        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "4        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "...                                                    ...   \n",
       "1503772  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503773  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503774  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503775  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503776  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "\n",
       "                                          PathName_HOECHST  \\\n",
       "0        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "2        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "3        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "4        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "...                                                    ...   \n",
       "1503772  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503773  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503774  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503775  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503776  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "\n",
       "                                        PathName_PHAandWGA  \\\n",
       "0        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "2        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "3        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "4        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "...                                                    ...   \n",
       "1503772  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503773  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503774  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503775  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "1503776  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...   \n",
       "\n",
       "                                             PathName_SYTO  Location_Center_X  \\\n",
       "0        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...          27.141176   \n",
       "1        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...         557.783142   \n",
       "2        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        2473.148682   \n",
       "3        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        2632.066895   \n",
       "4        /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        2551.355225   \n",
       "...                                                    ...                ...   \n",
       "1503772  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...         138.535400   \n",
       "1503773  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...         967.298523   \n",
       "1503774  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        2806.061523   \n",
       "1503775  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        2950.103516   \n",
       "1503776  /share/mikro/squid/AROS-Reproducibility-MoA-Fu...        1416.403931   \n",
       "\n",
       "         Location_Center_Y  AreaShape_MajorAxisLength  \n",
       "0                 1.658823                  21.362976  \n",
       "1                 2.975904                  25.859978  \n",
       "2                12.694836                  55.841057  \n",
       "3                57.518616                  88.254044  \n",
       "4                77.630051                  63.285812  \n",
       "...                    ...                        ...  \n",
       "1503772        2987.422607                  41.186794  \n",
       "1503773        2987.430908                  59.916874  \n",
       "1503774        2987.622314                  81.681999  \n",
       "1503775        2987.438721                  39.091148  \n",
       "1503776        2989.416748                  50.504890  \n",
       "\n",
       "[1503777 rows x 16 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_metadata_specs(metadata, location_df):\n",
    "    metadata_filt = metadata[[\"barcode\", \"well\", \"Metadata_Site\", \"cbkid\", \"compound_name\",\"cmpd_conc\"]]\n",
    "    new_column_names_metadata = {\n",
    "    'barcode': 'Metadata_Plate',\n",
    "    'well': 'Metadata_Well',\n",
    "    'cbkid': 'Metadata_cmpdName',\n",
    "    'cmpd_conc': \"Metadata_cmpdConc\"}\n",
    "    metadata_filt = metadata_filt.rename(columns=new_column_names_metadata)\n",
    "\n",
    "    location_filt = location_df[[\"Metadata_Barcode\", \"Metadata_Site\", \"Metadata_Well\", \"FileName_CONC\", \"FileName_HOECHST\", \"FileName_PHAandWGA\", \"FileName_SYTO\", \"FileName_MITO\"]]\n",
    "    location_filt = location_filt.drop_duplicates().reset_index(drop = True)\n",
    "    new_column_names_locations = {\n",
    "    'Metadata_Barcode': 'Metadata_Plate',\n",
    "    'FileName_CONC': 'ER',\n",
    "    'FileName_HOECHST': 'DNA',\n",
    "    'FileName_PHAandWGA': \"AGP\",\n",
    "    \"FileName_SYTO\": \"RNA\", \n",
    "    \"FileName_MITO\": \"Mito\"}\n",
    "    location_filt = location_filt.rename(columns=new_column_names_locations)\n",
    "    result = pd.merge(metadata_filt, location_filt, on=['Metadata_Plate', 'Metadata_Well', \"Metadata_Site\"], how='left')\n",
    "    result[\"DNA\"] = result[\"Metadata_Plate\"] + \"/\" + result[\"DNA\"] \n",
    "    result[\"ER\"] = result[\"Metadata_Plate\"] + \"/\" + result[\"ER\"] \n",
    "    result[\"RNA\"] = result[\"Metadata_Plate\"] + \"/\" + result[\"RNA\"] \n",
    "    result[\"AGP\"] = result[\"Metadata_Plate\"] + \"/\" + result[\"AGP\"] \n",
    "    result[\"Mito\"] = result[\"Metadata_Plate\"] + \"/\" + result[\"Mito\"] \n",
    "    \n",
    "    return result\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_metadata = generate_metadata_specs(ref_metadata_full_flags, check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Metadata_Plate</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>Metadata_Site</th>\n",
       "      <th>Metadata_cmpdName</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>Metadata_cmpdConc</th>\n",
       "      <th>ER</th>\n",
       "      <th>DNA</th>\n",
       "      <th>AGP</th>\n",
       "      <th>RNA</th>\n",
       "      <th>Mito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P013725</td>\n",
       "      <td>A02</td>\n",
       "      <td>s1</td>\n",
       "      <td>CBK309344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013725/A02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P013725</td>\n",
       "      <td>A02</td>\n",
       "      <td>s2</td>\n",
       "      <td>CBK309344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013725/A02_s2_x1_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s2_x1_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s2_x1_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s2_x1_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s2_x1_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P013725</td>\n",
       "      <td>A02</td>\n",
       "      <td>s3</td>\n",
       "      <td>CBK309344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013725/A02_s3_x2_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s3_x2_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s3_x2_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s3_x2_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s3_x2_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P013725</td>\n",
       "      <td>A02</td>\n",
       "      <td>s4</td>\n",
       "      <td>CBK309344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013725/A02_s4_x0_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s4_x0_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s4_x0_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s4_x0_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s4_x0_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P013725</td>\n",
       "      <td>A02</td>\n",
       "      <td>s5</td>\n",
       "      <td>CBK309344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013725/A02_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013725/A02_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865</th>\n",
       "      <td>P013726</td>\n",
       "      <td>P23</td>\n",
       "      <td>s5</td>\n",
       "      <td>CBK288271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013726/P23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5866</th>\n",
       "      <td>P013726</td>\n",
       "      <td>P23</td>\n",
       "      <td>s6</td>\n",
       "      <td>CBK288271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013726/P23_s6_x2_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s6_x2_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s6_x2_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s6_x2_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s6_x2_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5867</th>\n",
       "      <td>P013726</td>\n",
       "      <td>P23</td>\n",
       "      <td>s7</td>\n",
       "      <td>CBK288271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013726/P23_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5868</th>\n",
       "      <td>P013726</td>\n",
       "      <td>P23</td>\n",
       "      <td>s8</td>\n",
       "      <td>CBK288271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013726/P23_s8_x1_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s8_x1_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s8_x1_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s8_x1_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s8_x1_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5869</th>\n",
       "      <td>P013726</td>\n",
       "      <td>P23</td>\n",
       "      <td>s9</td>\n",
       "      <td>CBK288271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.125</td>\n",
       "      <td>P013726/P23_s9_x2_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s9_x2_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s9_x2_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s9_x2_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>P013726/P23_s9_x2_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5870 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Metadata_Plate Metadata_Well Metadata_Site Metadata_cmpdName  \\\n",
       "0           P013725           A02            s1         CBK309344   \n",
       "1           P013725           A02            s2         CBK309344   \n",
       "2           P013725           A02            s3         CBK309344   \n",
       "3           P013725           A02            s4         CBK309344   \n",
       "4           P013725           A02            s5         CBK309344   \n",
       "...             ...           ...           ...               ...   \n",
       "5865        P013726           P23            s5         CBK288271   \n",
       "5866        P013726           P23            s6         CBK288271   \n",
       "5867        P013726           P23            s7         CBK288271   \n",
       "5868        P013726           P23            s8         CBK288271   \n",
       "5869        P013726           P23            s9         CBK288271   \n",
       "\n",
       "      compound_name  Metadata_cmpdConc  \\\n",
       "0               NaN              3.125   \n",
       "1               NaN              3.125   \n",
       "2               NaN              3.125   \n",
       "3               NaN              3.125   \n",
       "4               NaN              3.125   \n",
       "...             ...                ...   \n",
       "5865            NaN              3.125   \n",
       "5866            NaN              3.125   \n",
       "5867            NaN              3.125   \n",
       "5868            NaN              3.125   \n",
       "5869            NaN              3.125   \n",
       "\n",
       "                                                    ER  \\\n",
       "0     P013725/A02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "1     P013725/A02_s2_x1_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "2     P013725/A02_s3_x2_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "3     P013725/A02_s4_x0_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "4     P013725/A02_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "...                                                ...   \n",
       "5865  P013726/P23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "5866  P013726/P23_s6_x2_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "5867  P013726/P23_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "5868  P013726/P23_s8_x1_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "5869  P013726/P23_s9_x2_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "\n",
       "                                                   DNA  \\\n",
       "0     P013725/A02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "1     P013725/A02_s2_x1_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "2     P013725/A02_s3_x2_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "3     P013725/A02_s4_x0_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "4     P013725/A02_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "...                                                ...   \n",
       "5865  P013726/P23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "5866  P013726/P23_s6_x2_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "5867  P013726/P23_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "5868  P013726/P23_s8_x1_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "5869  P013726/P23_s9_x2_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "\n",
       "                                                   AGP  \\\n",
       "0     P013725/A02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "1     P013725/A02_s2_x1_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "2     P013725/A02_s3_x2_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "3     P013725/A02_s4_x0_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "4     P013725/A02_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "...                                                ...   \n",
       "5865  P013726/P23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "5866  P013726/P23_s6_x2_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "5867  P013726/P23_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "5868  P013726/P23_s8_x1_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "5869  P013726/P23_s9_x2_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "\n",
       "                                                   RNA  \\\n",
       "0     P013725/A02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "1     P013725/A02_s2_x1_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "2     P013725/A02_s3_x2_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "3     P013725/A02_s4_x0_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "4     P013725/A02_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "...                                                ...   \n",
       "5865  P013726/P23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "5866  P013726/P23_s6_x2_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "5867  P013726/P23_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "5868  P013726/P23_s8_x1_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "5869  P013726/P23_s9_x2_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "\n",
       "                                                  Mito  \n",
       "0     P013725/A02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff  \n",
       "1     P013725/A02_s2_x1_y0_Fluorescence_638_nm_Ex.tiff  \n",
       "2     P013725/A02_s3_x2_y0_Fluorescence_638_nm_Ex.tiff  \n",
       "3     P013725/A02_s4_x0_y1_Fluorescence_638_nm_Ex.tiff  \n",
       "4     P013725/A02_s5_x1_y1_Fluorescence_638_nm_Ex.tiff  \n",
       "...                                                ...  \n",
       "5865  P013726/P23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff  \n",
       "5866  P013726/P23_s6_x2_y1_Fluorescence_638_nm_Ex.tiff  \n",
       "5867  P013726/P23_s7_x0_y2_Fluorescence_638_nm_Ex.tiff  \n",
       "5868  P013726/P23_s8_x1_y2_Fluorescence_638_nm_Ex.tiff  \n",
       "5869  P013726/P23_s9_x2_y2_Fluorescence_638_nm_Ex.tiff  \n",
       "\n",
       "[5870 rows x 11 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_metadata.to_csv(\"AROS_Metadata.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate folders for DP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_locations_deepprofiler(meta_centers, root_folder):\n",
    "    plates = meta_centers[\"Metadata_Barcode\"].unique()\n",
    "    for plate in tqdm.tqdm(plates):\n",
    "        output_folder = root_folder + \"/\" + plate\n",
    "        os.makedirs(output_folder, exist_ok=True)\n",
    "        plate_data = meta_centers[meta_centers[\"Metadata_Barcode\"] == plate]\n",
    "# Group the data by 'well' and 'site' and save each group as a separate CSV file\n",
    "        grouped = plate_data.groupby(['Metadata_Well', 'Metadata_Site'])\n",
    "        for group_name, group_data in grouped:\n",
    "            well, site = group_name\n",
    "            filename = f\"{well}-s{site}-Nuclei.csv\"\n",
    "            file_path = os.path.join(output_folder, filename)\n",
    "            group_data['Nuclei_Location_Center_X'] = group_data['Location_Center_X'].astype(int)\n",
    "            group_data['Nuclei_Location_Center_Y'] = group_data['Location_Center_Y'].astype(int)\n",
    "            #group_data.rename(columns={'X_coord': 'Nuclei_Location_Center_X', 'Y_coord': 'Nuclei_Location_Center_Y'}, inplace=True) \n",
    "            group_data[['Nuclei_Location_Center_X','Nuclei_Location_Center_Y']].to_csv(file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:23<00:00, 11.86s/it]\n"
     ]
    }
   ],
   "source": [
    "generate_locations_deepprofiler(check,'/share/data/analyses/benjamin/Single_cell_project/DP_AROS/inputs/locations/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "def copy_image_data(feat_df, output_root_folder: str):\n",
    "    source_folders = feat_df[\"PathName_HOECHST\"].unique()\n",
    "    for folder in tqdm.tqdm(source_folders):\n",
    "        destination_folder = os.path.join(output_root_folder, feat_df[feat_df[\"PathName_HOECHST\"] == folder][\"Metadata_Barcode\"].unique()[0])\n",
    "        os.makedirs(destination_folder, exist_ok=True)\n",
    "        print(\"Copying plate:\", feat_df[feat_df[\"PathName_HOECHST\"] == folder][\"Metadata_Barcode\"].unique()[0])\n",
    "        for filename in os.listdir(folder):\n",
    "            if filename.lower().endswith('.tiff') and 'thumb' not in filename.lower():\n",
    "                source_file_path = os.path.join(folder, filename)\n",
    "                destination_file_path = os.path.join(destination_folder, filename)\n",
    "                if not os.path.isfile(destination_file_path):\n",
    "                    shutil.copy(source_file_path, destination_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tqdm\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "def copy_file(source_file_path, destination_file_path):\n",
    "    if not os.path.isfile(destination_file_path):\n",
    "        shutil.copy(source_file_path, destination_file_path)\n",
    "\n",
    "def copy_image_data(feat_df, output_root_folder: str, max_workers=10):\n",
    "    source_folders = feat_df[\"PathName_HOECHST\"].unique()\n",
    "\n",
    "    with ThreadPoolExecutor(max_workers=max_workers) as executor:\n",
    "        for folder in tqdm.tqdm(source_folders):\n",
    "            destination_folder = os.path.join(output_root_folder, feat_df[feat_df[\"PathName_HOECHST\"] == folder][\"Metadata_Barcode\"].unique()[0])\n",
    "            os.makedirs(destination_folder, exist_ok=True)\n",
    "            tasks = []\n",
    "\n",
    "            for filename in os.listdir(folder):\n",
    "                if filename.lower().endswith('.tiff') and 'thumb' not in filename.lower():\n",
    "                    source_file_path = os.path.join(folder, filename)\n",
    "                    destination_file_path = os.path.join(destination_folder, filename)\n",
    "\n",
    "                    # Submit a copy task\n",
    "                    tasks.append(executor.submit(copy_file, source_file_path, destination_file_path))\n",
    "\n",
    "            # Wait for all tasks to complete\n",
    "            for task in tqdm.tqdm(tasks, desc=\"Copying files\"):\n",
    "                task.result()  # This will re-raise any exceptions that occurred in the thread\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tqdm\n",
    "\n",
    "def create_image_data_symlinks(feat_df, output_root_folder: str):\n",
    "    source_folders = feat_df[\"PathName_HOECHST\"].unique()\n",
    "    for folder in tqdm.tqdm(source_folders):\n",
    "        destination_folder = os.path.join(output_root_folder, feat_df[feat_df[\"PathName_HOECHST\"] == folder][\"Metadata_Barcode\"].unique()[0])\n",
    "        os.makedirs(destination_folder, exist_ok=True)\n",
    "        print(\"Linking plate:\", feat_df[feat_df[\"PathName_HOECHST\"] == folder][\"Metadata_Barcode\"].unique()[0])\n",
    "        for filename in os.listdir(folder):\n",
    "            if filename.lower().endswith('.tiff') and 'thumb' not in filename.lower():\n",
    "                source_file_path = os.path.join(folder, filename)\n",
    "                destination_file_path = os.path.join(destination_folder, filename)\n",
    "                if not os.path.islink(destination_file_path) and not os.path.exists(destination_file_path):\n",
    "                    os.symlink(source_file_path, destination_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/share/mikro/squid/AROS-Reproducibility-MoA-Full/P013725_2023-03-20_16.56.49'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check[\"PathName_MITO\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linking plate: P013725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 1/2 [00:21<00:21, 21.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linking plate: P013726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:42<00:00, 21.49s/it]\n"
     ]
    }
   ],
   "source": [
    "create_image_data_symlinks(check, '/share/data/analyses/benjamin/Single_cell_project/DP_AROS/inputs/images/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image size is: (3000, 3000)\n"
     ]
    }
   ],
   "source": [
    "from skimage import io\n",
    "\n",
    "def check_tiff_image_size_skimage(file_path):\n",
    "    img = io.imread(file_path)\n",
    "    print(f\"The image size is: {img.shape}\")  # img.shape returns a tuple (height, width) for 2D, (plane, height, width) for 3D\n",
    "\n",
    "# Example usage:\n",
    "check_tiff_image_size_skimage( '/share/mikro2/squid/AROS-Reproducibility-MoA-Full/P013725_2023-03-20_16.56.49/A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG: Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "DEBUG: Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "DEBUG: findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerif.ttf', name='FreeSerif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBold.ttf', name='FreeMono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBoldOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBold.ttf', name='FreeSerif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMono.ttf', name='FreeMono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBold.ttf', name='FreeSans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSans.ttf', name='FreeSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBoldOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBoldItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "DEBUG: findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "DEBUG: findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.8/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skimage import io\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io\n",
    "import pandas as pd\n",
    "\n",
    "def add_crosses_with_boxes(image_path, df_coordinates, brightness_factor=10.2, cross_color='red', cross_size=10, box_size=250):\n",
    "    # Read the image\n",
    "    image = io.imread(image_path)\n",
    "\n",
    "    # Adjust the brightness\n",
    "    image = image * brightness_factor\n",
    "    #image[image > 255] = 255  # Ensure the maximum value is 255 for uint8 images\n",
    "\n",
    "    # Create a figure and axis\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    # Display the image\n",
    "    ax.imshow(image, cmap='gray')\n",
    "\n",
    "    # Add red crosses and boxes for each coordinate in the DataFrame\n",
    "    for index, row in df_coordinates.iterrows():\n",
    "        x, y = row['Location_Center_X'], row['Location_Center_Y']\n",
    "        \n",
    "        # Draw the cross\n",
    "        ax.plot(x, y, 'x', color=cross_color, markersize=cross_size)\n",
    "        \n",
    "        # Draw the box\n",
    "        half_box_size = box_size / 2\n",
    "        rect = plt.Rectangle((x - half_box_size, y - half_box_size), box_size, box_size, \n",
    "                             linewidth=0.3, edgecolor=cross_color, facecolor='none')\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    # Remove axis ticks and labels\n",
    "    ax.axis('off')\n",
    "\n",
    "    # Save or show the image\n",
    "    plt.show()\n",
    "\n",
    "# Example usage (assuming you have a DataFrame named df_coordinates):\n",
    "# file_path = 'your_image_path.tiff'\n",
    "# df_coordinates = pd.DataFrame({'Location_Center_X': [x_values], 'Location_Center_Y': [y_values]})\n",
    "# add_crosses_with_boxes(file_path, df_coordinates, box_size=20)\n",
    "\n",
    "\n",
    "# Example usage\n",
    "file_path = '/share/mikro2/squid/AROS-Reproducibility-MoA-Full/P013725_2023-03-20_16.56.49/A03_s6_x2_y1_Fluorescence_488_nm_Ex.tiff'\n",
    "pixel_length = 250  # Length of the scale bar in pixels\n",
    "add_crosses_with_boxes(file_path, check[(check[\"Metadata_Barcode\"] == \"P013725\") & (check[\"Metadata_Well\"] == \"A03\") & (check[\"Metadata_Site\"] == 6)], cross_size= 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Metadata_Barcode</th>\n",
       "      <th>Metadata_Site</th>\n",
       "      <th>Metadata_AcqID</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>FileName_CONC</th>\n",
       "      <th>FileName_HOECHST</th>\n",
       "      <th>FileName_PHAandWGA</th>\n",
       "      <th>FileName_SYTO</th>\n",
       "      <th>FileName_MITO</th>\n",
       "      <th>PathName_MITO</th>\n",
       "      <th>PathName_HOECHST</th>\n",
       "      <th>PathName_PHAandWGA</th>\n",
       "      <th>PathName_SYTO</th>\n",
       "      <th>Location_Center_X</th>\n",
       "      <th>Location_Center_Y</th>\n",
       "      <th>AreaShape_MajorAxisLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>771674</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>117.719742</td>\n",
       "      <td>13.794754</td>\n",
       "      <td>82.116081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771675</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>595.775635</td>\n",
       "      <td>8.512265</td>\n",
       "      <td>83.824562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771676</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>850.056824</td>\n",
       "      <td>1.556818</td>\n",
       "      <td>23.123064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771677</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>710.561340</td>\n",
       "      <td>38.673599</td>\n",
       "      <td>73.847916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771678</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>556.103638</td>\n",
       "      <td>42.926830</td>\n",
       "      <td>65.491951</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771770</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>2344.065674</td>\n",
       "      <td>2394.468262</td>\n",
       "      <td>98.319923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771771</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>259.741669</td>\n",
       "      <td>2402.177246</td>\n",
       "      <td>59.810719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771772</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>2191.670410</td>\n",
       "      <td>2414.588867</td>\n",
       "      <td>80.025642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771773</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>1086.725586</td>\n",
       "      <td>2453.921143</td>\n",
       "      <td>65.023720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771774</th>\n",
       "      <td>P101334</td>\n",
       "      <td>1</td>\n",
       "      <td>3172</td>\n",
       "      <td>B02</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101334_specs3k_U2...</td>\n",
       "      <td>137.716278</td>\n",
       "      <td>2474.874268</td>\n",
       "      <td>62.267124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Metadata_Barcode  Metadata_Site  Metadata_AcqID Metadata_Well  \\\n",
       "771674          P101334              1            3172           B02   \n",
       "771675          P101334              1            3172           B02   \n",
       "771676          P101334              1            3172           B02   \n",
       "771677          P101334              1            3172           B02   \n",
       "771678          P101334              1            3172           B02   \n",
       "...                 ...            ...             ...           ...   \n",
       "771770          P101334              1            3172           B02   \n",
       "771771          P101334              1            3172           B02   \n",
       "771772          P101334              1            3172           B02   \n",
       "771773          P101334              1            3172           B02   \n",
       "771774          P101334              1            3172           B02   \n",
       "\n",
       "                                   FileName_CONC  \\\n",
       "771674  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771675  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771676  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771677  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771678  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "...                                          ...   \n",
       "771770  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771771  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771772  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771773  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "771774  B02_s1_x0_y0_Fluorescence_730_nm_Ex.tiff   \n",
       "\n",
       "                                FileName_HOECHST  \\\n",
       "771674  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771675  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771676  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771677  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771678  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "...                                          ...   \n",
       "771770  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771771  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771772  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771773  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "771774  B02_s1_x0_y0_Fluorescence_405_nm_Ex.tiff   \n",
       "\n",
       "                              FileName_PHAandWGA  \\\n",
       "771674  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771675  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771676  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771677  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771678  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "...                                          ...   \n",
       "771770  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771771  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771772  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771773  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "771774  B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff   \n",
       "\n",
       "                                   FileName_SYTO  \\\n",
       "771674  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771675  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771676  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771677  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771678  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "...                                          ...   \n",
       "771770  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771771  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771772  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771773  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "771774  B02_s1_x0_y0_Fluorescence_488_nm_Ex.tiff   \n",
       "\n",
       "                                   FileName_MITO  \\\n",
       "771674  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771675  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771676  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771677  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771678  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "...                                          ...   \n",
       "771770  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771771  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771772  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771773  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "771774  B02_s1_x0_y0_Fluorescence_638_nm_Ex.tiff   \n",
       "\n",
       "                                            PathName_MITO  \\\n",
       "771674  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771675  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771676  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771677  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771678  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "...                                                   ...   \n",
       "771770  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771771  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771772  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771773  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771774  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "\n",
       "                                         PathName_HOECHST  \\\n",
       "771674  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771675  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771676  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771677  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771678  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "...                                                   ...   \n",
       "771770  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771771  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771772  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771773  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771774  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "\n",
       "                                       PathName_PHAandWGA  \\\n",
       "771674  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771675  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771676  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771677  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771678  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "...                                                   ...   \n",
       "771770  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771771  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771772  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771773  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "771774  /share/mikro2/squid/specs3k/P101334_specs3k_U2...   \n",
       "\n",
       "                                            PathName_SYTO  Location_Center_X  \\\n",
       "771674  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         117.719742   \n",
       "771675  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         595.775635   \n",
       "771676  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         850.056824   \n",
       "771677  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         710.561340   \n",
       "771678  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         556.103638   \n",
       "...                                                   ...                ...   \n",
       "771770  /share/mikro2/squid/specs3k/P101334_specs3k_U2...        2344.065674   \n",
       "771771  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         259.741669   \n",
       "771772  /share/mikro2/squid/specs3k/P101334_specs3k_U2...        2191.670410   \n",
       "771773  /share/mikro2/squid/specs3k/P101334_specs3k_U2...        1086.725586   \n",
       "771774  /share/mikro2/squid/specs3k/P101334_specs3k_U2...         137.716278   \n",
       "\n",
       "        Location_Center_Y  AreaShape_MajorAxisLength  \n",
       "771674          13.794754                  82.116081  \n",
       "771675           8.512265                  83.824562  \n",
       "771676           1.556818                  23.123064  \n",
       "771677          38.673599                  73.847916  \n",
       "771678          42.926830                  65.491951  \n",
       "...                   ...                        ...  \n",
       "771770        2394.468262                  98.319923  \n",
       "771771        2402.177246                  59.810719  \n",
       "771772        2414.588867                  80.025642  \n",
       "771773        2453.921143                  65.023720  \n",
       "771774        2474.874268                  62.267124  \n",
       "\n",
       "[101 rows x 16 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check[(check[\"Metadata_Barcode\"] == \"P101334\") & (check[\"Metadata_Well\"] == \"B02\") & (check[\"Metadata_Site\"] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: <tifffile.TiffFile 'B02_s1_x0_y0_Flu…_561_nm_Ex.tiff'> shaped series shape does not match page shape\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tifffile\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Replace 'your_file.tiff' with the path to your TIFF file\n",
    "file_path = '/share/mikro2/squid/specs3k/P101334_specs3k_U2OS_P02_L1_2023-04-03_16.15.49/B02_s1_x0_y0_Fluorescence_561_nm_Ex.tiff'\n",
    "# Read the TIFF file using tifffile\n",
    "with tifffile.TiffFile(file_path) as tif:\n",
    "    # Assuming you want to display the first page if it's a multi-page TIFF\n",
    "    image = tif.pages[0].asarray()\n",
    "img = io.imread(file_path)\n",
    "# Display the image using matplotlib\n",
    "plt.imshow(img, cmap='gray')  # Use appropriate colormap if it's not grayscale\n",
    "plt.axis('off')  # Hide axis\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = '/home/jovyan/share/mikro/RMS/RMS-e02-v01-P014563-RH30-48h/2021-10-28/821/RMS-e02-v01-P014563-RH30-48h_A01_s1_w3175E8FC9-08D4-4033-97FB-D4268F243903.tif'\n",
    "img_test = io.imread(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.imshow(img_test, cmap='gray')  # Use appropriate colormap if it's not grayscale\n",
    "plt.axis('off')  # Hide axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show distributions of cell sizes for cropping window determination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_kde_colored_by_group(df, column_to_plot, color_column):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    # Generate a color palette with as many colors as there are unique treatments\n",
    "    palette = sns.color_palette(\"husl\", len(df[color_column].unique()))\n",
    "    \n",
    "    # Loop through the unique values of the color column and plot the KDE for each\n",
    "    for idx, (value, color) in enumerate(zip(df[color_column].unique(), palette)):\n",
    "        subset = df[df[color_column] == value]\n",
    "        sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
    "    \n",
    "    plt.title(f'KDE of {column_to_plot} colored by {color_column}')\n",
    "    plt.legend(title=color_column, loc='upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
    "    plt.xlabel(column_to_plot)\n",
    "    plt.ylabel('Density')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n",
      "<ipython-input-117-05c2678f4855>:13: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(subset[column_to_plot], color=color, label=str(value), shade=True, alpha=0.3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kde_colored_by_group(check, \"AreaShape_MajorAxisLength\", \"Metadata_Barcode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Metadata_Barcode</th>\n",
       "      <th>Metadata_Site</th>\n",
       "      <th>Metadata_AcqID</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>FileName_CONC</th>\n",
       "      <th>FileName_HOECHST</th>\n",
       "      <th>FileName_PHAandWGA</th>\n",
       "      <th>FileName_SYTO</th>\n",
       "      <th>FileName_MITO</th>\n",
       "      <th>PathName_MITO</th>\n",
       "      <th>PathName_HOECHST</th>\n",
       "      <th>PathName_PHAandWGA</th>\n",
       "      <th>PathName_SYTO</th>\n",
       "      <th>Location_Center_X</th>\n",
       "      <th>Location_Center_Y</th>\n",
       "      <th>AreaShape_MajorAxisLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P101336</td>\n",
       "      <td>5</td>\n",
       "      <td>3173</td>\n",
       "      <td>J23</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>858.628784</td>\n",
       "      <td>8.912724</td>\n",
       "      <td>54.831848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P101336</td>\n",
       "      <td>5</td>\n",
       "      <td>3173</td>\n",
       "      <td>J23</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>970.331665</td>\n",
       "      <td>8.481988</td>\n",
       "      <td>48.315880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P101336</td>\n",
       "      <td>5</td>\n",
       "      <td>3173</td>\n",
       "      <td>J23</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>1588.492065</td>\n",
       "      <td>20.345093</td>\n",
       "      <td>68.443581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P101336</td>\n",
       "      <td>5</td>\n",
       "      <td>3173</td>\n",
       "      <td>J23</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>1967.644775</td>\n",
       "      <td>4.938914</td>\n",
       "      <td>43.810356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P101336</td>\n",
       "      <td>5</td>\n",
       "      <td>3173</td>\n",
       "      <td>J23</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101336_specs3k_U2...</td>\n",
       "      <td>770.534912</td>\n",
       "      <td>53.407974</td>\n",
       "      <td>53.642841</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28952461</th>\n",
       "      <td>P101366</td>\n",
       "      <td>7</td>\n",
       "      <td>3190</td>\n",
       "      <td>J06</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>1024.148071</td>\n",
       "      <td>2463.276611</td>\n",
       "      <td>45.953186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28952462</th>\n",
       "      <td>P101366</td>\n",
       "      <td>7</td>\n",
       "      <td>3190</td>\n",
       "      <td>J06</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>813.819031</td>\n",
       "      <td>2484.063232</td>\n",
       "      <td>48.014126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28952463</th>\n",
       "      <td>P101366</td>\n",
       "      <td>7</td>\n",
       "      <td>3190</td>\n",
       "      <td>J06</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>331.064819</td>\n",
       "      <td>2488.360107</td>\n",
       "      <td>56.518444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28952464</th>\n",
       "      <td>P101366</td>\n",
       "      <td>7</td>\n",
       "      <td>3190</td>\n",
       "      <td>J06</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>743.933350</td>\n",
       "      <td>2489.288574</td>\n",
       "      <td>49.213100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28952465</th>\n",
       "      <td>P101366</td>\n",
       "      <td>7</td>\n",
       "      <td>3190</td>\n",
       "      <td>J06</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff</td>\n",
       "      <td>J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>/share/mikro2/squid/specs3k/P101366_specs3k_U2...</td>\n",
       "      <td>1875.898804</td>\n",
       "      <td>2492.571777</td>\n",
       "      <td>53.233303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28952466 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Metadata_Barcode  Metadata_Site  Metadata_AcqID Metadata_Well  \\\n",
       "0                 P101336              5            3173           J23   \n",
       "1                 P101336              5            3173           J23   \n",
       "2                 P101336              5            3173           J23   \n",
       "3                 P101336              5            3173           J23   \n",
       "4                 P101336              5            3173           J23   \n",
       "...                   ...            ...             ...           ...   \n",
       "28952461          P101366              7            3190           J06   \n",
       "28952462          P101366              7            3190           J06   \n",
       "28952463          P101366              7            3190           J06   \n",
       "28952464          P101366              7            3190           J06   \n",
       "28952465          P101366              7            3190           J06   \n",
       "\n",
       "                                     FileName_CONC  \\\n",
       "0         J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "1         J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "2         J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "3         J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "4         J23_s5_x1_y1_Fluorescence_730_nm_Ex.tiff   \n",
       "...                                            ...   \n",
       "28952461  J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "28952462  J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "28952463  J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "28952464  J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "28952465  J06_s7_x0_y2_Fluorescence_730_nm_Ex.tiff   \n",
       "\n",
       "                                  FileName_HOECHST  \\\n",
       "0         J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "1         J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "2         J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "3         J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "4         J23_s5_x1_y1_Fluorescence_405_nm_Ex.tiff   \n",
       "...                                            ...   \n",
       "28952461  J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "28952462  J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "28952463  J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "28952464  J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "28952465  J06_s7_x0_y2_Fluorescence_405_nm_Ex.tiff   \n",
       "\n",
       "                                FileName_PHAandWGA  \\\n",
       "0         J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "1         J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "2         J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "3         J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "4         J23_s5_x1_y1_Fluorescence_561_nm_Ex.tiff   \n",
       "...                                            ...   \n",
       "28952461  J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "28952462  J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "28952463  J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "28952464  J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "28952465  J06_s7_x0_y2_Fluorescence_561_nm_Ex.tiff   \n",
       "\n",
       "                                     FileName_SYTO  \\\n",
       "0         J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "1         J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "2         J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "3         J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "4         J23_s5_x1_y1_Fluorescence_488_nm_Ex.tiff   \n",
       "...                                            ...   \n",
       "28952461  J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "28952462  J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "28952463  J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "28952464  J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "28952465  J06_s7_x0_y2_Fluorescence_488_nm_Ex.tiff   \n",
       "\n",
       "                                     FileName_MITO  \\\n",
       "0         J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "1         J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "2         J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "3         J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "4         J23_s5_x1_y1_Fluorescence_638_nm_Ex.tiff   \n",
       "...                                            ...   \n",
       "28952461  J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff   \n",
       "28952462  J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff   \n",
       "28952463  J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff   \n",
       "28952464  J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff   \n",
       "28952465  J06_s7_x0_y2_Fluorescence_638_nm_Ex.tiff   \n",
       "\n",
       "                                              PathName_MITO  \\\n",
       "0         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "1         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "2         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "3         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "4         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "...                                                     ...   \n",
       "28952461  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952462  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952463  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952464  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952465  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "\n",
       "                                           PathName_HOECHST  \\\n",
       "0         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "1         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "2         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "3         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "4         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "...                                                     ...   \n",
       "28952461  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952462  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952463  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952464  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952465  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "\n",
       "                                         PathName_PHAandWGA  \\\n",
       "0         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "1         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "2         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "3         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "4         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "...                                                     ...   \n",
       "28952461  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952462  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952463  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952464  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952465  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "\n",
       "                                              PathName_SYTO  \\\n",
       "0         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "1         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "2         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "3         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "4         /share/mikro2/squid/specs3k/P101336_specs3k_U2...   \n",
       "...                                                     ...   \n",
       "28952461  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952462  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952463  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952464  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "28952465  /share/mikro2/squid/specs3k/P101366_specs3k_U2...   \n",
       "\n",
       "          Location_Center_X  Location_Center_Y  AreaShape_MajorAxisLength  \n",
       "0                858.628784           8.912724                  54.831848  \n",
       "1                970.331665           8.481988                  48.315880  \n",
       "2               1588.492065          20.345093                  68.443581  \n",
       "3               1967.644775           4.938914                  43.810356  \n",
       "4                770.534912          53.407974                  53.642841  \n",
       "...                     ...                ...                        ...  \n",
       "28952461        1024.148071        2463.276611                  45.953186  \n",
       "28952462         813.819031        2484.063232                  48.014126  \n",
       "28952463         331.064819        2488.360107                  56.518444  \n",
       "28952464         743.933350        2489.288574                  49.213100  \n",
       "28952465        1875.898804        2492.571777                  53.233303  \n",
       "\n",
       "[28952466 rows x 16 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check"
   ]
  },
  {
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}