api_playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20edabb6-5273-49aa-a9d5-4eaba676c761",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ds/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n",
      "/home/ds/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.0, the latest is 0.5.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from dclpatch.main import PatchProject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81a4e996-283c-4b23-bef4-8c75001c7c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_dir_path = Path('/mnt/c/Users/dsege/TEMP/Alexia/analysis_with_dcl_patch/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "345be5ea-9140-46c9-8e57-5d1417314aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = PatchProject(root_dir = root_dir_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7621efd4-b0af-432d-9fe1-6a26f0dd4e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________________________________________________________________________\n",
      "Warning! When I tried to load the data of the following path: \"/mnt/c/Users/dsege/TEMP/Alexia/sample_data/2022_04_11_C5\", the following error occured:\n",
      "\"Could not find a file with recorded events for cell: 2022_04_11_C5 for inhibitory_voltage_clamp_recordings with stimulation type: 20-Hz_for_200-ms. Please check the corresponding files!\"\n",
      "For now, I skipped loading these data to continue. Try loading that dataset individually to see more details about the error or try to follow the instructions that might be given in the error message above.\n",
      "_____________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "path_to_all_cell_recordings = Path('/mnt/c/Users/dsege/TEMP/Alexia/sample_data/')\n",
    "api.add_all_cells_to_database(path_with_cell_recordings_as_subdirs = path_to_all_cell_recordings, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d703701-6884-4b36-b217-0126f2dcc252",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = api.database.excitatory_voltage_clamp_recordings.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04bff57e-1ca5-424e-9ef5-b4fff32a49a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['global_cell_id', 'date', 'session_cell_id', 'mouse_line',\n",
       "       'brain_region', 'cell_type', 'sex', 'age', 'stimulation_in_percent',\n",
       "       'internal_solution', 'recording_type', 'stimulation_string',\n",
       "       'stimulation_frequency-Hz', 'stimulation_duration-ms', 'pharmacology',\n",
       "       'time_since_cutting-M', 'Cap I (pA)', 'Tau (ms)', 'Rs (MΩ)',\n",
       "       'Cell Cap (pF)', 'Iinf(nA)', 'Rm (MΩ)', 'Access change (%)',\n",
       "       'Total time (s)', 'Time of counting (s)', 'Nb of  sweeps',\n",
       "       'Mean amp (pA)', 'Rise time 10-90 (ms)', 'Decay time 63% (ms)',\n",
       "       'Half width (ms)', 'filepath_main_excel_sheet',\n",
       "       'filepath_detected_events', 'Time of counting (s) '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34aa7b1c-f7aa-4bdc-b7cb-f4faea9e46c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "key = 'stimulation_duration-ms'\n",
    "value = [200, 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0652cf3e-6741-4f71-92cc-a3de650475b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 2000.,  200.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[key].isin(value)) | (df[key].isnull())][key].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28e16dfb-92b7-4817-8d28-43ecf1fcac79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['baseline', '4-Hz_for_1000-ms', '20-Hz_for_100-ms',\n",
       "       '20-Hz_for_500-ms', '4-Hz_for_5000-ms', '4-Hz_for_2000-ms',\n",
       "       '20-Hz_for_200-ms', 'control_baseline'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stimulation_string'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba6c25c4-8de3-42ed-a658-342e969dc137",
   "metadata": {},
   "outputs": [],
   "source": [
    "inclusion_criteria = {'stimulation_string': ['baseline', '4-Hz_for_2000-ms', '20-Hz_for_200-ms']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aee5fc65-5d4e-4447-9f6c-5897b3501a9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Please be aware that I detected inconsistent values for the column \"mouse_line\":\n",
      "\n",
      "--> ['NET-ires-cre:vGat-flp', 'NET-cre:vGat-flp']\n",
      "\n",
      "You might consider adding all but one to the exclusion criteria!\n",
      "_______________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#exclusion_criteria = {'internal_solution': 'Kglu = bio', 'internal_solution': 'Kglu + 1.78ug bio'}\n",
    "#inclusion_criteria = {'cell_type': 'Glutamatergic'}\n",
    "dataset = api.compare_within_group(group_column = 'cell_type', group_id = 'GABA', analysis_type = 'CDF', recording_type = 'IPSPs', include = inclusion_criteria, export = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "817996c9-b79a-4367-b461-7c52d3407788",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2063b6d1-5526-4f2c-9dd7-6429800498b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['4-Hz_for_1000-ms', '20-Hz_for_100-ms', '4-Hz_for_2000-ms', '20-Hz_for_200-ms'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e56b9e89-0a6d-4730-be3c-55e3d323b2d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'amplitude': {'baseline': [-7.396280000000001, -8.67289],\n",
       "  'stimulated': [-6.19072, -7.45901],\n",
       "  'global_cell_id': ['0000', '0001']},\n",
       " 'inter_event_interval': {'baseline': [315.7333333333336, 1306.0083333333332],\n",
       "  'stimulated': [312.6000000000001, 717.3166666666664],\n",
       "  'global_cell_id': ['0000', '0001']}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4adc2fc6-89b0-4c7a-bb53-96c6dbd31a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "data group_id subject_id measurement_type\n",
    "\n",
    "data condition cell_id measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea9f3470-f2dc-4c42-9384-aefc214ad74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stimulation_key = '4-Hz_for_1000-ms'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f40194f-9a62-4a83-a960-a676289d42fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_stimulation_type_data = {'data': list(),\n",
    "                                'stimulation_type': list(),\n",
    "                                'global_cell_id': list(),\n",
    "                                'measurement': list()}\n",
    "\n",
    "for measurement in dataset[stimulation_key].keys():\n",
    "    single_stimulation_type_data['data'] += dataset[stimulation_key]['stimulated']\n",
    "    single_stimulation_type_data['data'] += dataset[stimulation_key]['baseline']\n",
    "    single_stimulation_type_data['data'] += \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc3127ca-33f6-453c-b646-aa5e42cbd9cb",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'api' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1306/720768201.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_database\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'api' is not defined"
     ]
    }
   ],
   "source": [
    "api.save_database()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa89fcb-1398-42e1-b6c1-20dc44ed21e8",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Code development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e53676f-7473-4465-9b80-6ef4aba8723c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d3d3fd1-beb7-4a87-b832-a2a2dfd7b45d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dclpatch.analysis import CDFAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e673380-8e21-46d7-9aba-573f019a955a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf_analysis = CDFAnalysis(database = api.database, df_to_use = api.database.excitatory_voltage_clamp_recordings, recording_type = 'EPSPs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "879de66c-f088-474f-bfad-61b05b11da2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf_analysis.group_column = 'cell_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b13baef2-9a1d-480f-ae69-e15d5d216b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf_analysis.group_id = 'Glutamatergic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a545a150-f4be-4ac6-9723-001faa87edcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths = cdf_analysis.get_all_recording_filepaths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71570434-b7b0-4670-99fd-2dc9afe51b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "for recording_filepath in filepaths:\n",
    "    cdf_analysis.get_data_for_cumulative_distributions(filepath = recording_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fde92f65-102d-418b-b10c-c4539c0a0811",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>inter_event_interval</th>\n",
       "      <th>sweep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-18.76700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-12.01080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-6.19985</td>\n",
       "      <td>223.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.21860</td>\n",
       "      <td>166.033333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-5.97182</td>\n",
       "      <td>274.283333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-6.12733</td>\n",
       "      <td>95.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-24.10140</td>\n",
       "      <td>190.866667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-34.68450</td>\n",
       "      <td>228.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-14.76840</td>\n",
       "      <td>237.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-7.49855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3447 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     global_cell_id stimulation_string  amplitude  inter_event_interval  sweep\n",
       "0              0000           baseline  -18.76700                   NaN      0\n",
       "1              0000           baseline  -12.01080                   NaN      0\n",
       "2              0000           baseline   -6.19985            223.166667      0\n",
       "3              0000           baseline   -8.21860            166.033333      0\n",
       "4              0000           baseline   -5.97182            274.283333      0\n",
       "...             ...                ...        ...                   ...    ...\n",
       "3442           0007   20-Hz_for_200-ms   -6.12733             95.566667      5\n",
       "3443           0007   20-Hz_for_200-ms  -24.10140            190.866667      5\n",
       "3444           0007   20-Hz_for_200-ms  -34.68450            228.566667      5\n",
       "3445           0007   20-Hz_for_200-ms  -14.76840            237.100000      5\n",
       "3446           0007   20-Hz_for_200-ms   -7.49855                   NaN      5\n",
       "\n",
       "[3447 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_events = pd.DataFrame(data=cdf_analysis.events_all_stim_paradigms)\n",
    "df_all_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cd7a044a-0818-485f-9993-9891fefe4744",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_all_events.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec375479-817d-4a22-8bd1-a137a29bc2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "stim_paradigms = list(data['stimulation_string'].unique())\n",
    "stim_paradigms.remove('baseline')\n",
    "\n",
    "probability_to_compare = 50\n",
    "measurements = ['amplitude', 'inter_event_interval']\n",
    "\n",
    "percentile_data_per_stim_string = dict()\n",
    "stim_paradigms = list(df_all_events['stimulation_string'].unique())\n",
    "stim_paradigms.remove('baseline')\n",
    "for stim_string in stim_paradigms:\n",
    "    percentile_data_per_stim_string[stim_string] = dict()\n",
    "    for measurement in measurements:\n",
    "        percentile_data_per_stim_string[stim_string][measurement] = {'baseline': list(),\n",
    "                                                                     'stimulated': list()}\n",
    "        for global_cell_id in df_all_events.loc[df_all_events['stimulation_string'] == stim_string, 'global_cell_id'].unique():\n",
    "            baseline = np.nanpercentile(df_all_events.loc[(df_all_events['stimulation_string'] == 'baseline') &\n",
    "                                                          (df_all_events['global_cell_id'] == global_cell_id), measurement].values,\n",
    "                                        probability_to_compare)\n",
    "            stimulated = np.nanpercentile(df_all_events.loc[(df_all_events['stimulation_string'] == stim_string) &\n",
    "                                                            (df_all_events['global_cell_id'] == global_cell_id), measurement].values,\n",
    "                                          probability_to_compare)\n",
    "            \n",
    "            percentile_data_per_stim_string[stim_string][measurement]['baseline'] += [baseline]\n",
    "            percentile_data_per_stim_string[stim_string][measurement]['stimulated'] += [stimulated]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "03b592cd-7adc-46ec-bf7b-05e9c2204fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['4-Hz_for_1000-ms',\n",
       " '20-Hz_for_100-ms',\n",
       " '4-Hz_for_2000-ms',\n",
       " '20-Hz_for_200-ms']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim_paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "03dde132-71ae-43e0-b3b8-383743b09c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "for stim_string in stim_paradigms:\n",
    "    df_temp = data.loc[data['stimulation_string'].isin([stim_string, 'baseline'])]\n",
    "    #for column_name in columns_for_cdfs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ccef9014-50ac-4f00-bcaa-817da442d3f2",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>inter_event_interval</th>\n",
       "      <th>sweep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-18.76700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-12.01080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-6.19985</td>\n",
       "      <td>223.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.21860</td>\n",
       "      <td>166.033333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-5.97182</td>\n",
       "      <td>274.283333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-6.12733</td>\n",
       "      <td>95.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-24.10140</td>\n",
       "      <td>190.866667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-34.68450</td>\n",
       "      <td>228.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-14.76840</td>\n",
       "      <td>237.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-7.49855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2087 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     global_cell_id stimulation_string  amplitude  inter_event_interval  sweep\n",
       "0              0000           baseline  -18.76700                   NaN      0\n",
       "1              0000           baseline  -12.01080                   NaN      0\n",
       "2              0000           baseline   -6.19985            223.166667      0\n",
       "3              0000           baseline   -8.21860            166.033333      0\n",
       "4              0000           baseline   -5.97182            274.283333      0\n",
       "...             ...                ...        ...                   ...    ...\n",
       "3442           0007   20-Hz_for_200-ms   -6.12733             95.566667      5\n",
       "3443           0007   20-Hz_for_200-ms  -24.10140            190.866667      5\n",
       "3444           0007   20-Hz_for_200-ms  -34.68450            228.566667      5\n",
       "3445           0007   20-Hz_for_200-ms  -14.76840            237.100000      5\n",
       "3446           0007   20-Hz_for_200-ms   -7.49855                   NaN      5\n",
       "\n",
       "[2087 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "16f80a56-1583-47c9-8a14-54b3cc34e469",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>inter_event_interval</th>\n",
       "      <th>sweep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-18.76700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-12.01080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-6.19985</td>\n",
       "      <td>223.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.21860</td>\n",
       "      <td>166.033333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-5.97182</td>\n",
       "      <td>274.283333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-6.12733</td>\n",
       "      <td>95.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-24.10140</td>\n",
       "      <td>190.866667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-34.68450</td>\n",
       "      <td>228.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-14.76840</td>\n",
       "      <td>237.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-7.49855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3447 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     global_cell_id stimulation_string  amplitude  inter_event_interval  sweep\n",
       "0              0000           baseline  -18.76700                   NaN      0\n",
       "1              0000           baseline  -12.01080                   NaN      0\n",
       "2              0000           baseline   -6.19985            223.166667      0\n",
       "3              0000           baseline   -8.21860            166.033333      0\n",
       "4              0000           baseline   -5.97182            274.283333      0\n",
       "...             ...                ...        ...                   ...    ...\n",
       "3442           0007   20-Hz_for_200-ms   -6.12733             95.566667      5\n",
       "3443           0007   20-Hz_for_200-ms  -24.10140            190.866667      5\n",
       "3444           0007   20-Hz_for_200-ms  -34.68450            228.566667      5\n",
       "3445           0007   20-Hz_for_200-ms  -14.76840            237.100000      5\n",
       "3446           0007   20-Hz_for_200-ms   -7.49855                   NaN      5\n",
       "\n",
       "[3447 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9b5438c1-bcdb-4a54-ba66-8c5c6319898b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[np.nan, 7., 4.], [3., 2., 1.]])\n",
    "\n",
    "np.nanpercentile(a, 50)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1ae93db9-81a9-47eb-b3ca-aca451f5a875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_events.loc[(df_all_events['stimulation_string'] == stim_string) & (df_all_events['global_cell_id'] == global_cell_id), 'amplitude'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "2ed4911b-0fa6-418b-939e-5e903361d26e",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>inter_event_interval</th>\n",
       "      <th>sweep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-18.76700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-12.01080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-6.19985</td>\n",
       "      <td>223.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.21860</td>\n",
       "      <td>166.033333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-5.97182</td>\n",
       "      <td>274.283333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-6.12733</td>\n",
       "      <td>95.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-24.10140</td>\n",
       "      <td>190.866667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-34.68450</td>\n",
       "      <td>228.566667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-14.76840</td>\n",
       "      <td>237.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>0007</td>\n",
       "      <td>20-Hz_for_200-ms</td>\n",
       "      <td>-7.49855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3447 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     global_cell_id stimulation_string  amplitude  inter_event_interval  sweep\n",
       "0              0000           baseline  -18.76700                   NaN      0\n",
       "1              0000           baseline  -12.01080                   NaN      0\n",
       "2              0000           baseline   -6.19985            223.166667      0\n",
       "3              0000           baseline   -8.21860            166.033333      0\n",
       "4              0000           baseline   -5.97182            274.283333      0\n",
       "...             ...                ...        ...                   ...    ...\n",
       "3442           0007   20-Hz_for_200-ms   -6.12733             95.566667      5\n",
       "3443           0007   20-Hz_for_200-ms  -24.10140            190.866667      5\n",
       "3444           0007   20-Hz_for_200-ms  -34.68450            228.566667      5\n",
       "3445           0007   20-Hz_for_200-ms  -14.76840            237.100000      5\n",
       "3446           0007   20-Hz_for_200-ms   -7.49855                   NaN      5\n",
       "\n",
       "[3447 rows x 5 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "6e91e570-6927-430d-ad25-3eaebe5bb69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_to_compare = 50\n",
    "measurements = ['amplitude', 'inter_event_interval']\n",
    "\n",
    "percentile_data_per_stim_string = dict()\n",
    "stim_paradigms = list(df_all_events['stimulation_string'].unique())\n",
    "stim_paradigms.remove('baseline')\n",
    "for stim_string in stim_paradigms:\n",
    "    percentile_data_per_stim_string[stim_string] = dict()\n",
    "    for measurement in measurements:\n",
    "        percentile_data_per_stim_string[stim_string][measurement] = {'baseline': list(),\n",
    "                                                                     'stimulated': list()}\n",
    "        for global_cell_id in df_all_events.loc[df_all_events['stimulation_string'] == stim_string, 'global_cell_id'].unique():\n",
    "            baseline = np.nanpercentile(df_all_events.loc[(df_all_events['stimulation_string'] == 'baseline') &\n",
    "                                                          (df_all_events['global_cell_id'] == global_cell_id), measurement].values,\n",
    "                                        probability_to_compare)\n",
    "            stimulated = np.nanpercentile(df_all_events.loc[(df_all_events['stimulation_string'] == stim_string) &\n",
    "                                                            (df_all_events['global_cell_id'] == global_cell_id), measurement].values,\n",
    "                                          probability_to_compare)\n",
    "            \n",
    "            percentile_data_per_stim_string[stim_string][measurement]['baseline'] += [baseline]\n",
    "            percentile_data_per_stim_string[stim_string][measurement]['stimulated'] += [stimulated]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa9873b9-5cd6-4918-b9a4-5da506155b88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "950ff016-cb8c-46ea-8eed-67e569028461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=percentile_data_per_stim_string['4-Hz_for_1000-ms']['amplitude'])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c926db44-1bda-4c86-9728-19c8cb888c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = pg.wilcoxon(x=df['baseline'], y=df['stimulated'])\n",
    "\n",
    "stats['p-val']['Wilcoxon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "39ae3f0b-57b9-42ff-92c6-14ab6ba31cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['4-Hz_for_1000-ms', '20-Hz_for_100-ms', '4-Hz_for_2000-ms', '20-Hz_for_200-ms'])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for stimulation_type in percentile_data_per_stim_string.keys():\n",
    "    for measurement in percentile_data_per_stim_string[stimulation_type].keys():\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "e6251cfe-7350-4f8d-943d-75ba317d40dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'amplitude': {'baseline': [-7.396280000000001, -8.67289],\n",
       "  'stimulated': [-6.19072, -7.45901]},\n",
       " 'inter_event_interval': {'baseline': [315.7333333333336, 1306.0083333333332],\n",
       "  'stimulated': [312.6000000000001, 717.3166666666664]}}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_data_per_stim_string['4-Hz_for_1000-ms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "30bd6cda-6701-459a-a66a-8ec2b86a8907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hund 0\n",
      "katze 1\n"
     ]
    }
   ],
   "source": [
    "n_rows = 2\n",
    "lala = ['hund', 'katze']\n",
    "\n",
    "for animal, row_index in zip(lala, range(n_rows)):\n",
    "    print(animal, row_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "6c1d87cd-4711-41bf-a437-790fee158551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jitter = 0.1\n",
    "df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=df.values.shape), columns=df.columns)\n",
    "df_x_jitter += np.arange(len(df.columns))\n",
    "\n",
    "colormixer = plt.cm.viridis(np.linspace(0, 1, len(df.columns) + 2))\n",
    "colormixer = colormixer[1 : -1]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for index in range(len(df.columns)):\n",
    "    column = df.columns[index]\n",
    "    color = colormixer[index]\n",
    "    ax.plot(df_x_jitter[column], df[column], 'o', color=color, alpha=.60, zorder=1, ms=8, mew=1)\n",
    "ax.set_xticks(range(2))\n",
    "ax.set_xticklabels(['baseline', 'stimulated'])\n",
    "ax.set_xlim(-0.5,2-0.5)\n",
    "\n",
    "for idx in df.index:\n",
    "    ax.plot(df_x_jitter.loc[idx,['baseline','stimulated']], df.loc[idx,['baseline','stimulated']], color = 'grey', linewidth = 0.5, linestyle = '--', zorder=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "939604d4-8924-4dfb-bb8f-614dca3f6921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['4-Hz_for_1000-ms', '20-Hz_for_100-ms', '4-Hz_for_2000-ms', '20-Hz_for_200-ms'])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_data_per_stim_string.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97563d4-bca8-4ae7-b7a7-1776553ef929",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "508b3d17-2948-4178-a1fa-d84549d49171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0007']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_l = []\n",
    "e_l += [global_cell_id]\n",
    "e_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "833b3d8f-b791-4f2e-92e7-848e8f6cb6f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cell_id, baseline, stimulated, stimulation_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9c9b464f-fda6-4bde-80ed-6cd9db806f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0006', '0007'], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_events.loc[df_all_events['stimulation_string'] == stim_string, 'global_cell_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "efd5d159-2903-4399-9ea4-990d636500c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['4-Hz_for_1000-ms', '20-Hz_for_100-ms', '4-Hz_for_2000-ms', '20-Hz_for_200-ms'])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_data_per_stim_string.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "931bf4c5-5408-4288-8b44-1c2034ff6b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'global_cell_id': ['0006', '0006', '0007', '0007'],\n",
       " 'stimulation_string': ['baseline',\n",
       "  '20-Hz_for_200-ms',\n",
       "  'baseline',\n",
       "  '20-Hz_for_200-ms'],\n",
       " 'percentile_amplitude': [-6.94073, -7.33049, -16.9545, -7.674175],\n",
       " 'percentile_inter_event_interval': [373.89999999999964,\n",
       "  492.03333333333404,\n",
       "  350.8000000000002,\n",
       "  197.81666666666692]}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_data_per_stim_string['20-Hz_for_200-ms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "dfee04b6-a218-4b6b-a490-bbbad89853d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reorganized = {'baseline': df.loc[df['stimulation_string'] == 'baseline', 'percentile_amplitude'].values,\n",
    "               'stimulated': df.loc[df['stimulation_string'] == '4-Hz_for_1000-ms', 'percentile_amplitude'].values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "58c5dd5f-9e0f-43c8-a4d5-83154a39ec94",
   "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>baseline</th>\n",
       "      <th>stimulated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.39628</td>\n",
       "      <td>-6.19072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-8.67289</td>\n",
       "      <td>-7.45901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   baseline  stimulated\n",
       "0  -7.39628    -6.19072\n",
       "1  -8.67289    -7.45901"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reorganized = pd.DataFrame(data=reorganized)\n",
    "df_reorganized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "642c09f9-dd72-4e75-9350-c8e9921b1a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jitter = 0.1\n",
    "df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=df_reorganized.values.shape), columns=df_reorganized.columns)\n",
    "df_x_jitter += np.arange(len(df_reorganized.columns))\n",
    "\n",
    "colormixer = plt.cm.viridis(np.linspace(0, 1, len(df_reorganized.columns) + 2))\n",
    "colormixer = colormixer[1 : -1]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for index in range(len(df_reorganized.columns)):\n",
    "    column = df_reorganized.columns[index]\n",
    "    color = colormixer[index]\n",
    "    ax.plot(df_x_jitter[column], df_reorganized[column], 'o', color=color, alpha=.60, zorder=1, ms=8, mew=1)\n",
    "ax.set_xticks(range(2))\n",
    "ax.set_xticklabels(['baseline', 'stimulated'])\n",
    "ax.set_xlim(-0.5,2-0.5)\n",
    "\n",
    "for idx in df_reorganized.index:\n",
    "    ax.plot(df_x_jitter.loc[idx,['baseline','stimulated']], df_reorganized.loc[idx,['baseline','stimulated']], color = 'grey', linewidth = 0.5, linestyle = '--', zorder=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f880f5f-5866-40e0-bd2a-ca7aecefc3eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1802d8a-05e1-4ba3-ac1b-2538cdd3eba1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a48c71bb-50d1-4ecf-aafd-83fe73d34fa4",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>percentile_amplitude</th>\n",
       "      <th>percentile_inter_event_interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-7.39628</td>\n",
       "      <td>315.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>4-Hz_for_1000-ms</td>\n",
       "      <td>-6.19072</td>\n",
       "      <td>312.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.67289</td>\n",
       "      <td>1306.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001</td>\n",
       "      <td>4-Hz_for_1000-ms</td>\n",
       "      <td>-7.45901</td>\n",
       "      <td>717.316667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  global_cell_id stimulation_string  percentile_amplitude  \\\n",
       "0           0000           baseline              -7.39628   \n",
       "1           0000   4-Hz_for_1000-ms              -6.19072   \n",
       "2           0001           baseline              -8.67289   \n",
       "3           0001   4-Hz_for_1000-ms              -7.45901   \n",
       "\n",
       "   percentile_inter_event_interval  \n",
       "0                       315.733333  \n",
       "1                       312.600000  \n",
       "2                      1306.008333  \n",
       "3                       717.316667  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "27d164cc-1b9f-4241-938a-78d182209f9e",
   "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>global_cell_id</th>\n",
       "      <th>stimulation_string</th>\n",
       "      <th>percentile_amplitude</th>\n",
       "      <th>percentile_inter_event_interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>4-Hz_for_1000-ms</td>\n",
       "      <td>-6.19072</td>\n",
       "      <td>312.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001</td>\n",
       "      <td>4-Hz_for_1000-ms</td>\n",
       "      <td>-7.45901</td>\n",
       "      <td>717.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-7.39628</td>\n",
       "      <td>315.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001</td>\n",
       "      <td>baseline</td>\n",
       "      <td>-8.67289</td>\n",
       "      <td>1306.008333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  global_cell_id stimulation_string  percentile_amplitude  \\\n",
       "1           0000   4-Hz_for_1000-ms              -6.19072   \n",
       "3           0001   4-Hz_for_1000-ms              -7.45901   \n",
       "0           0000           baseline              -7.39628   \n",
       "2           0001           baseline              -8.67289   \n",
       "\n",
       "   percentile_inter_event_interval  \n",
       "1                       312.600000  \n",
       "3                       717.316667  \n",
       "0                       315.733333  \n",
       "2                      1306.008333  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=percentile_data_per_stim_string['4-Hz_for_1000-ms'])\n",
    "df.sort_values(by=['stimulation_string', 'global_cell_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd3268c0-f59c-47d1-8e1c-f603276e3c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ds/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.0, the latest is 0.5.1.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n"
     ]
    }
   ],
   "source": [
    "import pingouin as pg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "deaeabae-fab4-42a7-85c7-7593396da8ce",
   "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>percentile_amplitude</th>\n",
       "      <th>percentile_inter_event_interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.032500</td>\n",
       "      <td>0.936994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.004802</td>\n",
       "      <td>0.946024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.040314</td>\n",
       "      <td>0.956480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.068725</td>\n",
       "      <td>1.032449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percentile_amplitude  percentile_inter_event_interval\n",
       "0              0.032500                         0.936994\n",
       "1             -0.004802                         0.946024\n",
       "2             -0.040314                         0.956480\n",
       "3              0.068725                         1.032449"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x_jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3396c83a-a849-4c8d-98de-46604830620a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=df.values.shape), columns=df.columns)\n",
    "df_x_jitter += np.arange(len(df.columns))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89d0f379-1c72-4b03-a151-d2654f4941ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e65b3a8c-126b-4a7e-afc1-9b29253a0b85",
   "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>percentile_amplitude</th>\n",
       "      <th>percentile_inter_event_interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.032500</td>\n",
       "      <td>0.936994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.004802</td>\n",
       "      <td>0.946024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.040314</td>\n",
       "      <td>0.956480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.068725</td>\n",
       "      <td>1.032449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percentile_amplitude  percentile_inter_event_interval\n",
       "0              0.032500                         0.936994\n",
       "1             -0.004802                         0.946024\n",
       "2             -0.040314                         0.956480\n",
       "3              0.068725                         1.032449"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x_jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bda13d3c-8b6c-401a-bd92-b8925b274360",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['baseline', '4-Hz_for_1000-ms'], dtype='object')] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_784/2557484137.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_x_jitter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'baseline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'4-Hz_for_1000-ms'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'baseline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'4-Hz_for_1000-ms'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'grey'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    923\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    926\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1098\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIndexingError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m         \u001b[0;31m# no multi-index, so validate all of the indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    860\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0;31m# This is an elided recursive call to iloc/loc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"not applicable\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m         \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[1;32m   1095\u001b[0m             \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[0;32m~/anaconda3/envs/DCLwidgets_clone/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['baseline', '4-Hz_for_1000-ms'], dtype='object')] are in the [index]\""
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "N=20\n",
    "# dummy dataset\n",
    "data = np.random.normal(size=(N,))\n",
    "\n",
    "jitter = 0.05\n",
    "df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=df[['percentile_amplitude', 'percentile_inter_event_interval']].values.shape), columns=['percentile_amplitude', 'percentile_inter_event_interval'])\n",
    "df_x_jitter += np.arange(2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for col in ['percentile_amplitude', 'percentile_inter_event_interval']:\n",
    "    ax.plot(df_x_jitter[col], df[col], 'o', alpha=.40, zorder=1, ms=8, mew=1)\n",
    "ax.set_xticks(range(2))\n",
    "ax.set_xticklabels(['percentile_amplitude', 'percentile_inter_event_interval'])\n",
    "ax.set_xlim(-0.5,2-0.5)\n",
    "\n",
    "for idx in df.index:\n",
    "    ax.plot(df_x_jitter.loc[idx,['baseline','4-Hz_for_1000-ms']], df.loc[idx,['baseline','4-Hz_for_1000-ms']], color = 'grey', linewidth = 0.5, linestyle = '--', zorder=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7632e7-6aa7-47ef-b0fc-1c718a342109",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a42af6-de10-4567-b83c-f1c3b8eb2e0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5826bc8b-6aff-433a-80a2-c3567fab324b",
   "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>W-val</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>RBC</th>\n",
       "      <th>CLES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wilcoxon</th>\n",
       "      <td>0.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W-val alternative  p-val  RBC  CLES\n",
       "Wilcoxon    0.0   two-sided    0.5 -1.0  0.25"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.wilcoxon(x=df.loc[df['stimulation_string'] == 'baseline', 'percentile_amplitude'].values, \n",
    "            y=df.loc[df['stimulation_string'] == '4-Hz_for_1000-ms', 'percentile_amplitude'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4209d3-1b7f-497e-9f10-902a50233062",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "122eb835-7e18-4ef9-b9b1-a6057463c15e",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cell_id, stimulation_string, percentile_amplitude, percentile_inter_event_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "be2d4354-8ced-4bf2-8208-84f27c661a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0000': {'baseline': -7.396280000000001,\n",
       "  '4-Hz_for_1000-ms': -6.19072,\n",
       "  '20-Hz_for_100-ms': -7.4271650000000005},\n",
       " '0001': {'baseline': -8.67289,\n",
       "  '4-Hz_for_1000-ms': -7.45901,\n",
       "  '20-Hz_for_100-ms': -11.4995},\n",
       " '0006': {'baseline': -6.94073,\n",
       "  '4-Hz_for_2000-ms': -5.989545,\n",
       "  '20-Hz_for_200-ms': -7.33049},\n",
       " '0007': {'baseline': -16.9545,\n",
       "  '4-Hz_for_2000-ms': -12.2297,\n",
       "  '20-Hz_for_200-ms': -7.674175}}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9c238d1-f800-4e2e-b0e1-de86430f58e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f3497a-8940-493f-ae74-8be258ed91c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "492f626f-d349-48f8-a20a-4cb458886df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_ecdf(a):\n",
    "    x, y = ecdf(a)\n",
    "    x = np.insert(x, 0, x[0])\n",
    "    y = np.insert(y, 0, 0.)\n",
    "    plt.plot(x, y, drawstyle='steps-post')\n",
    "    for percentile in [10, 20, 30, 40, 50, 60, 90]:\n",
    "        plt.vlines(np.percentile(a, percentile), ymin=0, ymax=1, color='red')\n",
    "        plt.hlines(percentile/100, min(a), max(a), color='red')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    #plt.savefig('ecdf.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "33ee77d3-db0d-43a1-9757-6aebed50da6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def ecdf(a):\n",
    "    x, counts = np.unique(a, return_counts=True)\n",
    "    cusum = np.cumsum(counts)\n",
    "    return x, cusum / cusum[-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f055da2f-4494-410a-b567-3cd4df86c0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df_all_events.loc[(df_all_events['stimulation_string'] == '4-Hz_for_1000-ms'), 'amplitude'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9efa090-3da4-4cdc-a528-24e80ba2dbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6.41832"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "546636c4-0f91-4a27-a524-43651ae912ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-33.7484"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(a, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "731c32af-1a3e-4743-836c-8bc66e6a6027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ecdf(a = a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc2c24a2-8f19-4626-a61a-58c561aa49e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_analysis.plot_cdfs(data = df_all_events,\n",
    "                       columns_for_cdfs = ['amplitude', 'inter_event_interval'], \n",
    "                       show = True,\n",
    "                       save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3faabd79-2440-42a7-ba79-8e9812caac71",
   "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>global_cell_id</th>\n",
       "      <th>date</th>\n",
       "      <th>session_cell_id</th>\n",
       "      <th>mouse_line</th>\n",
       "      <th>brain_region</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>stimulation_in_percent</th>\n",
       "      <th>internal_solution</th>\n",
       "      <th>...</th>\n",
       "      <th>Total time (s)</th>\n",
       "      <th>Time of counting (s)</th>\n",
       "      <th>Nb of  sweeps</th>\n",
       "      <th>Mean amp (pA)</th>\n",
       "      <th>Rise time 10-90 (ms)</th>\n",
       "      <th>Decay time 63% (ms)</th>\n",
       "      <th>Half width (ms)</th>\n",
       "      <th>filepath_main_excel_sheet</th>\n",
       "      <th>filepath_detected_events</th>\n",
       "      <th>Time of counting (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>2021_11_16</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>135.45</td>\n",
       "      <td>19.35</td>\n",
       "      <td>7</td>\n",
       "      <td>8.74</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.56</td>\n",
       "      <td>4.86</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000</td>\n",
       "      <td>2021_11_16</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>16.5</td>\n",
       "      <td>8</td>\n",
       "      <td>7.21</td>\n",
       "      <td>2.07</td>\n",
       "      <td>3.52</td>\n",
       "      <td>6.46</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>2021_11_16</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>137.6</td>\n",
       "      <td>17.2</td>\n",
       "      <td>8</td>\n",
       "      <td>8.71</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.88</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001</td>\n",
       "      <td>2021_11_17</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu = bio</td>\n",
       "      <td>...</td>\n",
       "      <td>77.4</td>\n",
       "      <td>19.35</td>\n",
       "      <td>4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.08</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001</td>\n",
       "      <td>2021_11_17</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu = bio</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.31</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001</td>\n",
       "      <td>2021_11_17</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Male</td>\n",
       "      <td>145</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu = bio</td>\n",
       "      <td>...</td>\n",
       "      <td>137.6</td>\n",
       "      <td>17.2</td>\n",
       "      <td>8</td>\n",
       "      <td>13.11</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0002</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C2</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>8.59</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.66</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0002</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C2</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>8.38</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.93</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0002</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C2</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>8.71</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.71</td>\n",
       "      <td>3.56</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0002</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C2</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>222.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.22</td>\n",
       "      <td>4.32</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0002</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C2</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.17</td>\n",
       "      <td>4.12</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0003</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>9.25</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4.21</td>\n",
       "      <td>7.72</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0003</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2.07</td>\n",
       "      <td>3.67</td>\n",
       "      <td>6.74</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0003</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C3</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>10.01</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.51</td>\n",
       "      <td>6.59</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0004</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.46</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0004</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>8.13</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.77</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0004</td>\n",
       "      <td>2021_12_09</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>147</td>\n",
       "      <td>20</td>\n",
       "      <td>Kglu + 1.73ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10.42</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.41</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0005</td>\n",
       "      <td>2022_01_13</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>174</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.55</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0005</td>\n",
       "      <td>2022_01_13</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>12.52</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.68</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0005</td>\n",
       "      <td>2022_01_13</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGat-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>GABA</td>\n",
       "      <td>Female</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>184.2</td>\n",
       "      <td>30.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.47</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.01</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0006</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>186</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>6.62</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.45</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0006</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>7.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.96</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0006</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C4</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>15</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>184.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>6</td>\n",
       "      <td>8.09</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.57</td>\n",
       "      <td>4.91</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0007</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C6</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>17</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>19.61</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.23</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0007</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C6</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>17</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>155</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>15.51</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>3.15</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0007</td>\n",
       "      <td>2022_01_25</td>\n",
       "      <td>C6</td>\n",
       "      <td>NET-ires-cre:vGlut2-flp</td>\n",
       "      <td>vlPAG</td>\n",
       "      <td>Glutamatergic</td>\n",
       "      <td>Female</td>\n",
       "      <td>194</td>\n",
       "      <td>17</td>\n",
       "      <td>Kglu + 1.78ug bio</td>\n",
       "      <td>...</td>\n",
       "      <td>184.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>6</td>\n",
       "      <td>11.78</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.46</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>/mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   global_cell_id        date session_cell_id               mouse_line  \\\n",
       "0            0000  2021_11_16              C3       NET-cre:vGlut2-flp   \n",
       "1            0000  2021_11_16              C3       NET-cre:vGlut2-flp   \n",
       "2            0000  2021_11_16              C3       NET-cre:vGlut2-flp   \n",
       "3            0001  2021_11_17              C3  NET-ires-cre:vGlut2-flp   \n",
       "4            0001  2021_11_17              C3  NET-ires-cre:vGlut2-flp   \n",
       "5            0001  2021_11_17              C3  NET-ires-cre:vGlut2-flp   \n",
       "6            0002  2021_12_09              C2         NET-cre:vGat-flp   \n",
       "7            0002  2021_12_09              C2         NET-cre:vGat-flp   \n",
       "8            0002  2021_12_09              C2         NET-cre:vGat-flp   \n",
       "9            0002  2021_12_09              C2         NET-cre:vGat-flp   \n",
       "10           0002  2021_12_09              C2         NET-cre:vGat-flp   \n",
       "11           0003  2021_12_09              C3         NET-cre:vGat-flp   \n",
       "12           0003  2021_12_09              C3         NET-cre:vGat-flp   \n",
       "13           0003  2021_12_09              C3         NET-cre:vGat-flp   \n",
       "14           0004  2021_12_09              C4         NET-cre:vGat-flp   \n",
       "15           0004  2021_12_09              C4         NET-cre:vGat-flp   \n",
       "16           0004  2021_12_09              C4         NET-cre:vGat-flp   \n",
       "17           0005  2022_01_13              C4    NET-ires-cre:vGat-flp   \n",
       "18           0005  2022_01_13              C4    NET-ires-cre:vGat-flp   \n",
       "19           0005  2022_01_13              C4    NET-ires-cre:vGat-flp   \n",
       "20           0006  2022_01_25              C4  NET-ires-cre:vGlut2-flp   \n",
       "21           0006  2022_01_25              C4  NET-ires-cre:vGlut2-flp   \n",
       "22           0006  2022_01_25              C4  NET-ires-cre:vGlut2-flp   \n",
       "23           0007  2022_01_25              C6  NET-ires-cre:vGlut2-flp   \n",
       "24           0007  2022_01_25              C6  NET-ires-cre:vGlut2-flp   \n",
       "25           0007  2022_01_25              C6  NET-ires-cre:vGlut2-flp   \n",
       "\n",
       "   brain_region      cell_type     sex  age  stimulation_in_percent  \\\n",
       "0         vlPAG  Glutamatergic    Male  145                      20   \n",
       "1         vlPAG  Glutamatergic    Male  145                      20   \n",
       "2         vlPAG  Glutamatergic    Male  145                      20   \n",
       "3         vlPAG  Glutamatergic    Male  145                      15   \n",
       "4         vlPAG  Glutamatergic    Male  145                      15   \n",
       "5         vlPAG  Glutamatergic    Male  145                      15   \n",
       "6         vlPAG           GABA  Female  147                      15   \n",
       "7         vlPAG           GABA  Female  147                      15   \n",
       "8         vlPAG           GABA  Female  147                      15   \n",
       "9         vlPAG           GABA  Female  147                      15   \n",
       "10        vlPAG           GABA  Female  147                      15   \n",
       "11        vlPAG           GABA  Female  147                      20   \n",
       "12        vlPAG           GABA  Female  147                      20   \n",
       "13        vlPAG           GABA  Female  147                      20   \n",
       "14        vlPAG           GABA  Female  147                      20   \n",
       "15        vlPAG           GABA  Female  147                      20   \n",
       "16        vlPAG           GABA  Female  147                      20   \n",
       "17        vlPAG           GABA  Female  182                      15   \n",
       "18        vlPAG           GABA  Female  182                      15   \n",
       "19        vlPAG           GABA  Female  182                      15   \n",
       "20        vlPAG  Glutamatergic  Female  194                      15   \n",
       "21        vlPAG  Glutamatergic  Female  194                      15   \n",
       "22        vlPAG  Glutamatergic  Female  194                      15   \n",
       "23        vlPAG  Glutamatergic  Female  194                      17   \n",
       "24        vlPAG  Glutamatergic  Female  194                      17   \n",
       "25        vlPAG  Glutamatergic  Female  194                      17   \n",
       "\n",
       "    internal_solution  ... Total time (s) Time of counting (s) Nb of  sweeps  \\\n",
       "0      Kglu + 1ug bio  ...         135.45                19.35             7   \n",
       "1      Kglu + 1ug bio  ...            132                 16.5             8   \n",
       "2      Kglu + 1ug bio  ...          137.6                 17.2             8   \n",
       "3          Kglu = bio  ...           77.4                19.35             4   \n",
       "4          Kglu = bio  ...             66                 16.5             4   \n",
       "5          Kglu = bio  ...          137.6                 17.2             8   \n",
       "6   Kglu + 1.73ug bio  ...            272                  NaN             8   \n",
       "7   Kglu + 1.73ug bio  ...            270                  NaN            10   \n",
       "8   Kglu + 1.73ug bio  ...            313                  NaN            10   \n",
       "9   Kglu + 1.73ug bio  ...          222.6                  NaN             7   \n",
       "10  Kglu + 1.73ug bio  ...            321                  NaN            10   \n",
       "11  Kglu + 1.73ug bio  ...            150                  NaN             6   \n",
       "12  Kglu + 1.73ug bio  ...            145                  NaN             5   \n",
       "13  Kglu + 1.73ug bio  ...            216                  NaN             8   \n",
       "14  Kglu + 1.73ug bio  ...            116                  NaN             4   \n",
       "15  Kglu + 1.73ug bio  ...            270                  NaN            10   \n",
       "16  Kglu + 1.73ug bio  ...            250                  NaN            10   \n",
       "17     Kglu + 1ug bio  ...            174                   29             6   \n",
       "18     Kglu + 1ug bio  ...            170                   34             5   \n",
       "19     Kglu + 1ug bio  ...          184.2                 30.7             6   \n",
       "20  Kglu + 1.78ug bio  ...            186                   31             6   \n",
       "21  Kglu + 1.78ug bio  ...            136                   34             4   \n",
       "22  Kglu + 1.78ug bio  ...          184.8                 30.8             6   \n",
       "23  Kglu + 1.78ug bio  ...            102                   34             3   \n",
       "24  Kglu + 1.78ug bio  ...            155                   31             5   \n",
       "25  Kglu + 1.78ug bio  ...          184.8                 30.8             6   \n",
       "\n",
       "    Mean amp (pA) Rise time 10-90 (ms)  Decay time 63% (ms) Half width (ms)  \\\n",
       "0            8.74                 1.13                 2.56            4.86   \n",
       "1            7.21                 2.07                 3.52            6.46   \n",
       "2            8.71                 1.67                  3.2            5.88   \n",
       "3             9.9                 0.72                 1.03            2.08   \n",
       "4             8.6                  1.2                 1.57            3.31   \n",
       "5           13.11                  1.2                  2.6            5.05   \n",
       "6            8.59                 0.82                 1.85            3.66   \n",
       "7            8.38                 1.25                 1.98            3.93   \n",
       "8            8.71                 1.21                 1.71            3.56   \n",
       "9            7.75                 1.36                 2.22            4.32   \n",
       "10              8                  1.3                 2.17            4.12   \n",
       "11           9.25                 2.37                 4.21            7.72   \n",
       "12           11.1                 2.07                 3.67            6.74   \n",
       "13          10.01                 2.04                 3.51            6.59   \n",
       "14              8                 1.31                  1.7            3.46   \n",
       "15           8.13                 1.33                 1.92            3.77   \n",
       "16          10.42                 1.51                 2.93            5.41   \n",
       "17           11.5                  1.3                 1.78            3.55   \n",
       "18          12.52                 1.26                 1.88            3.68   \n",
       "19          11.47                 1.29                 2.05            4.01   \n",
       "20           6.62                 1.41                 2.34            4.45   \n",
       "21           7.27                 1.28                 2.02            3.96   \n",
       "22           8.09                 1.51                 2.57            4.91   \n",
       "23          19.61                 1.23                  1.6            3.23   \n",
       "24          15.51                 1.22                 1.53            3.15   \n",
       "25          11.78                 1.42                 1.67            3.46   \n",
       "\n",
       "                            filepath_main_excel_sheet  \\\n",
       "0   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "1   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "2   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "3   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "4   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "5   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "6   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "7   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "8   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "9   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "10  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "11  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "12  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "13  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "14  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "15  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "16  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "17  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "18  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "19  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "20  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "21  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "22  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "23  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "24  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "25  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...   \n",
       "\n",
       "                             filepath_detected_events Time of counting (s)   \n",
       "0   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "1   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "2   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "3   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "4   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "5   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "6   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    34  \n",
       "7   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    27  \n",
       "8   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                  31.3  \n",
       "9   /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                  31.8  \n",
       "10  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                  32.1  \n",
       "11  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    25  \n",
       "12  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    29  \n",
       "13  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    27  \n",
       "14  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    29  \n",
       "15  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    27  \n",
       "16  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                    25  \n",
       "17  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "18  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "19  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "20  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "21  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "22  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "23  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "24  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "25  /mnt/c/Users/dsege/TEMP/Alexia/sample_data/202...                   NaN  \n",
       "\n",
       "[26 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.database.excitatory_voltage_clamp_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e6a7db2-fefb-4d41-a936-741cf80a6585",
   "metadata": {},
   "outputs": [],
   "source": [
    "api.database.excitatory_voltage_clamp_recordings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f89ceed-7d31-46b8-94b2-fa37fc5e5645",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ec029e-df00-437f-8464-ac36ce6cdebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "include = {'cell_type': 'GABA'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "145e7ff5-bbf9-411e-b8ff-0951003c688c",
   "metadata": {},
   "outputs": [],
   "source": [
    "exclude = {'session_cell_id': 'C3'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4631fc6-df6a-44e8-9893-ee45fec36f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_inclusion_criteria(df: DataFrame, inclusion_criteria: Dict) -> DataFrame:\n",
    "    chunks_of_df_to_include = []\n",
    "    for key, value in inclusion_criteria.items():\n",
    "        if key not in df.columns:\n",
    "            raise ValueError(f'{key} has to be in columns of the DataFrame. Please find a list of all possible columns: {list(df.columns)}')\n",
    "        else:\n",
    "            if type(value) == str:\n",
    "                chunks_of_df_to_include.append(df.loc[df[key] == value].copy())\n",
    "            else:\n",
    "                raise ValueError(f'{value} is not a valid input! Only strings (e.g. \"vlPAG\") are possible, simply add a column multiple times with different values if needed.')\n",
    "    df_after_inclusions = pd.concat(chunks_of_df_to_include, ignore_index = True)\n",
    "    df_after_inclusions = df_after_inclusions.drop_duplicates(ignore_index = True)\n",
    "    return df_after_inclusions\n",
    "\n",
    "def apply_exclusion_criteria(df: DataFrame, exclusion_criteria: Dict) -> DataFrame:\n",
    "    df_after_exclusions = df.copy()\n",
    "    for key, value in exclusion_criteria.items():\n",
    "        if key not in df.columns:\n",
    "            raise ValueError(f'{key} has to be in columns of the DataFrame. Please find a list of all possible columns: {list(df.columns)}')\n",
    "        else:\n",
    "            if type(value) == str:\n",
    "                df_after_exclusions = df_after_exclusions.loc[df_after_exclusions[key] != value]\n",
    "            else:\n",
    "                raise ValueError(f'{value} is not a valid input! Only strings (e.g. \"vlPAG\") are possible, simply add a column multiple times with different values if needed.')\n",
    "    return df_after_exclusions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08365ae-c274-4bd9-97b5-5358538db866",
   "metadata": {},
   "outputs": [],
   "source": [
    "4 % 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcb77e96-09c9-45b7-ab34-8baa0f6df6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_after_inclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ad6933-7c0a-430a-a223-df882535f9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cell_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3fdfa6e-33c4-4bd2-9092-e727bcacbb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "columns = list(df.columns)\n",
    "relevant_columns = columns[columns.index('pharmacology') + 1 : columns.index('filepath_main_excel_sheet')]\n",
    "\n",
    "for column in relevant_columns:\n",
    "    sns.stripplot(data=df.loc[df['cell_type'] == 'GABA'], y = column, x = 'stimulation_string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7645f782-3775-43c0-bf1a-bbf6ad3d40c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_analysis(self, group_column: str, group_id: str, show: bool, save: bool):\n",
    "    self.group_column = group_column\n",
    "    self.group_id = group_id\n",
    "    \n",
    "    \n",
    "    \n",
    "    filepaths = self.get_all_recording_filepaths()\n",
    "    for recording_filepath in filepaths:\n",
    "        self.get_data_for_cumulative_distributions(filepath = recording_filepath)\n",
    "    df_all_events = pd.DataFrame(data=self.events_all_stim_paradigms)\n",
    "    self.plot_cdfs(data = df_all_events,\n",
    "                   columns_for_cdfs = ['amplitude', 'inter_event_interval'], \n",
    "                   show = show,\n",
    "                   save = save)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a41200a-b33c-4739-8ec4-f1dd4f7c82ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b18bb0-3194-46ae-8560-67f2b1b6906f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f5bc4b3-0b98-4e94-b66b-b96db1937a0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ad656ed-481a-46cb-a538-6d24d8c9ce38",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = '.2021_12_09_C2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28ff0e3a-8704-4404-8b5c-4a2abbf46ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if name.startswith('.') == False:\n",
    "    print('no_hidden_file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cb0c9e3-0f9e-4674-9d4a-b96515cf44ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "[3] * 5 + [7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "722853f9-df2d-498c-b1fd-32077ccab196",
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_columns1 = ['recording_type','pharmacology', 'filepath_main_excel_sheet', 'filepath_detected_events']\n",
    "relevant_columns2 = ['stimulation_string', 'stimulation_frequency-Hz', 'stimulation_duration-ms']\n",
    "\n",
    "relevant_columns1 + relevant_columns2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc6115e-77bc-4b2b-a116-dc5bf84919bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eea31c7-95cc-4889-a55c-68fefc263454",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('/mnt/c/Users/dsege/TEMP/Alexia/NET-cre_vGat-flp/2021_12_09_C2/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c71f5c3-61aa-49bc-bef9-9ade4f8c5b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_excel('/mnt/c/Users/dsege/TEMP/Alexia/NET-cre_vGat-flp/2021_12_09_C2/2021_12_09_C2.xlsx', sheet_name = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79767687-0191-434d-a32f-f0674acdef4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = test['Recordings and cell properties']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c97a2ee-1d3a-45a2-bd9c-6598e76b8008",
   "metadata": {},
   "outputs": [],
   "source": [
    "'stimulation_string', 'stimulation_frequency-Hz', 'stimulation_duration-ms', 'filepath_detected_events'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76462201-358f-4023-8047-4b44c482539a",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df.columns = input_df.iloc[1]\n",
    "input_df = input_df.iloc[2:, 1:]\n",
    "input_df = input_df.loc[input_df['Type of protocol'].notnull()]\n",
    "\n",
    "columns = list(input_df.columns)\n",
    "relevant_data_columns = columns[columns.index('Pharmacology') +1 :]\n",
    "\n",
    "all_final_single_stimulation_dfs = []\n",
    "for row_index in range(input_df.shape[0]):\n",
    "    single_stimulation_df = input_df.iloc[[row_index]].copy()\n",
    "    if type(single_stimulation_df['Time since cutting'].iloc[0]) == datetime.time:\n",
    "        time_since_cutting = single_stimulation_df['Time since cutting'].iloc[0].hour * 60 + single_stimulation_df['Time since cutting'].iloc[0].minute\n",
    "    else:\n",
    "        time_since_cutting = 'not_available'\n",
    "\n",
    "    if single_stimulation_df['Vh (mV)'].iloc[0] == -70:\n",
    "        postsynaptic_current_type = 'excitatory'\n",
    "    elif single_stimulation_df['Vh (mV)'].iloc[0] == 0:\n",
    "        postsynaptic_current_type = 'inhibitory'\n",
    "    else:\n",
    "        raise ValueError(f'{single_stimulation_df[\"Vh (mV)\"].iloc[0]} is not a valid entry for holding potential!')\n",
    "\n",
    "    if pd.isna(single_stimulation_df['Pharmacology'].iloc[0]):\n",
    "        pharmacology = 'none'\n",
    "    else:\n",
    "        pharmacology = single_stimulation_df['Pharmacology'].iloc[0]\n",
    "\n",
    "    if single_stimulation_df['Type of protocol'].iloc[0] == 'Bsl':\n",
    "        stimulation_frequency = np.NaN\n",
    "        stimulation_duration = np.NaN\n",
    "        stimulation_string = 'baseline'\n",
    "        # filepath?\n",
    "    elif single_stimulation_df['Type of protocol'].iloc[0] == 'Bsl2':\n",
    "        stimulation_frequency = np.NaN\n",
    "        stimulation_duration = np.NaN\n",
    "        stimulation_string = 'control_baseline'\n",
    "        # filepath?\n",
    "    elif 'Hz' in single_stimulation_df['Type of protocol'].iloc[0]:\n",
    "        stimulation_frequency = single_stimulation_df['Type of protocol'].iloc[0].replace(' Hz', '')\n",
    "        stimulation_duration = int(single_stimulation_df['Timing of stim (s)'].iloc[0] * 1000)\n",
    "        stimulation_string = f'{stimulation_frequency}-Hz_for_{stimulation_duration}-ms'\n",
    "        # filepath\n",
    "\n",
    "\n",
    "    sorted_data = {'postsynaptic_current_type': [postsynaptic_current_type],\n",
    "                   'stimulation_string': [stimulation_string],\n",
    "                   'stimulation_frequency-Hz': [stimulation_frequency],\n",
    "                   'stimulation_duration-ms': [stimulation_duration],\n",
    "                   'pharmacology': [pharmacology],\n",
    "                   'time_since_cutting-M': [time_since_cutting]}\n",
    "\n",
    "    sorted_single_stimulation_df = pd.DataFrame(data = sorted_data)\n",
    "    remaining_data_df = single_stimulation_df[relevant_data_columns].copy()\n",
    "    remaining_data_df = remaining_data_df.reset_index(drop=True)\n",
    "    all_final_single_stimulation_dfs.append(pd.concat([sorted_single_stimulation_df, remaining_data_df], axis=1))\n",
    "recording_specific_dataframe = pd.concat(all_final_single_stimulation_dfs)\n",
    "final_df = pd.concat([general_metadata_df, recording_specific_dataframe], axis=1)\n",
    "final_df.insert(0, 'global_cell_id', [global_cell_id]*final_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7823a81-c8e6-4606-afa5-bff3167a200e",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = test['General information']\n",
    "general_metadata = {'date': path.name[:10],\n",
    "                    'session_cell_id': path.name[path.name.rfind('_') + 1:],\n",
    "                    'mouse_line': metadata_df['Animal line'][0],\n",
    "                    'brain_region': metadata_df['Region'][0],\n",
    "                    'cell_type': metadata_df['Type'][0],\n",
    "                    'sex': metadata_df['Sex'][0],\n",
    "                    'age': metadata_df['Age (days)'][0],\n",
    "                    'stimulation_in_percent': metadata_df['Stimulation (%)'][0],\n",
    "                    'internal_patch_solution': metadata_df['Internal used'][0]}\n",
    "general_metadata_df = pd.DataFrame(general_metadata, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23f1efef-5f65-4048-b5fc-4a7d0a767603",
   "metadata": {},
   "outputs": [],
   "source": [
    "general_metadata_df = pd.DataFrame([general_metadata]*3)\n",
    "general_metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f040e7ac-c534-4073-8793-e1ec73a89216",
   "metadata": {},
   "outputs": [],
   "source": [
    "list(general_metadata_df['date'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ff6dda7-56bf-44af-a492-3f3e8c6db0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cell_id = '0000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "babe90b9-8ee0-4365-9f17-0a79d4070867",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat([general_metadata_df, recording_specific_dataframe], axis=1)\n",
    "final_df.insert(0, 'global_cell_id', [global_cell_id]*final_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db9fd66-aea3-4174-af6f-d56b010d2e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = {'hund': 'dog', \n",
    "     'katze': 'cat'}\n",
    "\n",
    "for key, value in a.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d8809a-0bc9-4f13-b588-42ae2e9653b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([final_df, final_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08756a59-99bc-40c0-8c18-5ffafd71a1e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9146d43-48f4-44de-9f06-9ae043204120",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bb79c50-e888-42a0-9a40-ac530674f5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_since_cutting = single_stimulation_df['Time since cutting'].iloc[0].hour * 60 + single_stimulation_df['Time since cutting'].iloc[0].minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f58b21-216d-411a-9608-13c9fd396b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "remaining_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d8f8dc4-a7de-4dfb-8afd-7e958ee6656c",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_stimulation_df[data_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85cdd116-41f0-4ae4-b6ed-078d15cbcff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "isinstance(time_since_cutting, datetime.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daa3357b-5610-4d83-8421-9c4e202c01a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_str = tmp_str.replace(' Hz', '')\n",
    "tmp_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77422e4-9068-4b11-afd9-593ec54b1658",
   "metadata": {},
   "outputs": [],
   "source": [
    "'stimulation_string', 'stimulation_frequency-Hz', 'stimulation_duration-ms', 'filepath_detected_events'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11e0560c-d768-4505-81de-02686804b52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "database = api.database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3caea1f1-1c87-4c81-930f-0721b7fef8f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "database.__dict__.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8878f9ab-1b74-4aaf-961d-3d0c41b8a23c",
   "metadata": {},
   "outputs": [],
   "source": [
    "database.subdirectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae57e3f-60ef-44bd-8719-aa2c6722ff40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c95a853-a0bc-4e50-b07f-972b214424af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = api.database.cell_recordings_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4bad9a3-9fe9-42af-88a2-c864779e7cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9addf6b7-9404-4084-9d6d-d17b9676d6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "list(df.loc[df['sex'] == 'Female', 'global_cell_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3e068b9-baf2-4eef-8595-3af864799ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10de3ee8-428e-4c26-9773-0d5cca179878",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.path.isdir('/mnt/c/Users/dsege/TEMP/Alexia/analysis_with_dcl_patch/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6114c07-9295-480d-ba93-46dda9bb8197",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = Path('/mnt/c/Users/dsege/TEMP/Alexia/analysis_with_dcl_patch/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf56483-5888-4a3c-bc49-584db86c2cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "p.joinpath('results/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce93fcde-492a-4805-8fd2-5b7638320fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "342e3bf4-9214-40d0-8912-2714c0cc3d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "from typing import Dict\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b539ed0-3878-4b24-a750-c59c108252d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b845d9a-0d5d-4099-90f9-8f8feedad87b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cee7600-f5c4-4528-9242-f64bb514b635",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_general_metadata_df(path_to_recordings_dir: Path) -> pd.DataFrame:\n",
    "    metadata_df = pd.read_excel(path_to_recordings_dir.joinpath(f'{path_to_recordings_dir.name}.xlsx'),\n",
    "                                sheet_name = 'General information')\n",
    "    general_metadata = {'date': path_to_recordings_dir.name[:10],\n",
    "                        'session_cell_id': path_to_recordings_dir.name[path_to_recordings_dir.name.rfind('_') + 1:],\n",
    "                        'mouse_line': metadata_df['Animal line'][0],\n",
    "                        'brain_region': metadata_df['Region'][0],\n",
    "                        'cell_type': metadata_df['Type'][0],\n",
    "                        'sex': metadata_df['Sex'][0]}\n",
    "    return pd.DataFrame(general_metadata, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42e5b914-2afe-4e19-836e-e1690a278279",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stimulation_paradigms_df(path_to_recordings_dir: Path) -> pd.DataFrame:\n",
    "    filepaths_stimulation_recordings = list()\n",
    "    for elem in path_to_recordings_dir.iterdir():\n",
    "        if 'datapoints' not in elem.name:\n",
    "            if elem.name.endswith('.csv'):\n",
    "                filepaths_stimulation_recordings.append(elem)\n",
    "    stimulation_paradigms = {'stimulation_string': list(),\n",
    "                             'stimulation_frequency-Hz': list(),\n",
    "                             'stimulation_duration-ms': list(), \n",
    "                             'filepath_detected_events': list()}\n",
    "    for filepath in filepaths_stimulation_recordings:\n",
    "        filename = filepath.name\n",
    "        filename = filename.replace('.csv', '')\n",
    "        yyyy_mm_dd_ = filename[:11]\n",
    "        filename = filename.replace(yyyy_mm_dd_, '')\n",
    "        cell_id = filename[:filename.index('_')]\n",
    "        filename = filename.replace(cell_id + '_', '')\n",
    "        if 'Hz' in filename:\n",
    "            stimulation_frequency = int(filename[:filename.index('Hz')])\n",
    "            stimulation_duration = filename[filename.index('Hz') + 2 :]\n",
    "            if 'ms' not in stimulation_duration:\n",
    "                stimulation_duration = int(stimulation_duration[:stimulation_duration.find('s')]) * 1000\n",
    "            else:\n",
    "                stimulation_duration = int(stimulation_duration[:stimulation_duration.find('ms')])\n",
    "            stimulation_paradigm = f'{stimulation_frequency}-Hz_for_{stimulation_duration}-ms'\n",
    "        elif 'Bsl' in filename:\n",
    "            stimulation_frequency = np.NaN\n",
    "            stimulation_duration = np.NaN\n",
    "            stimulation_paradigm = 'baseline'\n",
    "        else:\n",
    "            print(f'Warning: stimulation paradigm could not be identified for: {filepath.name}')\n",
    "            stimulation_frequency = np.NaN\n",
    "            stimulation_duration = np.NaN\n",
    "            stimulation_paradigm = 'unknown'\n",
    "        stimulation_paradigms['stimulation_string'].append(stimulation_paradigm)\n",
    "        stimulation_paradigms['stimulation_frequency-Hz'].append(stimulation_frequency)\n",
    "        stimulation_paradigms['stimulation_duration-ms'].append(stimulation_duration)\n",
    "        stimulation_paradigms['filepath_detected_events'].append(filepath)\n",
    "    return pd.DataFrame(stimulation_paradigms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9617fd2d-2b91-4660-a261-a767c650fde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cell_id = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77534877-335d-49ed-a263-a8051593df18",
   "metadata": {},
   "outputs": [],
   "source": [
    "str(global_cell_id).zfill(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "670b919e-79f9-490a-8a50-55deaaa86a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get\n",
    "    general_metadata = get_general_metadata_df(cell_recording_dir)\n",
    "    stimulation_paradigms = get_stimulation_paradigms_df(cell_recording_dir)\n",
    "    stimulations_adjusted_general_metadata = pd.concat([general_metadata]*stimulation_paradigms.shape[0], ignore_index=True)\n",
    "    recordings_overview = pd.concat([stimulations_adjusted_general_metadata, stimulation_paradigms], axis=1)\n",
    "    recordings_overview['global_cell_id'] = str(global_cell_id).zfill(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52114cf-3b4e-41b5-8967-57f23afc5aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['global_cell_id', 'date', 'session_cell_id', 'mouse_line', 'sex', 'brain_region', 'cell_type', \n",
    "           'stimulation_string', 'stimulation_frequency-Hz', 'stimulation_duration-ms', 'filepath_detected_events']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cf7ef19-757d-4a5a-878a-0b5a37dcbd10",
   "metadata": {},
   "outputs": [],
   "source": [
    "recordings_overview[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab068ff-3bb9-48d6-9efd-0305ac68dabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "recordings_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c74a7c9e-3a9b-4c8a-aeb7-0617fac1e39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stimulation_paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b5b8bd6-8d48-4975-b0d5-ffa1c251c8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cell = pd.DataFrame(recording_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40ac4cc6-1d3a-42fe-a5a9-14a6bd8bd022",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "696e8894-9ed0-44cc-b386-4a1094eafad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_recording_dir = Path('/mnt/c/Users/dsege/TEMP/Alexia/NET-cre_vGat-flp/2021_12_09_C2/')\n",
    "\n",
    "\n",
    "\n",
    "metadata_df = pd.read_excel(cell_recording_dir.joinpath(f'{cell_recording_dir.name}.xlsx'),\n",
    "                            sheet_name = 'General information')\n",
    "\n",
    "mix_and_matchable_categories = dict()\n",
    "\n",
    "general_metadata = {'mouse_line': metadata_df['Animal line'][0],\n",
    "                    'brain_region': metadata_df['Region'][0],\n",
    "                    'cell_type': metadata_df['Type'][0],\n",
    "                    'sex': metadata_df['Sex'][0]}\n",
    "\n",
    "filepaths_stimulation_recordings = list()\n",
    "for elem in cell_recording_dir.iterdir():\n",
    "    if 'datapoints' not in elem.name:\n",
    "        if elem.name.endswith('.csv'):\n",
    "            filepaths_stimulation_recordings.append(elem)\n",
    "\n",
    "\n",
    "stimulation_paradigms = dict()\n",
    "for filepath in filepaths_stimulation_recordings:\n",
    "    filename = filepath.name\n",
    "    filename = filename.replace('.csv', '')\n",
    "    yyyy_mm_dd_ = filename[:11]\n",
    "    filename = filename.replace(yyyy_mm_dd_, '')\n",
    "    cell_id = filename[:filename.index('_')]\n",
    "    filename = filename.replace(cell_id + '_', '')\n",
    "    if 'Hz' in filename:\n",
    "        stimulation_frequency = filename[:filename.index('Hz') + 2]\n",
    "        stimulation_duration = filename[filename.index('Hz') + 2 :]\n",
    "        stimulation_paradigm = f'{stimulation_frequency}_for_{stimulation_duration}'\n",
    "    elif 'Bsl' in filename:\n",
    "        stimulation_paradigm = 'baseline'\n",
    "    else:\n",
    "        print(f'Warning: stimulation paradigm could not be identified for: {filepath.name}')\n",
    "        stimulation_paradigm = 'unknown'\n",
    "    stimulation_paradigms[stimulation_paradigm] = filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70c86837-1b7a-49af-adbc-8ba37f19ab9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d0e54b-5516-4425-8929-ef28a8b9ad8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths_stimulation_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a69de78d-0cc6-4282-a9f5-f13b83827d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b6bd54-008a-49f6-acb2-1208339f079c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stimulation_paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c265412d-4744-45c2-9cd7-89e70d80fe93",
   "metadata": {},
   "outputs": [],
   "source": [
    "?filename.replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2365adab-9beb-4e21-8f12-d12fcb5866cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df['Animal line'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "123ec728-a4a9-452f-a65c-7cc6ea5b548c",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "036b7f7a-5efd-4200-a798-9a7156c36d26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a7d3949-48d2-474e-9bcf-6fb0852e08fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e283c39e-069d-4ec7-bbee-0a25e10c212f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(cell_recording_dir.as_posix() + '/' + cell_recording_dir.name + '.xlsx', sheet_name = 'General information')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e22426b4-8abc-4541-b701-1b7422f04af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(cell_recording_dir.as_posix() + '/' + cell_recording_dir.name + '.xlsx', sheet_name = 'Recordings and cell properties', skiprows=2)\n",
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d87c447f-a489-496e-9771-b8b98f48c4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs['General information']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30a26853-f81e-4850-bc20-5d9c06ffd72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs['Recordings and cell properties']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81461a7c-a3f0-43e6-acf4-8f8a763e8d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "for elem in cell_recording_dir.iterdir():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df15321a-60ec-4446-a8e0-7f0f962691c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "815ad5fb-c30f-48a2-b87f-7f2b3b577f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('/mnt/c/Users/dsege/TEMP/Alexia/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb32b06f-7b8d-4e08-83e6-7714cb0e41c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for elem in path.iterdir():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44903f33-b3ca-4e16-ba42-046dd4fccff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for elem in path.glob(pattern='*'):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c702871-62ad-49d1-a570-79d895aef92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path.parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b0794d-2163-4638-b713-f8a87477ec9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "database = Database(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3592b935-9bbe-4fa5-a804-3499f4df1a94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb571cc5-7bba-4b72-9c39-08c44b01f7a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "from abc import ABC, abstractmethod\n",
    "\n",
    "class Feature(ABC):\n",
    "    \n",
    "    @abstractmethod\n",
    "    def add_to_database(self):\n",
    "        pass\n",
    "    \n",
    "\n",
    "class CellRecording(Feature):\n",
    "    \n",
    "    def add_to_database(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "class Subdirectories:\n",
    "    \n",
    "    def __init__(self, root_dir: Path):\n",
    "        self.root_dir = root_dir\n",
    "        self.create_missing_subdirectories()\n",
    "        self.assign_subdirectories_as_attributes()\n",
    "        \n",
    "    def create_missing_subdirectories(self):\n",
    "        # check for each element in a list of subdirs, whether they exist --> create if not\n",
    "        pass\n",
    "    \n",
    "    def assign_subdirectories_as_attributes(self):\n",
    "        # use list of subdirs and set the path to each as attribute\n",
    "        pass   \n",
    "\n",
    "\n",
    "class Database:\n",
    "    \n",
    "    \"\"\"\n",
    "    The database is supposed to hold all information about all recorded cells that were added to it.\n",
    "    These information exclude the raw data (only contain filepaths to raw data),\n",
    "    but include general metadata like celltype, stimulus type, brain region, pharamacological treatment.\n",
    "    Potentially, some intermediate data could be added (like how the cell reacted upon stimulation, see dashboard).\n",
    "    Overall, the database should allow the selection of cells based on mix-and-match criteria \n",
    "    for further (statistical) analyses and plotting.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, root_dir: Path):\n",
    "        self.root_dir = root_dir\n",
    "        self.subdirectories = Subdirectories(root_dir = root_dir)\n",
    "\n",
    "    def load_database_from_disk(self):\n",
    "        # Option to load previously created and saved database\n",
    "        pass\n",
    "    \n",
    "    def save_database_to_disk(self):\n",
    "        # Save all information to disk\n",
    "        pass\n",
    "    \n",
    "    def add_new_feature(self, new_feature: Feature):\n",
    "        # By providing the Path to a new directory, the user has the option to add new recordings to the database\n",
    "        # should trigger the database to update itself (e.g. categories dictionary)\n",
    "        pass\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11254dbd-659f-4f6a-9fef-c27fa8e86b8d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}