nwbDataExploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuroconv.datainterfaces import PhySortingInterface, CsvTimeIntervalsInterface\n",
    "from neuroconv import NWBConverter\n",
    "from neuroconv.utils.dict import load_dict_from_file, dict_deep_update\n",
    "from pathlib import Path\n",
    "from datetime import datetime,timezone\n",
    "from zoneinfo import ZoneInfo\n",
    "\n",
    "from pynwb import NWBHDF5IO, NWBFile\n",
    "from pynwb.ecephys import LFP, ElectricalSeries\n",
    "import matplotlib.pyplot as plt\n",
    "import phylib\n",
    "from phylib.io.model import load_model\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# colorcycler = (cycler(color=[\"darkorange\", \"gold\", \"lawngreen\", \"seagreen\", \"cornflowerblue\", \n",
    "#           \"mediumorchid\",  \"mediumvioletred\", \"crimson\", \"saddlebrown\",\"darkgoldenrod\", \n",
    "#           \"olivedrab\", \"seagreen\", \"darkslategray\", \"deepskyblue\",\"maroon\"]))\n",
    "# #map1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "#, \"saddlebrown\"\n",
    "#\"maroon\", \"saddlebrown\"\n",
    "\n",
    "colorlist=[\"orangered\", \"darkorange\", \"gold\", \"lawngreen\", \"seagreen\", \"cornflowerblue\", \n",
    "           \"deepskyblue\", \"darkblue\", \"mediumorchid\", \"indigo\", \"mediumvioletred\", \n",
    "           \"crimson\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract unit and trial data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'EMR22009_20230323'\n",
    "\n",
    "with NWBHDF5IO(f\"C:\\\\Users\\\\Ellen\\\\OneDrive - University of Massachusetts\\\\20220622_PersonalComp\\\\NPX\\\\NWBFiles\\\\{filename}.nwb\", \"r\") as io:\n",
    "    read_nwbfile = io.read()\n",
    "    \n",
    "    #print(read_nwbfile.trials)\n",
    "    units=read_nwbfile.units\n",
    "    units_df=units.to_dataframe()\n",
    "    units_df=units_df[units_df['quality']!='noise']\n",
    "    #goodunits_df=units_df[units_df['quality']=='good']\n",
    "    trials=read_nwbfile.trials\n",
    "    trials_df=trials.to_dataframe()\n",
    "    \n",
    "io.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spike_times</th>\n",
       "      <th>unit_name</th>\n",
       "      <th>amp</th>\n",
       "      <th>original_cluster_id</th>\n",
       "      <th>sh</th>\n",
       "      <th>n_spikes</th>\n",
       "      <th>KSLabel</th>\n",
       "      <th>depth</th>\n",
       "      <th>ch</th>\n",
       "      <th>quality</th>\n",
       "      <th>ContamPct</th>\n",
       "      <th>fr</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>waveform_shape</th>\n",
       "      <th>p2t_time</th>\n",
       "      <th>p2t_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.12276666666666666, 0.3393333333333333, 0.55...</td>\n",
       "      <td>12</td>\n",
       "      <td>93.629334</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10727.0</td>\n",
       "      <td>mua</td>\n",
       "      <td>7380.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>good</td>\n",
       "      <td>29.4</td>\n",
       "      <td>3.122623</td>\n",
       "      <td>907.2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>93.629333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[20.1984, 51.2817, 51.58906666666667, 51.94156...</td>\n",
       "      <td>14</td>\n",
       "      <td>143.688965</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78723.0</td>\n",
       "      <td>good</td>\n",
       "      <td>7380.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.916214</td>\n",
       "      <td>3053.5</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>143.688965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[2.040233333333333, 3.2939666666666665, 6.0238...</td>\n",
       "      <td>20</td>\n",
       "      <td>134.820572</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58448.0</td>\n",
       "      <td>good</td>\n",
       "      <td>7340.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>good</td>\n",
       "      <td>2.5</td>\n",
       "      <td>17.014175</td>\n",
       "      <td>2318.9</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>134.820572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.1852, 0.3183666666666667, 0.373433333333333...</td>\n",
       "      <td>21</td>\n",
       "      <td>146.746918</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>mua</td>\n",
       "      <td>7340.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>good</td>\n",
       "      <td>33.9</td>\n",
       "      <td>4.948689</td>\n",
       "      <td>3784.7</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>146.746918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.37293333333333334, 0.7979, 2.3471, 3.103433...</td>\n",
       "      <td>23</td>\n",
       "      <td>121.027695</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20578.0</td>\n",
       "      <td>good</td>\n",
       "      <td>7340.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>good</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.990242</td>\n",
       "      <td>1044.9</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>121.027695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          spike_times unit_name         amp  \\\n",
       "id                                                                            \n",
       "0   [0.12276666666666666, 0.3393333333333333, 0.55...        12   93.629334   \n",
       "1   [20.1984, 51.2817, 51.58906666666667, 51.94156...        14  143.688965   \n",
       "3   [2.040233333333333, 3.2939666666666665, 6.0238...        20  134.820572   \n",
       "4   [0.1852, 0.3183666666666667, 0.373433333333333...        21  146.746918   \n",
       "5   [0.37293333333333334, 0.7979, 2.3471, 3.103433...        23  121.027695   \n",
       "\n",
       "    original_cluster_id   sh  n_spikes KSLabel   depth    ch quality  \\\n",
       "id                                                                     \n",
       "0                  12.0  0.0   10727.0     mua  7380.0  10.0    good   \n",
       "1                  14.0  0.0   78723.0    good  7380.0  11.0    good   \n",
       "3                  20.0  0.0   58448.0    good  7340.0  14.0    good   \n",
       "4                  21.0  0.0   17000.0     mua  7340.0  15.0    good   \n",
       "5                  23.0  0.0   20578.0    good  7340.0  15.0    good   \n",
       "\n",
       "    ContamPct         fr  Amplitude  \\\n",
       "id                                    \n",
       "0        29.4   3.122623      907.2   \n",
       "1         0.0  22.916214     3053.5   \n",
       "3         2.5  17.014175     2318.9   \n",
       "4        33.9   4.948689     3784.7   \n",
       "5         4.6   5.990242     1044.9   \n",
       "\n",
       "                                       waveform_shape  p2t_time    p2t_dist  \n",
       "id                                                                           \n",
       "0   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... -0.200000   93.629333  \n",
       "1   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... -0.166667  143.688965  \n",
       "3   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... -0.166667  134.820572  \n",
       "4   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... -0.166667  146.746918  \n",
       "5   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... -0.133333  121.027695  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## add location data to file - need to check parameters for each file manually first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make location data - a list that we will use to add column to NWB file \n",
    "#need to manually identify parameters for where is LC - for this file it is based on depth \n",
    "\n",
    "locationdata=[]\n",
    "\n",
    "for i in range(len(units_df)):\n",
    "    if units_df.iloc[i].quality == 'noise':\n",
    "        locationdata.append('nan')    \n",
    "    elif units_df.iloc[i].depth < 6900:\n",
    "        locationdata.append('cerebellum')\n",
    "    elif units_df.iloc[i].depth > 7300:\n",
    "        locationdata.append('brainstem')\n",
    "    else:\n",
    "        locationdata.append('lc')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filename = 'EMR22009_20230323'\n",
    "with NWBHDF5IO(f\"C:\\\\Users\\\\Ellen\\\\OneDrive - University of Massachusetts\\\\20220622_PersonalComp\\\\NPX\\\\NWBFiles\\\\{filename}.nwb\", \"r+\") as io:\n",
    "    read_nwbfile = io.read()\n",
    "    \n",
    "    read_nwbfile.units.add_column(name='location', description='brain region of recording site', data=locationdata)\n",
    "\n",
    "    units=read_nwbfile.units\n",
    "    units_df=units.to_dataframe()\n",
    "    # units_df=units_df[units_df['quality']!='noise']\n",
    "    # goodunits_df=units_df[units_df['quality']=='good']\n",
    "\n",
    "    io.write(read_nwbfile)\n",
    "    io.close()\n",
    "    #print(read_nwbfile.acquisition)\n",
    "    units_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "potential_lc=units_df[units_df['location']=='lc']\n",
    "potential_cerebellum=units_df[units_df['location']=='cerebellum']\n",
    "potential_brainstem=units_df[units_df['location']=='brainstem']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ZSCORE FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'cue'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Ellen\\anaconda3\\envs\\NWB\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'cue'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 61\u001b[0m\n\u001b[0;32m     59\u001b[0m zscore_lc_leverpress_aversive\u001b[38;5;241m=\u001b[39m[]\n\u001b[0;32m     60\u001b[0m bin_lc_leverpress_aversive\u001b[38;5;241m=\u001b[39m[]\n\u001b[1;32m---> 61\u001b[0m cueevent\u001b[38;5;241m=\u001b[39mtrials_df[\u001b[43mtrials_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m     62\u001b[0m cueevent\u001b[38;5;241m=\u001b[39mcueevent[cueevent[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     63\u001b[0m event_times\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(cueevent[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mleverpress_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[1;32mc:\\Users\\Ellen\\anaconda3\\envs\\NWB\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\Ellen\\anaconda3\\envs\\NWB\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'cue'"
     ]
    }
   ],
   "source": [
    "# GET ZSCORE OF LC NEURONS AREOUND CUE, LP, LEVEROUT\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def bin_whole_file(data, neuron, binsize):\n",
    "    temp = data.iloc[neuron]\n",
    "    temp2=temp['spike_times']\n",
    "    wholefile_bin = np.histogram(temp2, bins=int(5000*(1000/binsize)), range=(0,5000))\n",
    "    return wholefile_bin[0]\n",
    "    \n",
    "\n",
    "def bin_around_event(cue_times, spikes, seconds, binsize):\n",
    "    col=((2*seconds)*(1000/binsize))\n",
    "    temp_allevents_oneneuron=np.zeros((1,int(col)))\n",
    "\n",
    "    for event in cue_times:\n",
    "        hold= np.histogram(spikes, bins=int((2*seconds)*(1000/binsize)), range=((event-seconds),(event+seconds)))\n",
    "        hold=hold[0]\n",
    "        hold=np.atleast_2d(hold)\n",
    "        temp_allevents_oneneuron= np.vstack((temp_allevents_oneneuron,(hold)))\n",
    "\n",
    "    temp_allevents_oneneuron=np.delete(temp_allevents_oneneuron,0,0)\n",
    "    allevets_oneneuron=temp_allevents_oneneuron.mean(axis=0)\n",
    "\n",
    "    return(allevets_oneneuron)\n",
    "\n",
    "\n",
    "\n",
    "def zscore_around_event(binned_data, binsize, neuron, data):\n",
    "    ytemp=binned_data\n",
    "    xtemp=range(len(binned_data))\n",
    "\n",
    "    wholetemp=bin_whole_file(data, neuron, binsize)\n",
    "    z_mean=np.mean(wholetemp)\n",
    "    z_sd=np.std(wholetemp)\n",
    "\n",
    "    z_zscore = []\n",
    "    for i in ytemp:\n",
    "        z_zscore.append((i-z_mean)/z_sd)\n",
    "        #print(z_zscore)\n",
    "    \n",
    "    return z_zscore\n",
    "\n",
    "# row=potential_lc.iloc[0]\n",
    "# spikes=row['spike_times']\n",
    "# averleverout=trials_df[trials_df['cue']==2]\n",
    "# avercue_times=np.array(avercue['start_time'])\n",
    "# seconds=1\n",
    "# binsize=100\n",
    "# neuron=0\n",
    "\n",
    "# neuron_one=bin_around_event(avercue_times,spikes,seconds, binsize)\n",
    "# zscore_neuronone = zscore_around_event(neuron_one,binsize, neuron)\n",
    "\n",
    "seconds=1\n",
    "binsize=100\n",
    "\n",
    "\n",
    "zscore_lc_leverpress_aversive=[]\n",
    "bin_lc_leverpress_aversive=[]\n",
    "cueevent=trials_df[trials_df['cue']==2]\n",
    "cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['leverpress_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverpress_aversive.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverpress_aversive.append(zscore_around_event(bin_lc_leverpress_aversive[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_leverpress_appetative=[]\n",
    "bin_lc_leverpress_appetative=[]\n",
    "cueevent=trials_df[trials_df['cue']==1]\n",
    "cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['leverpress_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverpress_appetative.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverpress_appetative.append(zscore_around_event(bin_lc_leverpress_appetative[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_leverpress_neutral=[]\n",
    "bin_lc_leverpress_neutral=[]\n",
    "cueevent=trials_df[trials_df['cue']==3]\n",
    "\n",
    "cueevent=cueevent[cueevent['response']!=0]\n",
    "event_times=np.array(cueevent['leverpress_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverpress_neutral.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverpress_neutral.append(zscore_around_event(bin_lc_leverpress_neutral[i],binsize, neuron,potential_lc))\n",
    "\n",
    "###CUE##############\n",
    "\n",
    "zscore_lc_cue_aversive=[]\n",
    "bin_lc_cue_aversive=[]\n",
    "cueevent=trials_df[trials_df['cue']==2]\n",
    "#cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_cue_aversive.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_cue_aversive.append(zscore_around_event(bin_lc_cue_aversive[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_cue_appetative=[]\n",
    "bin_lc_cue_appetative=[]\n",
    "cueevent=trials_df[trials_df['cue']==1]\n",
    "#cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_cue_appetative.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_cue_appetative.append(zscore_around_event(bin_lc_cue_appetative[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_cue_neutral=[]\n",
    "bin_lc_cue_neutral=[]\n",
    "cueevent=trials_df[trials_df['cue']==3]\n",
    "#cueevent=cueevent[cueevent['response']!=0]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_cue_neutral.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_cue_neutral.append(zscore_around_event(bin_lc_cue_neutral[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "######### LEVEROUT #################\n",
    "\n",
    "zscore_lc_leverout_aversive=[]\n",
    "bin_lc_leverout_aversive=[]\n",
    "cueevent=trials_df[trials_df['cue']==2]\n",
    "#cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['leverout_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverout_aversive.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverout_aversive.append(zscore_around_event(bin_lc_leverout_aversive[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_leverout_appetative=[]\n",
    "bin_lc_leverout_appetative=[]\n",
    "cueevent=trials_df[trials_df['cue']==1]\n",
    "#cueevent=cueevent[cueevent['response']==1]\n",
    "event_times=np.array(cueevent['leverout_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverout_appetative.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverout_appetative.append(zscore_around_event(bin_lc_leverout_appetative[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_leverout_neutral=[]\n",
    "bin_lc_leverout_neutral=[]\n",
    "cueevent=trials_df[trials_df['cue']==3]\n",
    "#cueevent=cueevent[cueevent['response']!=0]\n",
    "event_times=np.array(cueevent['leverout_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_leverout_neutral.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_leverout_neutral.append(zscore_around_event(bin_lc_leverout_neutral[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT ZSCORE AROUND CUE ONSET \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "xaxis=np.arange(-seconds, seconds, (binsize/1000) )\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(25,5))\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_cue_aversive:\n",
    "    ax1.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax1.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax1.set_title('Aversive Cue')\n",
    "ax1.set_ylabel('z score')\n",
    "ax1.set_xlabel('Time from cue onset (s)')\n",
    "ax1.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_cue_appetative:\n",
    "    ax2.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax2.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax2.set_title('Appetative Cue')\n",
    "ax2.set_ylabel('z score')\n",
    "ax2.set_xlabel('Time from cue onset (s)')\n",
    "ax2.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_cue_neutral:\n",
    "    ax3.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax3.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax3.set_title('Neutral Cue')\n",
    "ax3.set_ylabel('z score')\n",
    "ax3.set_xlabel('Time from cue onset (s)')\n",
    "ax3.set_ylim(-.5,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PLOT RAW BIN AORUND CUE ONSET\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "xaxis=np.arange(-seconds, seconds, (binsize/1000) )\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(25,5))\n",
    "i=0\n",
    "for neuron in bin_lc_cue_aversive:\n",
    "    ax1.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax1.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax1.set_title('Aversive Cue')\n",
    "ax1.set_ylabel('bin')\n",
    "ax1.set_xlabel('Time from cue onset (s)')\n",
    "\n",
    "i=0\n",
    "for neuron in bin_lc_cue_appetative:\n",
    "    ax2.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax2.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax2.set_title('Appetative Cue')\n",
    "ax2.set_ylabel('avg bin')\n",
    "ax2.set_xlabel('Time from cue onset (s)')\n",
    "\n",
    "i=0\n",
    "for neuron in bin_lc_cue_neutral:\n",
    "    ax3.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax3.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax3.set_title('Neutral Cue')\n",
    "ax3.set_ylabel('avg bin')\n",
    "ax3.set_xlabel('Time from cue onset (s)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT ZSCORE AROUND LEVER OUT\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "xaxis=np.arange(-seconds, seconds, (binsize/1000) )\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(25,5))\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_leverout_aversive:\n",
    "    ax1.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax1.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax1.set_title('Aversive Cue')\n",
    "ax1.set_ylabel('z score')\n",
    "ax1.set_xlabel('Time from lever out (s)')\n",
    "ax1.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_leverout_appetative:\n",
    "    ax2.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax2.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax2.set_title('Appetative Cue')\n",
    "ax2.set_ylabel('z score')\n",
    "ax2.set_xlabel('Time from lever out (s)')\n",
    "ax2.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_leverout_neutral:\n",
    "    ax3.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax3.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax3.set_title('Neutral Cue')\n",
    "ax3.set_ylabel('z score')\n",
    "ax3.set_xlabel('Time from lever out (s)')\n",
    "ax3.set_ylim(-.5,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PLOT ZSCORE AROUND LEVERPRESS ON CORRECT TRIALS ONLY\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "xaxis=np.arange(-seconds, seconds, (binsize/1000) )\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(25,5))\n",
    "i=0\n",
    "for neuron in zscore_lc_leverpress_aversive:\n",
    "    ax1.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax1.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax1.set_title('Aversive Cue')\n",
    "ax1.set_ylabel('z score')\n",
    "ax1.set_xlabel('Time from lever press (s)')\n",
    "ax1.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_leverpress_appetative:\n",
    "    ax2.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax2.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax2.set_title('Appetative Cue')\n",
    "ax2.set_ylabel('z score')\n",
    "ax2.set_xlabel('Time from lever press (s)')\n",
    "ax2.set_ylim(-.5,2)\n",
    "\n",
    "i=0\n",
    "for neuron in zscore_lc_leverpress_neutral:\n",
    "    ax3.plot(xaxis,neuron, c=colorlist[i])\n",
    "    i+=1\n",
    "ax3.legend(potential_lc['unit_name'], loc='upper left')\n",
    "ax3.set_title('Neutral Cue')\n",
    "ax3.set_ylabel('z score')\n",
    "ax3.set_xlabel('Time from lever press (s)')\n",
    "ax3.set_ylim(-.5,2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ZSCORE  for anes recording"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'potential_lc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 55\u001b[0m\n\u001b[0;32m     52\u001b[0m cueevent\u001b[38;5;241m=\u001b[39mtrials_df[trials_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfootpinch\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     53\u001b[0m event_times\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(cueevent[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m---> 55\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[43mpotential_lc\u001b[49m)):\n\u001b[0;32m     56\u001b[0m     neuron\u001b[38;5;241m=\u001b[39mi\n\u001b[0;32m     57\u001b[0m     row\u001b[38;5;241m=\u001b[39mpotential_lc\u001b[38;5;241m.\u001b[39miloc[i]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'potential_lc' is not defined"
     ]
    }
   ],
   "source": [
    "#FOR ANES - GET ZSCORE OF LC NEURONS AREOUND JAW AND FOOTPINCH\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def bin_whole_file(data, neuron, binsize):\n",
    "    temp = data.iloc[neuron]\n",
    "    temp2=temp['spike_times']\n",
    "    wholefile_bin = np.histogram(temp2, bins=int(5000*(1000/binsize)), range=(0,5000))\n",
    "    return wholefile_bin[0]\n",
    "    \n",
    "\n",
    "def bin_around_event(cue_times, spikes, seconds, binsize):\n",
    "    col=((2*seconds)*(1000/binsize))\n",
    "    temp_allevents_oneneuron=np.zeros((1,int(col)))\n",
    "\n",
    "    for event in cue_times:\n",
    "        hold= np.histogram(spikes, bins=int((2*seconds)*(1000/binsize)), range=((event-seconds),(event+seconds)))\n",
    "        hold=hold[0]\n",
    "        hold=np.atleast_2d(hold)\n",
    "        temp_allevents_oneneuron= np.vstack((temp_allevents_oneneuron,(hold)))\n",
    "\n",
    "    temp_allevents_oneneuron=np.delete(temp_allevents_oneneuron,0,0)\n",
    "    allevets_oneneuron=temp_allevents_oneneuron.mean(axis=0)\n",
    "\n",
    "    return(allevets_oneneuron)\n",
    "\n",
    "\n",
    "\n",
    "def zscore_around_event(binned_data, binsize, neuron, data):\n",
    "    ytemp=binned_data\n",
    "    xtemp=range(len(binned_data))\n",
    "\n",
    "    wholetemp=bin_whole_file(data, neuron, binsize)\n",
    "    z_mean=np.mean(wholetemp)\n",
    "    z_sd=np.std(wholetemp)\n",
    "\n",
    "    z_zscore = []\n",
    "    for i in ytemp:\n",
    "        z_zscore.append((i-z_mean)/z_sd)\n",
    "        #print(z_zscore)\n",
    "    \n",
    "    return z_zscore\n",
    "\n",
    "\n",
    "\n",
    "seconds=2\n",
    "binsize=100\n",
    "\n",
    "\n",
    "zscore_lc_contrafootpinch=[]\n",
    "bin_lc_contrafootpinch=[]\n",
    "cueevent=trials_df[trials_df['footpinch']==1]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_contrafootpinch.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_contrafootpinch.append(zscore_around_event(bin_lc_contrafootpinch[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_ipsifootpinch=[]\n",
    "bin_lc_ipsifootpinch=[]\n",
    "cueevent=trials_df[trials_df['footpinch']==2]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_ipsifootpinch.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_ipsifootpinch.append(zscore_around_event(bin_lc_ipsifootpinch[i],binsize, neuron,potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_contrafootpinch_post=[]\n",
    "bin_lc_contrafootpinch_post=[]\n",
    "cueevent=trials_df[trials_df['footpinch']==3]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_contrafootpinch_post.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_contrafootpinch_post.append(zscore_around_event(bin_lc_contrafootpinch_post[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n",
    "\n",
    "zscore_lc_ipsifootpinch_post=[]\n",
    "bin_lc_ipsifootpinch_post=[]\n",
    "cueevent=trials_df[trials_df['footpinch']==4]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_ipsifootpinch_post.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_ipsifootpinch_post.append(zscore_around_event(bin_lc_ipsifootpinch_post[i],binsize, neuron,potential_lc))\n",
    "\n",
    "### jaw ##############\n",
    "\n",
    "zscore_lc_jaw=[]\n",
    "bin_lc_jaw=[]\n",
    "cueevent=trials_df[trials_df['jaw']==1]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_jaw.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_jaw.append(zscore_around_event(bin_lc_jaw[i],binsize, neuron, potential_lc))\n",
    "\n",
    "zscore_lc_jaw_post=[]\n",
    "bin_lc_jaw_post=[]\n",
    "cueevent=trials_df[trials_df['jaw']==2]\n",
    "event_times=np.array(cueevent['start_time'])\n",
    "\n",
    "for i in range(len(potential_lc)):\n",
    "    neuron=i\n",
    "    row=potential_lc.iloc[i]\n",
    "    spikes=row['spike_times']\n",
    "    bin_lc_jaw_post.append(bin_around_event(event_times,spikes,seconds, binsize))\n",
    "    zscore_lc_jaw_post.append(zscore_around_event(bin_lc_jaw_post[i],binsize, neuron, potential_lc))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'average_score_lc_contrafootpinch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(zscore_lc_contrafootpinch)):\n\u001b[0;32m      2\u001b[0m     row\u001b[38;5;241m=\u001b[39mzscore_lc_contrafootpinch[i]\n\u001b[1;32m----> 6\u001b[0m \u001b[43maverage_score_lc_contrafootpinch\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m      7\u001b[0m temp\u001b[38;5;241m=\u001b[39m(zscore_lc_contrafootpinch[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      8\u001b[0m (temp[\u001b[38;5;241m15\u001b[39m:\u001b[38;5;241m25\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'average_score_lc_contrafootpinch' is not defined"
     ]
    }
   ],
   "source": [
    "for i in range(len(zscore_lc_contrafootpinch)):\n",
    "    row=zscore_lc_contrafootpinch[i]\n",
    "    \n",
    "\n",
    "\n",
    "average_score_lc_contrafootpinch[0]\n",
    "temp=(zscore_lc_contrafootpinch[0])\n",
    "(temp[15:25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT ZSCORE AROUND footpinch\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "xaxis=np.arange(-seconds, seconds, (binsize/1000) )\n",
    "\n",
    "fig, ax = plt.subplots(2,2, figsize=(25,25))\n",
    "#ax1.legend(potential_lc['unit_name'], loc='upper left')\n",
    "\n",
    "for neuron in zscore_lc_contrafootpinch:\n",
    "    ax[0,0].plot(xaxis,neuron) #, c=colorlist[i])\n",
    "ax[0,0].set_title('Contra FP')\n",
    "ax[0,0].set_ylabel('z score')\n",
    "\n",
    "for neuron in zscore_lc_ipsifootpinch:\n",
    "    ax[0,1].plot(xaxis,neuron) #, c=colorlist[i])\n",
    "ax[0,1].set_title('Ipsi FP')\n",
    "ax[0,1].set_ylabel('z score')\n",
    "\n",
    "\n",
    "for neuron in zscore_lc_contrafootpinch_post:\n",
    "    ax[1,0].plot(xaxis,neuron) #, c=colorlist[i])\n",
    "ax[1,0].set_title('Contra FP post clon')\n",
    "ax[1,0].set_ylabel('z score')\n",
    "\n",
    "\n",
    "for neuron in zscore_lc_ipsifootpinch_post:\n",
    "    ax[1,1].plot(xaxis,neuron) #, c=colorlist[i])\n",
    "ax[1,1].set_title('Ipsi FP post clon')\n",
    "ax[1,1].set_ylabel('z score')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all potential LC around contra FP - 5 per figure\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(3,3, figsize=(25,25))\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[0,0].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,0].legend(legend, loc='upper left')\n",
    "ax[0,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[0,1].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,1].legend(legend, loc='upper left')\n",
    "ax[0,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,15):\n",
    "    ax[0,2].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,2].legend(legend, loc='upper left')\n",
    "ax[0,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(15,20):\n",
    "    ax[1,0].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,0].legend(legend, loc='upper left')\n",
    "ax[1,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(20,25):\n",
    "    ax[1,1].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,1].legend(legend, loc='upper left')\n",
    "ax[1,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(25,30):\n",
    "    ax[1,2].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,2].legend(legend, loc='upper left')\n",
    "ax[1,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(30,35):\n",
    "    ax[2,0].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,0].legend(legend, loc='upper left')\n",
    "ax[2,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(35,40):\n",
    "    ax[2,1].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,1].legend(legend, loc='upper left')\n",
    "ax[2,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(40,43):\n",
    "    ax[2,2].plot(xaxis,zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,2].legend(legend, loc='upper left')\n",
    "ax[2,2].set_ylim(-1,8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all potential LC around ipsi FP - 5 per figure\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(3,3, figsize=(25,25))\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[0,0].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,0].legend(legend, loc='upper left')\n",
    "ax[0,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[0,1].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,1].legend(legend, loc='upper left')\n",
    "ax[0,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,15):\n",
    "    ax[0,2].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,2].legend(legend, loc='upper left')\n",
    "ax[0,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(15,20):\n",
    "    ax[1,0].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,0].legend(legend, loc='upper left')\n",
    "ax[1,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(20,25):\n",
    "    ax[1,1].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,1].legend(legend, loc='upper left')\n",
    "ax[1,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(25,30):\n",
    "    ax[1,2].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,2].legend(legend, loc='upper left')\n",
    "ax[1,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(30,35):\n",
    "    ax[2,0].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,0].legend(legend, loc='upper left')\n",
    "ax[2,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(35,40):\n",
    "    ax[2,1].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,1].legend(legend, loc='upper left')\n",
    "ax[2,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(40,43):\n",
    "    ax[2,2].plot(xaxis,zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,2].legend(legend, loc='upper left')\n",
    "ax[2,2].set_ylim(-1,8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all potential LC around contra FP post clon - 5 per figure\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(3,3, figsize=(25,25))\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[0,0].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,0].legend(legend, loc='upper left')\n",
    "ax[0,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[0,1].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,1].legend(legend, loc='upper left')\n",
    "ax[0,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,15):\n",
    "    ax[0,2].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[0,2].legend(legend, loc='upper left')\n",
    "ax[0,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(15,20):\n",
    "    ax[1,0].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,0].legend(legend, loc='upper left')\n",
    "ax[1,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(20,25):\n",
    "    ax[1,1].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,1].legend(legend, loc='upper left')\n",
    "ax[1,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(25,30):\n",
    "    ax[1,2].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[1,2].legend(legend, loc='upper left')\n",
    "ax[1,2].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(30,35):\n",
    "    ax[2,0].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,0].legend(legend, loc='upper left')\n",
    "ax[2,0].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(35,40):\n",
    "    ax[2,1].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,1].legend(legend, loc='upper left')\n",
    "ax[2,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(40,43):\n",
    "    ax[2,2].plot(xaxis,zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(potential_lc['unit_name'].iloc[neuron])\n",
    "ax[2,2].legend(legend, loc='upper left')\n",
    "ax[2,2].set_ylim(-1,8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## extra trim lc from anes recording\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import compress\n",
    "\n",
    "lc_unitnames=['60','73','851','1261','1255','1257','1265','1275','1288','1303','1307','1744','1292']\n",
    "\n",
    "temp=potential_lc['unit_name'].isin(lc_unitnames)\n",
    "trim_lc=potential_lc[temp]\n",
    "\n",
    "\n",
    "trim_zscore_lc_contrafootpinch=list(compress(zscore_lc_contrafootpinch,temp))\n",
    "trim_zscore_lc_ipsifootpinch=list(compress(zscore_lc_ipsifootpinch,temp))\n",
    "trim_zscore_lc_contrafootpinch_post=list(compress(zscore_lc_contrafootpinch_post,temp))\n",
    "trim_zscore_lc_ipsifootpinch_post=list(compress(zscore_lc_ipsifootpinch_post,temp))\n",
    "\n",
    "trim_zscore_lc_jaw=list(compress(zscore_lc_jaw,temp))\n",
    "trim_zscore_lc_jaw_post=list(compress(zscore_lc_jaw_post,temp))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(4,3, figsize=(25,25))\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[0,0].plot(xaxis,trim_zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,0].legend(legend, loc='upper left')\n",
    "ax[0,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[0,1].plot(xaxis,trim_zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,1].legend(legend, loc='upper left')\n",
    "ax[0,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[0,2].plot(xaxis,trim_zscore_lc_contrafootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,2].legend(legend, loc='upper left')\n",
    "ax[0,2].set_ylim(-1,8)\n",
    "\n",
    "###\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[1,0].plot(xaxis,trim_zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,0].legend(legend, loc='upper left')\n",
    "ax[1,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[1,1].plot(xaxis,trim_zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,1].legend(legend, loc='upper left')\n",
    "ax[1,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[1,2].plot(xaxis,trim_zscore_lc_contrafootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,2].legend(legend, loc='upper left')\n",
    "ax[1,2].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "###\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[2,0].plot(xaxis,trim_zscore_lc_ipsifootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[2,0].legend(legend, loc='upper left')\n",
    "ax[2,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[2,1].plot(xaxis,trim_zscore_lc_ipsifootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[2,1].legend(legend, loc='upper left')\n",
    "ax[2,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[2,2].plot(xaxis,trim_zscore_lc_ipsifootpinch[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[2,2].legend(legend, loc='upper left')\n",
    "ax[2,2].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "\n",
    "###\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[3,0].plot(xaxis,trim_zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[3,0].legend(legend, loc='upper left')\n",
    "ax[3,0].set_ylim(-1,8)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[3,1].plot(xaxis,trim_zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[3,1].legend(legend, loc='upper left')\n",
    "ax[3,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[3,2].plot(xaxis,trim_zscore_lc_ipsifootpinch_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[3,2].legend(legend, loc='upper left')\n",
    "ax[3,2].set_ylim(-1,8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(2,3, figsize=(15,15))\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[0,0].plot(xaxis,trim_zscore_lc_jaw[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,0].legend(legend, loc='upper left')\n",
    "ax[0,0].set_ylim(-1,15)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[0,1].plot(xaxis,trim_zscore_lc_jaw[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,1].legend(legend, loc='upper left')\n",
    "ax[0,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[0,2].plot(xaxis,trim_zscore_lc_jaw[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[0,2].legend(legend, loc='upper left')\n",
    "ax[0,2].set_ylim(-1,8)\n",
    "\n",
    "###\n",
    "legend=[]\n",
    "for neuron in range(0,5):\n",
    "    ax[1,0].plot(xaxis,trim_zscore_lc_jaw_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,0].legend(legend, loc='upper left')\n",
    "ax[1,0].set_ylim(-1,15)\n",
    "\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(5,10):\n",
    "    ax[1,1].plot(xaxis,trim_zscore_lc_jaw_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,1].legend(legend, loc='upper left')\n",
    "ax[1,1].set_ylim(-1,8)\n",
    "\n",
    "legend=[]\n",
    "for neuron in range(10,13):\n",
    "    ax[1,2].plot(xaxis,trim_zscore_lc_jaw_post[neuron])\n",
    "    legend.append(trim_lc['unit_name'].iloc[neuron])\n",
    "ax[1,2].legend(legend, loc='upper left')\n",
    "ax[1,2].set_ylim(-1,8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## raster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def calc_raster(neuron):\n",
    "    raster=[]\n",
    "    for event in range(len(trials_df['start_time'])):\n",
    "        eventtime=trials_df['leverout_time'].iloc[event]\n",
    "        tempneuron=trim_lc['spike_times'].iloc[neuron]\n",
    "        tempneuron=tempneuron-eventtime\n",
    "        tempraster=tempneuron[tempneuron>-1]\n",
    "        tempraster=tempraster[tempraster<1]\n",
    "        raster.append(tempraster)\n",
    "    return raster \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors1=[\"seagreen\", \"seagreen\", \"deepskyblue\", \"red\",\"deepskyblue\", \"red\",\"deepskyblue\",\"deepskyblue\",\"deepskyblue\",\n",
    "         \"black\",\"seagreen\",\"seagreen\",\"deepskyblue\",\"red\",\"deepskyblue\",\"red\", \"deepskyblue\" ]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.eventplot(raster, colors=colors1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "colorcuelist=[]\n",
    "for cue in trials_df['cue']:\n",
    "    if cue == 1:\n",
    "        colorcuelist.append('seagreen')\n",
    "    elif cue == 2:\n",
    "        colorcuelist.append('red')\n",
    "    elif cue == 3:\n",
    "        colorcuelist.append('black')\n",
    "\n",
    "\n",
    "\n",
    "colorresplist=[]\n",
    "for response in trials_df['response']:\n",
    "    if response == 1:\n",
    "        colorresplist.append('seagreen')\n",
    "    elif response == 3:\n",
    "        colorresplist.append('red')\n",
    "    elif response == 2:\n",
    "        colorresplist.append('blue')\n",
    "    elif response == 0:\n",
    "        colorresplist.append('black')\n",
    "    else:\n",
    "        colorresplist.append('purple')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# raster OF LC ISI - ONE NEURON/PLOT\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "nrows, ncols= 2,5\n",
    "fig, axs = plt.subplots(nrows,ncols, figsize=(25,10), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "\n",
    "    raster=calc_raster(i)\n",
    "    ax.eventplot(raster, colors=colorresplist)\n",
    "    ax.axvline(x = 0, color = 'black')\n",
    "\n",
    "    # ax.hist(isidata[i], bins=50, range=(0,.1), \n",
    "    #                   density=True, label=trim_lc['unit_name'].iloc[i], color=colorlist[i])\n",
    "    # ax.set_title(f'Unit {trim_lc['unit_name'].iloc[i]}')\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "\n",
    "# axs[0,0].legend()\n",
    "#plt.hist(isidata, bins=10, range=(0,.1), histtype='bar', density=True)\n",
    "#plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ISI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIND AND INSERT ISI DATA FOR EACH UNIT TO POTENTIAL LC \n",
    "def find_isi(data, neuron):\n",
    "    isi=[]\n",
    "    tempspikes=data.iloc[neuron].spike_times\n",
    "    for x in range(len(tempspikes)-2):\n",
    "        isi.append(tempspikes[x+1]-tempspikes[x])\n",
    "    return np.array(isi)\n",
    "\n",
    "#temp=find_isi(potential_lc,0)\n",
    "\n",
    "isidata=[]\n",
    "\n",
    "for i in range(len(trim_lc)):\n",
    "    temp=find_isi(trim_lc,i)\n",
    "    isidata.append(temp)\n",
    "\n",
    "trim_lc[\"isi\"]=isidata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HISTOGRAM OF ALL POTENTIAL LC NEURONS - ISI\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#used to delete rows that are not LC - prob not the best way to do this\n",
    "#isidatatrim=isidata\n",
    "#isidataoutlierthree=isidatatrim.pop(3)\n",
    "#isidataoutlierone=isidatatrim.pop(1)\n",
    "\n",
    "\n",
    "\n",
    "plt.hist(isidata, bins=10, range=(0,.5), histtype='bar', density=True, color=colorlist)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HISTOGRAM OF LC ISI - ONE NEURON/PLOT\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "nrows, ncols= 2,5\n",
    "fig, axs = plt.subplots(nrows,ncols, figsize=(25,10), sharex=False, sharey=False)\n",
    "\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    ax.hist(isidata[i], bins=50, range=(0,.1), \n",
    "                      density=True, label=trim_lc['unit_name'].iloc[i], color=colorlist[i])\n",
    "    ax.set_title(f'Unit {trim_lc['unit_name'].iloc[i]}')\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "\n",
    "# axs[0,0].legend()\n",
    "#plt.hist(isidata, bins=10, range=(0,.1), histtype='bar', density=True)\n",
    "#plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## RETURN DENSITY PLOT ATTEMPT - scatter for all units\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "nrows, ncols= 2,5\n",
    "fig, axs = plt.subplots(nrows,ncols, figsize=(30,10), sharex=False, sharey=False)\n",
    "\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    temp=isidata[i]\n",
    "    temp_x=temp[0:-1]\n",
    "    temp_y=temp[1:]\n",
    "    temp_x_log=np.log(temp_x)\n",
    "    temp_y_log=np.log(temp_y)\n",
    "\n",
    "    ax.scatter(temp_x_log, temp_y_log, s=3, c=colorlist[i])\n",
    "    ax.set_xlabel('log(ISI n)')\n",
    "    ax.set_ylabel('log(ISI n+1)')\n",
    "    ax.set_title(f'Unit {trim_lc['unit_name'].iloc[i]}')\n",
    "\n",
    "    \n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a return density and histogram for one unit \n",
    "\n",
    "def scatter_hist(x, y, ax, ax_histx, ax_histy,i):\n",
    "    ax_histx.tick_params(axis=\"x\", labelbottom=False)\n",
    "    ax_histy.tick_params(axis=\"y\", labelleft=False)\n",
    "\n",
    "    # the scatter plot:\n",
    "    ax.scatter(x, y, s=2, c=colorlist[i])\n",
    "\n",
    "    # now determine nice limits by hand:\n",
    "    binwidth = 0.25\n",
    "    xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))\n",
    "    lim = (int(xymax/binwidth) + 1) * binwidth\n",
    "\n",
    "    bins = np.arange(-lim, lim + binwidth, binwidth)\n",
    "    ax_histx.hist(x, bins=bins, color=colorlist[i])\n",
    "    ax_histy.hist(y, bins=bins, orientation='horizontal', color=colorlist[i])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Create a Figure, which doesn't have to be square.\n",
    "fig = plt.figure(layout='constrained')\n",
    "# Create the main Axes, leaving 25% of the figure space at the top and on the\n",
    "# right to position marginals.\n",
    "ax = fig.add_gridspec(top=0.75, right=0.75).subplots()\n",
    "# The main Axes' aspect can be fixed.\n",
    "ax.set(aspect=1)\n",
    "# Create marginal Axes, which have 25% of the size of the main Axes.  Note that\n",
    "# the inset Axes are positioned *outside* (on the right and the top) of the\n",
    "# main Axes, by specifying axes coordinates greater than 1.  Axes coordinates\n",
    "# less than 0 would likewise specify positions on the left and the bottom of\n",
    "# the main Axes.\n",
    "ax_histx = ax.inset_axes([0, 1.05, 1, 0.25], sharex=ax)\n",
    "ax_histy = ax.inset_axes([1.05, 0, 0.25, 1], sharey=ax)\n",
    "# Draw the scatter plot and marginals.\n",
    "#scatter_hist(x, y, ax, ax_histx, ax_histy)\n",
    "\n",
    "\n",
    "\n",
    "i=8\n",
    "\n",
    "temp=isidata[i]\n",
    "temp_x=temp[0:-1]\n",
    "temp_y=temp[1:]\n",
    "temp_x_log=np.log(temp_x)\n",
    "temp_y_log=np.log(temp_y)\n",
    "\n",
    "scatter_hist(temp_x_log, temp_y_log, ax, ax_histx, ax_histy,i)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot unit data basics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT AMPLITUDE, FR, DEPTH SCATTERPLOTS\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(25,5))\n",
    "ax1.plot(units_df['fr'],(-units_df['depth']),'o')\n",
    "ax1.set_title('Unit FR x Depth')\n",
    "ax1.set_xlabel('Firing Rate')\n",
    "ax1.set_ylabel('Depth')\n",
    "\n",
    "ax2.plot(units_df['Amplitude'],(-units_df['depth']),'o')\n",
    "ax2.set_title('Unit Amplitude x Depth')\n",
    "ax2.set_xlabel('Amplitude')\n",
    "ax2.set_ylabel('Depth')\n",
    "\n",
    "\n",
    "ax3.plot(units_df['Amplitude'],(units_df['fr']),'o')\n",
    "ax3.set_title('Unit Amplitude x FR')\n",
    "ax3.set_xlabel('Amplitude')\n",
    "ax3.set_ylabel('Firing Rate')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PLOT DEPTH, FR, AMPLITUDE SCATTER FOR LC NEURONS\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, (ax1, ax2, ax3,ax4) = plt.subplots(1,4, figsize=(25,5))\n",
    "ax1.plot(trim_lc['fr'],(-trim_lc['depth']),'o')\n",
    "ax1.set_title('Unit FR x Depth')\n",
    "ax1.set_xlabel('Firing Rate')\n",
    "ax1.set_ylabel('Depth')\n",
    "\n",
    "ax2.plot(trim_lc['Amplitude'],(-trim_lc['depth']),'o')\n",
    "ax2.set_title('Unit Amplitude x Depth')\n",
    "ax2.set_xlabel('Amplitude')\n",
    "ax2.set_ylabel('Depth')\n",
    "\n",
    "\n",
    "ax3.plot(trim_lc['Amplitude'],(trim_lc['fr']),'o')\n",
    "ax3.set_title('Unit Amplitude x FR')\n",
    "ax3.set_xlabel('Amplitude')\n",
    "ax3.set_ylabel('Firing Rate')\n",
    "\n",
    "ax4.plot(trim_lc['fr'],(trim_lc['ch']),'o')\n",
    "ax4.set_title('Unit FR x Channel')\n",
    "ax4.set_xlabel('Firing Rate')\n",
    "ax4.set_ylabel('Channel')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trim_lc=trim_lc[trim_lc['fr']<10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PLOT DEPTH, FR, AMPLITUDE SCATTER FOR ABOVE, LC, BELOW NEURONS\n",
    "# \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(3,3, figsize=(15,15))\n",
    "\n",
    "\n",
    "ax[0,0].plot(potential_cerebellum['fr'],(-potential_cerebellum['depth']),'o')\n",
    "ax[0,0].set_title('Unit FR x Depth')\n",
    "ax[0,0].set_xlabel('Firing Rate')\n",
    "ax[0,0].set_ylabel('Depth')\n",
    "\n",
    "ax[0,1].plot(potential_cerebellum['Amplitude'],(-potential_cerebellum['depth']),'o')\n",
    "ax[0,1].set_title('Unit Amplitude x Depth')\n",
    "ax[0,1].set_xlabel('Amplitude')\n",
    "ax[0,1].set_ylabel('Depth')\n",
    "\n",
    "ax[0,2].plot(potential_cerebellum['Amplitude'],(potential_cerebellum['fr']),'o')\n",
    "ax[0,2].set_title('Unit Amplitude x FR')\n",
    "ax[0,2].set_xlabel('Amplitude')\n",
    "ax[0,2].set_ylabel('Firing Rate')\n",
    "\n",
    "\n",
    "\n",
    "ax[1,0].plot(potential_lc['fr'],(-potential_lc['depth']),'o')\n",
    "ax[1,0].set_xlabel('Firing Rate')\n",
    "ax[1,0].set_ylabel('Depth')\n",
    "\n",
    "ax[1,1].plot(potential_lc['Amplitude'],(-potential_lc['depth']),'o')\n",
    "ax[1,1].set_xlabel('Amplitude')\n",
    "ax[1,1].set_ylabel('Depth')\n",
    "\n",
    "ax[1,2].plot(potential_lc['Amplitude'],(potential_lc['fr']),'o')\n",
    "ax[1,2].set_xlabel('Amplitude')\n",
    "ax[1,2].set_ylabel('Firing Rate')\n",
    "\n",
    "\n",
    "\n",
    "ax[2,0].plot(potential_brainstem['fr'],(-potential_brainstem['depth']),'o')\n",
    "ax[2,0].set_xlabel('Firing Rate')\n",
    "ax[2,0].set_ylabel('Depth')\n",
    "\n",
    "ax[2,1].plot(potential_brainstem['Amplitude'],(-potential_brainstem['depth']),'o')\n",
    "ax[2,1].set_xlabel('Amplitude')\n",
    "ax[2,1].set_ylabel('Depth')\n",
    "\n",
    "ax[2,2].plot(potential_brainstem['Amplitude'],(potential_brainstem['fr']),'o')\n",
    "ax[2,2].set_xlabel('Amplitude')\n",
    "ax[2,2].set_ylabel('Firing Rate')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NWB widget panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nwbwidgets.panel import Panel\n",
    "\n",
    "Panel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pynwb import NWBHDF5IO\n",
    "from nwbwidgets import nwb2widget\n",
    "filename = 'EMR22009_20230323'\n",
    "\n",
    "io = NWBHDF5IO(f\"C:\\\\Users\\\\Ellen\\\\OneDrive - University of Massachusetts\\\\20220622_PersonalComp\\\\NPX\\\\NWBFiles\\\\{filename}.nwb\", \"r\")\n",
    "nwb = io.read()\n",
    "\n",
    "#nwb2widget(nwb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCRAPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row=len(avercue_times)\n",
    "col=(2*(5000*25))\n",
    "temp_allevents_oneneuron=np.zeros((row,col))\n",
    "\n",
    "check=(np.histogram(spikes, bins=2*(5000*25), range=((event-(5000*25)),(event+(5000*25)))))\n",
    "check=check[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "\n",
    "\n",
    "avercue=trials_df[trials_df['cue']==2]\n",
    "avercue_times=np.array(avercue['start_time'])\n",
    "\n",
    "row=potential_lc.iloc[0]\n",
    "spikes=row['spike_times']\n",
    "\n",
    "\n",
    "#for index, row in potential_lc.iterrows():     \n",
    "#spikes=row['spike_times']\n",
    "\n",
    "\n",
    "ytemp=neuron_one\n",
    "xtemp=range(2000*50)\n",
    "\n",
    "#grab cooresponding whole bin value for mean and std\n",
    "wholetemp=(wholefile_25mssbin[0])\n",
    "ytemp_whole=wholetemp[0]\n",
    "\n",
    "z_mean=np.mean(ytemp_whole)\n",
    "z_sd=np.std(ytemp_whole)\n",
    "\n",
    "\n",
    "z_zscore = []\n",
    "for i in ytemp:\n",
    "    z_zscore.append((i-z_mean)/z_sd)\n",
    "print(z_zscore)\n",
    "\n",
    "\n",
    "plt.plot(xtemp,z_zscore, linewidth=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this will plot the histogram of spikes over the recording session for one unit\n",
    "#not really useful big pictue but sanity check \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "arr=units_df.spike_times[14]\n",
    "plt.hist(arr[1:], bins=np.linspace(0,5000,500))\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from nwbwidgets.allen import load_allen_widgets\n",
    "from pynwb import NWBHDF5IO\n",
    "from nwbwidgets import nwb2widget\n",
    "#pip install allensdk\n",
    "import allensdk\n",
    "#import allensdk.brain_observatory.ecephys.nwb\n",
    "\n",
    "filename = 'EMR22009_20230327'\n",
    "fpath = f\"C:\\\\Users\\\\Ellen\\\\OneDrive - University of Massachusetts\\\\20220622_PersonalComp\\\\NPX\\\\NWBFiles\\\\{filename}.nwb\"\n",
    "nwb=NWBHDF5IO(fpath,'r').read()\n"
   ]
  }
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