{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "387db77a",
   "metadata": {},
   "source": [
    "# Zheng et al (2022) Dataset Tutorial \n",
    "\n",
    "*Author: Dhruv Mehrotra*\n",
    "\n",
    "\n",
    "This notebook demonstrates how we use Pynapple on various publicly available datasets in systems neuroscience to streamline analysis. In this notebook, we will examine the dataset from Zheng et al (2022), which was used to generate Figure 4c in the publication. \n",
    "\n",
    "The NWB file for the example used here is provided in this repository. The entire dataset can be downloaded  <a href=\"https://dandiarchive.org/dandiset/000207/0.220216.0323\" target=\"_blank\">here</a>. \n",
    "\n",
    "First, import the necessary libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73b93706",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import os\n",
    "import pandas as pd \n",
    "import pynapple as nap \n",
    "import pynwb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1bc46e",
   "metadata": {},
   "source": [
    "The first step is to load the data from the Neurodata Without Borders (NWB) file. This is done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac7b5584",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_directory = '/home/dhruv/Code/Projects/Steinmetz/000207/sub-4' #Path to your data\n",
    "nwbfilename = [f for f in os.listdir(data_directory) if \"nwb\" in f][0] #Find the NWB file in the directory\n",
    "nwbfilepath = os.path.join(data_directory, nwbfilename) #Path to the NWB file\n",
    "io = pynwb.NWBHDF5IO(nwbfilepath, \"r\") #Create I/O object for NWB files\n",
    "nwbfile = io.read() #Read the NWB file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d937cb7d",
   "metadata": {},
   "source": [
    "Now let's load the units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c0039b3",
   "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>electrodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.03942225, 0.224933, 0.57630925, 0.75585675,...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.11060299999999999, 0.159082, 0.170435249999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.0101695, 0.34445224999999996, 0.35126874999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.6591977499999999, 1.433779, 1.4394622499999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.083177, 0.36912849999999997, 2.747743, 3.76...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[1.9118342499999998, 2.19203725, 6.923858, 7.7...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[2.0798345, 3.18959125, 3.471756, 3.62455375, ...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.099465, 0.1986445, 0.21868374999999998, 0.3...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.30819325, 0.46718675, 1.6140295, 3.50645425...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[0.6436062499999999, 2.4721652499999998, 8.617...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.7324602499999999, 1.2706772499999999, 1.600...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[3.42395775, 3.483071, 3.6250035, 3.650293, 4....</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[0.0947015, 0.7298137499999999, 0.994587249999...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[2.37931875, 2.6579997499999997, 3.240213, 3.5...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.007858249999999999, 0.046433499999999996, 0...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.65015225, 2.93840375, 2.9422444999999997, 3...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.43972574999999997, 0.6502487499999999, 0.74...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[4.1091334999999996, 6.22830575, 7.39239675, 8...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.16679175, 0.21680149999999998, 1.1272725, 1...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[2.146346, 2.87994125, 13.21902875, 13.8206007...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[0.295531, 0.399404, 2.1373465, 2.698859499999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[2.89995175, 3.5633917499999996, 5.63561775, 5...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[0.239592, 0.5635145, 1.01331625, 1.4077864999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[1.67002875, 2.9530955, 3.4147382499999996, 3....</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[0.80507425, 28.350659, 30.35310825, 33.120475...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[0.33522599999999997, 0.54895125, 0.7414752499...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[0.0642745, 3.19509675, 3.4809935, 3.567417, 7...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[0.28973525, 1.61336175, 1.90000525, 1.9406234...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[0.37661649999999997, 1.5559345, 2.01996825, 2...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[0.41057875, 2.4227654999999997, 8.17502949999...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>[19.780521, 34.5957905, 36.25223175, 38.691635...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>[0.38641175, 0.41407725, 1.1401615, 1.1961875,...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>[0.9113844999999999, 1.14785775, 2.98555575, 1...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>[0.32259424999999997, 0.5832415, 6.41815425, 7...</td>\n",
       "      <td>x      y      z  imp              loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>[6.187681749999999, 9.90826175, 10.43789649999...</td>\n",
       "      <td>x     y      z  imp           location...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          spike_times  \\\n",
       "id                                                      \n",
       "0   [0.03942225, 0.224933, 0.57630925, 0.75585675,...   \n",
       "1   [0.11060299999999999, 0.159082, 0.170435249999...   \n",
       "2   [0.0101695, 0.34445224999999996, 0.35126874999...   \n",
       "3   [0.6591977499999999, 1.433779, 1.4394622499999...   \n",
       "4   [0.083177, 0.36912849999999997, 2.747743, 3.76...   \n",
       "5   [1.9118342499999998, 2.19203725, 6.923858, 7.7...   \n",
       "6   [2.0798345, 3.18959125, 3.471756, 3.62455375, ...   \n",
       "7   [0.099465, 0.1986445, 0.21868374999999998, 0.3...   \n",
       "8   [0.30819325, 0.46718675, 1.6140295, 3.50645425...   \n",
       "9   [0.6436062499999999, 2.4721652499999998, 8.617...   \n",
       "10  [0.7324602499999999, 1.2706772499999999, 1.600...   \n",
       "11  [3.42395775, 3.483071, 3.6250035, 3.650293, 4....   \n",
       "12  [0.0947015, 0.7298137499999999, 0.994587249999...   \n",
       "13  [2.37931875, 2.6579997499999997, 3.240213, 3.5...   \n",
       "14  [0.007858249999999999, 0.046433499999999996, 0...   \n",
       "15  [0.65015225, 2.93840375, 2.9422444999999997, 3...   \n",
       "16  [0.43972574999999997, 0.6502487499999999, 0.74...   \n",
       "17  [4.1091334999999996, 6.22830575, 7.39239675, 8...   \n",
       "18  [0.16679175, 0.21680149999999998, 1.1272725, 1...   \n",
       "19  [2.146346, 2.87994125, 13.21902875, 13.8206007...   \n",
       "20  [0.295531, 0.399404, 2.1373465, 2.698859499999...   \n",
       "21  [2.89995175, 3.5633917499999996, 5.63561775, 5...   \n",
       "22  [0.239592, 0.5635145, 1.01331625, 1.4077864999...   \n",
       "23  [1.67002875, 2.9530955, 3.4147382499999996, 3....   \n",
       "24  [0.80507425, 28.350659, 30.35310825, 33.120475...   \n",
       "25  [0.33522599999999997, 0.54895125, 0.7414752499...   \n",
       "26  [0.0642745, 3.19509675, 3.4809935, 3.567417, 7...   \n",
       "27  [0.28973525, 1.61336175, 1.90000525, 1.9406234...   \n",
       "28  [0.37661649999999997, 1.5559345, 2.01996825, 2...   \n",
       "29  [0.41057875, 2.4227654999999997, 8.17502949999...   \n",
       "30  [19.780521, 34.5957905, 36.25223175, 38.691635...   \n",
       "31  [0.38641175, 0.41407725, 1.1401615, 1.1961875,...   \n",
       "32  [0.9113844999999999, 1.14785775, 2.98555575, 1...   \n",
       "33  [0.32259424999999997, 0.5832415, 6.41815425, 7...   \n",
       "34  [6.187681749999999, 9.90826175, 10.43789649999...   \n",
       "\n",
       "                                           electrodes  \n",
       "id                                                     \n",
       "0           x      y      z  imp              loca...  \n",
       "1           x      y      z  imp              loca...  \n",
       "2           x      y      z  imp              loca...  \n",
       "3           x      y      z  imp              loca...  \n",
       "4           x     y      z  imp           location...  \n",
       "5           x     y      z  imp           location...  \n",
       "6           x     y      z  imp           location...  \n",
       "7           x     y      z  imp           location...  \n",
       "8           x     y      z  imp           location...  \n",
       "9           x     y      z  imp           location...  \n",
       "10          x     y      z  imp           location...  \n",
       "11          x     y      z  imp           location...  \n",
       "12          x     y      z  imp           location...  \n",
       "13          x      y      z  imp              loca...  \n",
       "14          x      y      z  imp              loca...  \n",
       "15          x      y      z  imp              loca...  \n",
       "16          x      y      z  imp              loca...  \n",
       "17          x      y      z  imp              loca...  \n",
       "18          x      y      z  imp              loca...  \n",
       "19          x      y      z  imp              loca...  \n",
       "20          x      y      z  imp              loca...  \n",
       "21          x      y      z  imp              loca...  \n",
       "22          x      y      z  imp              loca...  \n",
       "23          x      y      z  imp              loca...  \n",
       "24          x      y      z  imp              loca...  \n",
       "25          x      y      z  imp              loca...  \n",
       "26          x      y      z  imp              loca...  \n",
       "27          x      y      z  imp              loca...  \n",
       "28          x      y      z  imp              loca...  \n",
       "29          x      y      z  imp              loca...  \n",
       "30          x      y      z  imp              loca...  \n",
       "31          x      y      z  imp              loca...  \n",
       "32          x      y      z  imp              loca...  \n",
       "33          x      y      z  imp              loca...  \n",
       "34          x     y      z  imp           location...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units = nwbfile.units.to_dataframe() #Make a DataFrame of units\n",
    "\n",
    "#What does this look like?\n",
    "units"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e53d5d6",
   "metadata": {},
   "source": [
    "This DataFrame has 3 columns: the unit ID, spike timings, and the electrode locations. Let's extract the spike timings and put them in a Pynapple TsGroup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb6d063f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Index    Freq. (Hz)\n",
       "-------  ------------\n",
       "      0          7\n",
       "      1          7.24\n",
       "      2          6.09\n",
       "      3          6.92\n",
       "      4          0.4\n",
       "      5          0.46\n",
       "      6          1.81\n",
       "      7          4.79\n",
       "      8          1.31\n",
       "      9          0.62\n",
       "     10          1.51\n",
       "     11          0.77\n",
       "     12          1.46\n",
       "     13          0.4\n",
       "     14          6.27\n",
       "     15          1.15\n",
       "     16          2.15\n",
       "     17          0.63\n",
       "     18          2.88\n",
       "     19          0.35\n",
       "     20          1.75\n",
       "     21          0.58\n",
       "     22          2.13\n",
       "     23          1.51\n",
       "     24          0.27\n",
       "     25          7.35\n",
       "     26          0.37\n",
       "     27          1.89\n",
       "     28          1.13\n",
       "     29          0.6\n",
       "     30          0.18\n",
       "     31          2.33\n",
       "     32          0.31\n",
       "     33          0.69\n",
       "     34          0.66"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dictionary of spike timings\n",
    "spike_times = {\n",
    "            n: nap.Ts(t = units.loc[n, \"spike_times\"], time_units = \"s\")\n",
    "            for n in units.index\n",
    "        }\n",
    "\n",
    "spikes = nap.TsGroup(spike_times)\n",
    "\n",
    "#What does this look like? \n",
    "spikes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15bd7012",
   "metadata": {},
   "source": [
    "The spike times TsGroup has 2 columns: The unit ID, and the firing rate of the units in Hz.\n",
    "\n",
    "Let's also extract the electrode locations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bdd55b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'hippocampus_right', b'hippocampus_right', b'hippocampus_right',\n",
       "       b'hippocampus_right', b'hippocampus_right', b'amygdala_right',\n",
       "       b'amygdala_right', b'amygdala_right', b'amygdala_right',\n",
       "       b'amygdala_right', b'hippocampus_right', b'hippocampus_right',\n",
       "       b'hippocampus_right', b'hippocampus_right', b'hippocampus_right',\n",
       "       b'hippocampus_right', b'hippocampus_right', b'hippocampus_right',\n",
       "       b'hippocampus_right'], dtype='|S17')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrode_location = np.array(nwbfile.units.electrodes.get('location'))\n",
    "\n",
    "#What does this look like? \n",
    "electrode_location"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4202df5c",
   "metadata": {},
   "source": [
    "Next, let's get the table of all stimulus times, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90434e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "encoding_table pynwb.epoch.TimeIntervals at 0x139986732956448\n",
       "Fields:\n",
       "  colnames: ['start_time' 'stop_time' 'fixcross_time' 'ExperimentID' 'boundary1_time'\n",
       " 'boundary2_time' 'boundary3_time' 'stimCategory' 'Clip_name']\n",
       "  columns: (\n",
       "    start_time <class 'hdmf.common.table.VectorData'>,\n",
       "    stop_time <class 'hdmf.common.table.VectorData'>,\n",
       "    fixcross_time <class 'hdmf.common.table.VectorData'>,\n",
       "    ExperimentID <class 'hdmf.common.table.VectorData'>,\n",
       "    boundary1_time <class 'hdmf.common.table.VectorData'>,\n",
       "    boundary2_time <class 'hdmf.common.table.VectorData'>,\n",
       "    boundary3_time <class 'hdmf.common.table.VectorData'>,\n",
       "    stimCategory <class 'hdmf.common.table.VectorData'>,\n",
       "    Clip_name <class 'hdmf.common.table.VectorData'>\n",
       "  )\n",
       "  description: intervals for the encoding task\n",
       "  id: id <class 'hdmf.common.table.ElementIdentifiers'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding_table = nwbfile.intervals.get('encoding_table') #Get the encoding table for all stimuli\n",
    "\n",
    "#What does this look like?\n",
    "encoding_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfdb3ff",
   "metadata": {},
   "source": [
    "This table has, among other things, the scene boundary times for which we will plot the peri-event time histogram (PETH).\n",
    "\n",
    "There are 3 types of scene boundaries in this data. For the purposes of demonstration, we will use only the \"No boundary\" (NB) and the \"Hard boundary\" (HB conditions). The encoding table has a *stimCategory* field, which tells us the type of boundary corresponding to a given trial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6437181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 0, 0, 1, 0, 2, 1, 2, 0, 2, 0, 1, 1, 1, 1, 0, 0, 2, 0,\n",
       "       0, 2, 0, 1, 0, 2, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 1, 1, 1, 1,\n",
       "       0, 2, 1, 1, 0, 2, 1, 0, 2, 2, 2, 0, 1, 0, 1, 1, 2, 2, 0, 2, 2, 2,\n",
       "       1, 1, 2, 1, 0, 2, 2, 1, 0, 1, 2, 0, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2,\n",
       "       1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimCategory = np.array(encoding_table.stimCategory.data) #Get the scene boundary type for all trials\n",
    "\n",
    "#What does this look like? \n",
    "stimCategory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56aea81f",
   "metadata": {},
   "source": [
    "Trials marked 0 correspond to NB, while trials marked 2 correspond to HB. Let's extract the trial numbers for NB and HB trials, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4e13c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "indxNB = np.where(stimCategory == 0) #NB trial indices\n",
    "indxHB = np.where(stimCategory == 2) #HB trial indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dddc4d0",
   "metadata": {},
   "source": [
    "The encoding table also has 3 types of boundary times. For the purposes of our demonstration, we will focus on boundary1 times, and extract them as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "185337e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.06846275,  12.88060075,  23.071072  ,  32.1604755 ,\n",
       "        41.470909  ,  49.5747065 ,  56.07442325,  72.803867  ,\n",
       "        82.1299925 ,  92.77667275,  99.9925845 , 109.0787315 ,\n",
       "       118.0778575 , 133.12435825, 140.94827125, 147.8236585 ,\n",
       "       160.726736  , 170.441769  , 183.29262575, 191.11327175,\n",
       "       199.63382425, 208.5142425 , 217.6186295 , 232.62687825,\n",
       "       241.7270365 , 250.58327775, 259.5545145 , 268.6661045 ,\n",
       "       278.026902  , 287.04517875, 301.2426685 , 310.21093375,\n",
       "       319.1822215 , 328.13037175, 337.16751875, 363.737004  ,\n",
       "       372.785663  , 381.86749625, 391.2704085 , 400.5077995 ,\n",
       "       407.28660475, 416.0272855 , 431.792285  , 441.75272875,\n",
       "       448.88401175, 456.97766275, 463.88864475, 472.13035175,\n",
       "       490.53584775, 499.7064625 , 507.6917495 , 518.78793775,\n",
       "       528.140675  , 537.1412335 , 552.5116415 , 562.4287995 ,\n",
       "       569.44012625, 580.99649325, 591.974217  , 597.74463025,\n",
       "       608.3638655 , 624.44389125, 633.4175285 , 642.3969695 ,\n",
       "       651.776966  , 660.699774  , 676.9439885 , 681.3602465 ,\n",
       "       692.799878  , 699.53861175, 711.341688  , 720.4819275 ,\n",
       "       729.43782175, 772.99961425, 780.19490625, 788.1175045 ,\n",
       "       798.34264525, 807.54519   , 817.25781375, 835.1736475 ,\n",
       "       842.54760125, 852.294991  , 861.40924725, 868.63873425,\n",
       "       887.93252325, 898.284193  , 905.9068505 , 915.1383965 ,\n",
       "       921.90619325, 932.99710575])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundary1_time = np.array(encoding_table.boundary1_time.data) #Get timings of Boundary1\n",
    "\n",
    "#What does this look like? \n",
    "boundary1_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4070c05",
   "metadata": {},
   "source": [
    "This contains the timings of all boundaries in this block of trials. Note that we also have the type of boundary for each trial. Let's store the NB and HB boundary timings in separate variables, as Pynapple Ts objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "101376c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "NB = nap.Ts(boundary1_time[indxNB]) #NB timings\n",
    "HB = nap.Ts(boundary1_time[indxHB]) #HB timings\n",
    "\n",
    "io.close() #Close the I/O object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e664467f",
   "metadata": {},
   "source": [
    "Now the analysis can truly begin! \n",
    "\n",
    "***\n",
    "\n",
    "## Step-by-step tutorial\n",
    "\n",
    "### Peri-Event Time Histogram (PETH)\n",
    "\n",
    "A PETH is a plot where we align a variable of interest (for example, spikes) to an external event (in this case, to boundary times). This visualization helps us infer relationships between the two.\n",
    "\n",
    "For our demonstration, we will align the spikes of the first unit, which is located in the hippocampus, to the times of NB and HB. You can do a quick check to verify that the first unit is indeed located in the hippocampus, we leave it to you.\n",
    "\n",
    "With Pynapple, PETHs can be computed with a single line of code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f907532f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhruv/pynapple/pynapple/core/time_series.py:154: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  self.rate = len(t) / self.time_support.tot_length(\"s\")\n",
      "/home/dhruv/pynapple/pynapple/core/time_series.py:154: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  self.rate = len(t) / self.time_support.tot_length(\"s\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NB_peth = nap.compute_perievent(spikes[0], NB, minmax = (-0.5,1)) #Compute PETH of unit aligned to NB, for -0.5 to 1s windows\n",
    "HB_peth = nap.compute_perievent(spikes[0], HB, minmax = (-0.5,1)) #Compute PETH of unit aligned to HB, for -0.5 to 1s windows\n",
    "\n",
    "#Let's plot the PETH\n",
    "plt.figure()\n",
    "plt.rc('font', size = 12) \n",
    "plt.subplot(211) #Plot the figures in 2 rows \n",
    "for i,n in enumerate(NB_peth):\n",
    "     plt.plot(NB_peth[n].as_units('s').fillna(i), 'o', color = [102/255,204/255,0/255], markersize = 4) #Plot PETH\n",
    "plt.axvline(0, linewidth = 2, color = 'k', linestyle = '--') #Plot a line at t = 0 \n",
    "plt.yticks([0,30]) #Set ticks on Y-axis\n",
    "plt.gca().set_yticklabels(['1','30']) #Label the ticks\n",
    "plt.xlabel('Time from NB (s)') #Time from boundary in seconds, on X-axis\n",
    "plt.ylabel('Trial Number') #Trial number on Y-axis\n",
    "\n",
    "plt.subplot(212)\n",
    "for i,n in enumerate(HB_peth):\n",
    "     plt.plot(HB_peth[n].as_units('s').fillna(i), 'o', color = [255/255,99/255,71/255], markersize = 4) #Plot PETH\n",
    "plt.axvline(0, linewidth = 2, color = 'k', linestyle = '--') #Plot a line at t = 0 \n",
    "plt.yticks([0,30]) #Set ticks on Y-axis\n",
    "plt.gca().set_yticklabels(['1','30']) #Label the ticks\n",
    "plt.xlabel('Time from HB (s)') #Time from boundary in seconds, on X-axis\n",
    "plt.ylabel('Trial Number') #Trial number on Y-axis\n",
    "plt.subplots_adjust(wspace = 0.2, hspace = 0.5, top = 0.85) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1ee614",
   "metadata": {},
   "source": [
    "Awesome! From the PETH, we can see that this neuron fires after boundary onset in HB trials. This is an example of what the authors describe  <a href=\"https://www.nature.com/articles/s41593-022-01020-w\" target=\"_blank\">here</a> as a *boundary cell*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3b9d52",
   "metadata": {},
   "source": [
    "*** \n",
    "\n",
    "### PETH of firing rate for NB and HB trials\n",
    "\n",
    "Now that we have the PETH of spiking, we can go one step further. We will plot the mean firing rate of this cell aligned to the boundary for each trial type. Doing this in Pynapple is very simple!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cf4f333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f51305e5df0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_size = 0.2 #200ms bin size\n",
    "step_size = 0.01 #10ms step size, to make overlapping bins \n",
    "winsize = int(bin_size/step_size) #Window size \n",
    "\n",
    "#Use Pynapple to compute binned spike counts\n",
    "counts_NB = NB_peth.count(step_size) #Spike counts binned in 10ms steps, for NB trials\n",
    "counts_HB = HB_peth.count(step_size) #Spike counts binned in 10ms steps, for HB trials\n",
    "\n",
    "#Smoothing binned spike counts using a window of size 20, for both trial types\n",
    "counts_NB = counts_NB.as_dataframe().rolling(winsize, win_type = 'gaussian', min_periods = 1, center = True, axis = 0).mean(std = 0.2 * winsize) \n",
    "counts_HB = counts_HB.as_dataframe().rolling(winsize, win_type = 'gaussian', min_periods = 1, center = True, axis = 0).mean(std = 0.2 * winsize) \n",
    "\n",
    "#Compute firing rate for both trial types\n",
    "fr_NB = counts_NB * winsize\n",
    "fr_HB = counts_HB * winsize\n",
    "\n",
    "#Compute the mean firing rate for both trial types \n",
    "meanfr_NB = fr_NB.mean(axis = 1)\n",
    "meanfr_HB = fr_HB.mean(axis = 1)\n",
    "\n",
    "#Compute standard error of mean (SEM) of the firing rate for both trial types\n",
    "error_NB = fr_NB.sem(axis = 1)\n",
    "error_HB = fr_HB.sem(axis = 1)\n",
    "\n",
    "#Plot the mean +/- SEM of firing rate for both trial types\n",
    "plt.figure()\n",
    "plt.plot(meanfr_NB , color = [102/255, 204/255, 0/255], label = 'NB') #Plot mean firing rate for NB trials\n",
    "#Plot SEM for NB trials\n",
    "plt.fill_between(meanfr_NB.index.values, meanfr_NB.values - error_NB, meanfr_NB.values + error_NB, color = [102/255, 204/255, 0/255], alpha = 0.2)\n",
    "plt.plot(meanfr_HB , color = [255/255, 99/255, 71/255], label = 'HB')  #Plot mean firing rate for HB trials\n",
    "#Plot SEM for NB trials\n",
    "plt.fill_between(meanfr_HB.index.values, meanfr_HB.values - error_HB, meanfr_HB.values + error_HB, color = [255/255, 99/255, 71/255], alpha = 0.2)\n",
    "plt.axvline(0, linewidth = 2, color = 'k', linestyle = '--') #Plot a line at t = 0 \n",
    "plt.xlabel('Time from boundary (s)') #Time from boundary in seconds, on X-axis\n",
    "plt.ylabel('Firing rate (Hz)') #Firing rate in Hz on Y-axis\n",
    "plt.legend(loc = 'upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa4670b",
   "metadata": {},
   "source": [
    "This plot verifies what we visualized in the PETH rasters above, that this cell responds to a hard boundary. Hence, it is a *boundary cell*. To learn more about these cells, please check out the original study <a href=\"https://www.nature.com/articles/s41593-022-01020-w\" target=\"_blank\">here</a>. \n",
    "\n",
    "I hope this tutorial was helpful. If you have any questions, comments or suggestions, please feel free to reach out to the Pynapple Team!"
   ]
  }
 ],
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   "language": "python",
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