diff --git a/000363/MAP/demo/README.md b/000363/MAP/demo/README.md
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+++ b/000363/MAP/demo/README.md
@@ -0,0 +1,9 @@
+This folder contains a notebook that demonstrates how to work with dandiset 000363. 
+
+`browse_map_ephys_data.ipynb` demonstrates how to navigate a single `.nwb` file from
+dataset 000363. This dataset was generated from the [map
+ephys](https://github.com/mesoscale-activity-map/map-ephys) pipeline. 
+
+The notebook demonstrates the contents of the `.nwb` file including acquiring the ccf
+coordinates, creating rasters aligned to specific events, and browsing the dandiset
+without downloading the file locally.
\ No newline at end of file
diff --git a/000363/MAP/demo/browse_map_ephys_data.ipynb b/000363/MAP/demo/browse_map_ephys_data.ipynb
new file mode 100644
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@@ -0,0 +1,2005 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Browse datasets on DANDI\n",
+    "\n",
+    "This notebook will provide instructions to read a dataset from DANDI and browse\n",
+    "the corresponding NWB file. The full set of instructions on NWB can be found\n",
+    "[here](https://pynwb.readthedocs.io/en/stable/index.html). \n",
+    "\n",
+    "The following packages are necessary to run this notebook. Please install them\n",
+    "to your conda environment by typing each of the code blocks in your terminal:\n",
+    "\n",
+    "1. pynwb:\n",
+    "\n",
+    "    ```pip install pynwb```\n",
+    "\n",
+    "2. nwbwidgets:\n",
+    "\n",
+    "    ```pip install nwbwidgets```\n",
+    "\n",
+    "3. fsspec, requests, and aiohttp\n",
+    "\n",
+    "    ```pip install fsspec requests aiohttp```\n",
+    "\n",
+    "\n",
+    "This notebook allows you to stream data directly from DANDI to preview the contents of\n",
+    "the `.nwb` file. We currently recommend downloading the `.nwb`\n",
+    "file of interest to your local machine after you explore it with the nwbwidget\n",
+    "to ensure optimal performance for downstream analyses. \n",
+    "\n",
+    "If you want to explore the NWB file in streaming mode, you will find\n",
+    "instructions at the bottom of the notebook to do so. Please note that converting\n",
+    "the `units` object to a dataframe takes a long time in this mode and it is\n",
+    "recommended that you bring objects into memory one at a time for best performance.\n",
+    "\n",
+    "The dataset used in this notebook can be found at\n",
+    "[https://dandiarchive.org/dandiset/000363?pos=1](https://dandiarchive.org/dandiset/000363?pos=1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pynwb\n",
+    "import fsspec\n",
+    "import h5py\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pynwb import NWBHDF5IO\n",
+    "from nwbwidgets import nwb2widget\n",
+    "from nwbwidgets.panel import Panel\n",
+    "from dandi.dandiapi import DandiAPIClient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nwbwidgets `Panel()` class allows you to generate an exploratory widget for\n",
+    "any NWB file. This file can be on your local machine or on DANDI archive. To\n",
+    "find your dataset on DANDI please follow the steps below:\n",
+    "\n",
+    "1. Click on the `DANDI` radio button on the left side of the top panel.\n",
+    "2. Find your Dandiset: the MAP data is at Dandiset ID `000363`. \n",
+    "3. Find the file you want to browse.\n",
+    "4. Click load file to generate a widget that allows you to explore the contents\n",
+    "   of the `.nwb` file. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4729bd5bb454ca598457ae51c3a9b3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Panel(children=(HBox(children=(VBox(children=(RadioButtons(layout=Layout(width='100px'), options=('Local dir',…"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe2905db64fa450f98d2bd828260c866",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading dandiset metadata:   0%|          | 0/296 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\kusha\\miniconda3\\envs\\mapEphysNWB\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.7.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "c:\\Users\\kusha\\miniconda3\\envs\\mapEphysNWB\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.4.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "Panel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have found the file you want to use, please download it and update the\n",
+    "path below to point to the `.nwb` file on your local machine relative to the\n",
+    "current working directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "io = NWBHDF5IO(\"../../dandiset/sub-449141_ses-20190530T173316_behavior+ecephys+ogen.nwb\", mode=\"r\")\n",
+    "nwbfile = io.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performing exploratory and downstream analyses requires extracting data from the\n",
+    "NWB file and bringing it into memory. Below, we extract behavior trials and\n",
+    "units from ephys into dataframes - `trials_df` and `units_df`, respectively.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trials_df = nwbfile.trials.to_dataframe()\n",
+    "units_df = nwbfile.units.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>photostim_onset</th>\n",
+       "      <th>photostim_power</th>\n",
+       "      <th>photostim_duration</th>\n",
+       "      <th>trial_uid</th>\n",
+       "      <th>task</th>\n",
+       "      <th>task_protocol</th>\n",
+       "      <th>trial_instruction</th>\n",
+       "      <th>early_lick</th>\n",
+       "      <th>outcome</th>\n",
+       "      <th>auto_water</th>\n",
+       "      <th>free_water</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",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>5.5544</td>\n",
+       "      <td>1</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11.8208</td>\n",
+       "      <td>22.2921</td>\n",
+       "      <td>2</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>2</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0940</td>\n",
+       "      <td>30.4805</td>\n",
+       "      <td>3</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>33.1364</td>\n",
+       "      <td>39.9460</td>\n",
+       "      <td>4</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>early</td>\n",
+       "      <td>ignore</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41.7374</td>\n",
+       "      <td>46.7201</td>\n",
+       "      <td>5</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>4766.5699</td>\n",
+       "      <td>4771.5319</td>\n",
+       "      <td>656</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>656</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>4773.9950</td>\n",
+       "      <td>4778.8603</td>\n",
+       "      <td>657</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>657</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>right</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>4781.3120</td>\n",
+       "      <td>4785.1642</td>\n",
+       "      <td>658</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>658</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>left</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>4787.8274</td>\n",
+       "      <td>4792.9458</td>\n",
+       "      <td>659</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>659</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>left</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>hit</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>4795.4072</td>\n",
+       "      <td>4799.2389</td>\n",
+       "      <td>660</td>\n",
+       "      <td>1.891</td>\n",
+       "      <td>5.500</td>\n",
+       "      <td>0.5000</td>\n",
+       "      <td>660</td>\n",
+       "      <td>audio delay</td>\n",
+       "      <td>1</td>\n",
+       "      <td>left</td>\n",
+       "      <td>no early</td>\n",
+       "      <td>miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>660 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     start_time  stop_time  trial photostim_onset photostim_power  \\\n",
+       "id                                                                  \n",
+       "0        0.0000     5.5544      1             N/A             N/A   \n",
+       "1       11.8208    22.2921      2             N/A             N/A   \n",
+       "2       24.0940    30.4805      3             N/A             N/A   \n",
+       "3       33.1364    39.9460      4             N/A             N/A   \n",
+       "4       41.7374    46.7201      5             N/A             N/A   \n",
+       "..          ...        ...    ...             ...             ...   \n",
+       "655   4766.5699  4771.5319    656             N/A             N/A   \n",
+       "656   4773.9950  4778.8603    657             N/A             N/A   \n",
+       "657   4781.3120  4785.1642    658             N/A             N/A   \n",
+       "658   4787.8274  4792.9458    659             N/A             N/A   \n",
+       "659   4795.4072  4799.2389    660           1.891           5.500   \n",
+       "\n",
+       "    photostim_duration  trial_uid         task  task_protocol  \\\n",
+       "id                                                              \n",
+       "0                  N/A          1  audio delay              1   \n",
+       "1                  N/A          2  audio delay              1   \n",
+       "2                  N/A          3  audio delay              1   \n",
+       "3                  N/A          4  audio delay              1   \n",
+       "4                  N/A          5  audio delay              1   \n",
+       "..                 ...        ...          ...            ...   \n",
+       "655                N/A        656  audio delay              1   \n",
+       "656                N/A        657  audio delay              1   \n",
+       "657                N/A        658  audio delay              1   \n",
+       "658                N/A        659  audio delay              1   \n",
+       "659             0.5000        660  audio delay              1   \n",
+       "\n",
+       "    trial_instruction early_lick outcome  auto_water  free_water  \n",
+       "id                                                                \n",
+       "0               right      early     hit           0           0  \n",
+       "1               right      early     hit           0           0  \n",
+       "2               right      early     hit           0           0  \n",
+       "3               right      early  ignore           0           0  \n",
+       "4               right   no early     hit           0           0  \n",
+       "..                ...        ...     ...         ...         ...  \n",
+       "655             right   no early     hit           0           0  \n",
+       "656             right   no early     hit           0           0  \n",
+       "657              left   no early    miss           0           0  \n",
+       "658              left   no early     hit           0           0  \n",
+       "659              left   no early    miss           0           0  \n",
+       "\n",
+       "[660 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
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+       "      <th>sampling_rate</th>\n",
+       "      <th>unit_quality</th>\n",
+       "      <th>unit_posx</th>\n",
+       "      <th>unit_posy</th>\n",
+       "      <th>unit_amp</th>\n",
+       "      <th>unit_snr</th>\n",
+       "      <th>isi_violation</th>\n",
+       "      <th>avg_firing_rate</th>\n",
+       "      <th>drift_metric</th>\n",
+       "      <th>...</th>\n",
+       "      <th>velocity_above</th>\n",
+       "      <th>velocity_below</th>\n",
+       "      <th>classification</th>\n",
+       "      <th>anno_name</th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>obs_intervals</th>\n",
+       "      <th>electrodes</th>\n",
+       "      <th>electrode_group</th>\n",
+       "      <th>waveform_mean</th>\n",
+       "      <th>waveform_sd</th>\n",
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+       "    <tr>\n",
+       "      <th>id</th>\n",
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+       "      <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</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>147.217681</td>\n",
+       "      <td>8.037514</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.413650</td>\n",
+       "      <td>0.018182</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.686771</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[19.228361858212644, 25.16561393995109, 28.914...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[2.5724660462776656, 2.1456866197183095, 1.966...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>multi</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>86.126430</td>\n",
+       "      <td>4.787742</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.095506</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[8.136125511297582, 8.235526053858877, 14.2586...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[0.16850490196078433, 0.4238153594771242, 0.19...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>64.746094</td>\n",
+       "      <td>3.552861</td>\n",
+       "      <td>0.002653</td>\n",
+       "      <td>17.324200</td>\n",
+       "      <td>0.154545</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.988300</td>\n",
+       "      <td>-6.180940</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[-0.3295540305365834, -0.19785331167015974, 0....</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-2.0531439012096775, -1.9397366431451613, -1....</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>56.539984</td>\n",
+       "      <td>2.954013</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.285685</td>\n",
+       "      <td>0.072727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.515078</td>\n",
+       "      <td>0.480740</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[16.85488223630304, 17.939621490504805, 18.021...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[0.7641921397379913, 0.56632096069869, 0.82901...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>60.036058</td>\n",
+       "      <td>3.331454</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.005410</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.262633</td>\n",
+       "      <td>-0.343385</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[17.38181844582985, 441.68083391373926, 564.16...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-0.18028846153846154, 0.3605769230769231, -0....</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2111</th>\n",
+       "      <td>505</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>1440.0</td>\n",
+       "      <td>297.534180</td>\n",
+       "      <td>6.342309</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.099875</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.343388</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Midbrain reticular nucleus</td>\n",
+       "      <td>[15.20743573111292, 16.26698295628765, 16.4064...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-4.755859375, -5.0830078125, -3.9892578125, -...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2112</th>\n",
+       "      <td>506</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>1560.0</td>\n",
+       "      <td>98.612035</td>\n",
+       "      <td>2.571565</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.019559</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.037200</td>\n",
+       "      <td>-1.373550</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[8.793836569035015, 277.82287333129483, 354.28...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[5.759640957446808, 5.659906914893617, 6.65724...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2113</th>\n",
+       "      <td>507</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>multi</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>1640.0</td>\n",
+       "      <td>172.346166</td>\n",
+       "      <td>4.056073</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.121098</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.928090</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[1.2496709899752019, 33.18653497590302, 34.357...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-2.009503865979381, -2.3477770618556697, -2.7...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2114</th>\n",
+       "      <td>508</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>multi</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>124.163167</td>\n",
+       "      <td>3.079136</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.052642</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.637721</td>\n",
+       "      <td>-0.722299</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[1.2787047039727852, 1.4934741865060008, 80.67...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-1.1950345849802373, -1.0653409090909092, -1....</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115</th>\n",
+       "      <td>509</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>multi</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2740.0</td>\n",
+       "      <td>106.171875</td>\n",
+       "      <td>3.267464</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.002081</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.327770</td>\n",
+       "      <td>-0.302509</td>\n",
+       "      <td>unlabelled</td>\n",
+       "      <td></td>\n",
+       "      <td>[134.56330014589082, 877.815221847931, 877.999...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[3.28125, 2.578125, -0.9375, -1.640625, 3.9843...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2116 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      unit  sampling_rate unit_quality  unit_posx  unit_posy    unit_amp  \\\n",
+       "id                                                                         \n",
+       "0        0          30000         good       59.0        0.0  147.217681   \n",
+       "1        1          30000        multi       11.0       20.0   86.126430   \n",
+       "2        2          30000         good       43.0       20.0   64.746094   \n",
+       "3        3          30000         good       11.0       60.0   56.539984   \n",
+       "4        4          30000         good       27.0       40.0   60.036058   \n",
+       "...    ...            ...          ...        ...        ...         ...   \n",
+       "2111   505          30000         good       59.0     1440.0  297.534180   \n",
+       "2112   506          30000         good       59.0     1560.0   98.612035   \n",
+       "2113   507          30000        multi       59.0     1640.0  172.346166   \n",
+       "2114   508          30000        multi       11.0     2300.0  124.163167   \n",
+       "2115   509          30000        multi       11.0     2740.0  106.171875   \n",
+       "\n",
+       "      unit_snr  isi_violation  avg_firing_rate  drift_metric  ...  \\\n",
+       "id                                                            ...   \n",
+       "0     8.037514       0.000000         0.413650      0.018182  ...   \n",
+       "1     4.787742       0.000000         0.095506      0.000000  ...   \n",
+       "2     3.552861       0.002653        17.324200      0.154545  ...   \n",
+       "3     2.954013       0.000000         0.285685      0.072727  ...   \n",
+       "4     3.331454       0.000000         0.005410      1.000000  ...   \n",
+       "...        ...            ...              ...           ...  ...   \n",
+       "2111  6.342309       0.000000         0.099875      0.000000  ...   \n",
+       "2112  2.571565       0.000000         0.019559      1.000000  ...   \n",
+       "2113  4.056073       0.000000         0.121098      0.000000  ...   \n",
+       "2114  3.079136       0.000000         0.052642      0.000000  ...   \n",
+       "2115  3.267464       0.000000         0.002081      1.000000  ...   \n",
+       "\n",
+       "      velocity_above  velocity_below  classification  \\\n",
+       "id                                                     \n",
+       "0           0.686771             NaN      unlabelled   \n",
+       "1           0.000000        0.000000      unlabelled   \n",
+       "2          10.988300       -6.180940      unlabelled   \n",
+       "3          -0.515078        0.480740            good   \n",
+       "4          -0.262633       -0.343385      unlabelled   \n",
+       "...              ...             ...             ...   \n",
+       "2111        0.000000       -0.343388            good   \n",
+       "2112       24.037200       -1.373550      unlabelled   \n",
+       "2113        8.928090             NaN      unlabelled   \n",
+       "2114        0.637721       -0.722299      unlabelled   \n",
+       "2115       -1.327770       -0.302509      unlabelled   \n",
+       "\n",
+       "                       anno_name  \\\n",
+       "id                                 \n",
+       "0                                  \n",
+       "1                                  \n",
+       "2                                  \n",
+       "3     Anterior olfactory nucleus   \n",
+       "4                                  \n",
+       "...                          ...   \n",
+       "2111  Midbrain reticular nucleus   \n",
+       "2112                               \n",
+       "2113                               \n",
+       "2114                               \n",
+       "2115                               \n",
+       "\n",
+       "                                            spike_times  \\\n",
+       "id                                                        \n",
+       "0     [19.228361858212644, 25.16561393995109, 28.914...   \n",
+       "1     [8.136125511297582, 8.235526053858877, 14.2586...   \n",
+       "2     [-0.3295540305365834, -0.19785331167015974, 0....   \n",
+       "3     [16.85488223630304, 17.939621490504805, 18.021...   \n",
+       "4     [17.38181844582985, 441.68083391373926, 564.16...   \n",
+       "...                                                 ...   \n",
+       "2111  [15.20743573111292, 16.26698295628765, 16.4064...   \n",
+       "2112  [8.793836569035015, 277.82287333129483, 354.28...   \n",
+       "2113  [1.2496709899752019, 33.18653497590302, 34.357...   \n",
+       "2114  [1.2787047039727852, 1.4934741865060008, 80.67...   \n",
+       "2115  [134.56330014589082, 877.815221847931, 877.999...   \n",
+       "\n",
+       "                                          obs_intervals  \\\n",
+       "id                                                        \n",
+       "0     [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "1     [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2     [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "3     [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "4     [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "...                                                 ...   \n",
+       "2111  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2112  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2113  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2114  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2115  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "\n",
+       "                                             electrodes  \\\n",
+       "id                                                        \n",
+       "0                                                  l...   \n",
+       "1                                                  l...   \n",
+       "2                                                  l...   \n",
+       "3                                                  l...   \n",
+       "4                                                  l...   \n",
+       "...                                                 ...   \n",
+       "2111                                                ...   \n",
+       "2112                                                ...   \n",
+       "2113                                                ...   \n",
+       "2114                                                ...   \n",
+       "2115                                                ...   \n",
+       "\n",
+       "                                        electrode_group  \\\n",
+       "id                                                        \n",
+       "0     17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "1     17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2     17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "3     17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "4     17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "...                                                 ...   \n",
+       "2111  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2112  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2113  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2114  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2115  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "\n",
+       "                                          waveform_mean  waveform_sd  \n",
+       "id                                                                    \n",
+       "0     [2.5724660462776656, 2.1456866197183095, 1.966...        [nan]  \n",
+       "1     [0.16850490196078433, 0.4238153594771242, 0.19...        [nan]  \n",
+       "2     [-2.0531439012096775, -1.9397366431451613, -1....        [nan]  \n",
+       "3     [0.7641921397379913, 0.56632096069869, 0.82901...        [nan]  \n",
+       "4     [-0.18028846153846154, 0.3605769230769231, -0....        [nan]  \n",
+       "...                                                 ...          ...  \n",
+       "2111  [-4.755859375, -5.0830078125, -3.9892578125, -...        [nan]  \n",
+       "2112  [5.759640957446808, 5.659906914893617, 6.65724...        [nan]  \n",
+       "2113  [-2.009503865979381, -2.3477770618556697, -2.7...        [nan]  \n",
+       "2114  [-1.1950345849802373, -1.0653409090909092, -1....        [nan]  \n",
+       "2115  [3.28125, 2.578125, -0.9375, -1.640625, 3.9843...        [nan]  \n",
+       "\n",
+       "[2116 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "units_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The units dataframe contains information such as `unit_quality`, `spike_times`,\n",
+    "and even the CCF location of the electrode for each unit. In the cell below, we'll write a\n",
+    "function that returns the x, y and z CCF coordinates for a given unit in the\n",
+    "`units_df`.\n",
+    "\n",
+    "**Please Note:** x, y, and z are described in the pipeline as follows:\n",
+    "\n",
+    "    ccf_x   :  int   # (um)  Left-to-Right (ML axis)\n",
+    "    ccf_y   :  int   # (um)  Superior-to-Inferior (DV axis)\n",
+    "    ccf_z   :  int   # (um)  Anterior-to-Posterior (AP axis)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ccf(unit_number):\n",
+    "    x = float(units_df.electrodes[unit_number].reset_index().x.values)\n",
+    "    y = float(units_df.electrodes[unit_number].reset_index().y.values)\n",
+    "    z = float(units_df.electrodes[unit_number].reset_index().z.values)\n",
+    "    return x, y, z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_ccf = 7200.0\n",
+      "y_ccf = 2400.0\n",
+      "z_ccf = 2500.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_ccf, y_ccf, z_ccf = get_ccf(unit_number=150)\n",
+    "\n",
+    "print(f\"x_ccf = {x_ccf}\")\n",
+    "print(f\"y_ccf = {y_ccf}\")\n",
+    "print(f\"z_ccf = {z_ccf}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can further filter the dataframes to suit your analysis goals. For example,\n",
+    "we will filter the `trials_df` to only show the entries that contain `photostim`\n",
+    "and where the outcome is a `hit`.\n",
+    "We will also filter the `units_df` to only show units with \"good\"\n",
+    "`classification` quality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trials = trials_df.query(\"photostim_onset != 'N/A' and outcome == 'hit'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "units = units_df.query(\"classification == 'good'\").reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>id</th>\n",
+       "      <th>unit</th>\n",
+       "      <th>sampling_rate</th>\n",
+       "      <th>unit_quality</th>\n",
+       "      <th>unit_posx</th>\n",
+       "      <th>unit_posy</th>\n",
+       "      <th>unit_amp</th>\n",
+       "      <th>unit_snr</th>\n",
+       "      <th>isi_violation</th>\n",
+       "      <th>avg_firing_rate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>velocity_above</th>\n",
+       "      <th>velocity_below</th>\n",
+       "      <th>classification</th>\n",
+       "      <th>anno_name</th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>obs_intervals</th>\n",
+       "      <th>electrodes</th>\n",
+       "      <th>electrode_group</th>\n",
+       "      <th>waveform_mean</th>\n",
+       "      <th>waveform_sd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>56.539984</td>\n",
+       "      <td>2.954013</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.285685</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.515078</td>\n",
+       "      <td>0.480740</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[16.85488223630304, 17.939621490504805, 18.021...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[0.7641921397379913, 0.56632096069869, 0.82901...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>114.756332</td>\n",
+       "      <td>5.388086</td>\n",
+       "      <td>0.037909</td>\n",
+       "      <td>3.299630</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371609</td>\n",
+       "      <td>0.426272</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[-0.02451903222055085, 0.44261685089614433, 1....</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-1.3679750504032258, -1.216765372983871, -1.1...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>337.810382</td>\n",
+       "      <td>13.569296</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.196630</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.902027</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[-0.3466541238745928, -0.3245873367600467, 1.5...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-5.366918665276329, -5.335147288842545, -5.42...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>236.101562</td>\n",
+       "      <td>8.838941</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.249688</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.166600</td>\n",
+       "      <td>-1.983600</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[3.5245003395101864, 19.96236586465118, 24.589...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-3.87109375, -3.41015625, -3.62890625, -4.511...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>251.819987</td>\n",
+       "      <td>12.015720</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.554932</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.904400</td>\n",
+       "      <td>-2.895710</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Anterior olfactory nucleus</td>\n",
+       "      <td>[3.5243336719337925, 3.5277003569769483, 3.537...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>l...</td>\n",
+       "      <td>17216703352 1-301 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[4.350042182227221, 4.002038807649043, 4.17076...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>2091</td>\n",
+       "      <td>485</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>1860.0</td>\n",
+       "      <td>126.615050</td>\n",
+       "      <td>2.959896</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.417601</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.343388</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Midbrain reticular nucleus</td>\n",
+       "      <td>[8.770836267475506, 49.82652010675252, 51.0380...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[1.079035874439462, 0.5745515695067265, -0.304...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>2095</td>\n",
+       "      <td>489</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>163.973921</td>\n",
+       "      <td>4.276903</td>\n",
+       "      <td>0.069031</td>\n",
+       "      <td>7.943780</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.970959</td>\n",
+       "      <td>-1.013810</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Superior colliculus, motor related, intermedia...</td>\n",
+       "      <td>[-0.46711818570510166, -0.13818053963374188, 0...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-0.4069841675178754, -0.7229954034729316, -1....</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>2108</td>\n",
+       "      <td>502</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>multi</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3580.0</td>\n",
+       "      <td>75.383828</td>\n",
+       "      <td>4.470425</td>\n",
+       "      <td>0.126795</td>\n",
+       "      <td>3.250500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.139550</td>\n",
+       "      <td>-2.609750</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Retrosplenial area, ventral part, layer 1</td>\n",
+       "      <td>[3.584401600887656, 3.585334946458187, 3.98587...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[2.6987909416581375, 2.5068769191402254, 2.516...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>2110</td>\n",
+       "      <td>504</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3620.0</td>\n",
+       "      <td>143.412537</td>\n",
+       "      <td>7.007097</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.421970</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.905650</td>\n",
+       "      <td>-1.695770</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Retrosplenial area, ventral part, layer 1</td>\n",
+       "      <td>[22.52149829326651, 37.43265731395145, 73.9456...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[1.9138313609467454, 2.0005085059171597, 2.142...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>2111</td>\n",
+       "      <td>505</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>good</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>1440.0</td>\n",
+       "      <td>297.534180</td>\n",
+       "      <td>6.342309</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.099875</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.343388</td>\n",
+       "      <td>good</td>\n",
+       "      <td>Midbrain reticular nucleus</td>\n",
+       "      <td>[15.20743573111292, 16.26698295628765, 16.4064...</td>\n",
+       "      <td>[[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18005107871 1-384 pynwb.ecephys.ElectrodeGroup...</td>\n",
+       "      <td>[-4.755859375, -5.0830078125, -3.9892578125, -...</td>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>534 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       id  unit  sampling_rate unit_quality  unit_posx  unit_posy    unit_amp  \\\n",
+       "0       3     3          30000         good       11.0       60.0   56.539984   \n",
+       "1       8     8          30000         good       11.0      300.0  114.756332   \n",
+       "2       9     9          30000         good       27.0      320.0  337.810382   \n",
+       "3      10    10          30000         good       11.0      340.0  236.101562   \n",
+       "4      13    13          30000         good       59.0      360.0  251.819987   \n",
+       "..    ...   ...            ...          ...        ...        ...         ...   \n",
+       "529  2091   485          30000         good       43.0     1860.0  126.615050   \n",
+       "530  2095   489          30000         good       27.0     2400.0  163.973921   \n",
+       "531  2108   502          30000        multi       11.0     3580.0   75.383828   \n",
+       "532  2110   504          30000         good       11.0     3620.0  143.412537   \n",
+       "533  2111   505          30000         good       59.0     1440.0  297.534180   \n",
+       "\n",
+       "      unit_snr  isi_violation  avg_firing_rate  ...  velocity_above  \\\n",
+       "0     2.954013       0.000000         0.285685  ...       -0.515078   \n",
+       "1     5.388086       0.037909         3.299630  ...        0.371609   \n",
+       "2    13.569296       0.000000         1.196630  ...        0.902027   \n",
+       "3     8.838941       0.000000         0.249688  ...        2.166600   \n",
+       "4    12.015720       0.000000         0.554932  ...        1.904400   \n",
+       "..         ...            ...              ...  ...             ...   \n",
+       "529   2.959896       0.000000         0.417601  ...        0.000000   \n",
+       "530   4.276903       0.069031         7.943780  ...        0.970959   \n",
+       "531   4.470425       0.126795         3.250500  ...        3.139550   \n",
+       "532   7.007097       0.000000         0.421970  ...        1.905650   \n",
+       "533   6.342309       0.000000         0.099875  ...        0.000000   \n",
+       "\n",
+       "     velocity_below  classification  \\\n",
+       "0          0.480740            good   \n",
+       "1          0.426272            good   \n",
+       "2          0.000000            good   \n",
+       "3         -1.983600            good   \n",
+       "4         -2.895710            good   \n",
+       "..              ...             ...   \n",
+       "529        0.343388            good   \n",
+       "530       -1.013810            good   \n",
+       "531       -2.609750            good   \n",
+       "532       -1.695770            good   \n",
+       "533       -0.343388            good   \n",
+       "\n",
+       "                                             anno_name  \\\n",
+       "0                           Anterior olfactory nucleus   \n",
+       "1                           Anterior olfactory nucleus   \n",
+       "2                           Anterior olfactory nucleus   \n",
+       "3                           Anterior olfactory nucleus   \n",
+       "4                           Anterior olfactory nucleus   \n",
+       "..                                                 ...   \n",
+       "529                         Midbrain reticular nucleus   \n",
+       "530  Superior colliculus, motor related, intermedia...   \n",
+       "531          Retrosplenial area, ventral part, layer 1   \n",
+       "532          Retrosplenial area, ventral part, layer 1   \n",
+       "533                         Midbrain reticular nucleus   \n",
+       "\n",
+       "                                           spike_times  \\\n",
+       "0    [16.85488223630304, 17.939621490504805, 18.021...   \n",
+       "1    [-0.02451903222055085, 0.44261685089614433, 1....   \n",
+       "2    [-0.3466541238745928, -0.3245873367600467, 1.5...   \n",
+       "3    [3.5245003395101864, 19.96236586465118, 24.589...   \n",
+       "4    [3.5243336719337925, 3.5277003569769483, 3.537...   \n",
+       "..                                                 ...   \n",
+       "529  [8.770836267475506, 49.82652010675252, 51.0380...   \n",
+       "530  [-0.46711818570510166, -0.13818053963374188, 0...   \n",
+       "531  [3.584401600887656, 3.585334946458187, 3.98587...   \n",
+       "532  [22.52149829326651, 37.43265731395145, 73.9456...   \n",
+       "533  [15.20743573111292, 16.26698295628765, 16.4064...   \n",
+       "\n",
+       "                                         obs_intervals  \\\n",
+       "0    [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "1    [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "2    [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "3    [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "4    [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "..                                                 ...   \n",
+       "529  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "530  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "531  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "532  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "533  [[0.0, 5.5544], [11.8208, 22.2921], [24.094, 3...   \n",
+       "\n",
+       "                                            electrodes  \\\n",
+       "0                                                 l...   \n",
+       "1                                                 l...   \n",
+       "2                                                 l...   \n",
+       "3                                                 l...   \n",
+       "4                                                 l...   \n",
+       "..                                                 ...   \n",
+       "529                                                ...   \n",
+       "530                                                ...   \n",
+       "531                                                ...   \n",
+       "532                                                ...   \n",
+       "533                                                ...   \n",
+       "\n",
+       "                                       electrode_group  \\\n",
+       "0    17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "1    17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "2    17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "3    17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "4    17216703352 1-301 pynwb.ecephys.ElectrodeGroup...   \n",
+       "..                                                 ...   \n",
+       "529  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "530  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "531  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "532  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "533  18005107871 1-384 pynwb.ecephys.ElectrodeGroup...   \n",
+       "\n",
+       "                                         waveform_mean  waveform_sd  \n",
+       "0    [0.7641921397379913, 0.56632096069869, 0.82901...        [nan]  \n",
+       "1    [-1.3679750504032258, -1.216765372983871, -1.1...        [nan]  \n",
+       "2    [-5.366918665276329, -5.335147288842545, -5.42...        [nan]  \n",
+       "3    [-3.87109375, -3.41015625, -3.62890625, -4.511...        [nan]  \n",
+       "4    [4.350042182227221, 4.002038807649043, 4.17076...        [nan]  \n",
+       "..                                                 ...          ...  \n",
+       "529  [1.079035874439462, 0.5745515695067265, -0.304...        [nan]  \n",
+       "530  [-0.4069841675178754, -0.7229954034729316, -1....        [nan]  \n",
+       "531  [2.6987909416581375, 2.5068769191402254, 2.516...        [nan]  \n",
+       "532  [1.9138313609467454, 2.0005085059171597, 2.142...        [nan]  \n",
+       "533  [-4.755859375, -5.0830078125, -3.9892578125, -...        [nan]  \n",
+       "\n",
+       "[534 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can align spikes from good units to photostim onset and plot rasters to\n",
+    "explore how and whether there is evoked neural activity at `photostim_onset`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aNGiQZs2a1fwVAgAA1CHgIPPxxx9LkqZOnaqFCxcqKioqaEUBAAAEoknvWgIAAHCCRl/sCwAA4BQEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsWwNMllZWbr00ksVGRmpmJgYTZgwQbt37/abc+LECaWlpalr167q2LGjJk6cqNLSUpsqBgAATmJrkMnLy1NaWpo2btyodevW6eTJk7r66qtVUVHhm/PAAw/oz3/+s959913l5eXpwIEDuvHGG22sGgAAOEWjv6KgOa1Zs8bv9rJlyxQTE6OCggJddtllKisr06uvvqq33npLV1xxhaSfviKhX79+2rhxo37xi1/YUTYAAHAIR10jU1ZWJknq0qWLJKmgoEAnT55USkqKb07fvn3Vo0cPbdiwodbH8Hq9Ki8v99sAAEDr5JggU1NTo5kzZ2r06NEaMGCAJKmkpERut1udOnXymxsbG6uSkpJaHycrK0vR0dG+LTExMdilAwAAmzgmyKSlpenzzz/X8uXLz+lxMjMzVVZW5tuKi4ubqUIAODeVVaeUNHuVkmavUmXVKbvLAVoFW6+ROS09PV0ffvihPvnkE3Xv3t03HhcXp6qqKh09etRvVaa0tFRxcXG1PpbH45HH4wl2yQAAwAFsDTKWZWn69OlauXKlcnNz1atXL7/7hw0bpnbt2iknJ0cTJ06UJO3evVtFRUVKTk62o2QAaLIId5j2LRhndxlAq2JrkElLS9Nbb72l999/X5GRkb7rXqKjo9W+fXtFR0fr7rvvVkZGhrp06aKoqChNnz5dycnJvGMJAADYG2QWL14sSRozZozf+NKlS3XXXXdJkp5//nmFhIRo4sSJ8nq9Sk1N1YsvvtjClQIAACey/dRSQ8LDw5Wdna3s7OwWqKj5VVadUv85ayVJux5PVYTbEZclAQDQKjjmXUsAAACNxfJAkHFxH+rT2BU7VvgAwB8rMgAAwFj8cw6wUWNX7FjhAwB/rMgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxlRJDJzs5WUlKSwsPDNXLkSG3atMnukgAAgAM4Psi88847ysjI0Ny5c7V161YNGjRIqampOnTokN2lAQAAmzk+yDz33HO69957NXXqVPXv319LlixRRESEXnvtNbtLAwAANnN0kKmqqlJBQYFSUlJ8YyEhIUpJSdGGDRtq3cfr9aq8vNxvAwAArZOjg8zhw4dVXV2t2NhYv/HY2FiVlJTUuk9WVpaio6N9W2JiYkuUCgAAbODoINMUmZmZKisr823FxcV2lwQAAIIkzO4C6tOtWzeFhoaqtLTUb7y0tFRxcXG17uPxeOTxeFqiPAAAYDNHr8i43W4NGzZMOTk5vrGamhrl5OQoOTnZxsoAAIATOHpFRpIyMjI0ZcoUDR8+XCNGjNALL7ygiooKTZ061e7SAACAzRwfZG699VZ99913mjNnjkpKSjR48GCtWbPmrAuAAQBA2+P4ICNJ6enpSk9Pt7sMAADgMI6+RgYAAKA+BBkAAGAsI04tnQvLsiSJT/hto6qqqnTixAlJP/0OuN1umysCAATi9N/t03/H6+KyGpphuG+++YZP9wUAwFDFxcXq3r17nfe3+iBTU1OjAwcOKDIyUi6XK+D9ysvLlZiYqOLiYkVFRQWxQvu0hR6lttFnW+hRaht9toUepbbRZ1voUQpen5Zl6dixY0pISFBISN1XwrT6U0shISH1JrmGREVFtepfQKlt9Ci1jT7bQo9S2+izLfQotY0+20KPUnD6jI6ObnAOF/sCAABjEWQAAICxCDJ18Hg8mjt3bqv+Asq20KPUNvpsCz1KbaPPttCj1Db6bAs9Svb32eov9gUAAK0XKzIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGCsMLsLCLaamhodOHBAkZGRcrlcdpcDAAACYFmWjh07poSEBIWE1L3u0uqDzIEDB5SYmGh3GQAAoAmKi4vVvXv3Ou9v9UEmMjJS0k8/iKioKJurQUurqqrSs88+K0l68MEH5Xa7ba4IABCI8vJyJSYm+v6O16XVB5nTp5OioqIIMm1QVVWVwsPDJf30O0CQAQCzNHRZiFEX+y5YsEAul0szZ860uxQAAOAAxgSZzZs366WXXtLAgQPtLgUAADiEEUHm+PHjmjx5sl555RV17tzZ7nIAAIBDGBFk0tLSNG7cOKWkpNhdCgAAcBDHX+y7fPlybd26VZs3bw5ovtfrldfr9d0uLy8PVmkAAMBmjg4yxcXFmjFjhtatW+d750lDsrKyNG/evCBX9pOk2auavO++BeOasRK0NvxuAUBgHH1qqaCgQIcOHdLQoUMVFhamsLAw5eXladGiRQoLC1N1dfVZ+2RmZqqsrMy3FRcX21A5AABoCY5ekbnyyitVWFjoNzZ16lT17dtXjzzyiEJDQ8/ax+PxyOPxtFSJAADARo4OMpGRkRowYIDfWIcOHdS1a9ezxgEAQNvj6FNLAAAA9XH0ikxtcnNz7S4BAAA4BCsyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACM5eggs3jxYg0cOFBRUVGKiopScnKyVq9ebXdZAADAIRwdZLp3764FCxaooKBAW7Zs0RVXXKHrr79eO3futLs0AADgAGF2F1Cf8ePH+91+8skntXjxYm3cuFEXX3yxTVUBAACncHSQ+bnq6mq9++67qqioUHJyst3lAAAAB3B8kCksLFRycrJOnDihjh07auXKlerfv3+d871er7xer+92eXl5S5QJAABs4OhrZCSpT58+2r59u/Lz83XfffdpypQp2rVrV53zs7KyFB0d7dsSExNbsFoAANCSHB9k3G63LrzwQg0bNkxZWVkaNGiQFi5cWOf8zMxMlZWV+bbi4uIWrBYAALQkx59aOlNNTY3fqaMzeTweeTyeFqwIAADYxdFBJjMzU9dee6169OihY8eO6a233lJubq7Wrl1rd2kAAMABHB1kDh06pDvvvFMHDx5UdHS0Bg4cqLVr1+qqq66yuzQAAOAAjg4yr776qt0lAAAAB3P8xb4AAAB1IcgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsRweZrKwsXXrppYqMjFRMTIwmTJig3bt3210WAABwCEcHmby8PKWlpWnjxo1at26dTp48qauvvloVFRV2lwYAABwgzO4C6rNmzRq/28uWLVNMTIwKCgp02WWX2VQVAABwCkevyJyprKxMktSlSxebKwEAAE7g6BWZn6upqdHMmTM1evRoDRgwoM55Xq9XXq/Xd7u8vLwlygMAADYwJsikpaXp888/1/r16+udl5WVpXnz5rVQVUDdkmavsrsEY5zLz2rfgnHNWAkA0xhxaik9PV0ffvihPv74Y3Xv3r3euZmZmSorK/NtxcXFLVQlAABoaY5ekbEsS9OnT9fKlSuVm5urXr16NbiPx+ORx+NpgeoAAIDdHB1k0tLS9NZbb+n9999XZGSkSkpKJEnR0dFq3769zdUBAAC7OfrU0uLFi1VWVqYxY8YoPj7et73zzjt2lwYAABzA0SsylmXZXQIAAHAwR6/IAAAA1IcgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsRwfZD755BONHz9eCQkJcrlceu+99+wuCQAAOITjg0xFRYUGDRqk7Oxsu0sBAAAOE2Z3AQ259tprde2119pdBgAAcCDHr8gAAADUxfErMo3l9Xrl9Xp9t8vLy22sBgAABFOrCzJZWVmaN2+e3WU4WtLsVbY8774F45q8b1NrDlO17mjf5KdFI9j1e3UuzqVmO36f7Xzec2Hiz8pEJh7f5tDqTi1lZmaqrKzMtxUXF9tdEgAACJJWtyLj8Xjk8XjsLgMAALQAxweZ48eP66uvvvLd3rt3r7Zv364uXbqoR48eNlYGAADs5vggs2XLFo0dO9Z3OyMjQ5I0ZcoULVu2zKaqAACAEzg+yIwZM0aWZdldBgAAcKBWd7EvAABoOwgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYy4ggk52draSkJIWHh2vkyJHatGmT3SUBAAAHcHyQeeedd5SRkaG5c+dq69atGjRokFJTU3Xo0CG7SwMAADZzfJB57rnndO+992rq1Knq37+/lixZooiICL322mt2lwYAAGzm6CBTVVWlgoICpaSk+MZCQkKUkpKiDRs22FgZAABwgjC7C6jP4cOHVV1drdjYWL/x2NhY/f3vf691H6/XK6/X67tdVlYmSSovL2/2+mq8lU3eNxj1BOpc6j4X59JzU2uuUbVOuE74nt/tdje5hkY/t4E/53NhYr92vYZNfN5zYeLPykQmHt9AHteyrPonWg727bffWpKszz77zG/8oYceskaMGFHrPnPnzrUksbGxsbGxsbWCrbi4uN6s4OgVmW7duik0NFSlpaV+46WlpYqLi6t1n8zMTGVkZPhu19TU6IcfflDXrl3lcrkCfu7y8nIlJiaquLhYUVFRTWvA4dpCj1Lb6LMt9Ci1jT7bQo9S2+izLfQoBa9Py7J07NgxJSQk1DvP0UHG7XZr2LBhysnJ0YQJEyT9FExycnKUnp5e6z4ej0cej8dvrFOnTk2uISoqqlX/Akpto0epbfTZFnqU2kafbaFHqW302RZ6lILTZ3R0dINzHB1kJCkjI0NTpkzR8OHDNWLECL3wwguqqKjQ1KlT7S4NAADYzPFB5tZbb9V3332nOXPmqKSkRIMHD9aaNWvOugAYAAC0PY4PMpKUnp5e56mkYPF4PJo7d+5Zp6lak7bQo9Q2+mwLPUpto8+20KPUNvpsCz1K9vfpsqyG3tcEAADgTI7+QDwAAID6EGQAAICxCDIAAMBYBBkAAGAsgsz/e/LJJzVq1ChFREQE/AF6lmVpzpw5io+PV/v27ZWSkqIvv/wyuIWeox9++EGTJ09WVFSUOnXqpLvvvlvHjx+vd58xY8bI5XL5bf/+7//eQhUHJjs7W0lJSQoPD9fIkSO1adOmeue/++676tu3r8LDw3XJJZfoL3/5SwtV2nSN6XHZsmVnHbPw8PAWrLbxPvnkE40fP14JCQlyuVx67733GtwnNzdXQ4cOlcfj0YUXXqhly5YFvc5z1dg+c3NzzzqWLpdLJSUlLVNwE2RlZenSSy9VZGSkYmJiNGHCBO3evbvB/Ux6XTalRxNfl4sXL9bAgQN9H3aXnJys1atX17tPSx9Hgsz/q6qq0s0336z77rsv4H2efvppLVq0SEuWLFF+fr46dOig1NRUnThxIoiVnpvJkydr586dWrdunT788EN98sknmjZtWoP73XvvvTp48KBve/rpp1ug2sC88847ysjI0Ny5c7V161YNGjRIqampOnToUK3zP/vsM02aNEl33323tm3bpgkTJmjChAn6/PPPW7jywDW2R+mnT9n8+THbv39/C1bceBUVFRo0aJCys7MDmr93716NGzdOY8eO1fbt2zVz5kzdc889Wrt2bZArPTeN7fO03bt3+x3PmJiYIFV47vLy8pSWlqaNGzdq3bp1OnnypK6++mpVVFTUuY9pr8um9CiZ97rs3r27FixYoIKCAm3ZskVXXHGFrr/+eu3cubPW+bYcx2b5dsdWZOnSpVZ0dHSD82pqaqy4uDjrD3/4g2/s6NGjlsfjsd5+++0gVth0u3btsiRZmzdv9o2tXr3acrlc1rffflvnfpdffrk1Y8aMFqiwaUaMGGGlpaX5bldXV1sJCQlWVlZWrfNvueUWa9y4cX5jI0eOtP7t3/4tqHWei8b2GOjvsVNJslauXFnvnIcffti6+OKL/cZuvfVWKzU1NYiVNa9A+vz4448tSdaRI0dapKZgOHTokCXJysvLq3OOia/LnwukR9Nfl6d17tzZ+uMf/1jrfXYcR1Zkmmjv3r0qKSlRSkqKbyw6OlojR47Uhg0bbKysbhs2bFCnTp00fPhw31hKSopCQkKUn59f775vvvmmunXrpgEDBigzM1OVlfZ8XfyZqqqqVFBQ4HccQkJClJKSUudx2LBhg998SUpNTXXscWtKj5J0/Phx9ezZU4mJifX+C8pUph3HczV48GDFx8frqquu0qeffmp3OY1SVlYmSerSpUudc0w/noH0KJn9uqyurtby5ctVUVGh5OTkWufYcRyN+GRfJzp9fvrMr0qIjY117LnrkpKSs5ajw8LC1KVLl3prvv3229WzZ08lJCRox44deuSRR7R7926tWLEi2CU36PDhw6qurq71OPz973+vdZ+SkhKjjltTeuzTp49ee+01DRw4UGVlZXrmmWc0atQo7dy5U927d2+JsoOuruNYXl6uH3/8Ue3bt7epsuYVHx+vJUuWaPjw4fJ6vfrjH/+oMWPGKD8/X0OHDrW7vAbV1NRo5syZGj16tAYMGFDnPNNelz8XaI+mvi4LCwuVnJysEydOqGPHjlq5cqX69+9f61w7jmOrDjKzZ8/WU089Ve+cL774Qn379m2hioIj0D6b6ufX0FxyySWKj4/XlVdeqT179uiCCy5o8uMieJKTk/3+xTRq1Cj169dPL730kv7jP/7DxsrQWH369FGfPn18t0eNGqU9e/bo+eef1xtvvGFjZYFJS0vT559/rvXr19tdStAE2qOpr8s+ffpo+/btKisr05/+9CdNmTJFeXl5dYaZltaqg8yDDz6ou+66q945559/fpMeOy4uTpJUWlqq+Ph433hpaakGDx7cpMdsqkD7jIuLO+vi0FOnTumHH37w9ROIkSNHSpK++uor24NMt27dFBoaqtLSUr/x0tLSOnuKi4tr1Hy7NaXHM7Vr105DhgzRV199FYwSbVHXcYyKimo1qzF1GTFihBHBID093femgoZWHEx7XZ7WmB7PZMrr0u1268ILL5QkDRs2TJs3b9bChQv10ksvnTXXjuPYqq+ROe+889S3b996N7fb3aTH7tWrl+Li4pSTk+MbKy8vV35+fp3nDoMl0D6Tk5N19OhRFRQU+Pb96KOPVFNT4wsngdi+fbsk+QU4u7jdbg0bNszvONTU1CgnJ6fO45CcnOw3X5LWrVvX4sctUE3p8UzV1dUqLCx0xDFrLqYdx+a0fft2Rx9Ly7KUnp6ulStX6qOPPlKvXr0a3Me049mUHs9k6uuypqZGXq+31vtsOY5Bu4zYMPv377e2bdtmzZs3z+rYsaO1bds2a9u2bdaxY8d8c/r06WOtWLHCd3vBggVWp06drPfff9/asWOHdf3111u9evWyfvzxRztaCMg111xjDRkyxMrPz7fWr19vXXTRRdakSZN893/zzTdWnz59rPz8fMuyLOurr76yHn/8cWvLli3W3r17rffff986//zzrcsuu8yuFs6yfPlyy+PxWMuWLbN27dplTZs2zerUqZNVUlJiWZZl3XHHHdbs2bN98z/99FMrLCzMeuaZZ6wvvvjCmjt3rtWuXTursLDQrhYa1Nge582bZ61du9bas2ePVVBQYN12221WeHi4tXPnTrtaaNCxY8d8rztJ1nPPPWdt27bN2r9/v2VZljV79mzrjjvu8M3/+uuvrYiICOuhhx6yvvjiCys7O9sKDQ211qxZY1cLAWlsn88//7z13nvvWV9++aVVWFhozZgxwwoJCbH+53/+x64WGnTfffdZ0dHRVm5urnXw4EHfVllZ6Ztj+uuyKT2a+LqcPXu2lZeXZ+3du9fasWOHNXv2bMvlcll//etfLctyxnEkyPy/KVOmWJLO2j7++GPfHEnW0qVLfbdramqsxx57zIqNjbU8Ho915ZVXWrt372754hvh+++/tyZNmmR17NjRioqKsqZOneoX1vbu3evXd1FRkXXZZZdZXbp0sTwej3XhhRdaDz30kFVWVmZTB7X7z//8T6tHjx6W2+22RowYYW3cuNF33+WXX25NmTLFb/5///d/W71797bcbrd18cUXW6tWrWrhihuvMT3OnDnTNzc2Nta67rrrrK1bt9pQdeBOv834zO10X1OmTLEuv/zys/YZPHiw5Xa7rfPPP9/v9elUje3zqaeesi644AIrPDzc6tKlizVmzBjro48+sqf4ANXW35n//zT9ddmUHk18Xf7617+2evbsabndbuu8886zrrzySl+IsSxnHEeXZVlW8NZ7AAAAgqdVXyMDAABaN4IMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkALSY3N1cul0tHjx615flzcnLUr18/VVdXNzh3zZo1Gjx4sGpqalqgMgBNRZABEBRjxozRzJkz/cZGjRqlgwcPKjo62paaHn74YT366KMKDQ1tcO4111yjdu3a6c0332yBygA0FUEGQItxu92Ki4uTy+Vq8edev3699uzZo4kTJwa8z1133aVFixYFsSoA54ogA6DZ3XXXXcrLy9PChQvlcrnkcrm0b9++s04tLVu2TJ06ddKHH36oPn36KCIiQjfddJMqKyv1+uuvKykpSZ07d9b999/vdzrI6/Vq1qxZ+qd/+id16NBBI0eOVG5ubr01LV++XFdddZXCw8N9Y//7v/+rsWPHKjIyUlFRURo2bJi2bNniu3/8+PHasmWL9uzZ06w/HwDNJ8zuAgC0PgsXLtQ//vEPDRgwQI8//rgk6bzzztO+ffvOmltZWalFixZp+fLlOnbsmG688UbdcMMN6tSpk/7yl7/o66+/1sSJEzV69GjdeuutkqT09HTt2rVLy5cvV0JCglauXKlrrrlGhYWFuuiii2qt6W9/+5tuv/12v7HJkydryJAhWrx4sUJDQ7V9+3a1a9fOd3+PHj0UGxurv/3tb7rgggua6acDoDkRZAA0u+joaLndbkVERCguLq7euSdPntTixYt9QeGmm27SG2+8odLSUnXs2FH9+/fX2LFj9fHHH+vWW29VUVGRli5dqqKiIiUkJEiSZs2apTVr1mjp0qWaP39+rc+zf/9+3/zTioqK9NBDD6lv376SVGsISkhI0P79+xv9MwDQMggyAGwVERHht9oRGxurpKQkdezY0W/s0KFDkqTCwkJVV1erd+/efo/j9XrVtWvXOp/nxx9/9DutJEkZGRm655579MYbbyglJUU333zzWSsv7du3V2VlZZP7AxBcBBkAtvr5qRxJcrlctY6dfhv08ePHFRoaqoKCgrPeffTz8HOmbt266ciRI35jv//973X77bdr1apVWr16tebOnavly5frhhtu8M354YcfdN555zWpNwDBR5ABEBRutzugz2tprCFDhqi6ulqHDh3Sv/zLvzRqv127dp013rt3b/Xu3VsPPPCAJk2apKVLl/qCzIkTJ7Rnzx4NGTKk2eoH0Lx41xKAoEhKSlJ+fr727dunw4cPN9sHy/Xu3VuTJ0/WnXfeqRUrVmjv3r3atGmTsrKytGrVqjr3S01N1fr16323f/zxR6Wnpys3N1f79+/Xp59+qs2bN6tfv36+ORs3bpTH41FycnKz1A6g+RFkAATFrFmzFBoaqv79++u8885TUVFRsz320qVLdeedd+rBBx9Unz59NGHCBG3evFk9evSoc5/Jkydr586d2r17tyQpNDRU33//ve6880717t1bt9xyi6699lrNmzfPt8/bb7+tyZMnKyIiotlqB9C8XJZlWXYXAQAt4aGHHlJ5ebleeumlBucePnxYffr00ZYtW9SrV68WqA5AU7AiA6DN+N3vfqeePXsGdJpr3759evHFFwkxgMOxIgMAAIzFigwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMNb/Ac0xnNBBdl+6AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "before = 1.0  # in seconds\n",
+    "after = 3.0\n",
+    "\n",
+    "# Get the stimulus times for all stimuli\n",
+    "stim_on_times =trials[\"start_time\"] + trials[\"photostim_onset\"].values.astype(\"float\")\n",
+    "\n",
+    "for unit in range(3):\n",
+    "    unit_spike_times = units[\"spike_times\"][unit]\n",
+    "    trial_spikes = []\n",
+    "    for time in stim_on_times:\n",
+    "        # Compute spike times relative to stimulus onset\n",
+    "        aligned_spikes = unit_spike_times - time\n",
+    "        # Keep only spike times in a given time window around the stimulus onset\n",
+    "        aligned_spikes = aligned_spikes[\n",
+    "            (-before < aligned_spikes) & (aligned_spikes < after)\n",
+    "        ]\n",
+    "        trial_spikes.append(aligned_spikes)\n",
+    "    fig, axs = plt.subplots(2, 1, sharex=\"all\")\n",
+    "    plt.xlabel(\"time (s)\")\n",
+    "    axs[0].eventplot(trial_spikes)\n",
+    "\n",
+    "    axs[0].set_ylabel(\"trial\")\n",
+    "    axs[0].set_title(\"unit {}\".format(unit))\n",
+    "    axs[0].axvline(0, color=[0.5, 0.5, 0.5])\n",
+    "\n",
+    "    axs[1].hist(np.hstack(trial_spikes), 30)\n",
+    "    axs[1].axvline(0, color=[0.5, 0.5, 0.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The NWB file also contains behavior tracking data which is found in `nwbfile.acquisition`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'BehavioralEvents': BehavioralEvents pynwb.behavior.BehavioralEvents at 0x2750574574512\n",
+       " Fields:\n",
+       "   time_series: {\n",
+       "     delay_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     delay_stop_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     go_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     go_stop_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     left_lick_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     photostim_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     photostim_stop_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     presample_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     presample_stop_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     right_lick_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     sample_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     sample_stop_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     trialend_start_times <class 'pynwb.base.TimeSeries'>,\n",
+       "     trialend_stop_times <class 'pynwb.base.TimeSeries'>\n",
+       "   },\n",
+       " 'BehavioralTimeSeries': BehavioralTimeSeries pynwb.behavior.BehavioralTimeSeries at 0x2750574822736\n",
+       " Fields:\n",
+       "   time_series: {\n",
+       "     Camera0_side_JawTracking <class 'pynwb.base.TimeSeries'>,\n",
+       "     Camera0_side_NoseTracking <class 'pynwb.base.TimeSeries'>,\n",
+       "     Camera0_side_TongueTracking <class 'pynwb.base.TimeSeries'>\n",
+       "   }}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.acquisition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The cell below fetches the timestamps for jaw tracking for camera 0. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "camera0_jawTracking_ts = nwbfile.acquisition[\"BehavioralTimeSeries\"][\"Camera0_side_JawTracking\"].timestamps[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we can create a function to plot trialized jaw movement overlaid with\n",
+    "spiking activity from a particular unit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trialized_jaw_movement(unit, title='left lick trials', trial_offset=0, trial_limit=10, xlim=(-0.5, 1)):   \n",
+    "    tongue_likelihood = nwbfile.acquisition['BehavioralTimeSeries']['Camera0_side_TongueTracking'].data[:, -1]\n",
+    "    \n",
+    "    tracking_y = nwbfile.acquisition['BehavioralTimeSeries']['Camera0_side_JawTracking'].data[:, 0]\n",
+    "    tracking_ts = nwbfile.acquisition['BehavioralTimeSeries']['Camera0_side_JawTracking'].timestamps\n",
+    "    \n",
+    "    left_licks_trials = nwbfile.trials.to_dataframe().query(\"trial_instruction == 'left' and early_lick == 'no early' and outcome == 'hit'\")\n",
+    "    right_licks_trials = nwbfile.trials.to_dataframe().query(\"trial_instruction == 'right' and early_lick == 'no early' and outcome == 'hit'\")\n",
+    "    left_lick_times = nwbfile.acquisition['BehavioralEvents']['left_lick_times'].timestamps\n",
+    "    right_lick_times = nwbfile.acquisition['BehavioralEvents']['right_lick_times'].timestamps\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
+    "    h_spacing = 150\n",
+    "    \n",
+    "    unit_data = units_df.query(f'id == {unit}').iloc[0]\n",
+    "    \n",
+    "    for conditioned_trials_df, lick_times, ax, ax_name, spk_color in zip(\n",
+    "        (left_licks_trials, right_licks_trials),\n",
+    "        (left_lick_times, right_lick_times),\n",
+    "        axs, \n",
+    "        ('left lick trials', 'right lick trials'),\n",
+    "        ('b', 'r')): \n",
+    "        \n",
+    "        if trial_offset < 1 and isinstance(trial_offset, float):\n",
+    "            offset = int(len(conditioned_trials_df) * trial_offset)\n",
+    "        else:\n",
+    "            offset = trial_offset\n",
+    "        \n",
+    "        trial_numbers = conditioned_trials_df.trial[offset:offset+trial_limit].values\n",
+    "\n",
+    "        _, row_ind, _ = np.intersect1d(trials_df.trial.values, trial_numbers, return_indices=True)\n",
+    "\n",
+    "        for tr_id, row_id in enumerate(row_ind):\n",
+    "            trial_data = trials_df.iloc[row_id]\n",
+    "            align_time = lick_times[np.logical_and(lick_times >= trial_data.start_time, \n",
+    "                                           lick_times < trial_data.stop_time)][0]\n",
+    "\n",
+    "            trk_feat = tracking_y[np.logical_and(tracking_ts >= align_time + xlim[0], \n",
+    "                                                           tracking_ts < align_time + xlim[-1])]\n",
+    "            tongue_out_bool = tongue_likelihood[np.logical_and(tracking_ts >= align_time + xlim[0], \n",
+    "                                                           tracking_ts < align_time + xlim[-1])] > 0.9\n",
+    "\n",
+    "            spike_times = unit_data.spike_times[np.logical_and(unit_data.spike_times >= align_time + xlim[0], \n",
+    "                                                           unit_data.spike_times < align_time + xlim[-1])]\n",
+    "            spike_times -= align_time\n",
+    "\n",
+    "            tvec = np.linspace(*xlim, len(trk_feat))\n",
+    "\n",
+    "            # plot\n",
+    "            ax.plot(tvec, trk_feat + tr_id * h_spacing, '.k', markersize=1)\n",
+    "            ax.plot(tvec[tongue_out_bool], trk_feat[tongue_out_bool] + tr_id * h_spacing, '.', color='lime', markersize=2)\n",
+    "            ax.plot(spike_times, np.full_like(spike_times, trk_feat[tongue_out_bool].mean() + h_spacing/10) + tr_id * h_spacing,\n",
+    "                    '|', color=spk_color, markersize=4)\n",
+    "\n",
+    "\n",
+    "        ax.set_title(ax_name)\n",
+    "        ax.axvline(x=0, linestyle='--', color='k')\n",
+    "\n",
+    "        # cosmetic\n",
+    "        ax.set_xlim(xlim)\n",
+    "        ax.set_yticks([])\n",
+    "        ax.spines['left'].set_visible(False)\n",
+    "        ax.spines['right'].set_visible(False)\n",
+    "        ax.spines['top'].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\kusha\\AppData\\Local\\Temp\\ipykernel_32764\\3554794887.py:29: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
+      "  trial_numbers = conditioned_trials_df.trial[offset:offset+trial_limit].values\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_trialized_jaw_movement(unit=12, trial_offset=0.5, trial_limit=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would like to work with a `.nwb` file without downloading it locally,\n",
+    "please follow the steps below: \n",
+    "\n",
+    "1. Once you find a dataset you want to work with, please update the filepath\n",
+    "variable below to match the file path on DANDI. \n",
+    "\n",
+    "2. Once updated, run the three cells below.\n",
+    "\n",
+    "The `.nwb` file is now streaming and you can bring items into memory by using code\n",
+    "similar to that in the fourth cell below this comment. \n",
+    "\n",
+    "**Please ensure the file path information is entered correctly and you are\n",
+    "working with your intended dataset.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dandiset_id = \"000363\" \n",
+    "filepath = \"sub-440956/sub-440956_ses-20190207T120657_behavior+ecephys+ogen.nwb\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\kusha\\miniconda3\\envs\\mapEphysNWB\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.7.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "c:\\Users\\kusha\\miniconda3\\envs\\mapEphysNWB\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.5.0 because version 2.6.0-alpha is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "c:\\Users\\kusha\\miniconda3\\envs\\mapEphysNWB\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.4.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "with DandiAPIClient() as client:\n",
+    "    asset = client.get_dandiset(dandiset_id, \"draft\").get_asset_by_path(filepath)\n",
+    "    s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)\n",
+    "fs=fsspec.filesystem(\"http\")\n",
+    "file = h5py.File(fs.open(s3_url, \"rb\"))\n",
+    "io = pynwb.NWBHDF5IO(file=file, load_namespaces=True)\n",
+    "nwbfile_stream = io.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['session_description']\">session_description:</span> <span class=\"field-value\"></span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['identifier']\">identifier:</span> <span class=\"field-value\">SC015_20190207_120657_s1</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['session_start_time']\">session_start_time:</span> <span class=\"field-value\">2019-02-07 12:06:57-06:00</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['timestamps_reference_time']\">timestamps_reference_time:</span> <span class=\"field-value\">2019-02-07 12:06:57-06:00</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['file_create_date']\"><b>file_create_date</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-value\" title=\".fields['file_create_date'][0]\">2023-04-25 12:08:21.159278-05:00</span></div></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['experimenter']\">experimenter:</span> <span class=\"field-value\">('susu',)</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['related_publications']\">related_publications:</span> <span class=\"field-value\">('',)</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['acquisition']\"><b>acquisition (2)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents']\"><b>BehavioralEvents</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']\"><b>time_series (14)</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times']\"><b>delay_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: delay - Start Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times']\"><b>delay_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: delay - Stop Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['delay_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times']\"><b>go_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: go - Start Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times']\"><b>go_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: go - Stop Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['go_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times']\"><b>left_lick_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: left lick</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['left_lick_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times']\"><b>photostim_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps of the photo-stimulation and the corresponding powers (in mW) being applied</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">mW</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['control']\"><b>control</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['control_description']\"><b>control_description</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times']\"><b>photostim_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps of the photo-stimulation being switched off</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">mW</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['control']\"><b>control</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['control_description']\"><b>control_description</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['photostim_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times']\"><b>presample_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: presample - Start Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times']\"><b>presample_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: presample - Stop Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['presample_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times']\"><b>right_lick_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: right lick</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['right_lick_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times']\"><b>sample_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: sample - Start Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times']\"><b>sample_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: sample - Stop Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['sample_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times']\"><b>trialend_start_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: trialend - Start Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_start_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times']\"><b>trialend_stop_times</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['description']\">description:</span> <span class=\"field-value\">Timestamps for event type: trialend - Stop Time</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralEvents'].fields['time_series']['trialend_stop_times'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries']\"><b>BehavioralTimeSeries</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']\"><b>time_series (3)</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking']\"><b>Camera0_side_JawTracking</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['description']\">description:</span> <span class=\"field-value\">Time series for JawTracking position: ('jaw_x', 'jaw_y', 'jaw_likelihood')</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_JawTracking'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking']\"><b>Camera0_side_NoseTracking</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['description']\">description:</span> <span class=\"field-value\">Time series for NoseTracking position: ('nose_x', 'nose_y', 'nose_likelihood')</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_NoseTracking'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking']\"><b>Camera0_side_TongueTracking</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['description']\">description:</span> <span class=\"field-value\">Time series for TongueTracking position: ('tongue_x', 'tongue_y', 'tongue_likelihood')</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['unit']\">unit:</span> <span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['data']\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['timestamps']\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['timestamps_unit']\">timestamps_unit:</span> <span class=\"field-value\">seconds</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['BehavioralTimeSeries'].fields['time_series']['Camera0_side_TongueTracking'].fields['interval']\">interval:</span> <span class=\"field-value\">1</span></div></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['keywords']\"><b>keywords</b></summary></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['epoch_tags']\">epoch_tags:</span> <span class=\"field-value\">set()</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrodes'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'electrode', 'shank', 'shank_col', 'shank_row', 'x', 'y', 'z', 'imp', 'filtering', 'rel_x', 'rel_y', 'rel_z')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x00000280086C19D0>, <hdmf.common.table.VectorData object at 0x00000280086C1910>, <hdmf.common.table.VectorData object at 0x00000280086C17C0>, <hdmf.common.table.VectorData object at 0x00000280086C1790>, <hdmf.common.table.VectorData object at 0x00000280086C1B50>, <hdmf.common.table.VectorData object at 0x00000280086C1BB0>, <hdmf.common.table.VectorData object at 0x00000280086C1C10>, <hdmf.common.table.VectorData object at 0x00000280086C1C70>, <hdmf.common.table.VectorData object at 0x00000280086C1CD0>, <hdmf.common.table.VectorData object at 0x00000280086C1D30>, <hdmf.common.table.VectorData object at 0x00000280086C1970>, <hdmf.common.table.VectorData object at 0x00000280086C17F0>, <hdmf.common.table.VectorData object at 0x00000280086C1A30>, <hdmf.common.table.VectorData object at 0x00000280086C1A90>, <hdmf.common.table.VectorData object at 0x00000280086C1AF0>)</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']\"><b>electrode_groups (4)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['17131309211 1-281']\"><b>17131309211 1-281</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17131309211 1-281'].fields['description']\">description:</span> <span class=\"field-value\">{\"probe\": \"17131309211\", \"probe_type\": \"neuropixels 1.0 - 3B\", \"electrode_config_name\": \"1-281\", \"subject_id\": 440956, \"session\": 1, \"insertion_number\": 1, \"probe_comment\": \"\", \"electrode_config_hash\": \"1497a7be87e3c11ebd33da9f574200d9\"}</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17131309211 1-281'].fields['location']\">location:</span> <span class=\"field-value\">{\"brain_regions\": \"left ALM\", \"skull_reference\": \"Bregma\", \"ap_location\": \"2500.00\", \"ml_location\": \"-1500.00\", \"depth\": \"-2600.00\", \"theta\": \"15.00\", \"phi\": \"135.00\", \"beta\": \"-135.00\"}</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['17131309211 1-281'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17131309211 1-281'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['17216703352 1-281']\"><b>17216703352 1-281</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17216703352 1-281'].fields['description']\">description:</span> <span class=\"field-value\">{\"probe\": \"17216703352\", \"probe_type\": \"neuropixels 1.0 - 3B\", \"electrode_config_name\": \"1-281\", \"subject_id\": 440956, \"session\": 1, \"insertion_number\": 2, \"probe_comment\": \"\", \"electrode_config_hash\": \"1497a7be87e3c11ebd33da9f574200d9\"}</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17216703352 1-281'].fields['location']\">location:</span> <span class=\"field-value\">{\"brain_regions\": \"right ALM\", \"skull_reference\": \"Bregma\", \"ap_location\": \"2500.00\", \"ml_location\": \"1500.00\", \"depth\": \"-2600.00\", \"theta\": \"15.00\", \"phi\": \"45.00\", \"beta\": \"-45.00\"}</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['17216703352 1-281'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['17216703352 1-281'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['18005117122 1-384']\"><b>18005117122 1-384</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005117122 1-384'].fields['description']\">description:</span> <span class=\"field-value\">{\"probe\": \"18005117122\", \"probe_type\": \"neuropixels 1.0 - 3B\", \"electrode_config_name\": \"1-384\", \"subject_id\": 440956, \"session\": 1, \"insertion_number\": 4, \"probe_comment\": \"\", \"electrode_config_hash\": \"4ae1f6ed4687467836d43369e548fe00\"}</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005117122 1-384'].fields['location']\">location:</span> <span class=\"field-value\">{\"brain_regions\": \"right Striatum\", \"skull_reference\": \"Bregma\", \"ap_location\": \"0.00\", \"ml_location\": \"2500.00\", \"depth\": \"-4200.00\", \"theta\": \"10.00\", \"phi\": \"0.00\", \"beta\": \"60.00\"}</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['18005117122 1-384'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005117122 1-384'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['18005118502 1-384']\"><b>18005118502 1-384</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005118502 1-384'].fields['description']\">description:</span> <span class=\"field-value\">{\"probe\": \"18005118502\", \"probe_type\": \"neuropixels 1.0 - 3B\", \"electrode_config_name\": \"1-384\", \"subject_id\": 440956, \"session\": 1, \"insertion_number\": 3, \"probe_comment\": \"\", \"electrode_config_hash\": \"4ae1f6ed4687467836d43369e548fe00\"}</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005118502 1-384'].fields['location']\">location:</span> <span class=\"field-value\">{\"brain_regions\": \"left Striatum\", \"skull_reference\": \"Bregma\", \"ap_location\": \"0.00\", \"ml_location\": \"-2500.00\", \"depth\": \"-4200.00\", \"theta\": \"10.00\", \"phi\": \"180.00\", \"beta\": \"120.00\"}</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['18005118502 1-384'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['18005118502 1-384'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['devices']\"><b>devices (5)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['17131309211 (neuropixels 1.0 - 3B)']\"><b>17131309211 (neuropixels 1.0 - 3B)</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['17131309211 (neuropixels 1.0 - 3B)'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['17216703352 (neuropixels 1.0 - 3B)']\"><b>17216703352 (neuropixels 1.0 - 3B)</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['17216703352 (neuropixels 1.0 - 3B)'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['18005117122 (neuropixels 1.0 - 3B)']\"><b>18005117122 (neuropixels 1.0 - 3B)</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['18005117122 (neuropixels 1.0 - 3B)'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['18005118502 (neuropixels 1.0 - 3B)']\"><b>18005118502 (neuropixels 1.0 - 3B)</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['18005118502 (neuropixels 1.0 - 3B)'].fields['description']\">description:</span> <span class=\"field-value\">neuropixels 1.0 - 3B</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['OBIS470']\"><b>OBIS470</b></summary></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['ogen_sites']\"><b>ogen_sites (2)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['ogen_sites']['OBIS470_4']\"><b>OBIS470_4</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['ogen_sites']['OBIS470_4'].fields['device']\"><b>device</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_4'].fields['description']\">description:</span> <span class=\"field-value\">excitation_duration: 0.5000</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_4'].fields['excitation_lambda']\">excitation_lambda:</span> <span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_4'].fields['location']\">location:</span> <span class=\"field-value\">[{\"skull_reference\": \"Bregma\", \"ap_location\": \"2500.00\", \"ml_location\": \"-1500.00\", \"depth\": \"0.00\", \"theta\": \"15.00\", \"phi\": \"15.00\", \"brain_area\": \"ALM\"}]</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['ogen_sites']['OBIS470_6']\"><b>OBIS470_6</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['ogen_sites']['OBIS470_6'].fields['device']\"><b>device</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_6'].fields['description']\">description:</span> <span class=\"field-value\">excitation_duration: 0.5000</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_6'].fields['excitation_lambda']\">excitation_lambda:</span> <span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['ogen_sites']['OBIS470_6'].fields['location']\">location:</span> <span class=\"field-value\">[{\"skull_reference\": \"Bregma\", \"ap_location\": \"2500.00\", \"ml_location\": \"-1500.00\", \"depth\": \"0.00\", \"theta\": \"15.00\", \"phi\": \"15.00\", \"brain_area\": \"ALM\"}, {\"skull_reference\": \"Bregma\", \"ap_location\": \"2500.00\", \"ml_location\": \"1500.00\", \"depth\": \"0.00\", \"theta\": \"15.00\", \"phi\": \"15.00\", \"brain_area\": \"ALM\"}]</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['intervals']\"><b>intervals (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['intervals']['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['description']\">description:</span> <span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['intervals']['trials'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('start_time', 'stop_time', 'trial', 'photostim_onset', 'photostim_power', 'photostim_duration', 'trial_uid', 'task', 'task_protocol', 'trial_instruction', 'early_lick', 'outcome', 'auto_water', 'free_water')</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x00000280086C1C40>, <hdmf.common.table.VectorData object at 0x00000280086C1B80>, <hdmf.common.table.VectorData object at 0x00000280086CB0D0>, <hdmf.common.table.VectorData object at 0x00000280086C1F10>, <hdmf.common.table.VectorData object at 0x00000280086C1F70>, <hdmf.common.table.VectorData object at 0x00000280086C1EB0>, <hdmf.common.table.VectorData object at 0x00000280086CB190>, <hdmf.common.table.VectorData object at 0x00000280086C1DC0>, <hdmf.common.table.VectorData object at 0x00000280086CB070>, <hdmf.common.table.VectorData object at 0x00000280086CB130>, <hdmf.common.table.VectorData object at 0x00000280086C1D90>, <hdmf.common.table.VectorData object at 0x00000280086C1E50>, <hdmf.common.table.VectorData object at 0x00000280086C1850>, <hdmf.common.table.VectorData object at 0x00000280086C1DF0>)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['subject']\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['description']\">description:</span> <span class=\"field-value\">SC015</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['sex']\">sex:</span> <span class=\"field-value\">M</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['species']\">species:</span> <span class=\"field-value\">Mus musculus</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['subject_id']\">subject_id:</span> <span class=\"field-value\">440956</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['date_of_birth']\">date_of_birth:</span> <span class=\"field-value\">2018-10-09 00:00:00-05:00</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['trials']\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['description']\">description:</span> <span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['trials'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('start_time', 'stop_time', 'trial', 'photostim_onset', 'photostim_power', 'photostim_duration', 'trial_uid', 'task', 'task_protocol', 'trial_instruction', 'early_lick', 'outcome', 'auto_water', 'free_water')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x00000280086C1C40>, <hdmf.common.table.VectorData object at 0x00000280086C1B80>, <hdmf.common.table.VectorData object at 0x00000280086CB0D0>, <hdmf.common.table.VectorData object at 0x00000280086C1F10>, <hdmf.common.table.VectorData object at 0x00000280086C1F70>, <hdmf.common.table.VectorData object at 0x00000280086C1EB0>, <hdmf.common.table.VectorData object at 0x00000280086CB190>, <hdmf.common.table.VectorData object at 0x00000280086C1DC0>, <hdmf.common.table.VectorData object at 0x00000280086CB070>, <hdmf.common.table.VectorData object at 0x00000280086CB130>, <hdmf.common.table.VectorData object at 0x00000280086C1D90>, <hdmf.common.table.VectorData object at 0x00000280086C1E50>, <hdmf.common.table.VectorData object at 0x00000280086C1850>, <hdmf.common.table.VectorData object at 0x00000280086C1DF0>)</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['units']\"><b>units</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['description']\">description:</span> <span class=\"field-value\">Autogenerated by NWBFile</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['units'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('unit', 'sampling_rate', 'unit_quality', 'unit_posx', 'unit_posy', 'unit_amp', 'unit_snr', 'isi_violation', 'avg_firing_rate', 'drift_metric', 'presence_ratio', 'amplitude_cutoff', 'isolation_distance', 'l_ratio', 'd_prime', 'nn_hit_rate', 'nn_miss_rate', 'silhouette_score', 'max_drift', 'cumulative_drift', 'duration', 'halfwidth', 'pt_ratio', 'repolarization_slope', 'recovery_slope', 'spread', 'velocity_above', 'velocity_below', 'classification', 'anno_name', 'spike_times', 'obs_intervals', 'electrodes', 'electrode_group', 'waveform_mean', 'waveform_sd')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x000002800863F550>, <hdmf.common.table.VectorData object at 0x000002800863F820>, <hdmf.common.table.VectorData object at 0x00000280085FB730>, <hdmf.common.table.VectorData object at 0x000002800863F2B0>, <hdmf.common.table.VectorData object at 0x000002800863FE50>, <hdmf.common.table.VectorData object at 0x000002800863F2E0>, <hdmf.common.table.VectorData object at 0x0000028008631FA0>, <hdmf.common.table.VectorData object at 0x00000280086CB760>, <hdmf.common.table.VectorData object at 0x00000280086CB4C0>, <hdmf.common.table.VectorData object at 0x00000280086CB640>, <hdmf.common.table.VectorData object at 0x000002800863FDC0>, <hdmf.common.table.VectorData object at 0x00000280086CB400>, <hdmf.common.table.VectorData object at 0x00000280086CB7C0>, <hdmf.common.table.VectorData object at 0x00000280086CB820>, <hdmf.common.table.VectorData object at 0x00000280086CB5E0>, <hdmf.common.table.VectorData object at 0x000002800863FFD0>, <hdmf.common.table.VectorData object at 0x000002800863FDF0>, <hdmf.common.table.VectorData object at 0x000002800863F7F0>, <hdmf.common.table.VectorData object at 0x00000280086CB880>, <hdmf.common.table.VectorData object at 0x00000280086CB580>, <hdmf.common.table.VectorData object at 0x00000280086CB6A0>, <hdmf.common.table.VectorData object at 0x00000280086CB700>, <hdmf.common.table.VectorData object at 0x000002800863F4C0>, <hdmf.common.table.VectorData object at 0x000002800863FA90>, <hdmf.common.table.VectorData object at 0x000002800863FAC0>, <hdmf.common.table.VectorData object at 0x000002800863F580>, <hdmf.common.table.VectorData object at 0x0000028008631550>, <hdmf.common.table.VectorData object at 0x0000028008631640>, <hdmf.common.table.VectorData object at 0x00000280086CB520>, <hdmf.common.table.VectorData object at 0x00000280086CB460>, <hdmf.common.table.VectorIndex object at 0x00000280086C1820>, <hdmf.common.table.VectorData object at 0x00000280086C1EE0>, <hdmf.common.table.VectorIndex object at 0x00000280086C1E20>, <hdmf.common.table.VectorData object at 0x00000280086C1CA0>, <hdmf.common.table.VectorIndex object at 0x00000280086C1FD0>, electrodes hdmf.common.table.DynamicTableRegion at 0x2748920410656\n",
+       "    Target table: electrodes hdmf.common.table.DynamicTable at 0x2748920370704\n",
+       ", <hdmf.common.table.VectorData object at 0x00000280086CB280>, <hdmf.common.table.VectorData object at 0x00000280086CB160>, <hdmf.common.table.VectorData object at 0x00000280086CB2E0>)</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['waveform_unit']\">waveform_unit:</span> <span class=\"field-value\">volts</span></div></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['experiment_description']\">experiment_description:</span> <span class=\"field-value\">high tone vs. low tone</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['institution']\">institution:</span> <span class=\"field-value\">Janelia Research Campus</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_collection']\">data_collection:</span> <span class=\"field-value\"></span></div></div>"
+      ],
+      "text/plain": [
+       "root pynwb.file.NWBFile at 0x2748919558688\n",
+       "Fields:\n",
+       "  acquisition: {\n",
+       "    BehavioralEvents <class 'pynwb.behavior.BehavioralEvents'>,\n",
+       "    BehavioralTimeSeries <class 'pynwb.behavior.BehavioralTimeSeries'>\n",
+       "  }\n",
+       "  devices: {\n",
+       "    17131309211 (neuropixels 1.0 - 3B) <class 'pynwb.device.Device'>,\n",
+       "    17216703352 (neuropixels 1.0 - 3B) <class 'pynwb.device.Device'>,\n",
+       "    18005117122 (neuropixels 1.0 - 3B) <class 'pynwb.device.Device'>,\n",
+       "    18005118502 (neuropixels 1.0 - 3B) <class 'pynwb.device.Device'>,\n",
+       "    OBIS470 <class 'pynwb.device.Device'>\n",
+       "  }\n",
+       "  electrode_groups: {\n",
+       "    17131309211 1-281 <class 'pynwb.ecephys.ElectrodeGroup'>,\n",
+       "    17216703352 1-281 <class 'pynwb.ecephys.ElectrodeGroup'>,\n",
+       "    18005117122 1-384 <class 'pynwb.ecephys.ElectrodeGroup'>,\n",
+       "    18005118502 1-384 <class 'pynwb.ecephys.ElectrodeGroup'>\n",
+       "  }\n",
+       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTable'>\n",
+       "  experiment_description: high tone vs. low tone\n",
+       "  experimenter: ['susu']\n",
+       "  file_create_date: [datetime.datetime(2023, 4, 25, 12, 8, 21, 159278, tzinfo=tzoffset(None, -18000))]\n",
+       "  identifier: SC015_20190207_120657_s1\n",
+       "  institution: Janelia Research Campus\n",
+       "  intervals: {\n",
+       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  }\n",
+       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (1,), type \"|O\">\n",
+       "  ogen_sites: {\n",
+       "    OBIS470_4 <class 'pynwb.ogen.OptogeneticStimulusSite'>,\n",
+       "    OBIS470_6 <class 'pynwb.ogen.OptogeneticStimulusSite'>\n",
+       "  }\n",
+       "  related_publications: ['']\n",
+       "  session_start_time: 2019-02-07 12:06:57-06:00\n",
+       "  subject: subject pynwb.file.Subject at 0x2748920369984\n",
+       "Fields:\n",
+       "  date_of_birth: 2018-10-09 00:00:00-05:00\n",
+       "  description: SC015\n",
+       "  sex: M\n",
+       "  species: Mus musculus\n",
+       "  subject_id: 440956\n",
+       "\n",
+       "  timestamps_reference_time: 2019-02-07 12:06:57-06:00\n",
+       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  units: units <class 'pynwb.misc.Units'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile_stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   3.0615,   10.4911,   17.3014,   27.2102,   35.2372,   42.905 ,\n",
+       "         49.7081,   56.5462,   63.2105,   72.5735,   79.2202,   85.9121,\n",
+       "         92.7641,   99.6109,  106.76  ,  116.3083,  122.0923,  127.5653,\n",
+       "        132.9223,  138.5094,  143.8852,  149.3363,  156.0039,  162.8571,\n",
+       "        169.6084,  176.8017,  187.2051,  193.9074,  201.7581,  207.6403,\n",
+       "        213.0816,  218.8675,  227.4653,  238.0169,  244.8202,  251.4951,\n",
+       "        259.8815,  266.7619,  274.0136,  281.0376,  286.9664,  292.8265,\n",
+       "        298.5257,  304.507 ,  310.8796,  319.829 ,  326.9991,  334.2257,\n",
+       "        343.1744,  351.5623,  357.7447,  363.1195,  368.747 ,  374.229 ,\n",
+       "        380.9804,  387.861 ,  394.8456,  402.903 ,  409.7502,  415.9349,\n",
+       "        421.4966,  427.0599,  432.4702,  440.0814,  446.9801,  453.5642,\n",
+       "        458.9952,  464.777 ,  470.2857,  475.6348,  482.8147,  489.7204,\n",
+       "        496.8337,  502.1852,  508.4031,  514.0713,  519.5604,  525.4693,\n",
+       "        533.5183,  540.1865,  546.609 ,  553.3516,  559.3192,  564.9274,\n",
+       "        571.8408,  578.6202,  585.4115,  592.0502,  598.5403,  604.113 ,\n",
+       "        609.6726,  615.3222,  621.482 ,  628.8424,  635.5277,  641.1466,\n",
+       "        647.8861,  654.524 ,  662.4597,  668.7303,  674.2622,  679.8691,\n",
+       "        687.3476,  694.7024,  701.8313,  708.8122,  715.5963,  721.2613,\n",
+       "        728.0117,  733.819 ,  740.7576,  747.2409,  752.8439,  758.4469,\n",
+       "        765.3189,  772.1975,  778.0954,  783.711 ,  789.8348,  795.4362,\n",
+       "        803.3813,  810.2268,  816.2674,  823.1371,  830.7652,  837.1315,\n",
+       "        842.7459,  848.6374,  854.5288,  861.4798,  867.4804,  873.2311,\n",
+       "        880.0694,  886.9565,  893.791 ,  899.6515,  906.5144,  915.4797,\n",
+       "        922.2182,  928.2243,  935.2025,  942.1355,  947.9049,  953.9881,\n",
+       "        959.7521,  965.4442,  971.8904,  979.3864,  986.1739,  991.7749,\n",
+       "        997.8248, 1005.4567, 1011.4929, 1018.4516, 1024.0857, 1032.8678,\n",
+       "       1040.0284, 1048.1081, 1053.7386, 1061.3482, 1067.3598, 1073.5459,\n",
+       "       1081.0744, 1088.0905, 1093.7267, 1099.3782, 1106.3492, 1113.7095,\n",
+       "       1121.0334, 1128.1796, 1136.5811, 1144.1801, 1150.2133, 1156.9586,\n",
+       "       1164.0074, 1170.8265, 1178.9861, 1185.6197, 1193.5744, 1201.2893,\n",
+       "       1208.2729, 1215.1117, 1221.3972, 1228.2218, 1235.1996, 1242.0493,\n",
+       "       1248.8307, 1255.7514, 1262.5618, 1269.6227, 1276.4573, 1282.8724,\n",
+       "       1289.781 , 1298.0529, 1304.9496, 1310.7095, 1317.6408, 1323.7209,\n",
+       "       1330.6749, 1337.547 , 1344.6158, 1350.2868, 1357.197 , 1364.1247,\n",
+       "       1373.3989, 1380.7206, 1387.1818, 1392.7917, 1398.7521, 1404.4882,\n",
+       "       1411.2463, 1418.4645, 1425.4206, 1431.4931, 1438.5164, 1445.4296,\n",
+       "       1452.6841, 1459.5578, 1465.2738, 1472.2254, 1479.1548, 1486.0814,\n",
+       "       1492.9945, 1500.4024, 1507.9126, 1515.1422, 1522.0199, 1527.9301,\n",
+       "       1534.112 , 1541.1924, 1548.1025, 1554.3979, 1560.2494, 1567.3377,\n",
+       "       1573.2397, 1580.1079, 1588.0388, 1594.8545, 1602.0475, 1607.8144,\n",
+       "       1613.8026, 1620.8349, 1627.6857, 1633.2791, 1639.2581, 1646.3069,\n",
+       "       1653.3472, 1659.348 , 1666.3821, 1673.2448, 1680.7812, 1688.254 ,\n",
+       "       1695.1875, 1702.056 , 1708.9938, 1718.3716, 1726.6969, 1733.6456,\n",
+       "       1740.5025, 1746.6062, 1752.3556, 1759.3398, 1766.2942, 1772.0469,\n",
+       "       1779.0226, 1785.8113, 1792.8843, 1799.7211, 1806.1827, 1811.9459,\n",
+       "       1817.4387, 1824.4164, 1831.5255, 1838.3327, 1847.9201, 1855.0521,\n",
+       "       1862.4418, 1869.2029, 1876.7185, 1883.6657, 1890.1301, 1895.7593,\n",
+       "       1902.8746, 1909.9185, 1917.18  , 1924.2328, 1930.4628, 1935.9326,\n",
+       "       1942.9056, 1949.7816, 1956.8511, 1964.0187, 1971.0863, 1978.244 ,\n",
+       "       1985.6911, 1992.7111, 1999.6399, 2008.0716, 2014.2828, 2021.2891,\n",
+       "       2028.3184, 2035.6347, 2041.7165, 2048.9987, 2056.7764, 2064.5651,\n",
+       "       2071.6463, 2077.7539, 2083.4884, 2089.1549, 2096.6003, 2103.7163,\n",
+       "       2111.7586, 2117.7629, 2123.4962, 2130.5302, 2138.8126, 2145.6863,\n",
+       "       2152.7347, 2158.9162, 2165.9785, 2172.5589, 2178.0028, 2183.8062,\n",
+       "       2189.8809, 2196.9408, 2203.983 , 2211.0387, 2218.178 , 2224.4173,\n",
+       "       2230.1544, 2235.9259, 2241.4429, 2248.4345, 2255.538 , 2262.9897,\n",
+       "       2270.0826, 2277.2559, 2284.2673, 2291.2929, 2298.4562, 2305.4346,\n",
+       "       2312.5212, 2319.476 , 2326.438 , 2333.4585, 2339.9448, 2349.9509,\n",
+       "       2357.1298, 2364.3269, 2370.2008, 2377.4106, 2384.6028, 2391.7107,\n",
+       "       2397.9439, 2405.7404, 2411.4053, 2419.0319, 2426.117 , 2433.139 ,\n",
+       "       2440.3165, 2447.3106, 2454.4401, 2461.6609, 2468.8032, 2475.9751,\n",
+       "       2482.9792, 2490.0071])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile_stream.acquisition[\"BehavioralEvents\"].time_series[\"go_start_times\"].timestamps[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mapEphysNWB",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/000363/MAP/demo/environment.yml b/000363/MAP/demo/environment.yml
new file mode 100644
index 0000000..f526d71
--- /dev/null
+++ b/000363/MAP/demo/environment.yml
@@ -0,0 +1,11 @@
+name: base
+dependencies:
+  - pynwb
+  - nwbwidgets
+  - fsspec
+  - requests
+  - aiohttp
+  - dandi
+  - numpy
+  - matplotlib
+  - pandas
\ No newline at end of file