From ad7968966fd80f11579d8c69a8b2f10867647655 Mon Sep 17 00:00:00 2001
From: weiglszonja <wszonja@gmail.com>
Date: Sun, 1 Dec 2024 18:40:21 +0100
Subject: [PATCH] Add tutorials

---
 ...embargo_2024_processedbehaviorinterface.py |    2 +-
 .../tutorials/ephys_example_notebook.ipynb    | 2893 +++++++++++++++++
 .../tutorials/ephys_run_session_to_nwb.ipynb  |  231 ++
 3 files changed, 3125 insertions(+), 1 deletion(-)
 create mode 100644 src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb
 create mode 100644 src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb

diff --git a/src/constantinople_lab_to_nwb/schierek_embargo_2024/interfaces/schierek_embargo_2024_processedbehaviorinterface.py b/src/constantinople_lab_to_nwb/schierek_embargo_2024/interfaces/schierek_embargo_2024_processedbehaviorinterface.py
index 2513549..61cdf8f 100644
--- a/src/constantinople_lab_to_nwb/schierek_embargo_2024/interfaces/schierek_embargo_2024_processedbehaviorinterface.py
+++ b/src/constantinople_lab_to_nwb/schierek_embargo_2024/interfaces/schierek_embargo_2024_processedbehaviorinterface.py
@@ -107,7 +107,7 @@ def add_to_nwbfile(
             if column in data:
                 data[column] = list(np.array(data[column]).astype(bool))
 
-        columns_to_add = data.keys()
+        columns_to_add = list(data.keys())
         if column_name_mapping is not None:
             columns_to_add = [column for column in column_name_mapping.keys() if column in data.keys()]
 
diff --git a/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb
new file mode 100644
index 0000000..2c57009
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb
@@ -0,0 +1,2893 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90ae0edb-2fab-47dc-ad31-e79cf2905733",
+   "metadata": {},
+   "source": [
+    "# Electrophysiology demo\n",
+    "\n",
+    "This tutorial demonstrates how to access an NWB file from the [DANDI:001264](https://dandiarchive.org/dandiset/001264/draft) dataset using `pynwb`.\n",
+    "\n",
+    "This dataset contains extracellular electrophysiology recordings from rats performing a value-based decision-making task. \n",
+    "\n",
+    "Neural data were acquired using Neuropixels probes (384 channels, 30 kHz sampling rate) with Neuropix-PXI hardware and OpenEphys, and preprocessed using Kilosort 2.5 with manual curation in Phy. \n",
+    "\n",
+    "Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f58b841c-573c-4b9f-92d3-8c6d0cad2e08",
+   "metadata": {},
+   "source": [
+    "# Reading an NWB file\n",
+    "\n",
+    "This section demonstrates how to read an NWB file using `pynwb`.\n",
+    "Based on the [NWB File Basics](https://pynwb.readthedocs.io/en/stable/tutorials/general/plot_file.html#sphx-glr-tutorials-general-plot-file-py) tutorial from [PyNWB](https://pynwb.readthedocs.io/en/stable/#).\n",
+    "\n",
+    "An [NWBFile](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.NWBFile) represents a single session of an experiment. Each NWBFile must have a `session description`, `identifier`, and `session start time`.\n",
+    "\n",
+    "Reading is carried out using the [NWBHDF5IO](https://pynwb.readthedocs.io/en/stable/pynwb.html#pynwb.NWBHDF5IO) class. To read the NWB file use the read mode \"r\" to retrieve an NWBFile object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "55f744a2-8657-4bcc-927e-61be5c1b84c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import ndx_structured_behavior\n",
+    "\n",
+    "nwbfile_path = \"/Volumes/T9/Constantinople/nwbfiles/J076_2023-12-12_14-52-04.nwb\"\n",
+    "io = NWBHDF5IO(nwbfile_path, load_namespaces=True)\n",
+    "nwbfile = io.read()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4781be7e-7af0-492e-bfb0-a4af09ecf0c2",
+   "metadata": {},
+   "source": [
+    "# Streaming an NWB file\n",
+    "\n",
+    "This section demonstrates how to access the files on the [DANDI Archive](https://dandiarchive.org) without downloading them. Based on the [Streaming NWB files](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html) tutorial from [PyNWB](https://pynwb.readthedocs.io/en/stable/#).\n",
+    "\n",
+    "The `dandi.dandiapi.DandiAPIClient` can be used to get the S3 URL of the NWB file stored in the DANDI Archive.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f402c0a8-285a-4270-9c46-c3ff2d24b13a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from dandi.dandiapi import DandiAPIClient\n",
+    "\n",
+    "# client = DandiAPIClient.for_dandi_instance(\"dandi\")\n",
+    "\n",
+    "# dandiset_id = \"001264\"\n",
+    "# file_path = \"sub-J076/sub-J076_ecephys.nwb\"\n",
+    "\n",
+    "# with DandiAPIClient() as client:\n",
+    "#     asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(file_path)\n",
+    "#     s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b5f586b-c337-4c13-8a69-a83c640d1ebf",
+   "metadata": {},
+   "source": [
+    "We will use `remfile` for streaming the file. You can read more about `remfile` at [this tutorial section](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html#method-3-remfile)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25469dcf-3453-4391-be40-a1b9b199d31e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import h5py\n",
+    "# from pynwb import NWBHDF5IO\n",
+    "# import remfile\n",
+    "\n",
+    "# # We stream the file using remfile and open it with h5py and pynwb\n",
+    "# file = remfile.File(s3_url)\n",
+    "# h5_file = h5py.File(file, \"r\")\n",
+    "# io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n",
+    "\n",
+    "# nwbfile = io.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a5bb56-7dd1-4d4e-b56c-a65543811c89",
+   "metadata": {},
+   "source": [
+    "Importantly, the session start time is the reference time for all timestamps in the file. For instance, an event with a timestamp of 0 in the file means the event occurred exactly at the session start time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6562ea81-123b-41d4-acc3-c25b8434a1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2023, 12, 12, 14, 53, 12, tzinfo=tzoffset(None, -18000))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.session_start_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fcae1c6-d866-4bd1-897d-563323d83e28",
+   "metadata": {},
+   "source": [
+    "This section demonstrates how to access the [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field in an NWBFile.\n",
+    "\n",
+    "The [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field can be accessed as `nwbfile.subject`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4c98c8ab-dd37-448d-a28c-a7f8a61a76da",
+   "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>subject (Subject)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".age\">age: </span><span class=\"field-value\">TBD</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".sex\">sex: </span><span class=\"field-value\">U</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".species\">species: </span><span class=\"field-value\">Rattus norvegicus</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject_id\">subject_id: </span><span class=\"field-value\">J076</span></div></div>"
+      ],
+      "text/plain": [
+       "subject pynwb.file.Subject at 0x5090464480\n",
+       "Fields:\n",
+       "  age: TBD\n",
+       "  age__reference: birth\n",
+       "  sex: U\n",
+       "  species: Rattus norvegicus\n",
+       "  subject_id: J076"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.subject"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12b5b6aa-9a44-4de3-a8e2-a50b5d538f6f",
+   "metadata": {},
+   "source": [
+    "# Access Recording\n",
+    "\n",
+    "This section demonstrates how to access the raw `ElectricalSeries` data.\n",
+    "\n",
+    "`NWB` organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:\n",
+    "\n",
+    "- `acquisition`: raw, acquired data that should never change\n",
+    "- `processing`: processed data, typically the results of preprocessing algorithms and could change\n",
+    "\n",
+    "## Raw ElectricalSeries\n",
+    "\n",
+    "The raw ElectricalSeries data is stored in an [pynwb.ecephys.ElectricalSeries](https://pynwb.readthedocs.io/en/stable/pynwb.ecephys.html#pynwb.ecephys.ElectricalSeries) object which is added to `nwbfile.acquisition`. The data can be accessed as `nwbfile.acquisition[\"ElectricalSeries\"]`.\n",
+    "\n",
+    "The data in `ElectricalSeries` is stored as a two dimensional array: the first dimension is time, the second dimension represents electrodes/channels.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c694cd3-33e9-46b8-a87e-a34d4d4eaf64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electrical_series = nwbfile.acquisition[\"ElectricalSeries\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b83afa53-8f41-4dac-b812-607161e5ea13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Prepare data for plotting\n",
+    "data = electrical_series.data[:1000, :10]\n",
+    "timestamps = electrical_series.get_timestamps()[:1000]\n",
+    "df = pd.DataFrame(data)\n",
+    "df[\"Time (s)\"] = timestamps\n",
+    "df.set_index(\"Time (s)\", inplace=True)\n",
+    "df.columns.name = \"electrodes\"\n",
+    "channel_name_mapper = dict(zip(df.columns, electrical_series.electrodes[\"channel_name\"][:]))\n",
+    "df.rename(channel_name_mapper, axis=1, inplace=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=len(df.columns), sharex=True, sharey=True, dpi=200)\n",
+    "lines = df.plot(subplots=True, ax=axes, legend=False, linewidth=0.8)\n",
+    "\n",
+    "# Hide y-axis labels\n",
+    "for ax in axes:\n",
+    "    ax.yaxis.set_visible(False)\n",
+    "\n",
+    "# Remove box around the plots\n",
+    "for ax in axes:\n",
+    "    ax.set_frame_on(False)\n",
+    "\n",
+    "# Get handles and labels for all lines\n",
+    "handles, labels = [], []\n",
+    "for line in lines:\n",
+    "    h, l = line.get_legend_handles_labels()\n",
+    "    handles.extend(h)\n",
+    "    labels.extend(l)\n",
+    "\n",
+    "# Create a single legend box\n",
+    "fig.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.2, 0.8), frameon=False)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4cdef31-3032-4abd-8c31-107653a6b54a",
+   "metadata": {},
+   "source": [
+    "The electrodes table describe the electrodes that generated this data. Extracellular electrodes are stored in an \"electrodes\" table, which is a [DynamicTable](https://hdmf.readthedocs.io/en/stable/hdmf.common.table.html#hdmf.common.table.DynamicTable) and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.electrodes.to_dataframe()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "53e37829-e7ea-4ac6-a47a-cf6ac70f205b",
+   "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>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>channel_depth_um</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   location                                              group  \\\n",
+       "id                                                               \n",
+       "0   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "1   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "2   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "3   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "4   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "\n",
+       "        group_name channel_name  channel_depth_um  \n",
+       "id                                                 \n",
+       "0   ElectrodeGroup          AP1               NaN  \n",
+       "1   ElectrodeGroup          AP2               NaN  \n",
+       "2   ElectrodeGroup          AP3               NaN  \n",
+       "3   ElectrodeGroup          AP4               NaN  \n",
+       "4   ElectrodeGroup          AP5               NaN  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.electrodes.to_dataframe().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9f114f1-5e02-4382-96ef-53a9a5189260",
+   "metadata": {},
+   "source": [
+    "## Filtered ElectricalSeries\n",
+    "\n",
+    "\n",
+    "The processed ecephys data is stored in \"processing/ecephys\" which can be accessed as `nwbfile.processing[\"ecephys\"]`.\n",
+    "Within this processing module we can access the container of filtered traces as `nwbfile.processing[\"ecephys\"][\"LFP\"]` which can hold multiple processed `ElectricalSeries` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "281be40d-1a73-4b97-ad78-cf6ddbb8e0e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>ecephys (ProcessingModule)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">Intermediate data from extracellular electrophysiology recordings, e.g., LFP.</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP']\"><b>LFP</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series\"><b>electrical_series</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].starting_time\">starting_time: </span><span class=\"field-value\">1.1578276308196287</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].rate\">rate: </span><span class=\"field-value\">2500.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].description\">description: </span><span class=\"field-value\">Acquisition traces for the ElectricalSeries.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].conversion\">conversion: </span><span class=\"field-value\">1.949999928474426e-07</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].data\"><b>data</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].electrodes.table\"><b>table</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".data_interfaces['LFP'].electrical_series['ElectricalSeries'].electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>channel_depth_um</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5093309664\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x5082001440\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5093309664\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x5082001440\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5093309664\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x5082001440\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5093309664\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x5082001440\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 764 more rows.</p></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".data_interfaces['units']\"><b>units</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['units']\">description: </span><span class=\"field-value\">Autogenerated by neuroconv.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".data_interfaces['units']\">waveform_unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".data_interfaces['units']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>unit_name</th>\n",
+       "      <th>original_cluster_id</th>\n",
+       "      <th>ch</th>\n",
+       "      <th>channel_depth_um</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[1.229560964152963, 1.3083609641529605, 1.4348276308196297, 1.4672942974862941, 1.4885942974862942, 1.5085276308196285, 1.5370609641529605, 1.9147276308196304, 2.9152609641529637, 3.4175942974862963, 3.556427630819627, 3.7128942974862937, 6.825294297486295, 8.262494297486299, 8.427660964152963, 9.064727630819633]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>372</td>\n",
+       "      <td>3890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[1.2434276308196281, 1.25942763081963, 1.289860964152961, 1.3444276308196272, 1.4091276308196292, 1.4391942974862957, 1.4915942974862944, 1.5288276308196274, 1.5398942974862955, 1.5534276308196304, 1.560760964152962, 1.570360964152961, 1.587760964152963, 1.647494297486297, 1.687094297486297, 1.7204276308196285, 1.784694297486297, 1.8675609641529611, 1.897660964152962, 1.9601276308196276, 1.9868609641529602, 2.0486276308196274, 2.0730276308196274, 2.0811609641529607, 2.1615609641529616, 2.200594297486294, 2.2463276308196285, 2.335027630819628, 2.47732763081963, 2.7047276308196295, 2.733760964152964, 2.765194297486296, 2.7775942974862957, 2.79912763081963, 2.816094297486295, 2.894660964152962, 2.9151609641529603, 2.916060964152962, 2.9419276308196274, 2.9548276308196293, 2.9864276308196303, 3.003494297486295, 3.0837609641529617, 3.1048942974862968, 3.139594297486294, 3.1760609641529634, 3.204294297486296, 3.212060964152961, 3.2399942974862945, 3.284960964152962, 3.3018609641529615, 3.3212609641529625, 3.3363942974862937, 3.3583942974862957, 3.3937276308196296, 3.460694297486299, 3.4811276308196284, 3.525027630819629, 3.5486609641529583, 3.5714276308196276, 3.631994297486294, 3.7373609641529626, 3.7551276308196293, 3.7964609641529634, 3.8401609641529646, 3.903260964152963, 4.00472763081963, 4.020827630819632, 4.048127630819629, 4.07676096415296, 4.112960964152961, 4.19276096415296, 4.245194297486293, 4.260094297486297, 4.279060964152961, 4.284560964152959, 4.293727630819632, 4.3014276308196315, 4.314594297486295, 4.329460964152965, 4.340994297486297, 4.357760964152959, 4.367760964152964, 4.373960964152964, 4.38122763081963, 4.402727630819626, 4.4131276308196306, 4.4190276308196275, 4.434427630819627, 4.449994297486299, 4.4625609641529635, 4.475760964152961, 4.49156096415296, 4.501160964152959, 4.513527630819631, 4.5315942974862935, 4.539627630819631, 4.567527630819626, 4.579594297486295, 4.604627630819628, ...]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>376</td>\n",
+       "      <td>3930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[9.347527630819627, 9.576494297486292, 9.643327630819627, 9.664194297486297, 9.73236096415296, 9.79486096415296, 9.851260964152964, 9.883660964152966, 9.914327630819628, 9.971094297486296, 10.01576096415296, 10.061594297486295, 10.092560964152966, 10.16482763081963]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>340</td>\n",
+       "      <td>3570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[1.1631609641529614, 1.2562942974862956, 1.3536609641529616, 1.4228942974862946, 1.45302763081963, 1.5162609641529627, 1.5994276308196298, 1.6373276308196303, 1.6566609641529624, 1.6681276308196296, 1.6862942974862953, 1.719760964152961, 1.7319942974862954, 1.7612942974862946, 1.801827630819627, 1.949960964152961, 2.0402609641529637, 2.05522763081963, 2.1212276308196287, 2.248760964152961, 2.294460964152961, 2.3602276308196295, 2.586527630819628, 2.7032609641529604, 2.7758276308196272, 2.8612609641529616, 2.9922276308196274, 3.030494297486296, 5.580494297486297, 5.812894297486295, 5.901427630819626, 5.978294297486293, 6.024360964152962, 6.08412763081963, 8.198860964152964, 8.352594297486291, 8.45966096415296, 8.746694297486293, 8.806794297486292, 8.846260964152961, 9.109560964152962, 9.155527630819627, 9.25336096415296]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>42</td>\n",
+       "      <td>367</td>\n",
+       "      <td>3830</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 1 more row.</p></details></details></details></div>"
+      ],
+      "text/plain": [
+       "ecephys pynwb.base.ProcessingModule at 0x5037041344\n",
+       "Fields:\n",
+       "  data_interfaces: {\n",
+       "    LFP <class 'pynwb.ecephys.LFP'>,\n",
+       "    units <class 'pynwb.misc.Units'>\n",
+       "  }\n",
+       "  description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP."
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.processing[\"ecephys\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7ab8c6d2-06ca-4c31-93b8-fb5556e07c60",
+   "metadata": {},
+   "outputs": [
+    {
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+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>ElectricalSeries</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".starting_time\">starting_time: </span><span class=\"field-value\">29.74</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".rate\">rate: </span><span class=\"field-value\">2500.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">Acquisition traces for the ElectricalSeries.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".conversion\">conversion: </span><span class=\"field-value\">1.949999928474426e-07</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".data\"><b>data</b></summary></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes.table\"><b>table</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>rel_x</th>\n",
+       "      <th>inter_sample_shift</th>\n",
+       "      <th>rel_y</th>\n",
+       "      <th>channel_depth_um</th>\n",
+       "      <th>width</th>\n",
+       "      <th>contact_shapes</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0 pynwb.ecephys.ElectrodeGroup at 0x6075195872\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x6075383104\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AP1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3840.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0 pynwb.ecephys.ElectrodeGroup at 0x6075195872\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x6075383104\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AP2</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3840.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0 pynwb.ecephys.ElectrodeGroup at 0x6075195872\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x6075383104\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AP3</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>3860.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0 pynwb.ecephys.ElectrodeGroup at 0x6075195872\\nFields:\\n  description: no description\\n  device: DeviceEcephys pynwb.device.Device at 0x6075383104\\nFields:\\n  description: no description\\n\\n  location: unknown\\n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AP4</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>3860.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>square</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 764 more rows.</p></details></details></details></div>"
+      ],
+      "text/plain": [
+       "ElectricalSeries pynwb.ecephys.ElectricalSeries at 0x6085278896\n",
+       "Fields:\n",
+       "  comments: no comments\n",
+       "  conversion: 1.949999928474426e-07\n",
+       "  data: <HDF5 dataset \"data\": shape (100, 384), type \"<i2\">\n",
+       "  description: Acquisition traces for the ElectricalSeries.\n",
+       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\n",
+       "  offset: 0.0\n",
+       "  rate: 2500.0\n",
+       "  resolution: -1.0\n",
+       "  starting_time: 29.74\n",
+       "  starting_time_unit: seconds\n",
+       "  unit: volts"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "    processed_ecephys = nwbfile.processing[\"ecephys\"][\"LFP\"]\n",
+    "\n",
+    "filtered_electrical_series = processed_ecephys[\"ElectricalSeries\"]\n",
+    "filtered_electrical_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "462de2e0-d78f-409f-9527-ed386778e0c1",
+   "metadata": {},
+   "source": [
+    "# Access Units\n",
+    "\n",
+    "Spike times are stored in the `Units` table, which is a DynamicTable and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.units.to_dataframe()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fecc7acc-9288-4c07-abd2-f9391a3f89e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>unit_name</th>\n",
+       "      <th>ch</th>\n",
+       "      <th>sh</th>\n",
+       "      <th>amp</th>\n",
+       "      <th>n_spikes</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>original_cluster_id</th>\n",
+       "      <th>Amplitude</th>\n",
+       "      <th>fr</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>KSLabel</th>\n",
+       "      <th>ContamPct</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[20.509433333333334, 20.5924, 20.8509, 20.936,...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66.992470</td>\n",
+       "      <td>691</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2031.3</td>\n",
+       "      <td>0.103813</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>335.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[5.534833333333333, 11.725933333333334, 14.853...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>105</td>\n",
+       "      <td>0</td>\n",
+       "      <td>75.382370</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>940.9</td>\n",
+       "      <td>0.262612</td>\n",
+       "      <td>1900.0</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>152.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[20.592466666666667, 20.935966666666666, 21.27...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>105</td>\n",
+       "      <td>0</td>\n",
+       "      <td>82.178314</td>\n",
+       "      <td>278</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5098.0</td>\n",
+       "      <td>0.041765</td>\n",
+       "      <td>1900.0</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>495.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[175.66426666666666, 237.11613333333332, 423.7...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59.803703</td>\n",
+       "      <td>34</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>680.2</td>\n",
+       "      <td>0.005108</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[1.5859, 3.782366666666667, 4.171866666666666,...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36.706387</td>\n",
+       "      <td>17856</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>4</td>\n",
+       "      <td>476.2</td>\n",
+       "      <td>2.682606</td>\n",
+       "      <td>2180.0</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>99.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          spike_times unit_name   ch  sh  \\\n",
+       "id                                                                         \n",
+       "0   [20.509433333333334, 20.5924, 20.8509, 20.936,...         0   74   0   \n",
+       "1   [5.534833333333333, 11.725933333333334, 14.853...         1  105   0   \n",
+       "2   [20.592466666666667, 20.935966666666666, 21.27...         2  105   0   \n",
+       "3   [175.66426666666666, 237.11613333333332, 423.7...         3  125   0   \n",
+       "4   [1.5859, 3.782366666666667, 4.171866666666666,...         4  133   0   \n",
+       "\n",
+       "          amp  n_spikes quality  original_cluster_id  Amplitude        fr  \\\n",
+       "id                                                                          \n",
+       "0   66.992470       691     nan                    0     2031.3  0.103813   \n",
+       "1   75.382370      1748     nan                    1      940.9  0.262612   \n",
+       "2   82.178314       278     nan                    2     5098.0  0.041765   \n",
+       "3   59.803703        34     nan                    3      680.2  0.005108   \n",
+       "4   36.706387     17856     nan                    4      476.2  2.682606   \n",
+       "\n",
+       "     depth KSLabel  ContamPct  \n",
+       "id                             \n",
+       "0   1600.0     mua      335.4  \n",
+       "1   1900.0     mua      152.4  \n",
+       "2   1900.0     mua      495.3  \n",
+       "3   2100.0     mua        inf  \n",
+       "4   2180.0     mua       99.4  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.units[:].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1c15222-a974-4453-af2b-19bc93b414d3",
+   "metadata": {},
+   "source": [
+    "# Access raw behavior data\n",
+    "\n",
+    "This section demonstrates how to access the Bpod data in the NWBFile.\n",
+    "\n",
+    "## Accessing the task metadata\n",
+    "\n",
+    "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n",
+    "\n",
+    "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3dc8745-ee19-43ad-924b-7e4ac2d89f71",
+   "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>event_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>right_port_poke</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event_name\n",
+       "id                  \n",
+       "0        state_timer\n",
+       "1     left_port_poke\n",
+       "2   center_port_poke\n",
+       "3    right_port_poke"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].event_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b6be4b-26b5-45e1-b498-8367209cf0dd",
+   "metadata": {},
+   "source": [
+    "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "be87f496-6e1c-41c2-836d-2834eea6734d",
+   "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>action_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     action_name\n",
+       "id              \n",
+       "0   sound_output"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].action_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4e00955-134e-4ff6-9c7d-a2fbfba7a015",
+   "metadata": {},
+   "source": [
+    "The `StateTypesTable` is a column-based table to store the type of states that occur during the task (e.g. while the animal is waiting for reward), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].state_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "18e31ed3-7e60-4381-b0eb-9f83ecc273a7",
+   "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>state_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>go_cue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>announce_reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>stop_sound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>wait_for_side_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>opt_out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            state_name\n",
+       "id                    \n",
+       "0               go_cue\n",
+       "1       nose_in_center\n",
+       "2      announce_reward\n",
+       "3        wait_for_poke\n",
+       "4           stop_sound\n",
+       "5   wait_for_side_poke\n",
+       "6              opt_out\n",
+       "7               reward\n",
+       "8     punish_violation"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].state_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d599112f-80f4-44c5-9815-e2b81919ee26",
+   "metadata": {},
+   "source": [
+    "The arguments for the task is stored in a `TaskArgumentsTable` which can be accessed as `nwbfile.lab_meta_data[\"task\"].task_arguments`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0cb976d4-ba8e-4f4f-90c2-637f66999dc4",
+   "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>argument_name</th>\n",
+       "      <th>argument_description</th>\n",
+       "      <th>expression</th>\n",
+       "      <th>expression_type</th>\n",
+       "      <th>output_type</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>reward_volume_ul</td>\n",
+       "      <td>The volume of reward in microliters.</td>\n",
+       "      <td>5</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "      <td>The time in seconds when the animal is require...</td>\n",
+       "      <td>1.0953925215757918</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>time_increment_for_nose_in_center</td>\n",
+       "      <td>The time increment for nose in center in seconds.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>target_duration_for_nose_in_center</td>\n",
+       "      <td>The goal for how long the animal must poke cen...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>training_stage</td>\n",
+       "      <td>The stage of the training.</td>\n",
+       "      <td>9</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reward_delay</td>\n",
+       "      <td>The delay in seconds to receive reward, drawn ...</td>\n",
+       "      <td>100</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>target_reward_delay</td>\n",
+       "      <td>The target delay in seconds to receive reward.</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>time_increment_for_reward_delay</td>\n",
+       "      <td>The time increment during monotonic increase o...</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>violation_time_out</td>\n",
+       "      <td>The time-out if nose is center is not satisfie...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>block_type</td>\n",
+       "      <td>The block type (High, Low or Test). High and L...</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>string</td>\n",
+       "      <td>string</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>num_trials_in_test_blocks</td>\n",
+       "      <td>The number of trials in each mixed blocks.</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>num_trials_in_adaptation_blocks</td>\n",
+       "      <td>The number of trials in each high reward (20, ...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>punish_sound_enabled</td>\n",
+       "      <td>Whether to play a white noise pulse on error.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>catch_percentage</td>\n",
+       "      <td>The percentage of catch trials.</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>is_catch</td>\n",
+       "      <td>Whether the trial is a catch trial.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>current_trial</td>\n",
+       "      <td>The current trial number.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>cumulative_reward_volume_ul</td>\n",
+       "      <td>The cumulative volume received during session ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>is_warm_up</td>\n",
+       "      <td>Whether the trial is warm-up.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>override_nose_in_center</td>\n",
+       "      <td>Whether the required time for maintaining cent...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>trials_in_stage</td>\n",
+       "      <td>The cumulative number of trials in the stages.</td>\n",
+       "      <td>129054</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>min_reward_volume_ul</td>\n",
+       "      <td>The minimum volume of reward in microliters. (...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>auto_change_catch_probability</td>\n",
+       "      <td>Whether to change the probability automaticall...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>previous_was_violation</td>\n",
+       "      <td>Whether the previous trial was a violation.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>changed</td>\n",
+       "      <td>Whether a block transition occurred for the tr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>num_trials_in_stage_3</td>\n",
+       "      <td>Determines how many trials occur in stage 3 be...</td>\n",
+       "      <td>400</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>num_trials_in_stage_8</td>\n",
+       "      <td>Determines how many trials occur in stage 8 be...</td>\n",
+       "      <td>250</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>center_port_cue</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>cycle_blocks</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         argument_name  \\\n",
+       "id                                       \n",
+       "0                     reward_volume_ul   \n",
+       "1                       nose_in_center   \n",
+       "2    time_increment_for_nose_in_center   \n",
+       "3   target_duration_for_nose_in_center   \n",
+       "4                       training_stage   \n",
+       "5                         reward_delay   \n",
+       "6                  target_reward_delay   \n",
+       "7      time_increment_for_reward_delay   \n",
+       "8                   violation_time_out   \n",
+       "9                           block_type   \n",
+       "10           num_trials_in_test_blocks   \n",
+       "11     num_trials_in_adaptation_blocks   \n",
+       "12                punish_sound_enabled   \n",
+       "13                    catch_percentage   \n",
+       "14                            is_catch   \n",
+       "15                       current_trial   \n",
+       "16         cumulative_reward_volume_ul   \n",
+       "17                          is_warm_up   \n",
+       "18             override_nose_in_center   \n",
+       "19                     trials_in_stage   \n",
+       "20                min_reward_volume_ul   \n",
+       "21       auto_change_catch_probability   \n",
+       "22              previous_was_violation   \n",
+       "23                             changed   \n",
+       "24               num_trials_in_stage_3   \n",
+       "25               num_trials_in_stage_8   \n",
+       "26                     center_port_cue   \n",
+       "27                        cycle_blocks   \n",
+       "\n",
+       "                                 argument_description          expression  \\\n",
+       "id                                                                          \n",
+       "0                The volume of reward in microliters.                   5   \n",
+       "1   The time in seconds when the animal is require...  1.0953925215757918   \n",
+       "2   The time increment for nose in center in seconds.                   0   \n",
+       "3   The goal for how long the animal must poke cen...                   1   \n",
+       "4                          The stage of the training.                   9   \n",
+       "5   The delay in seconds to receive reward, drawn ...                 100   \n",
+       "6      The target delay in seconds to receive reward.                 1.5   \n",
+       "7   The time increment during monotonic increase o...               0.025   \n",
+       "8   The time-out if nose is center is not satisfie...                   1   \n",
+       "9   The block type (High, Low or Test). High and L...               Mixed   \n",
+       "10         The number of trials in each mixed blocks.                  40   \n",
+       "11  The number of trials in each high reward (20, ...                  40   \n",
+       "12      Whether to play a white noise pulse on error.                True   \n",
+       "13                    The percentage of catch trials.                0.25   \n",
+       "14                Whether the trial is a catch trial.               False   \n",
+       "15                          The current trial number.                   0   \n",
+       "16  The cumulative volume received during session ...                   0   \n",
+       "17                      Whether the trial is warm-up.               False   \n",
+       "18  Whether the required time for maintaining cent...               False   \n",
+       "19     The cumulative number of trials in the stages.              129054   \n",
+       "20  The minimum volume of reward in microliters. (...                   5   \n",
+       "21  Whether to change the probability automaticall...               False   \n",
+       "22        Whether the previous trial was a violation.               False   \n",
+       "23  Whether a block transition occurred for the tr...               False   \n",
+       "24  Determines how many trials occur in stage 3 be...                 400   \n",
+       "25  Determines how many trials occur in stage 8 be...                 250   \n",
+       "26                                    Task parameter.               False   \n",
+       "27                                    Task parameter.                True   \n",
+       "\n",
+       "   expression_type output_type  \n",
+       "id                              \n",
+       "0          integer     numeric  \n",
+       "1           double     numeric  \n",
+       "2           double     numeric  \n",
+       "3           double     numeric  \n",
+       "4          integer     numeric  \n",
+       "5           double     numeric  \n",
+       "6           double     numeric  \n",
+       "7           double     numeric  \n",
+       "8           double     numeric  \n",
+       "9           string      string  \n",
+       "10         integer     numeric  \n",
+       "11         integer     numeric  \n",
+       "12         boolean     boolean  \n",
+       "13          double     numeric  \n",
+       "14         boolean     boolean  \n",
+       "15         integer     numeric  \n",
+       "16          double     numeric  \n",
+       "17         boolean     boolean  \n",
+       "18         boolean     boolean  \n",
+       "19         integer     numeric  \n",
+       "20          double     numeric  \n",
+       "21         boolean     boolean  \n",
+       "22         boolean     boolean  \n",
+       "23         boolean     boolean  \n",
+       "24         integer     numeric  \n",
+       "25         integer     numeric  \n",
+       "26         boolean     boolean  \n",
+       "27         boolean     boolean  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].task_arguments[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d052694b-7783-4929-8ae2-f948810d61ba",
+   "metadata": {},
+   "source": [
+    "The `TaskRecording` object stores the data for events, states, and actions that occured during the task. The `TaskRecording` is added as acquisition which can be accessed as `nwbfile.acquisition[\"task_recording\"]`.\n",
+    "\n",
+    "The `EventsTable` is a column-based table to store the information about the events (e.g. poke times), one event per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].events`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7a007082-90eb-4949-9253-7a050965c030",
+   "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>timestamp</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.8822</td>\n",
+       "      <td>1</td>\n",
+       "      <td>In</td>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0315</td>\n",
+       "      <td>1</td>\n",
+       "      <td>In</td>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8682</th>\n",
+       "      <td>6313.5514</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8683</th>\n",
+       "      <td>6314.3740</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8684</th>\n",
+       "      <td>6315.0374</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8685</th>\n",
+       "      <td>6315.4097</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8686</th>\n",
+       "      <td>6316.4097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8687 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      timestamp  event_type    value        event_name\n",
+       "0       38.5442           2       In  center_port_poke\n",
+       "1       38.6237           2      Out  center_port_poke\n",
+       "2       39.6237           0  Expired       state_timer\n",
+       "3       39.8822           1       In    left_port_poke\n",
+       "4       40.0315           1       In    left_port_poke\n",
+       "...         ...         ...      ...               ...\n",
+       "8682  6313.5514           2      Out  center_port_poke\n",
+       "8683  6314.3740           0  Expired       state_timer\n",
+       "8684  6315.0374           2       In  center_port_poke\n",
+       "8685  6315.4097           2      Out  center_port_poke\n",
+       "8686  6316.4097           0  Expired       state_timer\n",
+       "\n",
+       "[8687 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].events[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].event_types[:],\n",
+    "    left_on=\"event_type\",\n",
+    "    right_on=\"id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcef31e2-52b9-4772-8ca5-c5ffcf6545b5",
+   "metadata": {},
+   "source": [
+    "The `ActionsTable` is a column-based table to store the information about the actions (e.g. sound onset times), one action per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].actions`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "405d4add-b42d-49c8-93a5-1d47f7f7a2b9",
+   "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>timestamp</th>\n",
+       "      <th>action_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9881</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.1782</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.5443</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.6238</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39.1240</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamp  action_type value   action_name\n",
+       "0    19.9881            0    On  sound_output\n",
+       "1    20.1782            0    On  sound_output\n",
+       "2    38.5443            0    On  sound_output\n",
+       "3    38.6238            0    On  sound_output\n",
+       "4    39.1240            0    On  sound_output"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].actions[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].action_types[:],\n",
+    "    left_on=\"action_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ecb784-f7ba-4de6-8da5-2e824bb7e28e",
+   "metadata": {},
+   "source": [
+    "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cf25d54a-9e06-4127-af67-429c2ae6c48b",
+   "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>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>state_type</th>\n",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9880</td>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>3</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>8</td>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>42.2451</td>\n",
+       "      <td>3</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42.2451</td>\n",
+       "      <td>42.5430</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   start_time  stop_time  state_type        state_name\n",
+       "0     19.9880    38.5442           3     wait_for_poke\n",
+       "1     38.5442    38.6237           1    nose_in_center\n",
+       "2     38.6237    39.6237           8  punish_violation\n",
+       "3     39.7154    42.2451           3     wait_for_poke\n",
+       "4     42.2451    42.5430           1    nose_in_center"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].states[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].state_types[:],\n",
+    "    left_on=\"state_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "536cc673-a323-4fde-80a4-dbd52c1573bd",
+   "metadata": {},
+   "source": [
+    "### Plot the events, actions, and states\n",
+    "\n",
+    "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5e0b1ea3-bf38-405e-935d-6159a8de81e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n",
+    "\n",
+    "# Get the events from file\n",
+    "events = nwbfile.get_acquisition(\"task_recording\").events\n",
+    "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_events(\n",
+    "    events=events[20:100],\n",
+    "    event_types=event_types,\n",
+    "    show_event_values=True,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Events\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3133c5a4-163b-4f1c-b3f1-0596c98f1e55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7lJ6enudxrOvOyMhQbGys8wUDAACUAQRIAAAAAFCK+fv7233dzc0tz20yMjJMr58/f972+sWLFx0+rOFNSkpKgd/H9RzVfG3d9mrPjaurq5YuXaqIiAhFRUVp4sSJuvPOOxUQEKAOHTroo48+yhF0WLeFJP3111+5bg/rvYSuXceFCxckSWFhYXmu88qVK7ZQq1q1ag7bVa9e3fb80qVLdtvkZTtevw2t68rr2NebPn262rdvr8TERL311ltq166dAgICdNddd2nSpEk6d+6cw76OpKam2p7bm3FnNWfOHDVp0kTR0dF69dVX1aJFC/n7++vee+/VG2+8kSMsu563t7fdMQEAAJATARIAAAAAlDHW8OKee+6RYRh5etwsmjRpoiNHjmjVqlV64okn1KhRI6WkpGjDhg36+9//rnr16unAgQO29tfOTjp8+HCetsWQIUNsfexdOvBWFxQUpB9++EHbtm3ThAkT1KpVK7m5uWnPnj2aMmWK6tSpo88++yxf6yxXrpzteW6zzmrUqKFff/1V3377rZ599lk1a9ZM2dnZ+vHHHzVhwgTVrl1bP/zwg8P+1wZM144JAACAnAiQAAAAAKCMqVy5sqSiuTRdaeDh4aFHH31Us2bN0oEDBxQdHa2ZM2cqJCREZ86c0eDBg21trdtCcm57OLMtQ0JC5OrqKkk6e/asw3bXLrN3nyNnWdeV20yhvMwiuvfeezVt2jRt375dsbGx+uKLL9S4cWOlpKRo2LBhunjxYp5ruva+RzeaReTi4qKOHTvqnXfe0e7du3XlyhUtXrxYNWrUUExMjPr37+/wsnbWdQcGBjq83xUAAACuIkACAAAAgDKmVatWkq5efs16P5xbWbly5fTkk09q2rRpkqS9e/fq8uXLkqTw8HDbpdy++uqrfK+7ZcuWkqTvv/8+z5dE8/Dw0O233y5J2rhxo8N2GzZskHQ1MGnatGm+a3PkrrvukiRt2rTJYZvcZvHY4+XlpYcfflirV6+WdPXycNu3b89z/+DgYFsYd+LEiXyN7e/vr/79+2vu3LmSpIsXL5pmmV3r5MmTkqT69evnawwAAICyiAAJAAAAAMqY9u3bq3bt2pKkMWPGOJytYXWjGSGlRVpaWq7Lr73/jYvL//4cHjlypCRp7ty52rt3b67ruH5bDBkyRK6urrp8+bImTZqU51r79esnSVq5cqUOHjyYY3liYqKmT58uSerSpYsCAwPzvO4b6du3ryRp+/bt2rx5c47lKSkpeuONN+z2zczMtN0Lyh5H2zgv2rRpI0n6+eef7S6/0X6al7F37dolSWrbtm2+agMAACiLCJAAAAAAoIxxc3PTzJkz5ebmpu3bt6tNmzbauHGjMjIybG1OnDihmTNn6u6779aHH35YgtXm3dKlS9WqVSvNmjXLNIslKytL69ev18SJEyVJf/vb3xQcHGxbPm7cODVu3Fipqalq37693n//fdsMJUmKjY3VunXrNGjQILVu3do0Zu3atTV+/HhJ0vTp0zVixAgdO3bMtjw+Pl7Lli3TI488Yuo3atQoRUREKCMjQ507d9a6detswcyBAwfUsWNHnTx5Up6enpo6dWohbaGrevbsaZvR1LNnT61atcp2L6jDhw+rc+fOio6Ottv37NmzqlOnjqZOnaq9e/cqMzPTtmz//v0aOHCgJMnX1zffIU27du0k/S/kud6OHTt0++23a8aMGTp8+LBtexmGoR07dmjUqFGSpOrVq9tmeF0rKytLe/bskUSABAAAkBduJV0AAAAAAKD43X///VqxYoUGDRqkXbt26YEHHpC7u7sCAgKUmJhoms3To0ePkis0H6xBwo4dOyRJnp6e8vPzU0xMjC1sqFq1qubNm2fq5+fnp2+//VY9e/bUzp07NXr0aD377LMKDAxUdna24uPjbW2tM7euNXXqVCUkJOiDDz7Q3LlzNXfuXPn5+cnd3V2xsbEyDCPHDCJ/f399+eWX6tSpk86ePasuXbrIy8tLHh4etvE8PT316aefqkmTJoW6ndzc3LRixQq1a9dOZ86cUa9eveTp6SkvLy/FxcXJw8NDK1asUPfu3e32P3HihF5++WW9/PLLcnV1VWBgoBITE20zhDw8PLRgwQKFhITkq66ePXvqH//4h44cOaJjx46pTp06OdocOHBAY8eO1dixY237a1xcnC3ICggI0JIlS2z3mLrWxo0blZSUpIoVK+qBBx7IV20AAABlETOQAAAAAKCM6tGjh44fP65JkyapefPm8vPzU2xsrDw9PdWkSRONGDFCn3/+uW2GTWn38MMPa9GiRRo6dKiaNGmiwMBAxcXFyd/fX82bN9err76qQ4cOqV69ejn6Vq1aVdu3b9dnn32mhx9+WFWqVFFycrLS09MVHh6ubt266e2339bWrVtz9HV1ddX777+v7du3a8CAAapRo4YyMjJkGIYaNGig4cOHa9WqVTn6NWrUSIcOHVJkZKTuuOMOubm5KS0tTbVq1dJTTz2lQ4cOqVevXkWyrWrWrKl9+/Zp7NixioiIkGEY8vLyUq9evbRjxw49/PDDdvtVq1ZNX375pcaMGaMWLVqoSpUqSkxMlJubmxo0aKCnn35aBw8edKruihUr2mZqLV68OMfyu+++W8uXL9eoUaPUrFkzlS9fXvHx8fLy8tIdd9yhCRMm6PDhwzlmiVlZ1zl06FC5u7vnuz4AAICyxmIYhlHSRQAAAAAAAGzdulVt27ZVrVq1dOzYMVkslkJZb1JSki0U/OOPP1SzZs1CWS8AAMCtjBlIAAAAAACgVGjTpo0efPBB/fe//9WKFSsKbb3vv/++EhISNGLECMIjAACAPGIGEgAAAAAAKDUOHDigO+64Q/Xr19f+/fvl4lKw//c1MTFRERERSk1N1fHjx1WpUqVCqhQAAODW5lbSBQAAAAAAAFg1btxYc+fO1alTp/Tnn3+qWrVqBVrfqVOn9PTTT+vOO+8kPAIAAMgHZiABAAAAAAAAAADAhHsgAQAAAAAAAAAAwIQACQAAAAAAAAAAACYESAAAAAAAAAAAADAhQAIAAAAAAAAAAIAJARIAAAAAAAAAAABMCJAAAAAAAAAAAABgQoAEAAAAAAAAAAAAEwIkAAAAAAAAAAAAmBAgAQAAAAAAAAAAwOT/AcC9bmAcxCZlAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the actions from file\n",
+    "actions = nwbfile.get_acquisition(\"task_recording\").actions\n",
+    "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_actions(\n",
+    "    actions=actions[20:100],\n",
+    "    action_types=action_types,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Actions\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5f90f877-5ecb-40fb-9c8d-50ee870f7198",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the states from file\n",
+    "states = nwbfile.get_acquisition(\"task_recording\").states\n",
+    "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n",
+    "\n",
+    "# Plot the data\n",
+    "plot_states(states=states[20:100],\n",
+    "            state_types=state_types,\n",
+    "            marker_size=500)\n",
+    "plt.title(\"States\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a761eef8-db30-445e-8f43-7ff4149e4f52",
+   "metadata": {},
+   "source": [
+    "## Accessing the trials\n",
+    "\n",
+    "The `TrialsTable` is a column-based table to store information about trials, one trial per row.\n",
+    "The table can be accessed from the file as `nwbfile.trials`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "547c05e1-e5ba-4144-a95d-c54b5ef49899",
+   "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>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>states</th>\n",
+       "      <th>events</th>\n",
+       "      <th>actions</th>\n",
+       "      <th>trials_in_stage</th>\n",
+       "      <th>changed</th>\n",
+       "      <th>cycle_blocks</th>\n",
+       "      <th>target_reward_delay</th>\n",
+       "      <th>catch_percentage</th>\n",
+       "      <th>...</th>\n",
+       "      <th>num_trials_in_adaptation_blocks</th>\n",
+       "      <th>is_catch</th>\n",
+       "      <th>punish_sound_enabled</th>\n",
+       "      <th>nose_in_center</th>\n",
+       "      <th>target_duration_for_nose_in_center</th>\n",
+       "      <th>is_warm_up</th>\n",
+       "      <th>num_trials_in_test_blocks</th>\n",
+       "      <th>block_type</th>\n",
+       "      <th>previous_was_violation</th>\n",
+       "      <th>center_port_cue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <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>19.9880</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>[0, 1]</td>\n",
+       "      <td>[0, 1]</td>\n",
+       "      <td>[0, 1, 2, 3, 4]</td>\n",
+       "      <td>129054</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.095400</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>43.5430</td>\n",
+       "      <td>[3, 4]</td>\n",
+       "      <td>[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...</td>\n",
+       "      <td>[5, 6, 7, 8, 9]</td>\n",
+       "      <td>129056</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.6313</td>\n",
+       "      <td>46.8007</td>\n",
+       "      <td>[6, 7]</td>\n",
+       "      <td>[26, 27, 28, 29, 30, 31, 32, 33]</td>\n",
+       "      <td>[10, 11, 12, 13, 14]</td>\n",
+       "      <td>129057</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46.8732</td>\n",
+       "      <td>59.3249</td>\n",
+       "      <td>[9, 10]</td>\n",
+       "      <td>[35, 36, 37, 38]</td>\n",
+       "      <td>[15, 16, 17, 18, 19]</td>\n",
+       "      <td>129058</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.109749</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>59.4011</td>\n",
+       "      <td>77.6604</td>\n",
+       "      <td>[12, 13, 14, 15, 16]</td>\n",
+       "      <td>[40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 5...</td>\n",
+       "      <td>[20, 21, 22, 23, 24, 25, 26, 27]</td>\n",
+       "      <td>129059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.018144</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    start_time  stop_time                states  \\\n",
+       "id                                                \n",
+       "0      19.9880    39.6237                [0, 1]   \n",
+       "1      39.7154    43.5430                [3, 4]   \n",
+       "2      43.6313    46.8007                [6, 7]   \n",
+       "3      46.8732    59.3249               [9, 10]   \n",
+       "4      59.4011    77.6604  [12, 13, 14, 15, 16]   \n",
+       "\n",
+       "                                               events  \\\n",
+       "id                                                      \n",
+       "0                                              [0, 1]   \n",
+       "1   [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...   \n",
+       "2                    [26, 27, 28, 29, 30, 31, 32, 33]   \n",
+       "3                                    [35, 36, 37, 38]   \n",
+       "4   [40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 5...   \n",
+       "\n",
+       "                             actions  trials_in_stage  changed  cycle_blocks  \\\n",
+       "id                                                                             \n",
+       "0                    [0, 1, 2, 3, 4]           129054    False          True   \n",
+       "1                    [5, 6, 7, 8, 9]           129056    False          True   \n",
+       "2               [10, 11, 12, 13, 14]           129057    False          True   \n",
+       "3               [15, 16, 17, 18, 19]           129058    False          True   \n",
+       "4   [20, 21, 22, 23, 24, 25, 26, 27]           129059    False          True   \n",
+       "\n",
+       "    target_reward_delay  catch_percentage  ...  \\\n",
+       "id                                         ...   \n",
+       "0                   1.5              0.25  ...   \n",
+       "1                   1.5              0.25  ...   \n",
+       "2                   1.5              0.25  ...   \n",
+       "3                   1.5              0.25  ...   \n",
+       "4                   1.5              0.25  ...   \n",
+       "\n",
+       "    num_trials_in_adaptation_blocks  is_catch  punish_sound_enabled  \\\n",
+       "id                                                                    \n",
+       "0                                40     False                  True   \n",
+       "1                                40      True                  True   \n",
+       "2                                40     False                  True   \n",
+       "3                                40      True                  True   \n",
+       "4                                40      True                  True   \n",
+       "\n",
+       "    nose_in_center  target_duration_for_nose_in_center  is_warm_up  \\\n",
+       "id                                                                   \n",
+       "0         1.095400                                   1       False   \n",
+       "1         1.188173                                   1       False   \n",
+       "2         1.188173                                   1       False   \n",
+       "3         1.109749                                   1       False   \n",
+       "4         1.018144                                   1       False   \n",
+       "\n",
+       "    num_trials_in_test_blocks  block_type  previous_was_violation  \\\n",
+       "id                                                                  \n",
+       "0                          40       Mixed                   False   \n",
+       "1                          40       Mixed                    True   \n",
+       "2                          40       Mixed                    True   \n",
+       "3                          40       Mixed                    True   \n",
+       "4                          40       Mixed                    True   \n",
+       "\n",
+       "    center_port_cue  \n",
+       "id                   \n",
+       "0             False  \n",
+       "1             False  \n",
+       "2             False  \n",
+       "3             False  \n",
+       "4             False  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = nwbfile.trials\n",
+    "\n",
+    "trials[:].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f55fdd71-2069-4468-a605-788e250095c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot trial 2 - 5\n",
+    "plot_trials(\n",
+    "    trials=trials[2:6],\n",
+    "    states=states, state_types=state_types,\n",
+    "    actions=actions, action_types=action_types,\n",
+    "    events=events, event_types=event_types,\n",
+    "    figsize=None,\n",
+    "    fontsize=18,\n",
+    "    rectangle_height=1,\n",
+    "    marker_size=500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8e5a9f8-d927-45fb-acc4-7017ddc2f874",
+   "metadata": {},
+   "source": [
+    "# Access processed behavior data\n",
+    "\n",
+    "This section demonstrates how to access the processed behavior data in the NWBFile.\n",
+    "The table can be accessed from the file as `nwbfile.intervals[\"processed_trials\"]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b13df93c-8de1-49d6-a40e-50967ed875a6",
+   "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>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>center_poke_onset_time</th>\n",
+       "      <th>center_poke_offset_time</th>\n",
+       "      <th>nose_in_center</th>\n",
+       "      <th>training_stage</th>\n",
+       "      <th>block_type</th>\n",
+       "      <th>num_trials_in_adaptation_blocks</th>\n",
+       "      <th>num_trials_in_test_blocks</th>\n",
+       "      <th>catch_percentage</th>\n",
+       "      <th>...</th>\n",
+       "      <th>is_opt_out</th>\n",
+       "      <th>wait_time</th>\n",
+       "      <th>rewarded_port</th>\n",
+       "      <th>left_poke_times</th>\n",
+       "      <th>right_poke_times</th>\n",
+       "      <th>left_opt_out_times</th>\n",
+       "      <th>right_opt_out_times</th>\n",
+       "      <th>reaction_time</th>\n",
+       "      <th>short_latency_reaction_time</th>\n",
+       "      <th>inter_trial_interval</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <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>19.9880</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>48.570172</td>\n",
+       "      <td>67.126643</td>\n",
+       "      <td>1.095400</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18.5562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>43.5430</td>\n",
+       "      <td>68.297872</td>\n",
+       "      <td>70.827609</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.5297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.6313</td>\n",
+       "      <td>46.8007</td>\n",
+       "      <td>72.213823</td>\n",
+       "      <td>73.734469</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.5206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46.8732</td>\n",
+       "      <td>59.3249</td>\n",
+       "      <td>75.455781</td>\n",
+       "      <td>86.787184</td>\n",
+       "      <td>1.109749</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.3312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>59.4011</td>\n",
+       "      <td>77.6604</td>\n",
+       "      <td>87.983920</td>\n",
+       "      <td>89.428742</td>\n",
+       "      <td>1.018144</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15.5943</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[90.4488554055054, 106.04339712480126, 106.043...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.8094</td>\n",
+       "      <td>1.4448</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>479</th>\n",
+       "      <td>6237.8301</td>\n",
+       "      <td>6265.6915</td>\n",
+       "      <td>6266.513190</td>\n",
+       "      <td>6293.131314</td>\n",
+       "      <td>1.184658</td>\n",
+       "      <td>9</td>\n",
+       "      <td>High</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>26.6177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>6266.2179</td>\n",
+       "      <td>6268.1972</td>\n",
+       "      <td>6294.901438</td>\n",
+       "      <td>6295.369935</td>\n",
+       "      <td>1.135084</td>\n",
+       "      <td>9</td>\n",
+       "      <td>High</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.4685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6268.7188</td>\n",
+       "      <td>6311.9109</td>\n",
+       "      <td>6297.402369</td>\n",
+       "      <td>6339.474865</td>\n",
+       "      <td>0.848517</td>\n",
+       "      <td>9</td>\n",
+       "      <td>High</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>42.0718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>6312.4316</td>\n",
+       "      <td>6314.3740</td>\n",
+       "      <td>6341.115891</td>\n",
+       "      <td>6341.979427</td>\n",
+       "      <td>0.915302</td>\n",
+       "      <td>9</td>\n",
+       "      <td>High</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.8635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>6314.8954</td>\n",
+       "      <td>6316.4097</td>\n",
+       "      <td>6343.579727</td>\n",
+       "      <td>6343.721751</td>\n",
+       "      <td>1.087845</td>\n",
+       "      <td>9</td>\n",
+       "      <td>High</td>\n",
+       "      <td>40</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>[nan, nan, nan, nan]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1420</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>484 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     start_time  stop_time  center_poke_onset_time  center_poke_offset_time  \\\n",
+       "id                                                                            \n",
+       "0       19.9880    39.6237               48.570172                67.126643   \n",
+       "1       39.7154    43.5430               68.297872                70.827609   \n",
+       "2       43.6313    46.8007               72.213823                73.734469   \n",
+       "3       46.8732    59.3249               75.455781                86.787184   \n",
+       "4       59.4011    77.6604               87.983920                89.428742   \n",
+       "..          ...        ...                     ...                      ...   \n",
+       "479   6237.8301  6265.6915             6266.513190              6293.131314   \n",
+       "480   6266.2179  6268.1972             6294.901438              6295.369935   \n",
+       "481   6268.7188  6311.9109             6297.402369              6339.474865   \n",
+       "482   6312.4316  6314.3740             6341.115891              6341.979427   \n",
+       "483   6314.8954  6316.4097             6343.579727              6343.721751   \n",
+       "\n",
+       "     nose_in_center  training_stage block_type  \\\n",
+       "id                                               \n",
+       "0          1.095400               9      Mixed   \n",
+       "1          1.188173               9      Mixed   \n",
+       "2          1.188173               9      Mixed   \n",
+       "3          1.109749               9      Mixed   \n",
+       "4          1.018144               9      Mixed   \n",
+       "..              ...             ...        ...   \n",
+       "479        1.184658               9       High   \n",
+       "480        1.135084               9       High   \n",
+       "481        0.848517               9       High   \n",
+       "482        0.915302               9       High   \n",
+       "483        1.087845               9       High   \n",
+       "\n",
+       "     num_trials_in_adaptation_blocks  num_trials_in_test_blocks  \\\n",
+       "id                                                                \n",
+       "0                                 40                         40   \n",
+       "1                                 40                         40   \n",
+       "2                                 40                         40   \n",
+       "3                                 40                         40   \n",
+       "4                                 40                         40   \n",
+       "..                               ...                        ...   \n",
+       "479                               40                         40   \n",
+       "480                               40                         40   \n",
+       "481                               40                         40   \n",
+       "482                               40                         40   \n",
+       "483                               40                         40   \n",
+       "\n",
+       "     catch_percentage  ...  is_opt_out  wait_time  rewarded_port  \\\n",
+       "id                     ...                                         \n",
+       "0                0.25  ...       False        NaN          Right   \n",
+       "1                0.25  ...       False        NaN           Left   \n",
+       "2                0.25  ...       False        NaN           Left   \n",
+       "3                0.25  ...       False        NaN           Left   \n",
+       "4                0.25  ...        True    15.5943           Left   \n",
+       "..                ...  ...         ...        ...            ...   \n",
+       "479              0.25  ...       False        NaN           Left   \n",
+       "480              0.25  ...       False        NaN          Right   \n",
+       "481              0.25  ...       False        NaN          Right   \n",
+       "482              0.25  ...       False        NaN          Right   \n",
+       "483              0.25  ...       False        NaN          Right   \n",
+       "\n",
+       "          left_poke_times      right_poke_times    left_opt_out_times  \\\n",
+       "id                                                                      \n",
+       "0    [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "1    [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "2    [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "3    [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "4    [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "..                    ...                   ...                   ...   \n",
+       "479  [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "480  [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "481  [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "482  [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "483  [nan, nan, nan, nan]  [nan, nan, nan, nan]  [nan, nan, nan, nan]   \n",
+       "\n",
+       "                                   right_opt_out_times reaction_time  \\\n",
+       "id                                                                     \n",
+       "0                                 [nan, nan, nan, nan]           NaN   \n",
+       "1                                 [nan, nan, nan, nan]           NaN   \n",
+       "2                                 [nan, nan, nan, nan]           NaN   \n",
+       "3                                 [nan, nan, nan, nan]           NaN   \n",
+       "4    [90.4488554055054, 106.04339712480126, 106.043...           NaN   \n",
+       "..                                                 ...           ...   \n",
+       "479                               [nan, nan, nan, nan]           NaN   \n",
+       "480                               [nan, nan, nan, nan]           NaN   \n",
+       "481                               [nan, nan, nan, nan]           NaN   \n",
+       "482                               [nan, nan, nan, nan]           NaN   \n",
+       "483                               [nan, nan, nan, nan]           NaN   \n",
+       "\n",
+       "    short_latency_reaction_time inter_trial_interval  \n",
+       "id                                                    \n",
+       "0                           NaN              18.5562  \n",
+       "1                           NaN               2.5297  \n",
+       "2                           NaN               1.5206  \n",
+       "3                           NaN              11.3312  \n",
+       "4                        0.8094               1.4448  \n",
+       "..                          ...                  ...  \n",
+       "479                         NaN              26.6177  \n",
+       "480                         NaN               0.4685  \n",
+       "481                         NaN              42.0718  \n",
+       "482                         NaN               0.8635  \n",
+       "483                         NaN               0.1420  \n",
+       "\n",
+       "[484 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.intervals[\"processed_trials\"][:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d515db92-9631-4bb5-aeb2-5855a83fedab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb
new file mode 100644
index 0000000..706f47b
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb
@@ -0,0 +1,231 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "352a2fb2-54fe-4418-ae34-eda86223c180",
+   "metadata": {},
+   "source": [
+    "# Neuropixels data conversion to NWB\n",
+    "\n",
+    "This notebook demonstrates how to convert an example session from the Neuropixels dataset to NWB.\n",
+    "\n",
+    "## Neuropixels dataset\n",
+    "\n",
+    "This dataset contains in vivo extracellular electrophysiology recordings from rats performing a value-based decision-making task. Neural data were acquired using Neuropixels probes (384 channels, 30 kHz sampling rate) with Neuropix-PXI hardware and OpenEphys, and preprocessed using Kilosort 2.5 with manual curation in Phy. Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials.\n",
+    "This notebook demonstrates how to convert an example session to NWB.\n",
+    "\n",
+    "This dataset have the following data streams:\n",
+    "- Behavior: Bpod output (.mat)\n",
+    "- Recording AP, LFP: OpenEphys (binary format)\n",
+    "- Units: Phy output\n",
+    "\n",
+    "## Notes on the conversion\n",
+    "\n",
+    "The conversion notes is located in `src/constantinople_lab_to_nwb/schierek_embargo_2024/schierek_embargo_2024_notes.md`. This file contains information about the expected file structure and the conversion process.\n",
+    "\n",
+    "## Running the conversion\n",
+    "\n",
+    "To run a specific conversion, you might need to install first some conversion specific dependencies that are located in each conversion directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6408c740-2030-4f51-a44b-5e6b6dece298",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -r ../schierek_embargo_2024_requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b221cfff-c7e5-44e7-bd3a-024bf2f26f67",
+   "metadata": {},
+   "source": [
+    "## Convert a single session to NWB\n",
+    "\n",
+    "The `schierek_embargo_2024_convert_session.py` script defines the `session_to_nwb` function that converts a session of Neuropixels data to NWB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "695d7f2c-5b40-4e0d-b4b7-38546a4dce52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source data is valid!\n",
+      "Metadata is valid!\n",
+      "conversion_options is valid!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/weian/catalystneuro/constantinople-lab-to-nwb/src/constantinople_lab_to_nwb/general_interfaces/bpodbehaviorinterface.py:496: UserWarning: Task argument 'HiITI' not in metadata. Skipping.\n",
+      "  warn(f\"Task argument '{task_argument_name}' not in metadata. Skipping.\")\n",
+      "/Users/weian/anaconda3/envs/constantinople_lab_to_nwb_env/lib/python3.12/site-packages/hdmf/container.py:536: UserWarning: The linked table for DynamicTableRegion 'event_type' does not share an ancestor with the DynamicTableRegion.\n",
+      "  child._validate_on_set_parent()\n",
+      "/Users/weian/anaconda3/envs/constantinople_lab_to_nwb_env/lib/python3.12/site-packages/hdmf/container.py:536: UserWarning: The linked table for DynamicTableRegion 'state_type' does not share an ancestor with the DynamicTableRegion.\n",
+      "  child._validate_on_set_parent()\n",
+      "/Users/weian/anaconda3/envs/constantinople_lab_to_nwb_env/lib/python3.12/site-packages/hdmf/container.py:536: UserWarning: The linked table for DynamicTableRegion 'action_type' does not share an ancestor with the DynamicTableRegion.\n",
+      "  child._validate_on_set_parent()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NWB file saved at /Volumes/T9/Constantinople/nwbfiles/sub-J076_ephys.nwb!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from constantinople_lab_to_nwb.schierek_embargo_2024.schierek_embargo_2024_convert_session import session_to_nwb\n",
+    "\n",
+    "# Parameters for conversion\n",
+    "\n",
+    "# The path to the NWB file to write.\n",
+    "nwbfile_path = \"/Volumes/T9/Constantinople/nwbfiles/sub-J076_ephys.nwb\"\n",
+    "# The OpenEphys recording folder path (make sure to include the 'Record Node #' in the folder path)\n",
+    "folder_path = \"/Volumes/T9/Constantinople/Ephys Data/J076_2023-12-12_14-52-04/Record Node 117\"\n",
+    "# The name of the *raw* recording stream (e.g. )\n",
+    "raw_stream_name = \"Neuropix-PXI-119.ProbeA-AP\"\n",
+    "# The name of the *LFP* recording stream (e.g. )\n",
+    "lfp_stream_name = \"Neuropix-PXI-119.ProbeA-LFP\"\n",
+    "# The path to the processed spike sorting file (.mat). This file contains the \"SU\" named struct that contains the processed spike data.\n",
+    "# This file also contains the \"S\" named struct containing the processed behavior data.\n",
+    "spike_sorting_mat_file_path = \"/Volumes/T9/Constantinople/Ephys Data/J076_2023-12-12.mat\"\n",
+    "# The path to the raw Bpod data\n",
+    "bpod_file_path = \"/Volumes/T9/Constantinople/raw_Bpod/J076/DataFiles/J076_RWTautowait2_20231212_145250.mat\"\n",
+    "\n",
+    "# Task specific parameters\n",
+    "\n",
+    "# The column name mapping is used to rename the columns in the processed data to more descriptive column names.\n",
+    "# New (unseen) task parameters should be added here.\n",
+    "column_name_mapping = dict(\n",
+    "    NoseInCenter=\"nose_in_center\",\n",
+    "    TrainingStage=\"training_stage\",\n",
+    "    Block=\"block_type\",\n",
+    "    BlockLengthAd=\"num_trials_in_adaptation_blocks\",\n",
+    "    BlockLengthTest=\"num_trials_in_test_blocks\",\n",
+    "    ProbCatch=\"catch_percentage\",\n",
+    "    RewardDelay=\"reward_delay\",\n",
+    "    RewardAmount=\"reward_volume_ul\",\n",
+    "    WaitForPoke=\"wait_for_center_poke\",\n",
+    "    hits=\"is_rewarded\",\n",
+    "    vios=\"is_violation\",\n",
+    "    optout=\"is_opt_out\",\n",
+    "    wait_time=\"wait_time\",\n",
+    "    wait_thresh=\"wait_time_threshold\",\n",
+    "    wait_for_cpoke=\"wait_for_center_poke\",\n",
+    "    zwait_for_cpoke=\"z_scored_wait_for_center_poke\",\n",
+    "    RewardedSide=\"rewarded_port\",\n",
+    "    Cled=\"center_poke_times\",\n",
+    "    Lled=\"left_poke_times\",\n",
+    "    Rled=\"right_poke_times\",\n",
+    "    l_opt=\"left_opt_out_times\",\n",
+    "    r_opt=\"right_opt_out_times\",\n",
+    "    ReactionTime=\"reaction_time\",\n",
+    "    slrt=\"short_latency_reaction_time\",\n",
+    "    iti=\"inter_trial_interval\",\n",
+    ")\n",
+    "# The column descriptions are used to add descriptions to the columns in the processed data.\n",
+    "# New (unseen) task parameter descriptions should be added here.\n",
+    "column_descriptions = dict(\n",
+    "    NoseInCenter=\"The time in seconds when the animal is required to maintain center port to initiate the trial (uniformly drawn from 0.8 - 1.2 seconds).\",\n",
+    "    TrainingStage=\"The stage of the training.\",\n",
+    "    Block=\"The block type (High, Low or Test). High and Low blocks are high reward (20, 40, or 80μL) or low reward (5, 10, or 20μL) blocks. Test blocks are mixed blocks.\",\n",
+    "    BlockLengthAd=\"The number of trials in each high reward (20, 40, or 80μL) or low reward (5, 10, or 20μL) blocks.\",\n",
+    "    BlockLengthTest=\"The number of trials in each mixed blocks.\",\n",
+    "    ProbCatch=\"The percentage of catch trials.\",\n",
+    "    RewardDelay=\"The delay in seconds to receive reward, drawn from exponential distribution with mean = 2.5 seconds.\",\n",
+    "    RewardAmount=\"The volume of reward in microliters.\",\n",
+    "    hits=\"Whether the subject received reward for each trial.\",\n",
+    "    vios=\"Whether the subject violated the trial by not maintaining center poke for the time required by 'nose_in_center'.\",\n",
+    "    optout=\"Whether the subject opted out for each trial.\",\n",
+    "    WaitForPoke=\"The time (s) between side port poke and center poke.\",\n",
+    "    wait_time=\"The wait time for the subject for for each trial in seconds, after removing outliers.\"\n",
+    "        \" For hit trials (when reward was delivered) the wait time is equal to the reward delay.\"\n",
+    "        \" For opt-out trials, the wait time is equal to the time waited from trial start to opting out.\",\n",
+    "    wait_for_cpoke=\"The time between side port poke and center poke in seconds, includes the time when the subject is consuming the reward.\",\n",
+    "    zwait_for_cpoke=\"The z-scored wait_for_cpoke using all trials.\",\n",
+    "    RewardedSide=\"The rewarded port (Left or Right) for each trial.\",\n",
+    "    Cled=\"The time of center port LED on/off for each trial (2 x ntrials).\",\n",
+    "    Lled=\"The time of left port LED on/off for each trial (2 x ntrials).\",\n",
+    "    Rled=\"The time of right port LED on/off for each trial (2 x ntrials).\",\n",
+    "    l_opt=\"The time of left port entered/exited for each trial (2 x ntrials).\",\n",
+    "    r_opt=\"The time of right port entered/exited for each trial (2 x ntrials).\",\n",
+    "    ReactionTime=\"The reaction time in seconds.\",\n",
+    "    slrt=\"The short-latency reaction time in seconds.\",\n",
+    "    iti=\"The time to initiate trial in seconds (the time between the end of the consummatory period and the time to initiate the next trial).\",\n",
+    "    wait_thresh=\"The threshold in seconds to remove wait-times (mean + 1*std of all cumulative wait-times).\",\n",
+    ")\n",
+    "\n",
+    "# Optional parameters\n",
+    "\n",
+    "# Whether to run a stub test conversion. The stubbed file will only contain a small portion of data.\n",
+    "# When running the full conversion stub_test should be disabled.\n",
+    "stub_test = True\n",
+    "# Whether to overwrite an existing NWB file.\n",
+    "overwrite = True\n",
+    "\n",
+    "# Run the `session_to_nwb` function with the parameters to convert to NWB\n",
+    "session_to_nwb(\n",
+    "    nwbfile_path=nwbfile_path,\n",
+    "    openephys_recording_folder_path=folder_path,\n",
+    "    ap_stream_name=raw_stream_name,\n",
+    "    lfp_stream_name=lfp_stream_name,\n",
+    "    processed_spike_sorting_file_path=spike_sorting_mat_file_path,\n",
+    "    raw_behavior_file_path=bpod_file_path,\n",
+    "    column_name_mapping=column_name_mapping,\n",
+    "    column_descriptions=column_descriptions,\n",
+    "    stub_test=stub_test,\n",
+    "    overwrite=overwrite,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e70b3e5-de74-4f15-bec0-688151d80ce4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37140fad-10d0-436d-a4a8-452083d33d10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}