000947/TurnerLab/public_demo/000947_demo.ipynb

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    "# Reach-related Single Unit Activity in the Parkinsonian Macaque Tutorial \n",
    "\n",
    "This tutorial demonstrates how to access an NWB file from the [DANDI:000947](https://dandiarchive.org/dandiset/000947/draft) dandiset using `pynwb` and `dandi`.\n",
    "\n",
    "This dataset contains recordings of single-unit activity from from multiple cortical areas, including globus pallidus-internus (GPi), ventrolateral nucleus of the thalamus (VLa and VLp) and the arm-related regions of primary motor cortex, including sulcus (M1-S) and gyrus (M1-G) subregions, in monkeys performing a choice reaction time reaching task. Small numbers of recordings were also obtained from supplementary motor area (SMA), external globus pallidus (GPe), the thalamic reticular nucleus (RTN), striatum (STR) and the region between RTN and VL thalamus (R-V). It contains data from two monkeys before and after the administration of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), which induces Parkinsonism. The neuronal activity was recorded using 16-contact linear probes (0.5–1.0 MΩ, V-probe, Plexon) or glass-insulated tungsten microelectrodes (0.5–1.5 MΩ, Alpha Omega). The neuronal data were amplified (4×, 2 Hz–7.5 kHz) and digitized at 24.414 kHz (approx., 16-bit resolution; Tucker Davis Technologies). The neuronal data were high-pass filtered (Fpass: 200 Hz, Matlab FIRPM) and thresholded, and candidate action potentials were sorted into clusters in principal components space (Off-line Sorter, Plexon).\n",
    "\n",
    "Contents:\n",
    "\n",
    "- [Streaming NWB files](#stream-nwb)\n",
    "- [Access Subject metadata](#access-subject)\n",
    "- [Access Trials](#access-trials)\n",
    "- [Access Recording](#access-recording)\n",
    "- [Access Units](#access-units)\n",
    "- [View PSTH](#view-PSTH)\n"
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    "# Streaming NWB files <a name=\"stream-nwb\"></a>\n",
    "\n",
    "This section demonstrates how to access the files on the [DANDI Archive](https://dandiarchive.org) without downloading them.\n",
    "\n",
    "Based on the [Streaming NWB files](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html) tutorial from PyNWB.\n",
    "\n",
    "The `dandi.dandiapi.DandiAPIClient` can be used to get the S3 URL of the NWB file stored in the DANDI Archive.\n",
    "\n"
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    "from dandi.dandiapi import DandiAPIClient\n",
    "\n",
    "client = DandiAPIClient.for_dandi_instance(\"dandi\")\n",
    "\n",
    "dandiset_id = \"000947\"\n",
    "file_path = \"sub-Isis/sub-Isis_ses-post-MPTP-I-190118-6_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)"
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    "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)."
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       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['session_description']\">session_description:</span> <span class=\"field-value\">This session contains raw and high-pass filtered (200 Hz) extracellular data from multiple cortical areas,\n",
       "including the globus pallidus-internus (GPi) and ventrolateral nucleus of the thalamus (VLa and VLp) in \n",
       "parkinsonian macaque performing a choice reaction time reaching task.\n",
       "</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['identifier']\">identifier:</span> <span class=\"field-value\">0b60cba4-1a1a-4a01-808b-6a50a9f74e5d</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['session_start_time']\">session_start_time:</span> <span class=\"field-value\">2019-01-20 14:51:30-08:00</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['timestamps_reference_time']\">timestamps_reference_time:</span> <span class=\"field-value\">2019-01-20 14:51:30-08:00</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['file_create_date']\"><b>file_create_date</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-value\" title=\".fields['file_create_date'][0]\">2024-04-25 17:04:56.027983+02:00</span></div></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['experimenter']\">experimenter:</span> <span class=\"field-value\">('Kase, Daisuke', 'Zimnik, Andrew')</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['related_publications']\">related_publications:</span> <span class=\"field-value\">('https://doi.org/10.1101/2023.09.08.556120',)</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['acquisition']\"><b>acquisition (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['description']\">description:</span> <span class=\"field-value\">Acquisition traces for the ElectricalSeries.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['unit']\">unit:</span> <span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['starting_time_unit']\">starting_time_unit:</span> <span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['table'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['acquisition']['ElectricalSeries'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['keywords']\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['processing']\"><b>processing (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys']\"><b>ecephys</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['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: 40px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']\"><b>data_interfaces (1)</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed']\"><b>Processed</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']\"><b>electrical_series (3)</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe']\"><b>ElectricalSeriesProcessedGPe</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from GPe region.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['unit']\">unit:</span> <span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['starting_time_unit']\">starting_time_unit:</span> <span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 140px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 160px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi']\"><b>ElectricalSeriesProcessedGPi</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from GPi region.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['unit']\">unit:</span> <span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['starting_time_unit']\">starting_time_unit:</span> <span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 140px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 160px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa']\"><b>ElectricalSeriesProcessedVLa</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from VLa region.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['unit']\">unit:</span> <span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['starting_time_unit']\">starting_time_unit:</span> <span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 140px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 160px;\" class=\"container-fields field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 160px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['processing']['ecephys'].fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details></details></details></details></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['epoch_tags']\">epoch_tags:</span> <span class=\"field-value\">set()</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrodes'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']\"><b>electrode_groups (3)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group GPe']\"><b>Group GPe</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPe'].fields['description']\">description:</span> <span class=\"field-value\">Group vp16 electrodes.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPe'].fields['location']\">location:</span> <span class=\"field-value\">GPe</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group GPe'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPe'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">TDT recording</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPe'].fields['device'].fields['manufacturer']\">manufacturer:</span> <span class=\"field-value\">Tucker-Davis Technologies (TDT)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group GPi']\"><b>Group GPi</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPi'].fields['description']\">description:</span> <span class=\"field-value\">Group vp16 electrodes.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPi'].fields['location']\">location:</span> <span class=\"field-value\">GPi</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group GPi'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPi'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">TDT recording</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group GPi'].fields['device'].fields['manufacturer']\">manufacturer:</span> <span class=\"field-value\">Tucker-Davis Technologies (TDT)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group VLa']\"><b>Group VLa</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group VLa'].fields['description']\">description:</span> <span class=\"field-value\">Group vp16 electrodes.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group VLa'].fields['location']\">location:</span> <span class=\"field-value\">VLa</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['electrode_groups']['Group VLa'].fields['device']\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group VLa'].fields['device'].fields['description']\">description:</span> <span class=\"field-value\">TDT recording</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrode_groups']['Group VLa'].fields['device'].fields['manufacturer']\">manufacturer:</span> <span class=\"field-value\">Tucker-Davis Technologies (TDT)</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['devices']\"><b>devices (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['devices']['DeviceEcephys']\"><b>DeviceEcephys</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['DeviceEcephys'].fields['description']\">description:</span> <span class=\"field-value\">TDT recording</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['devices']['DeviceEcephys'].fields['manufacturer']\">manufacturer:</span> <span class=\"field-value\">Tucker-Davis Technologies (TDT)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['intervals']\"><b>intervals (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['intervals']['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['description']\">description:</span> <span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['intervals']['trials'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('start_time', 'stop_time', 'error_onset_time', 'reward_start_time', 'reward_stop_time', 'movement_start_time', 'movement_stop_time', 'return_start_time', 'return_stop_time', 'cue_onset_time', 'target')</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['intervals']['trials'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f658910>, <hdmf.common.table.VectorData object at 0x17f659bd0>, <hdmf.common.table.VectorData object at 0x17f690190>, <hdmf.common.table.VectorData object at 0x17f692e10>, <hdmf.common.table.VectorData object at 0x17f692f90>, <hdmf.common.table.VectorData object at 0x17f690a50>, <hdmf.common.table.VectorData object at 0x17f690e10>, <hdmf.common.table.VectorData object at 0x17f692ad0>, <hdmf.common.table.VectorData object at 0x17f691890>, <hdmf.common.table.VectorData object at 0x17f691b10>, <hdmf.common.table.VectorData object at 0x17f693150>)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['subject']\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['age__reference']\">age__reference:</span> <span class=\"field-value\">birth</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['description']\">description:</span> <span class=\"field-value\">USDA registration number RQ7513</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['sex']\">sex:</span> <span class=\"field-value\">F</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['species']\">species:</span> <span class=\"field-value\">Macaca mulatta</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['subject_id']\">subject_id:</span> <span class=\"field-value\">Isis</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject'].fields['date_of_birth']\">date_of_birth:</span> <span class=\"field-value\">2005-06-14 00:00:00-07:00</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['lab_meta_data']\"><b>lab_meta_data (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['lab_meta_data']['MPTPMetaData']\"><b>MPTPMetaData</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['lab_meta_data']['MPTPMetaData'].fields['MPTP_status']\">MPTP_status:</span> <span class=\"field-value\">post_MPTP</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['trials']\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['description']\">description:</span> <span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['trials'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('start_time', 'stop_time', 'error_onset_time', 'reward_start_time', 'reward_stop_time', 'movement_start_time', 'movement_stop_time', 'return_start_time', 'return_stop_time', 'cue_onset_time', 'target')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['trials'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f658910>, <hdmf.common.table.VectorData object at 0x17f659bd0>, <hdmf.common.table.VectorData object at 0x17f690190>, <hdmf.common.table.VectorData object at 0x17f692e10>, <hdmf.common.table.VectorData object at 0x17f692f90>, <hdmf.common.table.VectorData object at 0x17f690a50>, <hdmf.common.table.VectorData object at 0x17f690e10>, <hdmf.common.table.VectorData object at 0x17f692ad0>, <hdmf.common.table.VectorData object at 0x17f691890>, <hdmf.common.table.VectorData object at 0x17f691b10>, <hdmf.common.table.VectorData object at 0x17f693150>)</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".fields['units']\"><b>units</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['description']\">description:</span> <span class=\"field-value\">The curated single-units from the Plexon Offline Sorter v3, selected based on the quality of spike sorting.</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['units'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('spike_times', 'unit_name', 'sort_label', 'location', 'channel_ids', 'unit_quality_post_sorting')</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorIndex object at 0x17f2591d0>, <hdmf.common.table.VectorData object at 0x17f691210>, <hdmf.common.table.VectorData object at 0x17f690110>, <hdmf.common.table.VectorData object at 0x17f693910>, <hdmf.common.table.VectorData object at 0x17f692c90>, <hdmf.common.table.VectorData object at 0x17f692b90>, <hdmf.common.table.VectorData object at 0x17f693c50>)</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['units'].fields['waveform_unit']\">waveform_unit:</span> <span class=\"field-value\">volts</span></div></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['experiment_description']\">experiment_description:</span> <span class=\"field-value\">This dataset contains recordings of single-unit activity from from multiple cortical areas, \n",
       "including globus pallidus-internus (GPi), ventrolateral nucleus of the thalamus (VLa and VLp) and \n",
       "the arm-related regions of primary motor cortex, including sulcus (M1-S) and gyrus (M1-G) subregions, in MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced parkinsonian monkeys performing a choice\n",
       "reaction time reaching task. Small numbers of recordings were also obtained from supplementary motor area (SMA),\n",
       "external globus pallidus (GPe), the thalamic reticular nucleus (RTN), striatum (STR) and the region between RTN\n",
       "and VL thalamus (R-V).\n",
       "The neuronal activity was recorded using 16-contact linear probes (0.5–1.0 MΩ, V-probe, Plexon) or glass-insulated\n",
       "tungsten microelectrodes (0.5–1.5 MΩ, Alpha Omega). The neuronal data were amplified (4×, 2 Hz–7.5 kHz) and\n",
       "digitized at 24.414 kHz (approx., 16-bit resolution; Tucker Davis Technologies).\n",
       "The neuronal data were high-pass filtered (Fpass: 200 Hz, Matlab FIRPM) and thresholded, and candidate action\n",
       "potentials were sorted into clusters in principal components space (Off-line Sorter, Plexon).\n",
       "</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['session_id']\">session_id:</span> <span class=\"field-value\">post-MPTP-I-190118-6</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['lab']\">lab:</span> <span class=\"field-value\">Turner</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['institution']\">institution:</span> <span class=\"field-value\">University of Pittsburgh</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['pharmacology']\">pharmacology:</span> <span class=\"field-value\">MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; the chemical compound that induces parkinsonism) administration date 2017-10-27 (ICA; internal carotid artery injection), 2017-11-28 (IM; intramuscular injection), 2017-12-01 (IM), 2018-06-08 (ICA), 2018-07-05 (IM), 2018-07-19 (IM), 2018-08-27 (IM)</span></div></div>"
      ],
      "text/plain": [
       "root pynwb.file.NWBFile at 0x6426136208\n",
       "Fields:\n",
       "  acquisition: {\n",
       "    ElectricalSeries <class 'pynwb.ecephys.ElectricalSeries'>\n",
       "  }\n",
       "  devices: {\n",
       "    DeviceEcephys <class 'pynwb.device.Device'>\n",
       "  }\n",
       "  electrode_groups: {\n",
       "    Group GPe <class 'pynwb.ecephys.ElectrodeGroup'>,\n",
       "    Group GPi <class 'pynwb.ecephys.ElectrodeGroup'>,\n",
       "    Group VLa <class 'pynwb.ecephys.ElectrodeGroup'>\n",
       "  }\n",
       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTable'>\n",
       "  experiment_description: This dataset contains recordings of single-unit activity from from multiple cortical areas, \n",
       "including globus pallidus-internus (GPi), ventrolateral nucleus of the thalamus (VLa and VLp) and \n",
       "the arm-related regions of primary motor cortex, including sulcus (M1-S) and gyrus (M1-G) subregions, in MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced parkinsonian monkeys performing a choice\n",
       "reaction time reaching task. Small numbers of recordings were also obtained from supplementary motor area (SMA),\n",
       "external globus pallidus (GPe), the thalamic reticular nucleus (RTN), striatum (STR) and the region between RTN\n",
       "and VL thalamus (R-V).\n",
       "The neuronal activity was recorded using 16-contact linear probes (0.5–1.0 MΩ, V-probe, Plexon) or glass-insulated\n",
       "tungsten microelectrodes (0.5–1.5 MΩ, Alpha Omega). The neuronal data were amplified (4×, 2 Hz–7.5 kHz) and\n",
       "digitized at 24.414 kHz (approx., 16-bit resolution; Tucker Davis Technologies).\n",
       "The neuronal data were high-pass filtered (Fpass: 200 Hz, Matlab FIRPM) and thresholded, and candidate action\n",
       "potentials were sorted into clusters in principal components space (Off-line Sorter, Plexon).\n",
       "\n",
       "  experimenter: ['Kase, Daisuke' 'Zimnik, Andrew']\n",
       "  file_create_date: [datetime.datetime(2024, 4, 25, 17, 4, 56, 27983, tzinfo=tzoffset(None, 7200))]\n",
       "  identifier: 0b60cba4-1a1a-4a01-808b-6a50a9f74e5d\n",
       "  institution: University of Pittsburgh\n",
       "  intervals: {\n",
       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
       "  }\n",
       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (9,), type \"|O\">\n",
       "  lab: Turner\n",
       "  lab_meta_data: {\n",
       "    MPTPMetaData <class 'abc.TurnerLabMetaData'>\n",
       "  }\n",
       "  pharmacology: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; the chemical compound that induces parkinsonism) administration date 2017-10-27 (ICA; internal carotid artery injection), 2017-11-28 (IM; intramuscular injection), 2017-12-01 (IM), 2018-06-08 (ICA), 2018-07-05 (IM), 2018-07-19 (IM), 2018-08-27 (IM)\n",
       "  processing: {\n",
       "    ecephys <class 'pynwb.base.ProcessingModule'>\n",
       "  }\n",
       "  related_publications: ['https://doi.org/10.1101/2023.09.08.556120']\n",
       "  session_description: This session contains raw and high-pass filtered (200 Hz) extracellular data from multiple cortical areas,\n",
       "including the globus pallidus-internus (GPi) and ventrolateral nucleus of the thalamus (VLa and VLp) in \n",
       "parkinsonian macaque performing a choice reaction time reaching task.\n",
       "\n",
       "  session_id: post-MPTP-I-190118-6\n",
       "  session_start_time: 2019-01-20 14:51:30-08:00\n",
       "  subject: subject pynwb.file.Subject at 0x6428144272\n",
       "Fields:\n",
       "  age__reference: birth\n",
       "  date_of_birth: 2005-06-14 00:00:00-07:00\n",
       "  description: USDA registration number RQ7513\n",
       "  sex: F\n",
       "  species: Macaca mulatta\n",
       "  subject_id: Isis\n",
       "\n",
       "  timestamps_reference_time: 2019-01-20 14:51:30-08:00\n",
       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>\n",
       "  units: units <class 'pynwb.misc.Units'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n",
    "nwbfile = io.read()\n",
    "nwbfile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78abdfd-03a0-45f9-ae07-e07a18286e86",
   "metadata": {},
   "source": [
    "# Access subject metadata <a name=\"access-subject\"></a>\n",
    "\n",
    "This section demonstrates how to access the [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field in an NWB file.\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": 3,
   "id": "e5a3c3bb-8c9a-41b6-b3d0-dd0a6581b030",
   "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=\".fields['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=\".fields['description']\">description:</span> <span class=\"field-value\">USDA registration number RQ7513</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['sex']\">sex:</span> <span class=\"field-value\">F</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['species']\">species:</span> <span class=\"field-value\">Macaca mulatta</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['subject_id']\">subject_id:</span> <span class=\"field-value\">Isis</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['date_of_birth']\">date_of_birth:</span> <span class=\"field-value\">2005-06-14 00:00:00-07:00</span></div></div>"
      ],
      "text/plain": [
       "subject pynwb.file.Subject at 0x6428144272\n",
       "Fields:\n",
       "  age__reference: birth\n",
       "  date_of_birth: 2005-06-14 00:00:00-07:00\n",
       "  description: USDA registration number RQ7513\n",
       "  sex: F\n",
       "  species: Macaca mulatta\n",
       "  subject_id: Isis"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwbfile.subject"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2f4116-f38f-45c4-b346-81c2db86afa7",
   "metadata": {},
   "source": [
    "The MPTP status is stored in a [TurnerLabMetaData](https://github.com/catalystneuro/ndx-turner-metadata) container which extends [pynwb.file.LabMetaData](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.LabMetaData), and can be accessed as `nwbfile.lab_meta_data[\"MPTPMetaData\"]`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e8c9994-6aad-45b2-a647-fef07440e211",
   "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>MPTPMetaData (TurnerLabMetaData)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['MPTP_status']\">MPTP_status:</span> <span class=\"field-value\">post_MPTP</span></div></div>"
      ],
      "text/plain": [
       "MPTPMetaData abc.TurnerLabMetaData at 0x6426137232\n",
       "Fields:\n",
       "  MPTP_status: post_MPTP"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwbfile.lab_meta_data[\"MPTPMetaData\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1ca78d-0aec-47ac-baad-96fda7ab1c37",
   "metadata": {},
   "source": [
    "# Access trials <a name=\"access-trials\"></a>\n",
    "\n",
    "Behavior trials are stored in `nwbfile.trials`. The `start_time` denotes the start time of each trial in seconds relative to the global session start time.\n",
    "The `stop_time` denotes the end time of each trial in seconds relative to the global session start time.\n",
    "\n",
    "`nwbfile.trials` can be converted to a pandas DataFrame for convenient analysis using `nwbfile.trials.to_dataframe()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67073433-ae57-4459-900a-6b79e55b7795",
   "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>error_onset_time</th>\n",
       "      <th>reward_start_time</th>\n",
       "      <th>reward_stop_time</th>\n",
       "      <th>movement_start_time</th>\n",
       "      <th>movement_stop_time</th>\n",
       "      <th>return_start_time</th>\n",
       "      <th>return_stop_time</th>\n",
       "      <th>cue_onset_time</th>\n",
       "      <th>target</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.992620</td>\n",
       "      <td>60.075663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.932647</td>\n",
       "      <td>54.212239</td>\n",
       "      <td>53.702328</td>\n",
       "      <td>53.919293</td>\n",
       "      <td>58.726072</td>\n",
       "      <td>60.075663</td>\n",
       "      <td>53.478851</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.086641</td>\n",
       "      <td>70.747259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.636644</td>\n",
       "      <td>63.962071</td>\n",
       "      <td>63.347057</td>\n",
       "      <td>63.622390</td>\n",
       "      <td>69.581988</td>\n",
       "      <td>70.747259</td>\n",
       "      <td>63.103672</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.757294</td>\n",
       "      <td>87.668572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.166679</td>\n",
       "      <td>75.442340</td>\n",
       "      <td>74.939474</td>\n",
       "      <td>75.144356</td>\n",
       "      <td>85.597430</td>\n",
       "      <td>87.668572</td>\n",
       "      <td>74.664960</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>87.680328</td>\n",
       "      <td>102.365225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.649436</td>\n",
       "      <td>90.927391</td>\n",
       "      <td>90.407362</td>\n",
       "      <td>90.637189</td>\n",
       "      <td>93.556941</td>\n",
       "      <td>102.365225</td>\n",
       "      <td>90.165125</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102.381896</td>\n",
       "      <td>118.222643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.212150</td>\n",
       "      <td>105.524838</td>\n",
       "      <td>104.894423</td>\n",
       "      <td>105.200640</td>\n",
       "      <td>115.487580</td>\n",
       "      <td>118.222643</td>\n",
       "      <td>104.665498</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>118.241034</td>\n",
       "      <td>133.235917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.586442</td>\n",
       "      <td>121.857434</td>\n",
       "      <td>121.264620</td>\n",
       "      <td>121.573253</td>\n",
       "      <td>126.400512</td>\n",
       "      <td>133.235917</td>\n",
       "      <td>121.009603</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>133.241979</td>\n",
       "      <td>148.448256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.416051</td>\n",
       "      <td>136.696095</td>\n",
       "      <td>136.139940</td>\n",
       "      <td>136.393155</td>\n",
       "      <td>146.674319</td>\n",
       "      <td>148.448256</td>\n",
       "      <td>135.913800</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>148.459520</td>\n",
       "      <td>165.340938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.901140</td>\n",
       "      <td>153.242665</td>\n",
       "      <td>152.581325</td>\n",
       "      <td>152.887132</td>\n",
       "      <td>162.919014</td>\n",
       "      <td>165.340938</td>\n",
       "      <td>152.366694</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>165.350031</td>\n",
       "      <td>180.914258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.070920</td>\n",
       "      <td>169.386926</td>\n",
       "      <td>168.770929</td>\n",
       "      <td>169.049989</td>\n",
       "      <td>173.979689</td>\n",
       "      <td>180.914258</td>\n",
       "      <td>168.523612</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>180.927775</td>\n",
       "      <td>197.386732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.650588</td>\n",
       "      <td>184.983347</td>\n",
       "      <td>184.306893</td>\n",
       "      <td>184.624456</td>\n",
       "      <td>194.873057</td>\n",
       "      <td>197.386732</td>\n",
       "      <td>184.101397</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start_time   stop_time  error_onset_time  reward_start_time  \\\n",
       "id                                                                \n",
       "0    50.992620   60.075663               NaN          53.932647   \n",
       "1    60.086641   70.747259               NaN          63.636644   \n",
       "2    70.757294   87.668572               NaN          75.166679   \n",
       "3    87.680328  102.365225               NaN          90.649436   \n",
       "4   102.381896  118.222643               NaN         105.212150   \n",
       "5   118.241034  133.235917               NaN         121.586442   \n",
       "6   133.241979  148.448256               NaN         136.416051   \n",
       "7   148.459520  165.340938               NaN         152.901140   \n",
       "8   165.350031  180.914258               NaN         169.070920   \n",
       "9   180.927775  197.386732               NaN         184.650588   \n",
       "\n",
       "    reward_stop_time  movement_start_time  movement_stop_time  \\\n",
       "id                                                              \n",
       "0          54.212239            53.702328           53.919293   \n",
       "1          63.962071            63.347057           63.622390   \n",
       "2          75.442340            74.939474           75.144356   \n",
       "3          90.927391            90.407362           90.637189   \n",
       "4         105.524838           104.894423          105.200640   \n",
       "5         121.857434           121.264620          121.573253   \n",
       "6         136.696095           136.139940          136.393155   \n",
       "7         153.242665           152.581325          152.887132   \n",
       "8         169.386926           168.770929          169.049989   \n",
       "9         184.983347           184.306893          184.624456   \n",
       "\n",
       "    return_start_time  return_stop_time  cue_onset_time target  \n",
       "id                                                              \n",
       "0           58.726072         60.075663       53.478851   Left  \n",
       "1           69.581988         70.747259       63.103672  Right  \n",
       "2           85.597430         87.668572       74.664960   Left  \n",
       "3           93.556941        102.365225       90.165125   Left  \n",
       "4          115.487580        118.222643      104.665498  Right  \n",
       "5          126.400512        133.235917      121.009603  Right  \n",
       "6          146.674319        148.448256      135.913800   Left  \n",
       "7          162.919014        165.340938      152.366694  Right  \n",
       "8          173.979689        180.914258      168.523612   Left  \n",
       "9          194.873057        197.386732      184.101397  Right  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials = nwbfile.trials.to_dataframe()\n",
    "\n",
    "trials.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29abc49-b06c-48bb-8cfa-8f4ce3200ae0",
   "metadata": {},
   "source": [
    "# Access Recording <a name=\"access-recording\"></a>\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": "60c80beb-9785-425c-8f6a-705f1795d707",
   "metadata": {},
   "outputs": [],
   "source": [
    "electrical_series = nwbfile.acquisition[\"ElectricalSeries\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3c486f2-0f1d-4d41-b756-e03b042dd8ec",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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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": "540565a6-abad-4f63-a6a1-b06b656f6b3a",
   "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": "d4a56a82-e135-4f41-941c-d25b2b6fc5ba",
   "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>gain_to_uV</th>\n",
       "      <th>offset_to_uV</th>\n",
       "      <th>channel_ids</th>\n",
       "      <th>electrode_type</th>\n",
       "      <th>chamber_type</th>\n",
       "      <th>chamber_x</th>\n",
       "      <th>chamber_y</th>\n",
       "      <th>chamber_z</th>\n",
       "      <th>acx_x</th>\n",
       "      <th>acx_y</th>\n",
       "      <th>acx_z</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VLa</td>\n",
       "      <td>Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...</td>\n",
       "      <td>Group VLa</td>\n",
       "      <td>Conx 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>vp16</td>\n",
       "      <td>Sagittal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.50</td>\n",
       "      <td>-3.920903</td>\n",
       "      <td>-5.335591</td>\n",
       "      <td>4.325490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VLa</td>\n",
       "      <td>Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...</td>\n",
       "      <td>Group VLa</td>\n",
       "      <td>Conx 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>vp16</td>\n",
       "      <td>Sagittal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.10</td>\n",
       "      <td>-3.887668</td>\n",
       "      <td>-5.651801</td>\n",
       "      <td>3.816661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VLa</td>\n",
       "      <td>Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...</td>\n",
       "      <td>Group VLa</td>\n",
       "      <td>Conx 7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>vp16</td>\n",
       "      <td>Sagittal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.40</td>\n",
       "      <td>-3.871051</td>\n",
       "      <td>-5.809906</td>\n",
       "      <td>3.562247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VLa</td>\n",
       "      <td>Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...</td>\n",
       "      <td>Group VLa</td>\n",
       "      <td>Conx 11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>vp16</td>\n",
       "      <td>Sagittal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>-3.945830</td>\n",
       "      <td>-5.098434</td>\n",
       "      <td>4.707112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VLa</td>\n",
       "      <td>Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...</td>\n",
       "      <td>Group VLa</td>\n",
       "      <td>Conx 12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>vp16</td>\n",
       "      <td>Sagittal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.90</td>\n",
       "      <td>-3.954138</td>\n",
       "      <td>-5.019382</td>\n",
       "      <td>4.834319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location                                              group group_name  \\\n",
       "id                                                                          \n",
       "0       VLa  Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...  Group VLa   \n",
       "1       VLa  Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...  Group VLa   \n",
       "2       VLa  Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...  Group VLa   \n",
       "3       VLa  Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...  Group VLa   \n",
       "4       VLa  Group VLa pynwb.ecephys.ElectrodeGroup at 0x64...  Group VLa   \n",
       "\n",
       "   channel_name  gain_to_uV  offset_to_uV channel_ids electrode_type  \\\n",
       "id                                                                     \n",
       "0        Conx 1         1.0           0.0           1           vp16   \n",
       "1        Conx 5         1.0           0.0           5           vp16   \n",
       "2        Conx 7         1.0           0.0           7           vp16   \n",
       "3       Conx 11         1.0           0.0          11           vp16   \n",
       "4       Conx 12         1.0           0.0          12           vp16   \n",
       "\n",
       "   chamber_type  chamber_x  chamber_y  chamber_z     acx_x     acx_y     acx_z  \n",
       "id                                                                              \n",
       "0      Sagittal        4.0        2.0      42.50 -3.920903 -5.335591  4.325490  \n",
       "1      Sagittal        4.0        2.0      43.10 -3.887668 -5.651801  3.816661  \n",
       "2      Sagittal        4.0        2.0      43.40 -3.871051 -5.809906  3.562247  \n",
       "3      Sagittal        4.0        2.0      42.05 -3.945830 -5.098434  4.707112  \n",
       "4      Sagittal        4.0        2.0      41.90 -3.954138 -5.019382  4.834319  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwbfile.electrodes.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e898a7-8920-45d1-aa1e-a86fbfc3d13b",
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   "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\"][\"Processed\"]` which can hold multiple processed `ElectricalSeries` objects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21f92c95-f391-43d9-a4e1-9bab82c4d499",
   "metadata": {},
   "outputs": [
    {
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       "        <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=\".fields['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=\".fields['data_interfaces']\"><b>data_interfaces (1)</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['data_interfaces']['Processed']\"><b>Processed</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']\"><b>electrical_series (3)</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe']\"><b>ElectricalSeriesProcessedGPe</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from GPe region.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['description']\">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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPe'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi']\"><b>ElectricalSeriesProcessedGPi</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from GPi region.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['description']\">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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedGPi'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa']\"><b>ElectricalSeriesProcessedVLa</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from VLa region.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['description']\">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=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['data_interfaces']['Processed'].fields['electrical_series']['ElectricalSeriesProcessedVLa'].fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></details></details></details></details></div>"
      ],
      "text/plain": [
       "ecephys pynwb.base.ProcessingModule at 0x6432526288\n",
       "Fields:\n",
       "  data_interfaces: {\n",
       "    Processed <class 'pynwb.ecephys.FilteredEphys'>\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": 10,
   "id": "92063ea2-4d07-4bed-b0c8-6c52c63d8aa5",
   "metadata": {},
   "outputs": [
    {
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       "        \n",
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       "\n",
       "            document.addEventListener('DOMContentLoaded', function() {\n",
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       "                        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>ElectricalSeriesProcessedGPi (ElectricalSeries)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['starting_time']\">starting_time:</span> <span class=\"field-value\">9.5367431640625e-07</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['rate']\">rate:</span> <span class=\"field-value\">24414.0625</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['resolution']\">resolution:</span> <span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['comments']\">comments:</span> <span class=\"field-value\">no comments</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['description']\">description:</span> <span class=\"field-value\">High-pass filtered traces (200 Hz) from GPi region.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['conversion']\">conversion:</span> <span class=\"field-value\">1e-06</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['offset']\">offset:</span> <span class=\"field-value\">0.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['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=\".fields['data']\"><b>data</b></summary></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['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=\".fields['electrodes']\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['description']\">description:</span> <span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".fields['electrodes'].fields['table']\"><b>table</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['table'].fields['description']\">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=\".fields['electrodes'].fields['table'].fields['id']\"><b>id</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['table'].fields['colnames']\">colnames:</span> <span class=\"field-value\">('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['electrodes'].fields['table'].fields['columns']\">columns:</span> <span class=\"field-value\">(<hdmf.common.table.VectorData object at 0x17f46d250>, <hdmf.common.table.VectorData object at 0x17f46c550>, <hdmf.common.table.VectorData object at 0x17f46ddd0>, <hdmf.common.table.VectorData object at 0x17f536690>, <hdmf.common.table.VectorData object at 0x17f537950>, <hdmf.common.table.VectorData object at 0x17f46ed50>, <hdmf.common.table.VectorData object at 0x17f536290>, <hdmf.common.table.VectorData object at 0x17f537c50>, <hdmf.common.table.VectorData object at 0x17f536950>, <hdmf.common.table.VectorData object at 0x17f536090>, <hdmf.common.table.VectorData object at 0x17f537e90>, <hdmf.common.table.VectorData object at 0x17f536b10>, <hdmf.common.table.VectorData object at 0x17f0709d0>, <hdmf.common.table.VectorData object at 0x17f61e250>, <hdmf.common.table.VectorData object at 0x17f4f1290>)</span></div></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".fields['filtering']\">filtering:</span> <span class=\"field-value\">Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.</span></div></div>"
      ],
      "text/plain": [
       "ElectricalSeriesProcessedGPi pynwb.ecephys.ElectricalSeries at 0x6432443792\n",
       "Fields:\n",
       "  comments: no comments\n",
       "  conversion: 1e-06\n",
       "  data: <HDF5 dataset \"data\": shape (33588268, 4), type \"<f4\">\n",
       "  description: High-pass filtered traces (200 Hz) from GPi region.\n",
       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\n",
       "  filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.\n",
       "  offset: 0.0\n",
       "  rate: 24414.0625\n",
       "  resolution: -1.0\n",
       "  starting_time: 9.5367431640625e-07\n",
       "  starting_time_unit: seconds\n",
       "  unit: volts"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_ecephys = nwbfile.processing[\"ecephys\"][\"Processed\"]\n",
    "\n",
    "filtered_electrical_series = processed_ecephys[\"ElectricalSeriesProcessedGPi\"]\n",
    "filtered_electrical_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342da7d2-42aa-407f-9c00-e29ce9e0e1b7",
   "metadata": {},
   "source": [
    "# Access Units <a name=\"access-units\"></a>\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": 11,
   "id": "35d72c09-8b23-4949-a1f3-28d3e50365a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>sort_label</th>\n",
       "      <th>location</th>\n",
       "      <th>channel_ids</th>\n",
       "      <th>unit_quality_post_sorting</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.03395584, 0.0520192, 0.12259328, 0.16719872...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>1</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1.32456448, 5.83467008, 5.99814144, 6.0211609...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>5</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.00217088, 0.10825728, 0.1626112, 0.23588864...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>5</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.8450048, 1.18214656, 4.81329152, 6.4045056,...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>7</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.09691136, 0.1282048, 0.15757312, 0.25923584...</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>11</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.04907008, 0.116736, 0.15835136, 0.18751488,...</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>12</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.01056768, 0.08716288, 0.09531392, 0.1458585...</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VLa</td>\n",
       "      <td>15</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[2.70639104, 9.90437376, 10.7485184, 25.959055...</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.02125824, 0.09744384, 0.17870848, 0.2081587...</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>22</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[0.01036288, 0.055296, 0.09613312, 0.1349632, ...</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>22</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.10190848, 0.13770752, 0.1859584, 0.26558464...</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>22</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.063488, 0.0784384, 0.72880128, 1.0031104, 1...</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>22</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[0.1062912, 0.19480576, 0.33542144, 0.34828288...</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>23</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[0.00405504, 0.00933888, 0.0514048, 0.06397952...</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>23</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.00131072, 0.00364544, 0.0063488, 0.00970752...</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPi</td>\n",
       "      <td>26</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.34803712, 1.18255616, 1.44543744, 1.4502707...</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPe</td>\n",
       "      <td>28</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.03166208, 0.05963776, 0.0618496, 0.07155712...</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GPe</td>\n",
       "      <td>31</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                          spike_times unit_name  sort_label  \\\n",
       "id                                                                            \n",
       "0   [0.03395584, 0.0520192, 0.12259328, 0.16719872...         0         1.0   \n",
       "1   [1.32456448, 5.83467008, 5.99814144, 6.0211609...         1         1.0   \n",
       "2   [0.00217088, 0.10825728, 0.1626112, 0.23588864...         2         2.0   \n",
       "3   [0.8450048, 1.18214656, 4.81329152, 6.4045056,...         3         1.0   \n",
       "4   [0.09691136, 0.1282048, 0.15757312, 0.25923584...         4         1.0   \n",
       "5   [0.04907008, 0.116736, 0.15835136, 0.18751488,...         5         1.0   \n",
       "6   [0.01056768, 0.08716288, 0.09531392, 0.1458585...         6         1.0   \n",
       "7   [2.70639104, 9.90437376, 10.7485184, 25.959055...         7         1.0   \n",
       "8   [0.02125824, 0.09744384, 0.17870848, 0.2081587...         8         1.0   \n",
       "9   [0.01036288, 0.055296, 0.09613312, 0.1349632, ...         9         2.0   \n",
       "10  [0.10190848, 0.13770752, 0.1859584, 0.26558464...        10         3.0   \n",
       "11  [0.063488, 0.0784384, 0.72880128, 1.0031104, 1...        11         4.0   \n",
       "12  [0.1062912, 0.19480576, 0.33542144, 0.34828288...        12         1.0   \n",
       "13  [0.00405504, 0.00933888, 0.0514048, 0.06397952...        13         2.0   \n",
       "14  [0.00131072, 0.00364544, 0.0063488, 0.00970752...        14         1.0   \n",
       "15  [0.34803712, 1.18255616, 1.44543744, 1.4502707...        15         1.0   \n",
       "16  [0.03166208, 0.05963776, 0.0618496, 0.07155712...        16         1.0   \n",
       "\n",
       "   location  channel_ids unit_quality_post_sorting  \n",
       "id                                                  \n",
       "0       VLa            1                 excellent  \n",
       "1       VLa            5                 excellent  \n",
       "2       VLa            5                 excellent  \n",
       "3       VLa            7                      good  \n",
       "4       VLa           11                      good  \n",
       "5       VLa           12                 excellent  \n",
       "6       VLa           15                 excellent  \n",
       "7       GPi           20                         F  \n",
       "8       GPi           22                 excellent  \n",
       "9       GPi           22                      good  \n",
       "10      GPi           22                      good  \n",
       "11      GPi           22                      good  \n",
       "12      GPi           23                 excellent  \n",
       "13      GPi           23                      good  \n",
       "14      GPi           26                 excellent  \n",
       "15      GPe           28                 excellent  \n",
       "16      GPe           31                 excellent  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwbfile.units[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ba02f3-f725-492f-9258-d867499c0e6c",
   "metadata": {},
   "source": [
    "### Interactive visualization of spike times\n",
    "\n",
    "![Showing a raster plot in Neurosift](images/neurosift_units.png)\n",
    "\n",
    "Visit [Neurosift](https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/2bf73c66-8534-4d9a-bee5-a42025dea7a2/download/&dandisetId=000947&dandisetVersion=draft&tab=view:DirectRasterPlot|/units&tab-time=146.82820800814233,157.46102593869216,undefined)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4068f8f-eeea-475e-abe8-a60a04632b1e",
   "metadata": {},
   "source": [
    "### PSTH  <a name=\"view-PSTH\"></a>\n",
    "\n",
    "In this example we will show the activity of two single units sampled from VLa and GPi region aligned to the time of movement onset.\n",
    "\n",
    "We will use [Pynapple](https://pynapple-org.github.io/pynapple/) to compute and visualize the PSTH using the `pynapple.compute_perievent()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54b5e5c9-3f64-40c9-ad8f-2d7208204132",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pynapple as nap\n",
    "\n",
    "movement_onset_ts = nap.Ts(nwbfile.trials[\"movement_start_time\"][:], time_units=\"s\")\n",
    "spike_times_group = nap.TsGroup({unit_ind: nap.Ts(spikes) for unit_ind, spikes in enumerate(nwbfile.units[\"spike_times\"])})\n",
    "\n",
    "perievent_histogram = nap.compute_perievent(\n",
    "    data=spike_times_group,\n",
    "    tref=movement_onset_ts,\n",
    "    minmax=(-0.5, 1),\n",
    "    time_unit=\"s\",\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 6), dpi=200, sharex=True, sharey=True)\n",
    "ax[0].plot(\n",
    "    perievent_histogram[0].to_tsd(),\n",
    "    \"|\",\n",
    "    color=\"black\",\n",
    "    markersize=2,\n",
    ")\n",
    "ax[0].set_title(nwbfile.units[0][\"location\"].values[0])\n",
    "ax[0].axvline(0.0)\n",
    "\n",
    "ax[1].plot(\n",
    "    perievent_histogram[11].to_tsd(),\n",
    "    \"|\",\n",
    "    color=\"black\",\n",
    "    markersize=2,\n",
    ")\n",
    "ax[1].set_title(nwbfile.units[11][\"location\"].values[0])\n",
    "ax[1].axvline(0.0)\n",
    "\n",
    "plt.xlabel(\"Time from Movement onset (s)\")\n",
    "plt.ylabel(\"Trials\")\n",
    "plt.xlim(-0.5, 1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b472b7-8daf-4ebc-87a1-171844aea5d8",
   "metadata": {},
   "source": [
    "We can also use [Neurosift](https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/2bf73c66-8534-4d9a-bee5-a42025dea7a2/download/&dandisetId=000947&dandisetVersion=draft&tab=view:PSTH|/intervals/trials&tab-time=undefined,undefined,undefined) for this visualization:\n",
    "\n",
    "![Showing PSTH in Neurosift](images/neurosift_psth.png)\n"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}