diff --git a/001343/README.md b/001343/README.md new file mode 100644 index 0000000..fdabc8c --- /dev/null +++ b/001343/README.md @@ -0,0 +1,9 @@ +# Example Session for Dandiset 001343 + +This submission provides a notebook showcasing the example session for the Dandiset 001343. + +This notebook provides an example of how to access the critical data and metadata for each of the 3 data streams: + +- Extracellular electrophysiology with raw recordings, LFP, and sorted spike times +- Behavioral events during the shuttle task +- DeepLabCut pose estimation \ No newline at end of file diff --git a/001343/environment.yml b/001343/environment.yml new file mode 100644 index 0000000..1d89976 --- /dev/null +++ b/001343/environment.yml @@ -0,0 +1,13 @@ +# run: conda env create --file environment.yml +name: jadhav_notebook_env +channels: + - conda-forge +dependencies: + - python==3.12 + - ipykernel + - matplotlib + - dandi + - pip + - pip: + - remfile + - jadhav-lab-to-nwb @ git+https://github.com/catalystneuro/jadhav-lab-to-nwb.git@main \ No newline at end of file diff --git a/001343/example_notebook.ipynb b/001343/example_notebook.ipynb new file mode 100644 index 0000000..1eed58b --- /dev/null +++ b/001343/example_notebook.ipynb @@ -0,0 +1,1665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example Notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from stream_nwbfile import stream_nwbfile\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from bisect import bisect_left" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook showcases the example session from the 001343 dataset containing navigation behavior and concurrent SpikeGadgets recordings in hippocampal subfield CA1 and subiculum." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: CA1 and subiculum (SUB) are two main output regions of the hippocampus, projecting to highly overlapping cortical and subcortical regions. The manner and extent of coordination between rodent CA1 and SUB during the learning of memory-guided navigation is largely unknown. We are therefore recording these two regions simultaneously while rats learn a memory-guided navigation task in a complex track environment.
identifier: d579aa2d-e91c-46a6-85aa-e6bfc7f39cfd
session_start_time2023-05-03 11:26:42-04:00
timestamps_reference_time2023-05-03 11:26:42-04:00
file_create_date
02025-06-25 14:29:37.362833-07:00
experimenter('Olson, Jacob M.', 'Jadhav, Shantanu P.')
acquisition
ElectricalSeries
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description: Raw acquisition of extracellular electrophysiology data recorded by SpikeGadgets.
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electrodes
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1Right SubiculumnTrode56 abc.NwbElectrodeGroup at 0x5967102368\\nFields:\\n description: ElectrodeGroup for tetrode 56\\n device: nTrode56_probe abc.Probe at 0x5966894848\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 49\\n probe_description: nTrode56_probe description\\n probe_type: nTrode56_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right Subiculum\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode56hwChan32True32nTrode56_elec1032False
2Right hippocampal subfield CA1nTrode63 abc.NwbElectrodeGroup at 0x5967102896\\nFields:\\n description: ElectrodeGroup for tetrode 63\\n device: nTrode63_probe abc.Probe at 0x5966897872\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 55\\n probe_description: nTrode63_probe description\\n probe_type: nTrode63_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode63hwChan96True96nTrode63_elec1096False
3Left hippocampal subfield CA1nTrode24 abc.NwbElectrodeGroup at 0x5966902528\\nFields:\\n description: ElectrodeGroup for tetrode 24\\n device: nTrode24_probe abc.Probe at 0x5966759168\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 19\\n probe_description: nTrode24_probe description\\n probe_type: nTrode24_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode24hwChan160True160nTrode24_elec10160False

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Video_S06_F01_Home+4_HomeAltVisitAll
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processing
behavior
description: processed behavioral data
data_interfaces
PoseEstimation_S01_F01_BOX_SLP
pose_estimation_series
PoseEstimationSeriesBaseoftail
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
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description: Pose estimation series for haunch.
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
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description: Pose estimation series for redled.
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
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comments: no comments
description: Pose estimation series for shoulder.
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confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnSleepBoxRedLightJun26shuffle1_100000
source_software: DeepLabCut
skeleton
nodes
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[b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S02_F01_Home+4_HomeAltVisitAll
pose_estimation_series
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesGreenled
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comments: no comments
description: Pose estimation series for greenled.
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confidence
HDF5 dataset
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
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data
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timestamps
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confidence
HDF5 dataset
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confidence__definition: Softmax output of the deep neural network.
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confidence
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confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
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comments: no comments
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timestamps
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confidence
HDF5 dataset
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Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnTrackOct12shuffle1_520000
source_software: DeepLabCut
skeleton
nodes
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Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S03_F01_BOX_SLP
pose_estimation_series
PoseEstimationSeriesBaseoftail
resolution: -1.0
comments: no comments
description: Pose estimation series for baseoftail.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(67816, 2)
Array size1.03 MiB
Chunk shape(67816, 2)
Compressiongzip
Compression opts4
Compression ratio2.2908779204546894
timestamps
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shape(67816,)
Compressiongzip
Compression opts4
Compression ratio1.9145434270146662
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
resolution: -1.0
comments: no comments
description: Pose estimation series for haunch.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(67816, 2)
Array size1.03 MiB
Chunk shape(67816, 2)
Compressiongzip
Compression opts4
Compression ratio2.257791057944205
timestamps
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shape(67816,)
Compressiongzip
Compression opts4
Compression ratio1.9145434270146662
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
resolution: -1.0
comments: no comments
description: Pose estimation series for redled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(67816, 2)
Array size1.03 MiB
Chunk shape(67816, 2)
Compressiongzip
Compression opts4
Compression ratio2.3080552288047445
timestamps
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shape(67816,)
Compressiongzip
Compression opts4
Compression ratio1.9145434270146662
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
resolution: -1.0
comments: no comments
description: Pose estimation series for shoulder.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(67816, 2)
Array size1.03 MiB
Chunk shape(67816, 2)
Compressiongzip
Compression opts4
Compression ratio2.242815094090022
timestamps
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shape(67816,)
Compressiongzip
Compression opts4
Compression ratio1.9145434270146662
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(67816,)
Array size529.81 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnSleepBoxRedLightJun26shuffle1_100000
source_software: DeepLabCut
skeleton
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S04_F01_Home+4_HomeAltVisitAll
pose_estimation_series
PoseEstimationSeriesBaseoftail
resolution: -1.0
comments: no comments
description: Pose estimation series for baseoftail.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(74414, 2)
Array size1.14 MiB
Chunk shape(74414, 2)
Compressiongzip
Compression opts4
Compression ratio2.1620624345138544
timestamps
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shape(74414,)
Compressiongzip
Compression opts4
Compression ratio1.8329643667579076
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesGreenled
resolution: -1.0
comments: no comments
description: Pose estimation series for greenled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(74414, 2)
Array size1.14 MiB
Chunk shape(74414, 2)
Compressiongzip
Compression opts4
Compression ratio2.076591291608166
timestamps
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shape(74414,)
Compressiongzip
Compression opts4
Compression ratio1.8329643667579076
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
resolution: -1.0
comments: no comments
description: Pose estimation series for haunch.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(74414, 2)
Array size1.14 MiB
Chunk shape(74414, 2)
Compressiongzip
Compression opts4
Compression ratio2.149187347018347
timestamps
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shape(74414,)
Compressiongzip
Compression opts4
Compression ratio1.8329643667579076
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
resolution: -1.0
comments: no comments
description: Pose estimation series for redled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(74414, 2)
Array size1.14 MiB
Chunk shape(74414, 2)
Compressiongzip
Compression opts4
Compression ratio2.0813030869301485
timestamps
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shape(74414,)
Compressiongzip
Compression opts4
Compression ratio1.8329643667579076
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
resolution: -1.0
comments: no comments
description: Pose estimation series for shoulder.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(74414, 2)
Array size1.14 MiB
Chunk shape(74414, 2)
Compressiongzip
Compression opts4
Compression ratio2.1205365173392132
timestamps
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shape(74414,)
Compressiongzip
Compression opts4
Compression ratio1.8329643667579076
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(74414,)
Array size581.36 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnTrackOct12shuffle1_520000
source_software: DeepLabCut
skeleton
nodes
HDF5 dataset
Data typeobject
Shape(5,)
Array size40.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S05_F01_BOX_SLP
pose_estimation_series
PoseEstimationSeriesBaseoftail
resolution: -1.0
comments: no comments
description: Pose estimation series for baseoftail.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(80065, 2)
Array size1.22 MiB
Chunk shape(80065, 2)
Compressiongzip
Compression opts4
Compression ratio2.4606613805396766
timestamps
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shape(80065,)
Compressiongzip
Compression opts4
Compression ratio1.8781870269858165
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
resolution: -1.0
comments: no comments
description: Pose estimation series for haunch.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(80065, 2)
Array size1.22 MiB
Chunk shape(80065, 2)
Compressiongzip
Compression opts4
Compression ratio2.3901872907951742
timestamps
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shape(80065,)
Compressiongzip
Compression opts4
Compression ratio1.8781870269858165
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
resolution: -1.0
comments: no comments
description: Pose estimation series for redled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(80065, 2)
Array size1.22 MiB
Chunk shape(80065, 2)
Compressiongzip
Compression opts4
Compression ratio2.6043221045355667
timestamps
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shape(80065,)
Compressiongzip
Compression opts4
Compression ratio1.8781870269858165
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
resolution: -1.0
comments: no comments
description: Pose estimation series for shoulder.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(80065, 2)
Array size1.22 MiB
Chunk shape(80065, 2)
Compressiongzip
Compression opts4
Compression ratio2.3662142492999476
timestamps
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shape(80065,)
Compressiongzip
Compression opts4
Compression ratio1.8781870269858165
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(80065,)
Array size625.51 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnSleepBoxRedLightJun26shuffle1_100000
source_software: DeepLabCut
skeleton
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S06_F01_Home+4_HomeAltVisitAll
pose_estimation_series
PoseEstimationSeriesBaseoftail
resolution: -1.0
comments: no comments
description: Pose estimation series for baseoftail.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(59049, 2)
Array size922.64 KiB
Chunk shape(59049, 2)
Compressiongzip
Compression opts4
Compression ratio2.1884640419169306
timestamps
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shape(59049,)
Compressiongzip
Compression opts4
Compression ratio1.875032746151831
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesGreenled
resolution: -1.0
comments: no comments
description: Pose estimation series for greenled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(59049, 2)
Array size922.64 KiB
Chunk shape(59049, 2)
Compressiongzip
Compression opts4
Compression ratio2.1034089694724467
timestamps
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shape(59049,)
Compressiongzip
Compression opts4
Compression ratio1.875032746151831
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
resolution: -1.0
comments: no comments
description: Pose estimation series for haunch.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(59049, 2)
Array size922.64 KiB
Chunk shape(59049, 2)
Compressiongzip
Compression opts4
Compression ratio2.1717325193029557
timestamps
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shape(59049,)
Compressiongzip
Compression opts4
Compression ratio1.875032746151831
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
resolution: -1.0
comments: no comments
description: Pose estimation series for redled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(59049, 2)
Array size922.64 KiB
Chunk shape(59049, 2)
Compressiongzip
Compression opts4
Compression ratio2.1077119738717816
timestamps
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shape(59049,)
Compressiongzip
Compression opts4
Compression ratio1.875032746151831
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
resolution: -1.0
comments: no comments
description: Pose estimation series for shoulder.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(59049, 2)
Array size922.64 KiB
Chunk shape(59049, 2)
Compressiongzip
Compression opts4
Compression ratio2.1431983830464874
timestamps
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shape(59049,)
Compressiongzip
Compression opts4
Compression ratio1.875032746151831
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(59049,)
Array size461.32 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnTrackOct12shuffle1_520000
source_software: DeepLabCut
skeleton
nodes
HDF5 dataset
Data typeobject
Shape(5,)
Array size40.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
PoseEstimation_S07_F01_BOX_SLP
pose_estimation_series
PoseEstimationSeriesBaseoftail
resolution: -1.0
comments: no comments
description: Pose estimation series for baseoftail.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(73606, 2)
Array size1.12 MiB
Chunk shape(73606, 2)
Compressiongzip
Compression opts4
Compression ratio2.357211767484498
timestamps
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shape(73606,)
Compressiongzip
Compression opts4
Compression ratio1.8562881047103255
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesHaunch
resolution: -1.0
comments: no comments
description: Pose estimation series for haunch.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(73606, 2)
Array size1.12 MiB
Chunk shape(73606, 2)
Compressiongzip
Compression opts4
Compression ratio2.3341651025573436
timestamps
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shape(73606,)
Compressiongzip
Compression opts4
Compression ratio1.8562881047103255
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesRedled
resolution: -1.0
comments: no comments
description: Pose estimation series for redled.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(73606, 2)
Array size1.12 MiB
Chunk shape(73606, 2)
Compressiongzip
Compression opts4
Compression ratio2.421150446116525
timestamps
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shape(73606,)
Compressiongzip
Compression opts4
Compression ratio1.8562881047103255
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
PoseEstimationSeriesShoulder
resolution: -1.0
comments: no comments
description: Pose estimation series for shoulder.
conversion: 1.0
offset: 0.0
unit: pixels
data
HDF5 dataset
Data typefloat64
Shape(73606, 2)
Array size1.12 MiB
Chunk shape(73606, 2)
Compressiongzip
Compression opts4
Compression ratio2.3632758883635807
timestamps
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shape(73606,)
Compressiongzip
Compression opts4
Compression ratio1.8562881047103255
timestamps_unit: seconds
interval: 1
reference_frame: (0,0) corresponds to the bottom left corner of the video.
confidence
HDF5 dataset
Data typefloat64
Shape(73606,)
Array size575.05 KiB
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio1.0
confidence__definition: Softmax output of the deep neural network.
description: 2D keypoint coordinates estimated using DeepLabCut.
scorer: DLC_resnet50_SubLearnSleepBoxRedLightJun26shuffle1_100000
source_software: DeepLabCut
skeleton
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
Skeletons
skeletons
SkeletonPoseEstimation_S01_F01_BOX_SLP_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S02_F01_Home+4_HomeAltVisitAll_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(5,)
Array size40.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S03_F01_BOX_SLP_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S04_F01_Home+4_HomeAltVisitAll_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(5,)
Array size40.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S05_F01_BOX_SLP_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S06_F01_Home+4_HomeAltVisitAll_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(5,)
Array size40.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'greenled' b'redled' b'shoulder' b'haunch' b'baseoftail']
SkeletonPoseEstimation_S07_F01_BOX_SLP_Ind1
nodes
HDF5 dataset
Data typeobject
Shape(4,)
Array size32.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'redled' b'shoulder' b'haunch' b'baseoftail']
behavioral_events
time_series
reward_pump_1
resolution: -1.0
comments: no comments
description: Whenever a reward is delivered from Reward Pump 1
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(43, 1)
Array size344.00 bytes
Chunk shape(43, 1)
Compressiongzip
Compression opts4
Compression ratio18.105263157894736
timestamps
HDF5 dataset
Data typefloat64
Shape(43,)
Array size344.00 bytes
Chunk shape(43,)
Compressiongzip
Compression opts4
Compression ratio0.9690140845070423
timestamps_unit: seconds
interval: 1
reward_pump_2
resolution: -1.0
comments: no comments
description: Whenever a reward is delivered from Reward Pump 2
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(42, 1)
Array size336.00 bytes
Chunk shape(42, 1)
Compressiongzip
Compression opts4
Compression ratio17.68421052631579
timestamps
HDF5 dataset
Data typefloat64
Shape(42,)
Array size336.00 bytes
Chunk shape(42,)
Compressiongzip
Compression opts4
Compression ratio0.968299711815562
timestamps_unit: seconds
interval: 1
reward_pump_3
resolution: -1.0
comments: no comments
description: Whenever a reward is delivered from Reward Pump 3
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(43, 1)
Array size344.00 bytes
Chunk shape(43, 1)
Compressiongzip
Compression opts4
Compression ratio18.105263157894736
timestamps
HDF5 dataset
Data typefloat64
Shape(43,)
Array size344.00 bytes
Chunk shape(43,)
Compressiongzip
Compression opts4
Compression ratio0.9690140845070423
timestamps_unit: seconds
interval: 1
reward_pump_4
resolution: -1.0
comments: no comments
description: Whenever a reward is delivered from Reward Pump 4
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(44, 1)
Array size352.00 bytes
Chunk shape(44, 1)
Compressiongzip
Compression opts4
Compression ratio18.526315789473685
timestamps
HDF5 dataset
Data typefloat64
Shape(44,)
Array size352.00 bytes
Chunk shape(44,)
Compressiongzip
Compression opts4
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timestamps_unit: seconds
interval: 1
reward_pump_5
resolution: -1.0
comments: no comments
description: Whenever a reward is delivered from Reward Pump 5
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(288, 1)
Array size2.25 KiB
Chunk shape(288, 1)
Compressiongzip
Compression opts4
Compression ratio74.3225806451613
timestamps
HDF5 dataset
Data typefloat64
Shape(288,)
Array size2.25 KiB
Chunk shape(288,)
Compressiongzip
Compression opts4
Compression ratio1.0786516853932584
timestamps_unit: seconds
interval: 1
reward_well_1
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 1
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1370, 1)
Array size10.70 KiB
Chunk shape(1370, 1)
Compressiongzip
Compression opts4
Compression ratio238.2608695652174
timestamps
HDF5 dataset
Data typefloat64
Shape(1370,)
Array size10.70 KiB
Chunk shape(1370,)
Compressiongzip
Compression opts4
Compression ratio1.433616742969261
timestamps_unit: seconds
interval: 1
reward_well_2
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 2
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1054, 1)
Array size8.23 KiB
Chunk shape(1054, 1)
Compressiongzip
Compression opts4
Compression ratio196.09302325581396
timestamps
HDF5 dataset
Data typefloat64
Shape(1054,)
Array size8.23 KiB
Chunk shape(1054,)
Compressiongzip
Compression opts4
Compression ratio1.3898137464974452
timestamps_unit: seconds
interval: 1
reward_well_3
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 3
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1926, 1)
Array size15.05 KiB
Chunk shape(1926, 1)
Compressiongzip
Compression opts4
Compression ratio290.7169811320755
timestamps
HDF5 dataset
Data typefloat64
Shape(1926,)
Array size15.05 KiB
Chunk shape(1926,)
Compressiongzip
Compression opts4
Compression ratio1.4823936886665383
timestamps_unit: seconds
interval: 1
reward_well_4
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 4
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1548, 1)
Array size12.09 KiB
Chunk shape(1548, 1)
Compressiongzip
Compression opts4
Compression ratio258.0
timestamps
HDF5 dataset
Data typefloat64
Shape(1548,)
Array size12.09 KiB
Chunk shape(1548,)
Compressiongzip
Compression opts4
Compression ratio1.4624468587623995
timestamps_unit: seconds
interval: 1
reward_well_5
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 5
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(12203, 1)
Array size95.34 KiB
Chunk shape(12203, 1)
Compressiongzip
Compression opts4
Compression ratio567.5813953488372
timestamps
HDF5 dataset
Data typefloat64
Shape(12203,)
Array size95.34 KiB
Chunk shape(12203,)
Compressiongzip
Compression opts4
Compression ratio1.740767817977568
timestamps_unit: seconds
interval: 1
reward_well_6
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 6
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1, 1)
Array size8.00 bytes
Chunk shape(1, 1)
Compressiongzip
Compression opts4
Compression ratio0.6153846153846154

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timestamps
HDF5 dataset
Data typefloat64
Shape(1,)
Array size8.00 bytes
Chunk shape(1,)
Compressiongzip
Compression opts4
Compression ratio0.5

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timestamps_unit: seconds
interval: 1
reward_well_7
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 7
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1, 1)
Array size8.00 bytes
Chunk shape(1, 1)
Compressiongzip
Compression opts4
Compression ratio0.6153846153846154

[[1.]]
timestamps
HDF5 dataset
Data typefloat64
Shape(1,)
Array size8.00 bytes
Chunk shape(1,)
Compressiongzip
Compression opts4
Compression ratio0.5

[268.5614]
timestamps_unit: seconds
interval: 1
reward_well_8
resolution: -1.0
comments: no comments
description: Whenever the animal visits Reward Well 8
conversion: 1.0
offset: 0.0
unit: n.a.
data
HDF5 dataset
Data typefloat64
Shape(1, 1)
Array size8.00 bytes
Chunk shape(1, 1)
Compressiongzip
Compression opts4
Compression ratio0.6153846153846154

[[1.]]
timestamps
HDF5 dataset
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timestamps_unit: seconds
interval: 1
ecephys
description: Processed extracellular electrophysiology data.
data_interfaces
LFP
electrical_series
ElectricalSeriesLFP
resolution: -1.0
comments: no comments
description: Local field potential data recorded by SpikeGadgets (1 channel per tetrode).
conversion: 1.9499999999999999e-07
offset: 0.0
unit: volts
data
HDF5 dataset
Data typeint16
Shape(24069837, 56)
Array size2.51 GiB
Chunk shape(89285, 56)
Compressiongzip
Compression opts4
Compression ratio1.1984033432692385
timestamps
HDF5 dataset
Data typefloat64
Shape(24069837,)
Array size183.64 MiB
Chunk shape(1250000,)
Compressiongzip
Compression opts4
Compression ratio3.3997649598174933
timestamps_unit: seconds
interval: 1
electrodes
description: LFP electrodes
table
description: metadata about extracellular electrodes
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locationgroupgroup_namechannel_namehasLFPref_elect_idchIDprobe_shankprobe_electrodebad_channel
id
0Left SubiculumnTrode42 abc.NwbElectrodeGroup at 0x5966904448\\nFields:\\n description: ElectrodeGroup for tetrode 42\\n device: nTrode42_probe abc.Probe at 0x5966889376\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 37\\n probe_description: nTrode42_probe description\\n probe_type: nTrode42_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left Subiculum\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode42hwChan0True0nTrode42_elec100False
1Right SubiculumnTrode56 abc.NwbElectrodeGroup at 0x5967102368\\nFields:\\n description: ElectrodeGroup for tetrode 56\\n device: nTrode56_probe abc.Probe at 0x5966894848\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 49\\n probe_description: nTrode56_probe description\\n probe_type: nTrode56_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right Subiculum\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode56hwChan32True32nTrode56_elec1032False
2Right hippocampal subfield CA1nTrode63 abc.NwbElectrodeGroup at 0x5967102896\\nFields:\\n description: ElectrodeGroup for tetrode 63\\n device: nTrode63_probe abc.Probe at 0x5966897872\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 55\\n probe_description: nTrode63_probe description\\n probe_type: nTrode63_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode63hwChan96True96nTrode63_elec1096False
3Left hippocampal subfield CA1nTrode24 abc.NwbElectrodeGroup at 0x5966902528\\nFields:\\n description: ElectrodeGroup for tetrode 24\\n device: nTrode24_probe abc.Probe at 0x5966759168\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 19\\n probe_description: nTrode24_probe description\\n probe_type: nTrode24_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode24hwChan160True160nTrode24_elec10160False

... and 220 more rows.

tasks
description: tasks module
data_interfaces
HomeAltVisitAll
description: Shuttle task between home and 4 destinations.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
task_nametask_descriptiontask_environmentcamera_idled_configurationled_listled_positionstask_epochs
id
0HomeAltVisitAllShuttle task between home and 4 destinations.BOX[1]left/rightredled,greenledright,left[2, 4, 6]
Sleep
description: The animal sleeps in a small empty box.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
task_nametask_descriptiontask_environmentcamera_idled_configurationled_listled_positionstask_epochs
id
0SleepThe animal sleeps in a small empty box.SLP[0]singleredledcenter[1, 3, 5, 7]
electrodes
description: metadata about extracellular electrodes
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locationgroupgroup_namechannel_namehasLFPref_elect_idchIDprobe_shankprobe_electrodebad_channel
id
0Left SubiculumnTrode42 abc.NwbElectrodeGroup at 0x5966904448\\nFields:\\n description: ElectrodeGroup for tetrode 42\\n device: nTrode42_probe abc.Probe at 0x5966889376\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 37\\n probe_description: nTrode42_probe description\\n probe_type: nTrode42_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left Subiculum\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode42hwChan0True0nTrode42_elec100False
1Right SubiculumnTrode56 abc.NwbElectrodeGroup at 0x5967102368\\nFields:\\n description: ElectrodeGroup for tetrode 56\\n device: nTrode56_probe abc.Probe at 0x5966894848\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 49\\n probe_description: nTrode56_probe description\\n probe_type: nTrode56_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right Subiculum\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode56hwChan32True32nTrode56_elec1032False
2Right hippocampal subfield CA1nTrode63 abc.NwbElectrodeGroup at 0x5967102896\\nFields:\\n description: ElectrodeGroup for tetrode 63\\n device: nTrode63_probe abc.Probe at 0x5966897872\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 55\\n probe_description: nTrode63_probe description\\n probe_type: nTrode63_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Right hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode63hwChan96True96nTrode63_elec1096False
3Left hippocampal subfield CA1nTrode24 abc.NwbElectrodeGroup at 0x5966902528\\nFields:\\n description: ElectrodeGroup for tetrode 24\\n device: nTrode24_probe abc.Probe at 0x5966759168\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 19\\n probe_description: nTrode24_probe description\\n probe_type: nTrode24_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\nnTrode24hwChan160True160nTrode24_elec10160False

... and 220 more rows.

electrode_groups
nTrode1
description: ElectrodeGroup for tetrode 1
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

0
probe_type: nTrode1_probe_type
units: unknown
probe_description: nTrode1_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
65
rel_x: nan
rel_y: nan
rel_z: nan
67
rel_x: nan
rel_y: nan
rel_z: nan
69
rel_x: nan
rel_y: nan
rel_z: nan
71
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode12
description: ElectrodeGroup for tetrode 12
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

7
probe_type: nTrode12_probe_type
units: unknown
probe_description: nTrode12_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
137
rel_x: nan
rel_y: nan
rel_z: nan
139
rel_x: nan
rel_y: nan
rel_z: nan
141
rel_x: nan
rel_y: nan
rel_z: nan
143
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode13
description: ElectrodeGroup for tetrode 13
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

8
probe_type: nTrode13_probe_type
units: unknown
probe_description: nTrode13_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
145
rel_x: nan
rel_y: nan
rel_z: nan
147
rel_x: nan
rel_y: nan
rel_z: nan
149
rel_x: nan
rel_y: nan
rel_z: nan
151
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode14
description: ElectrodeGroup for tetrode 14
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

9
probe_type: nTrode14_probe_type
units: unknown
probe_description: nTrode14_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
153
rel_x: nan
rel_y: nan
rel_z: nan
155
rel_x: nan
rel_y: nan
rel_z: nan
157
rel_x: nan
rel_y: nan
rel_z: nan
159
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode15
description: ElectrodeGroup for tetrode 15
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

10
probe_type: nTrode15_probe_type
units: unknown
probe_description: nTrode15_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
161
rel_x: nan
rel_y: nan
rel_z: nan
163
rel_x: nan
rel_y: nan
rel_z: nan
165
rel_x: nan
rel_y: nan
rel_z: nan
167
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode16
description: ElectrodeGroup for tetrode 16
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

11
probe_type: nTrode16_probe_type
units: unknown
probe_description: nTrode16_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
169
rel_x: nan
rel_y: nan
rel_z: nan
171
rel_x: nan
rel_y: nan
rel_z: nan
173
rel_x: nan
rel_y: nan
rel_z: nan
175
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode17
description: ElectrodeGroup for tetrode 17
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

12
probe_type: nTrode17_probe_type
units: unknown
probe_description: nTrode17_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
177
rel_x: nan
rel_y: nan
rel_z: nan
179
rel_x: nan
rel_y: nan
rel_z: nan
181
rel_x: nan
rel_y: nan
rel_z: nan
183
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode18
description: ElectrodeGroup for tetrode 18
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

13
probe_type: nTrode18_probe_type
units: unknown
probe_description: nTrode18_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
185
rel_x: nan
rel_y: nan
rel_z: nan
187
rel_x: nan
rel_y: nan
rel_z: nan
189
rel_x: nan
rel_y: nan
rel_z: nan
191
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode19
description: ElectrodeGroup for tetrode 19
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

14
probe_type: nTrode19_probe_type
units: unknown
probe_description: nTrode19_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
184
rel_x: nan
rel_y: nan
rel_z: nan
186
rel_x: nan
rel_y: nan
rel_z: nan
188
rel_x: nan
rel_y: nan
rel_z: nan
190
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode2
description: ElectrodeGroup for tetrode 2
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

1
probe_type: nTrode2_probe_type
units: unknown
probe_description: nTrode2_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
81
rel_x: nan
rel_y: nan
rel_z: nan
83
rel_x: nan
rel_y: nan
rel_z: nan
85
rel_x: nan
rel_y: nan
rel_z: nan
87
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode20
description: ElectrodeGroup for tetrode 20
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

15
probe_type: nTrode20_probe_type
units: unknown
probe_description: nTrode20_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
176
rel_x: nan
rel_y: nan
rel_z: nan
178
rel_x: nan
rel_y: nan
rel_z: nan
180
rel_x: nan
rel_y: nan
rel_z: nan
182
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode21
description: ElectrodeGroup for tetrode 21
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

16
probe_type: nTrode21_probe_type
units: unknown
probe_description: nTrode21_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
152
rel_x: nan
rel_y: nan
rel_z: nan
154
rel_x: nan
rel_y: nan
rel_z: nan
156
rel_x: nan
rel_y: nan
rel_z: nan
158
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode22
description: ElectrodeGroup for tetrode 22
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

17
probe_type: nTrode22_probe_type
units: unknown
probe_description: nTrode22_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
168
rel_x: nan
rel_y: nan
rel_z: nan
170
rel_x: nan
rel_y: nan
rel_z: nan
172
rel_x: nan
rel_y: nan
rel_z: nan
174
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode23
description: ElectrodeGroup for tetrode 23
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

18
probe_type: nTrode23_probe_type
units: unknown
probe_description: nTrode23_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
144
rel_x: nan
rel_y: nan
rel_z: nan
146
rel_x: nan
rel_y: nan
rel_z: nan
148
rel_x: nan
rel_y: nan
rel_z: nan
150
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode24
description: ElectrodeGroup for tetrode 24
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

19
probe_type: nTrode24_probe_type
units: unknown
probe_description: nTrode24_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
160
rel_x: nan
rel_y: nan
rel_z: nan
162
rel_x: nan
rel_y: nan
rel_z: nan
164
rel_x: nan
rel_y: nan
rel_z: nan
166
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode25
description: ElectrodeGroup for tetrode 25
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

20
probe_type: nTrode25_probe_type
units: unknown
probe_description: nTrode25_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
136
rel_x: nan
rel_y: nan
rel_z: nan
138
rel_x: nan
rel_y: nan
rel_z: nan
140
rel_x: nan
rel_y: nan
rel_z: nan
142
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode26
description: ElectrodeGroup for tetrode 26
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

21
probe_type: nTrode26_probe_type
units: unknown
probe_description: nTrode26_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
216
rel_x: nan
rel_y: nan
rel_z: nan
218
rel_x: nan
rel_y: nan
rel_z: nan
220
rel_x: nan
rel_y: nan
rel_z: nan
222
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode27
description: ElectrodeGroup for tetrode 27
location: Left hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

22
probe_type: nTrode27_probe_type
units: unknown
probe_description: nTrode27_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
240
rel_x: nan
rel_y: nan
rel_z: nan
242
rel_x: nan
rel_y: nan
rel_z: nan
244
rel_x: nan
rel_y: nan
rel_z: nan
246
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode28
description: ElectrodeGroup for tetrode 28
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

23
probe_type: nTrode28_probe_type
units: unknown
probe_description: nTrode28_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
208
rel_x: nan
rel_y: nan
rel_z: nan
210
rel_x: nan
rel_y: nan
rel_z: nan
212
rel_x: nan
rel_y: nan
rel_z: nan
214
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode29
description: ElectrodeGroup for tetrode 29
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

24
probe_type: nTrode29_probe_type
units: unknown
probe_description: nTrode29_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
232
rel_x: nan
rel_y: nan
rel_z: nan
234
rel_x: nan
rel_y: nan
rel_z: nan
236
rel_x: nan
rel_y: nan
rel_z: nan
238
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode3
description: ElectrodeGroup for tetrode 3
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

2
probe_type: nTrode3_probe_type
units: unknown
probe_description: nTrode3_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
73
rel_x: nan
rel_y: nan
rel_z: nan
75
rel_x: nan
rel_y: nan
rel_z: nan
77
rel_x: nan
rel_y: nan
rel_z: nan
79
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode30
description: ElectrodeGroup for tetrode 30
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

25
probe_type: nTrode30_probe_type
units: unknown
probe_description: nTrode30_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
200
rel_x: nan
rel_y: nan
rel_z: nan
202
rel_x: nan
rel_y: nan
rel_z: nan
204
rel_x: nan
rel_y: nan
rel_z: nan
206
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode31
description: ElectrodeGroup for tetrode 31
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

26
probe_type: nTrode31_probe_type
units: unknown
probe_description: nTrode31_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
224
rel_x: nan
rel_y: nan
rel_z: nan
226
rel_x: nan
rel_y: nan
rel_z: nan
228
rel_x: nan
rel_y: nan
rel_z: nan
230
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode32
description: ElectrodeGroup for tetrode 32
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

27
probe_type: nTrode32_probe_type
units: unknown
probe_description: nTrode32_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
192
rel_x: nan
rel_y: nan
rel_z: nan
194
rel_x: nan
rel_y: nan
rel_z: nan
196
rel_x: nan
rel_y: nan
rel_z: nan
198
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode33
description: ElectrodeGroup for tetrode 33
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

28
probe_type: nTrode33_probe_type
units: unknown
probe_description: nTrode33_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
193
rel_x: nan
rel_y: nan
rel_z: nan
195
rel_x: nan
rel_y: nan
rel_z: nan
197
rel_x: nan
rel_y: nan
rel_z: nan
199
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode34
description: ElectrodeGroup for tetrode 34
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

29
probe_type: nTrode34_probe_type
units: unknown
probe_description: nTrode34_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
209
rel_x: nan
rel_y: nan
rel_z: nan
211
rel_x: nan
rel_y: nan
rel_z: nan
213
rel_x: nan
rel_y: nan
rel_z: nan
215
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode35
description: ElectrodeGroup for tetrode 35
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

30
probe_type: nTrode35_probe_type
units: unknown
probe_description: nTrode35_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
201
rel_x: nan
rel_y: nan
rel_z: nan
203
rel_x: nan
rel_y: nan
rel_z: nan
205
rel_x: nan
rel_y: nan
rel_z: nan
207
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode36
description: ElectrodeGroup for tetrode 36
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

31
probe_type: nTrode36_probe_type
units: unknown
probe_description: nTrode36_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
225
rel_x: nan
rel_y: nan
rel_z: nan
227
rel_x: nan
rel_y: nan
rel_z: nan
229
rel_x: nan
rel_y: nan
rel_z: nan
231
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode37
description: ElectrodeGroup for tetrode 37
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

32
probe_type: nTrode37_probe_type
units: unknown
probe_description: nTrode37_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
217
rel_x: nan
rel_y: nan
rel_z: nan
219
rel_x: nan
rel_y: nan
rel_z: nan
221
rel_x: nan
rel_y: nan
rel_z: nan
223
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode38
description: ElectrodeGroup for tetrode 38
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

33
probe_type: nTrode38_probe_type
units: unknown
probe_description: nTrode38_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
241
rel_x: nan
rel_y: nan
rel_z: nan
243
rel_x: nan
rel_y: nan
rel_z: nan
245
rel_x: nan
rel_y: nan
rel_z: nan
247
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode39
description: ElectrodeGroup for tetrode 39
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

34
probe_type: nTrode39_probe_type
units: unknown
probe_description: nTrode39_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
233
rel_x: nan
rel_y: nan
rel_z: nan
235
rel_x: nan
rel_y: nan
rel_z: nan
237
rel_x: nan
rel_y: nan
rel_z: nan
239
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode4
description: ElectrodeGroup for tetrode 4
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

3
probe_type: nTrode4_probe_type
units: unknown
probe_description: nTrode4_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
101
rel_x: nan
rel_y: nan
rel_z: nan
103
rel_x: nan
rel_y: nan
rel_z: nan
97
rel_x: nan
rel_y: nan
rel_z: nan
99
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode40
description: ElectrodeGroup for tetrode 40
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

35
probe_type: nTrode40_probe_type
units: unknown
probe_description: nTrode40_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
248
rel_x: nan
rel_y: nan
rel_z: nan
250
rel_x: nan
rel_y: nan
rel_z: nan
252
rel_x: nan
rel_y: nan
rel_z: nan
254
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode41
description: ElectrodeGroup for tetrode 41
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

36
probe_type: nTrode41_probe_type
units: unknown
probe_description: nTrode41_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
249
rel_x: nan
rel_y: nan
rel_z: nan
251
rel_x: nan
rel_y: nan
rel_z: nan
253
rel_x: nan
rel_y: nan
rel_z: nan
255
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode42
description: ElectrodeGroup for tetrode 42
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

37
probe_type: nTrode42_probe_type
units: unknown
probe_description: nTrode42_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
0
rel_x: nan
rel_y: nan
rel_z: nan
2
rel_x: nan
rel_y: nan
rel_z: nan
4
rel_x: nan
rel_y: nan
rel_z: nan
6
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode43
description: ElectrodeGroup for tetrode 43
location: Left Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

38
probe_type: nTrode43_probe_type
units: unknown
probe_description: nTrode43_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
1
rel_x: nan
rel_y: nan
rel_z: nan
3
rel_x: nan
rel_y: nan
rel_z: nan
5
rel_x: nan
rel_y: nan
rel_z: nan
7
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode44
description: ElectrodeGroup for tetrode 44
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

39
probe_type: nTrode44_probe_type
units: unknown
probe_description: nTrode44_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
11
rel_x: nan
rel_y: nan
rel_z: nan
13
rel_x: nan
rel_y: nan
rel_z: nan
15
rel_x: nan
rel_y: nan
rel_z: nan
9
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode45
description: ElectrodeGroup for tetrode 45
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

40
probe_type: nTrode45_probe_type
units: unknown
probe_description: nTrode45_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
17
rel_x: nan
rel_y: nan
rel_z: nan
19
rel_x: nan
rel_y: nan
rel_z: nan
21
rel_x: nan
rel_y: nan
rel_z: nan
23
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode46
description: ElectrodeGroup for tetrode 46
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

41
probe_type: nTrode46_probe_type
units: unknown
probe_description: nTrode46_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
25
rel_x: nan
rel_y: nan
rel_z: nan
27
rel_x: nan
rel_y: nan
rel_z: nan
29
rel_x: nan
rel_y: nan
rel_z: nan
31
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode47
description: ElectrodeGroup for tetrode 47
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

42
probe_type: nTrode47_probe_type
units: unknown
probe_description: nTrode47_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
33
rel_x: nan
rel_y: nan
rel_z: nan
35
rel_x: nan
rel_y: nan
rel_z: nan
37
rel_x: nan
rel_y: nan
rel_z: nan
39
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode48
description: ElectrodeGroup for tetrode 48
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

43
probe_type: nTrode48_probe_type
units: unknown
probe_description: nTrode48_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
41
rel_x: nan
rel_y: nan
rel_z: nan
43
rel_x: nan
rel_y: nan
rel_z: nan
45
rel_x: nan
rel_y: nan
rel_z: nan
47
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode49
description: ElectrodeGroup for tetrode 49
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

44
probe_type: nTrode49_probe_type
units: unknown
probe_description: nTrode49_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
49
rel_x: nan
rel_y: nan
rel_z: nan
51
rel_x: nan
rel_y: nan
rel_z: nan
53
rel_x: nan
rel_y: nan
rel_z: nan
55
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode5
description: ElectrodeGroup for tetrode 5
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

4
probe_type: nTrode5_probe_type
units: unknown
probe_description: nTrode5_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
89
rel_x: nan
rel_y: nan
rel_z: nan
91
rel_x: nan
rel_y: nan
rel_z: nan
93
rel_x: nan
rel_y: nan
rel_z: nan
95
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode50
description: ElectrodeGroup for tetrode 50
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

45
probe_type: nTrode50_probe_type
units: unknown
probe_description: nTrode50_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
57
rel_x: nan
rel_y: nan
rel_z: nan
59
rel_x: nan
rel_y: nan
rel_z: nan
61
rel_x: nan
rel_y: nan
rel_z: nan
63
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode51
description: ElectrodeGroup for tetrode 51
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

46
probe_type: nTrode51_probe_type
units: unknown
probe_description: nTrode51_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
56
rel_x: nan
rel_y: nan
rel_z: nan
58
rel_x: nan
rel_y: nan
rel_z: nan
60
rel_x: nan
rel_y: nan
rel_z: nan
62
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode53
description: ElectrodeGroup for tetrode 53
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

47
probe_type: nTrode53_probe_type
units: unknown
probe_description: nTrode53_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
24
rel_x: nan
rel_y: nan
rel_z: nan
26
rel_x: nan
rel_y: nan
rel_z: nan
28
rel_x: nan
rel_y: nan
rel_z: nan
30
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode54
description: ElectrodeGroup for tetrode 54
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

48
probe_type: nTrode54_probe_type
units: unknown
probe_description: nTrode54_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
40
rel_x: nan
rel_y: nan
rel_z: nan
42
rel_x: nan
rel_y: nan
rel_z: nan
44
rel_x: nan
rel_y: nan
rel_z: nan
46
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode56
description: ElectrodeGroup for tetrode 56
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

49
probe_type: nTrode56_probe_type
units: unknown
probe_description: nTrode56_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
32
rel_x: nan
rel_y: nan
rel_z: nan
34
rel_x: nan
rel_y: nan
rel_z: nan
36
rel_x: nan
rel_y: nan
rel_z: nan
38
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode57
description: ElectrodeGroup for tetrode 57
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

50
probe_type: nTrode57_probe_type
units: unknown
probe_description: nTrode57_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
10
rel_x: nan
rel_y: nan
rel_z: nan
12
rel_x: nan
rel_y: nan
rel_z: nan
14
rel_x: nan
rel_y: nan
rel_z: nan
8
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode58
description: ElectrodeGroup for tetrode 58
location: Right Subiculum
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

51
probe_type: nTrode58_probe_type
units: unknown
probe_description: nTrode58_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
88
rel_x: nan
rel_y: nan
rel_z: nan
90
rel_x: nan
rel_y: nan
rel_z: nan
92
rel_x: nan
rel_y: nan
rel_z: nan
94
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode60
description: ElectrodeGroup for tetrode 60
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

52
probe_type: nTrode60_probe_type
units: unknown
probe_description: nTrode60_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
80
rel_x: nan
rel_y: nan
rel_z: nan
82
rel_x: nan
rel_y: nan
rel_z: nan
84
rel_x: nan
rel_y: nan
rel_z: nan
86
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode61
description: ElectrodeGroup for tetrode 61
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

53
probe_type: nTrode61_probe_type
units: unknown
probe_description: nTrode61_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
104
rel_x: nan
rel_y: nan
rel_z: nan
106
rel_x: nan
rel_y: nan
rel_z: nan
108
rel_x: nan
rel_y: nan
rel_z: nan
110
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode62
description: ElectrodeGroup for tetrode 62
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

54
probe_type: nTrode62_probe_type
units: unknown
probe_description: nTrode62_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
72
rel_x: nan
rel_y: nan
rel_z: nan
74
rel_x: nan
rel_y: nan
rel_z: nan
76
rel_x: nan
rel_y: nan
rel_z: nan
78
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode63
description: ElectrodeGroup for tetrode 63
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

55
probe_type: nTrode63_probe_type
units: unknown
probe_description: nTrode63_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
100
rel_x: nan
rel_y: nan
rel_z: nan
102
rel_x: nan
rel_y: nan
rel_z: nan
96
rel_x: nan
rel_y: nan
rel_z: nan
98
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode7
description: ElectrodeGroup for tetrode 7
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

5
probe_type: nTrode7_probe_type
units: unknown
probe_description: nTrode7_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
105
rel_x: nan
rel_y: nan
rel_z: nan
107
rel_x: nan
rel_y: nan
rel_z: nan
109
rel_x: nan
rel_y: nan
rel_z: nan
111
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
nTrode9
description: ElectrodeGroup for tetrode 9
location: Right hippocampal subfield CA1
device
id
int64
Data typeint64
Shape()
Array size8.00 bytes

6
probe_type: nTrode9_probe_type
units: unknown
probe_description: nTrode9_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
121
rel_x: nan
rel_y: nan
rel_z: nan
123
rel_x: nan
rel_y: nan
rel_z: nan
125
rel_x: nan
rel_y: nan
rel_z: nan
127
rel_x: nan
rel_y: nan
rel_z: nan
targeted_location: unknown
targeted_x: nan
targeted_y: nan
targeted_z: nan
units: mm
devices
AdaptAMaze
description: Maze Control System
manufacturer: JadhavLab (JMOlson)
system: AdaptAMaze
amplifier: TBD
adc_circuit: TBD
ECU
description: Environmental Control Unit from SpikeGadgets - I/O control hardware
manufacturer: SpikeGadgets
system: ECU
amplifier: TBD
adc_circuit: TBD
MCU
description: Main Control Unit from SpikeGadgets - Handles Analog and Digital Signals, and Coordination across hardware
manufacturer: SpikeGadgets
system: MCU
amplifier: TBD
adc_circuit: TBD
camera_device 0
meters_per_pixel: 0.002
camera_name: SleepBox
model: Mako G-158C
lens: Theia SL183M
camera_device 1
meters_per_pixel: 0.0026
camera_name: Room
model: Mako G-158C
lens: Theia SL183M
nTrode12_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

7
probe_type: nTrode12_probe_type
units: unknown
probe_description: nTrode12_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
137
rel_x: nan
rel_y: nan
rel_z: nan
139
rel_x: nan
rel_y: nan
rel_z: nan
141
rel_x: nan
rel_y: nan
rel_z: nan
143
rel_x: nan
rel_y: nan
rel_z: nan
nTrode13_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

8
probe_type: nTrode13_probe_type
units: unknown
probe_description: nTrode13_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
145
rel_x: nan
rel_y: nan
rel_z: nan
147
rel_x: nan
rel_y: nan
rel_z: nan
149
rel_x: nan
rel_y: nan
rel_z: nan
151
rel_x: nan
rel_y: nan
rel_z: nan
nTrode14_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

9
probe_type: nTrode14_probe_type
units: unknown
probe_description: nTrode14_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
153
rel_x: nan
rel_y: nan
rel_z: nan
155
rel_x: nan
rel_y: nan
rel_z: nan
157
rel_x: nan
rel_y: nan
rel_z: nan
159
rel_x: nan
rel_y: nan
rel_z: nan
nTrode15_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

10
probe_type: nTrode15_probe_type
units: unknown
probe_description: nTrode15_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
161
rel_x: nan
rel_y: nan
rel_z: nan
163
rel_x: nan
rel_y: nan
rel_z: nan
165
rel_x: nan
rel_y: nan
rel_z: nan
167
rel_x: nan
rel_y: nan
rel_z: nan
nTrode16_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

11
probe_type: nTrode16_probe_type
units: unknown
probe_description: nTrode16_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
169
rel_x: nan
rel_y: nan
rel_z: nan
171
rel_x: nan
rel_y: nan
rel_z: nan
173
rel_x: nan
rel_y: nan
rel_z: nan
175
rel_x: nan
rel_y: nan
rel_z: nan
nTrode17_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

12
probe_type: nTrode17_probe_type
units: unknown
probe_description: nTrode17_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
177
rel_x: nan
rel_y: nan
rel_z: nan
179
rel_x: nan
rel_y: nan
rel_z: nan
181
rel_x: nan
rel_y: nan
rel_z: nan
183
rel_x: nan
rel_y: nan
rel_z: nan
nTrode18_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

13
probe_type: nTrode18_probe_type
units: unknown
probe_description: nTrode18_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
185
rel_x: nan
rel_y: nan
rel_z: nan
187
rel_x: nan
rel_y: nan
rel_z: nan
189
rel_x: nan
rel_y: nan
rel_z: nan
191
rel_x: nan
rel_y: nan
rel_z: nan
nTrode19_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

14
probe_type: nTrode19_probe_type
units: unknown
probe_description: nTrode19_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
184
rel_x: nan
rel_y: nan
rel_z: nan
186
rel_x: nan
rel_y: nan
rel_z: nan
188
rel_x: nan
rel_y: nan
rel_z: nan
190
rel_x: nan
rel_y: nan
rel_z: nan
nTrode1_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

0
probe_type: nTrode1_probe_type
units: unknown
probe_description: nTrode1_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
65
rel_x: nan
rel_y: nan
rel_z: nan
67
rel_x: nan
rel_y: nan
rel_z: nan
69
rel_x: nan
rel_y: nan
rel_z: nan
71
rel_x: nan
rel_y: nan
rel_z: nan
nTrode20_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

15
probe_type: nTrode20_probe_type
units: unknown
probe_description: nTrode20_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
176
rel_x: nan
rel_y: nan
rel_z: nan
178
rel_x: nan
rel_y: nan
rel_z: nan
180
rel_x: nan
rel_y: nan
rel_z: nan
182
rel_x: nan
rel_y: nan
rel_z: nan
nTrode21_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

16
probe_type: nTrode21_probe_type
units: unknown
probe_description: nTrode21_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
152
rel_x: nan
rel_y: nan
rel_z: nan
154
rel_x: nan
rel_y: nan
rel_z: nan
156
rel_x: nan
rel_y: nan
rel_z: nan
158
rel_x: nan
rel_y: nan
rel_z: nan
nTrode22_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

17
probe_type: nTrode22_probe_type
units: unknown
probe_description: nTrode22_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
168
rel_x: nan
rel_y: nan
rel_z: nan
170
rel_x: nan
rel_y: nan
rel_z: nan
172
rel_x: nan
rel_y: nan
rel_z: nan
174
rel_x: nan
rel_y: nan
rel_z: nan
nTrode23_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

18
probe_type: nTrode23_probe_type
units: unknown
probe_description: nTrode23_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
144
rel_x: nan
rel_y: nan
rel_z: nan
146
rel_x: nan
rel_y: nan
rel_z: nan
148
rel_x: nan
rel_y: nan
rel_z: nan
150
rel_x: nan
rel_y: nan
rel_z: nan
nTrode24_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

19
probe_type: nTrode24_probe_type
units: unknown
probe_description: nTrode24_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
160
rel_x: nan
rel_y: nan
rel_z: nan
162
rel_x: nan
rel_y: nan
rel_z: nan
164
rel_x: nan
rel_y: nan
rel_z: nan
166
rel_x: nan
rel_y: nan
rel_z: nan
nTrode25_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

20
probe_type: nTrode25_probe_type
units: unknown
probe_description: nTrode25_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
136
rel_x: nan
rel_y: nan
rel_z: nan
138
rel_x: nan
rel_y: nan
rel_z: nan
140
rel_x: nan
rel_y: nan
rel_z: nan
142
rel_x: nan
rel_y: nan
rel_z: nan
nTrode26_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

21
probe_type: nTrode26_probe_type
units: unknown
probe_description: nTrode26_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
216
rel_x: nan
rel_y: nan
rel_z: nan
218
rel_x: nan
rel_y: nan
rel_z: nan
220
rel_x: nan
rel_y: nan
rel_z: nan
222
rel_x: nan
rel_y: nan
rel_z: nan
nTrode27_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

22
probe_type: nTrode27_probe_type
units: unknown
probe_description: nTrode27_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
240
rel_x: nan
rel_y: nan
rel_z: nan
242
rel_x: nan
rel_y: nan
rel_z: nan
244
rel_x: nan
rel_y: nan
rel_z: nan
246
rel_x: nan
rel_y: nan
rel_z: nan
nTrode28_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

23
probe_type: nTrode28_probe_type
units: unknown
probe_description: nTrode28_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
208
rel_x: nan
rel_y: nan
rel_z: nan
210
rel_x: nan
rel_y: nan
rel_z: nan
212
rel_x: nan
rel_y: nan
rel_z: nan
214
rel_x: nan
rel_y: nan
rel_z: nan
nTrode29_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

24
probe_type: nTrode29_probe_type
units: unknown
probe_description: nTrode29_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
232
rel_x: nan
rel_y: nan
rel_z: nan
234
rel_x: nan
rel_y: nan
rel_z: nan
236
rel_x: nan
rel_y: nan
rel_z: nan
238
rel_x: nan
rel_y: nan
rel_z: nan
nTrode2_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

1
probe_type: nTrode2_probe_type
units: unknown
probe_description: nTrode2_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
81
rel_x: nan
rel_y: nan
rel_z: nan
83
rel_x: nan
rel_y: nan
rel_z: nan
85
rel_x: nan
rel_y: nan
rel_z: nan
87
rel_x: nan
rel_y: nan
rel_z: nan
nTrode30_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

25
probe_type: nTrode30_probe_type
units: unknown
probe_description: nTrode30_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
200
rel_x: nan
rel_y: nan
rel_z: nan
202
rel_x: nan
rel_y: nan
rel_z: nan
204
rel_x: nan
rel_y: nan
rel_z: nan
206
rel_x: nan
rel_y: nan
rel_z: nan
nTrode31_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

26
probe_type: nTrode31_probe_type
units: unknown
probe_description: nTrode31_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
224
rel_x: nan
rel_y: nan
rel_z: nan
226
rel_x: nan
rel_y: nan
rel_z: nan
228
rel_x: nan
rel_y: nan
rel_z: nan
230
rel_x: nan
rel_y: nan
rel_z: nan
nTrode32_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

27
probe_type: nTrode32_probe_type
units: unknown
probe_description: nTrode32_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
192
rel_x: nan
rel_y: nan
rel_z: nan
194
rel_x: nan
rel_y: nan
rel_z: nan
196
rel_x: nan
rel_y: nan
rel_z: nan
198
rel_x: nan
rel_y: nan
rel_z: nan
nTrode33_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

28
probe_type: nTrode33_probe_type
units: unknown
probe_description: nTrode33_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
193
rel_x: nan
rel_y: nan
rel_z: nan
195
rel_x: nan
rel_y: nan
rel_z: nan
197
rel_x: nan
rel_y: nan
rel_z: nan
199
rel_x: nan
rel_y: nan
rel_z: nan
nTrode34_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

29
probe_type: nTrode34_probe_type
units: unknown
probe_description: nTrode34_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
209
rel_x: nan
rel_y: nan
rel_z: nan
211
rel_x: nan
rel_y: nan
rel_z: nan
213
rel_x: nan
rel_y: nan
rel_z: nan
215
rel_x: nan
rel_y: nan
rel_z: nan
nTrode35_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

30
probe_type: nTrode35_probe_type
units: unknown
probe_description: nTrode35_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
201
rel_x: nan
rel_y: nan
rel_z: nan
203
rel_x: nan
rel_y: nan
rel_z: nan
205
rel_x: nan
rel_y: nan
rel_z: nan
207
rel_x: nan
rel_y: nan
rel_z: nan
nTrode36_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

31
probe_type: nTrode36_probe_type
units: unknown
probe_description: nTrode36_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
225
rel_x: nan
rel_y: nan
rel_z: nan
227
rel_x: nan
rel_y: nan
rel_z: nan
229
rel_x: nan
rel_y: nan
rel_z: nan
231
rel_x: nan
rel_y: nan
rel_z: nan
nTrode37_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

32
probe_type: nTrode37_probe_type
units: unknown
probe_description: nTrode37_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
217
rel_x: nan
rel_y: nan
rel_z: nan
219
rel_x: nan
rel_y: nan
rel_z: nan
221
rel_x: nan
rel_y: nan
rel_z: nan
223
rel_x: nan
rel_y: nan
rel_z: nan
nTrode38_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

33
probe_type: nTrode38_probe_type
units: unknown
probe_description: nTrode38_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
241
rel_x: nan
rel_y: nan
rel_z: nan
243
rel_x: nan
rel_y: nan
rel_z: nan
245
rel_x: nan
rel_y: nan
rel_z: nan
247
rel_x: nan
rel_y: nan
rel_z: nan
nTrode39_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

34
probe_type: nTrode39_probe_type
units: unknown
probe_description: nTrode39_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
233
rel_x: nan
rel_y: nan
rel_z: nan
235
rel_x: nan
rel_y: nan
rel_z: nan
237
rel_x: nan
rel_y: nan
rel_z: nan
239
rel_x: nan
rel_y: nan
rel_z: nan
nTrode3_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

2
probe_type: nTrode3_probe_type
units: unknown
probe_description: nTrode3_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
73
rel_x: nan
rel_y: nan
rel_z: nan
75
rel_x: nan
rel_y: nan
rel_z: nan
77
rel_x: nan
rel_y: nan
rel_z: nan
79
rel_x: nan
rel_y: nan
rel_z: nan
nTrode40_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

35
probe_type: nTrode40_probe_type
units: unknown
probe_description: nTrode40_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
248
rel_x: nan
rel_y: nan
rel_z: nan
250
rel_x: nan
rel_y: nan
rel_z: nan
252
rel_x: nan
rel_y: nan
rel_z: nan
254
rel_x: nan
rel_y: nan
rel_z: nan
nTrode41_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

36
probe_type: nTrode41_probe_type
units: unknown
probe_description: nTrode41_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
249
rel_x: nan
rel_y: nan
rel_z: nan
251
rel_x: nan
rel_y: nan
rel_z: nan
253
rel_x: nan
rel_y: nan
rel_z: nan
255
rel_x: nan
rel_y: nan
rel_z: nan
nTrode42_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

37
probe_type: nTrode42_probe_type
units: unknown
probe_description: nTrode42_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
0
rel_x: nan
rel_y: nan
rel_z: nan
2
rel_x: nan
rel_y: nan
rel_z: nan
4
rel_x: nan
rel_y: nan
rel_z: nan
6
rel_x: nan
rel_y: nan
rel_z: nan
nTrode43_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

38
probe_type: nTrode43_probe_type
units: unknown
probe_description: nTrode43_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
1
rel_x: nan
rel_y: nan
rel_z: nan
3
rel_x: nan
rel_y: nan
rel_z: nan
5
rel_x: nan
rel_y: nan
rel_z: nan
7
rel_x: nan
rel_y: nan
rel_z: nan
nTrode44_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

39
probe_type: nTrode44_probe_type
units: unknown
probe_description: nTrode44_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
11
rel_x: nan
rel_y: nan
rel_z: nan
13
rel_x: nan
rel_y: nan
rel_z: nan
15
rel_x: nan
rel_y: nan
rel_z: nan
9
rel_x: nan
rel_y: nan
rel_z: nan
nTrode45_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

40
probe_type: nTrode45_probe_type
units: unknown
probe_description: nTrode45_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
17
rel_x: nan
rel_y: nan
rel_z: nan
19
rel_x: nan
rel_y: nan
rel_z: nan
21
rel_x: nan
rel_y: nan
rel_z: nan
23
rel_x: nan
rel_y: nan
rel_z: nan
nTrode46_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

41
probe_type: nTrode46_probe_type
units: unknown
probe_description: nTrode46_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
25
rel_x: nan
rel_y: nan
rel_z: nan
27
rel_x: nan
rel_y: nan
rel_z: nan
29
rel_x: nan
rel_y: nan
rel_z: nan
31
rel_x: nan
rel_y: nan
rel_z: nan
nTrode47_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

42
probe_type: nTrode47_probe_type
units: unknown
probe_description: nTrode47_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
33
rel_x: nan
rel_y: nan
rel_z: nan
35
rel_x: nan
rel_y: nan
rel_z: nan
37
rel_x: nan
rel_y: nan
rel_z: nan
39
rel_x: nan
rel_y: nan
rel_z: nan
nTrode48_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

43
probe_type: nTrode48_probe_type
units: unknown
probe_description: nTrode48_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
41
rel_x: nan
rel_y: nan
rel_z: nan
43
rel_x: nan
rel_y: nan
rel_z: nan
45
rel_x: nan
rel_y: nan
rel_z: nan
47
rel_x: nan
rel_y: nan
rel_z: nan
nTrode49_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

44
probe_type: nTrode49_probe_type
units: unknown
probe_description: nTrode49_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
49
rel_x: nan
rel_y: nan
rel_z: nan
51
rel_x: nan
rel_y: nan
rel_z: nan
53
rel_x: nan
rel_y: nan
rel_z: nan
55
rel_x: nan
rel_y: nan
rel_z: nan
nTrode4_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

3
probe_type: nTrode4_probe_type
units: unknown
probe_description: nTrode4_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
101
rel_x: nan
rel_y: nan
rel_z: nan
103
rel_x: nan
rel_y: nan
rel_z: nan
97
rel_x: nan
rel_y: nan
rel_z: nan
99
rel_x: nan
rel_y: nan
rel_z: nan
nTrode50_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

45
probe_type: nTrode50_probe_type
units: unknown
probe_description: nTrode50_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
57
rel_x: nan
rel_y: nan
rel_z: nan
59
rel_x: nan
rel_y: nan
rel_z: nan
61
rel_x: nan
rel_y: nan
rel_z: nan
63
rel_x: nan
rel_y: nan
rel_z: nan
nTrode51_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

46
probe_type: nTrode51_probe_type
units: unknown
probe_description: nTrode51_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
56
rel_x: nan
rel_y: nan
rel_z: nan
58
rel_x: nan
rel_y: nan
rel_z: nan
60
rel_x: nan
rel_y: nan
rel_z: nan
62
rel_x: nan
rel_y: nan
rel_z: nan
nTrode53_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

47
probe_type: nTrode53_probe_type
units: unknown
probe_description: nTrode53_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
24
rel_x: nan
rel_y: nan
rel_z: nan
26
rel_x: nan
rel_y: nan
rel_z: nan
28
rel_x: nan
rel_y: nan
rel_z: nan
30
rel_x: nan
rel_y: nan
rel_z: nan
nTrode54_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

48
probe_type: nTrode54_probe_type
units: unknown
probe_description: nTrode54_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
40
rel_x: nan
rel_y: nan
rel_z: nan
42
rel_x: nan
rel_y: nan
rel_z: nan
44
rel_x: nan
rel_y: nan
rel_z: nan
46
rel_x: nan
rel_y: nan
rel_z: nan
nTrode56_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

49
probe_type: nTrode56_probe_type
units: unknown
probe_description: nTrode56_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
32
rel_x: nan
rel_y: nan
rel_z: nan
34
rel_x: nan
rel_y: nan
rel_z: nan
36
rel_x: nan
rel_y: nan
rel_z: nan
38
rel_x: nan
rel_y: nan
rel_z: nan
nTrode57_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

50
probe_type: nTrode57_probe_type
units: unknown
probe_description: nTrode57_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
10
rel_x: nan
rel_y: nan
rel_z: nan
12
rel_x: nan
rel_y: nan
rel_z: nan
14
rel_x: nan
rel_y: nan
rel_z: nan
8
rel_x: nan
rel_y: nan
rel_z: nan
nTrode58_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

51
probe_type: nTrode58_probe_type
units: unknown
probe_description: nTrode58_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
88
rel_x: nan
rel_y: nan
rel_z: nan
90
rel_x: nan
rel_y: nan
rel_z: nan
92
rel_x: nan
rel_y: nan
rel_z: nan
94
rel_x: nan
rel_y: nan
rel_z: nan
nTrode5_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

4
probe_type: nTrode5_probe_type
units: unknown
probe_description: nTrode5_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
89
rel_x: nan
rel_y: nan
rel_z: nan
91
rel_x: nan
rel_y: nan
rel_z: nan
93
rel_x: nan
rel_y: nan
rel_z: nan
95
rel_x: nan
rel_y: nan
rel_z: nan
nTrode60_probe
id
int64
Data typeint64
Shape()
Array size8.00 bytes

52
probe_type: nTrode60_probe_type
units: unknown
probe_description: nTrode60_probe description
contact_side_numbering
bool
Data typebool
Shape()
Array size1.00 bytes

False
contact_size: nan
shanks
0
shanks_electrodes
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intervals
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0268.5722002109.490567[01]
12446.7150005232.523233[02]
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... and 3 more rows.

subject
age: P63D/
age__reference: birth
description: Long Evans Rat
genotype: Wild Type
sex: M
species: Rattus norvegicus
subject_id: SL18
weight: 467g
epochs
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id
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12446.7150005232.523233[02]
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... and 3 more rows.

units
description: Autogenerated by NWBFile
waveform_unit: volts
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id
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1122nTrode12_unit270640.2141991842[268.598466666667, 268.608066666667, 270.852633333333, 317.061633333333, 323.111833333333, 326.534933333333, 326.569633333333, 326.6228, 326.977733333333, 327.128533333333, 327.1341, 327.261966666667, 327.266266666667, 327.270266666667, 327.531333333333, 330.7364, 341.596933333333, 400.483, 400.492, 400.606566666667, 401.319533333333, 401.696366666667, 410.588233333333, 411.062333333333, 414.8355, 436.4803, 446.0565, 471.386333333333, 482.810466666667, 483.743366666667, 542.9774, 542.989366666667, 543.0655, 543.071333333333, 543.0751, 543.199733333333, 543.2027, 543.212166666667, 543.307, 543.5316, 543.548533333333, 543.553633333333, 543.645666666667, 543.653633333333, 549.030366666667, 549.043566666667, 551.288666666667, 554.4421, 568.718466666667, 628.7038, 629.927966666667, 630.488766666667, 630.502633333333, 635.150966666667, 635.386533333333, 646.7583, 647.9241, 648.100166666667, 650.037533333333, 660.177566666667, 661.840366666667, 663.851133333333, 663.865866666667, 663.952833333333, 663.982733333333, 664.005766666667, 664.009633333333, 680.286233333333, 681.908266666667, 687.568766666667, 687.575966666667, 687.593033333333, 687.6046, 687.733566666667, 687.948066666667, 688.888966666667, 689.143333333333, 689.1601, 689.176133333333, 689.203066666667, 691.353233333333, 691.363, 691.567433333333, 691.719933333333, 691.7337, 691.752766666667, 691.8277, 691.839266666667, 691.863233333333, 691.911666666667, 691.998466666667, 692.007133333333, 692.152066666667, 696.269766666667, 696.276966666667, 698.167766666667, 698.326866666667, 699.726433333333, 705.469833333333, 705.5465, ...]nTrode12 abc.NwbElectrodeGroup at 0x5966901232\\nFields:\\n description: ElectrodeGroup for tetrode 12\\n device: nTrode12_probe abc.Probe at 0x5955867888\\nFields:\\n contact_side_numbering: False\\n contact_size: nan\\n id: 7\\n probe_description: nTrode12_probe description\\n probe_type: nTrode12_probe_type\\n shanks: {\\n 0 <class 'abc.Shank'>\\n }\\n units: unknown\\n\\n location: Left hippocampal subfield CA1\\n targeted_location: unknown\\n targeted_x: nan\\n targeted_y: nan\\n targeted_z: nan\\n units: mm\\n
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... and 375 more rows.

lab: Jadhav
institution: Brandeis University
source_script: Created using NeuroConv v0.7.4
source_script_file_name: /Users/pauladkisson/Documents/CatalystNeuro/Neuroconv/neuroconv/src/neuroconv/basedatainterface.py
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x5957965520\n", + "Fields:\n", + " acquisition: {\n", + " ElectricalSeries ,\n", + " Video_S01_F01_BOX_SLP ,\n", + " Video_S02_F01_Home+4_HomeAltVisitAll ,\n", + " Video_S03_F01_BOX_SLP ,\n", + " Video_S04_F01_Home+4_HomeAltVisitAll ,\n", + " Video_S05_F01_BOX_SLP ,\n", + " Video_S06_F01_Home+4_HomeAltVisitAll ,\n", + " Video_S07_F01_BOX_SLP \n", + " }\n", + " devices: {\n", + " AdaptAMaze ,\n", + " ECU ,\n", + " MCU ,\n", + " camera_device 0 ,\n", + " camera_device 1 ,\n", + " nTrode12_probe ,\n", + " nTrode13_probe ,\n", + " nTrode14_probe ,\n", + " nTrode15_probe ,\n", + " nTrode16_probe ,\n", + " nTrode17_probe ,\n", + " nTrode18_probe ,\n", + " nTrode19_probe ,\n", + " nTrode1_probe ,\n", + " nTrode20_probe ,\n", + " nTrode21_probe ,\n", + " nTrode22_probe ,\n", + " nTrode23_probe ,\n", + " nTrode24_probe ,\n", + " nTrode25_probe ,\n", + " nTrode26_probe ,\n", + " nTrode27_probe ,\n", + " nTrode28_probe ,\n", + " nTrode29_probe ,\n", + " nTrode2_probe ,\n", + " nTrode30_probe ,\n", + " nTrode31_probe ,\n", + " nTrode32_probe ,\n", + " nTrode33_probe ,\n", + " nTrode34_probe ,\n", + " nTrode35_probe ,\n", + " nTrode36_probe ,\n", + " nTrode37_probe ,\n", + " nTrode38_probe ,\n", + " nTrode39_probe ,\n", + " nTrode3_probe ,\n", + " nTrode40_probe ,\n", + " nTrode41_probe ,\n", + " nTrode42_probe ,\n", + " nTrode43_probe ,\n", + " nTrode44_probe ,\n", + " nTrode45_probe ,\n", + " nTrode46_probe ,\n", + " nTrode47_probe ,\n", + " nTrode48_probe ,\n", + " nTrode49_probe ,\n", + " nTrode4_probe ,\n", + " nTrode50_probe ,\n", + " nTrode51_probe ,\n", + " nTrode53_probe ,\n", + " nTrode54_probe ,\n", + " nTrode56_probe ,\n", + " nTrode57_probe ,\n", + " nTrode58_probe ,\n", + " nTrode5_probe ,\n", + " nTrode60_probe ,\n", + " nTrode61_probe ,\n", + " nTrode62_probe ,\n", + " nTrode63_probe ,\n", + " nTrode7_probe ,\n", + " nTrode9_probe \n", + " }\n", + " electrode_groups: {\n", + " nTrode1 ,\n", + " nTrode12 ,\n", + " nTrode13 ,\n", + " nTrode14 ,\n", + " nTrode15 ,\n", + " nTrode16 ,\n", + " nTrode17 ,\n", + " nTrode18 ,\n", + " nTrode19 ,\n", + " nTrode2 ,\n", + " nTrode20 ,\n", + " nTrode21 ,\n", + " nTrode22 ,\n", + " nTrode23 ,\n", + " nTrode24 ,\n", + " nTrode25 ,\n", + " nTrode26 ,\n", + " nTrode27 ,\n", + " nTrode28 ,\n", + " nTrode29 ,\n", + " nTrode3 ,\n", + " nTrode30 ,\n", + " nTrode31 ,\n", + " nTrode32 ,\n", + " nTrode33 ,\n", + " nTrode34 ,\n", + " nTrode35 ,\n", + " nTrode36 ,\n", + " nTrode37 ,\n", + " nTrode38 ,\n", + " nTrode39 ,\n", + " nTrode4 ,\n", + " nTrode40 ,\n", + " nTrode41 ,\n", + " nTrode42 ,\n", + " nTrode43 ,\n", + " nTrode44 ,\n", + " nTrode45 ,\n", + " nTrode46 ,\n", + " nTrode47 ,\n", + " nTrode48 ,\n", + " nTrode49 ,\n", + " nTrode5 ,\n", + " nTrode50 ,\n", + " nTrode51 ,\n", + " nTrode53 ,\n", + " nTrode54 ,\n", + " nTrode56 ,\n", + " nTrode57 ,\n", + " nTrode58 ,\n", + " nTrode60 ,\n", + " nTrode61 ,\n", + " nTrode62 ,\n", + " nTrode63 ,\n", + " nTrode7 ,\n", + " nTrode9 \n", + " }\n", + " electrodes: electrodes \n", + " epochs: epochs \n", + " experimenter: ['Olson, Jacob M.' 'Jadhav, Shantanu P.']\n", + " file_create_date: [datetime.datetime(2025, 6, 25, 14, 29, 37, 362833, tzinfo=tzoffset(None, -25200))]\n", + " identifier: d579aa2d-e91c-46a6-85aa-e6bfc7f39cfd\n", + " institution: Brandeis University\n", + " intervals: {\n", + " epochs \n", + " }\n", + " lab: Jadhav\n", + " processing: {\n", + " behavior ,\n", + " ecephys ,\n", + " tasks \n", + " }\n", + " session_description: CA1 and subiculum (SUB) are two main output regions of the hippocampus, projecting to highly overlapping cortical and subcortical regions. The manner and extent of coordination between rodent CA1 and SUB during the learning of memory-guided navigation is largely unknown. We are therefore recording these two regions simultaneously while rats learn a memory-guided navigation task in a complex track environment.\n", + " session_start_time: 2023-05-03 11:26:42-04:00\n", + " source_script: Created using NeuroConv v0.7.4\n", + " source_script_file_name: /Users/pauladkisson/Documents/CatalystNeuro/Neuroconv/neuroconv/src/neuroconv/basedatainterface.py\n", + " subject: subject pynwb.file.Subject at 0x5966899168\n", + "Fields:\n", + " age: P63D/\n", + " age__reference: birth\n", + " description: Long Evans Rat\n", + " genotype: Wild Type\n", + " sex: M\n", + " species: Rattus norvegicus\n", + " subject_id: SL18\n", + " weight: 467g\n", + "\n", + " timestamps_reference_time: 2023-05-03 11:26:42-04:00\n", + " units: units " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DANDISET_ID = '001343'\n", + "file_path = 'sub-SL18/sub-SL18_behavior+ecephys+image.nwb'\n", + "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)\n", + "display(nwbfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that task epochs alternate between Sleep and a behavioral shuttle task." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sleep\n" + ] + }, + { + "data": { + "text/html": [ + "
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task_nametask_descriptiontask_environmentcamera_idled_configurationled_listled_positionstask_epochs
id
0SleepThe animal sleeps in a small empty box.SLP[0]singleredledcenter[1, 3, 5, 7]
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" + ], + "text/plain": [ + " task_name task_description task_environment \\\n", + "id \n", + "0 Sleep The animal sleeps in a small empty box. SLP \n", + "\n", + " camera_id led_configuration led_list led_positions task_epochs \n", + "id \n", + "0 [0] single redled center [1, 3, 5, 7] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shuttle Task\n" + ] + }, + { + "data": { + "text/html": [ + "
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task_nametask_descriptiontask_environmentcamera_idled_configurationled_listled_positionstask_epochs
id
0HomeAltVisitAllShuttle task between home and 4 destinations.BOX[1]left/rightredled,greenledright,left[2, 4, 6]
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" + ], + "text/plain": [ + " task_name task_description \\\n", + "id \n", + "0 HomeAltVisitAll Shuttle task between home and 4 destinations. \n", + "\n", + " task_environment camera_id led_configuration led_list \\\n", + "id \n", + "0 BOX [1] left/right redled,greenled \n", + "\n", + " led_positions task_epochs \n", + "id \n", + "0 right,left [2, 4, 6] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epochs\n" + ] + }, + { + "data": { + "text/html": [ + "
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start_timestop_timetags
id
0268.5722002109.490567[01]
12446.7150005232.523233[02]
25385.8698337586.034767[03]
37807.94026710222.122933[04]
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" + ], + "text/plain": [ + " start_time stop_time tags\n", + "id \n", + "0 268.572200 2109.490567 [01]\n", + "1 2446.715000 5232.523233 [02]\n", + "2 5385.869833 7586.034767 [03]\n", + "3 7807.940267 10222.122933 [04]\n", + "4 10475.450200 13072.916633 [05]\n", + "5 13266.609733 15182.446400 [06]\n", + "6 15278.806033 17666.761833 [07]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sleep_df = nwbfile.processing[\"tasks\"].data_interfaces[\"Sleep\"].to_dataframe()\n", + "shuttle_task_df = nwbfile.processing[\"tasks\"].data_interfaces[\"HomeAltVisitAll\"].to_dataframe()\n", + "epochs_df = nwbfile.epochs.to_dataframe()\n", + "print(\"Sleep\")\n", + "display(sleep_df)\n", + "print(\"Shuttle Task\")\n", + "display(shuttle_task_df)\n", + "print(\"Epochs\")\n", + "display(epochs_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get plotting window (Middle of the first shuttle task epoch, i.e. epoch 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting window: 3500.0 to 3505.0 seconds\n", + "Plotting indices: 86443818 to 86593818 (exclusive)\n" + ] + } + ], + "source": [ + "plotting_start_time = 3500.0\n", + "plotting_stop_time = plotting_start_time + 5.0 # 5 second window\n", + "print(f\"Plotting window: {plotting_start_time} to {plotting_stop_time} seconds\")\n", + "plotting_start_index = bisect_left(nwbfile.acquisition[\"ElectricalSeries\"].timestamps, plotting_start_time)\n", + "plotting_stop_index = bisect_left(nwbfile.acquisition[\"ElectricalSeries\"].timestamps, plotting_stop_time)\n", + "plotting_slice = slice(plotting_start_index, plotting_stop_index)\n", + "print(f\"Plotting indices: {plotting_start_index} to {plotting_stop_index} (exclusive)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get electrodes for nTrode1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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locationgroupgroup_namechannel_namehasLFPref_elect_idchIDprobe_shankprobe_electrodebad_channel
id
8Right hippocampal subfield CA1nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...nTrode1hwChan65True65nTrode1_elec1065False
21Right hippocampal subfield CA1nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...nTrode1hwChan67False67nTrode1_elec2067False
34Right hippocampal subfield CA1nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...nTrode1hwChan69False69nTrode1_elec3069False
47Right hippocampal subfield CA1nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...nTrode1hwChan71False71nTrode1_elec4071False
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" + ], + "text/plain": [ + " location \\\n", + "id \n", + "8 Right hippocampal subfield CA1 \n", + "21 Right hippocampal subfield CA1 \n", + "34 Right hippocampal subfield CA1 \n", + "47 Right hippocampal subfield CA1 \n", + "\n", + " group group_name channel_name \\\n", + "id \n", + "8 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... nTrode1 hwChan65 \n", + "21 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... nTrode1 hwChan67 \n", + "34 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... nTrode1 hwChan69 \n", + "47 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... nTrode1 hwChan71 \n", + "\n", + " hasLFP ref_elect_id chID probe_shank probe_electrode \\\n", + "id \n", + "8 True 65 nTrode1_elec1 0 65 \n", + "21 False 67 nTrode1_elec2 0 67 \n", + "34 False 69 nTrode1_elec3 0 69 \n", + "47 False 71 nTrode1_elec4 0 71 \n", + "\n", + " bad_channel \n", + "id \n", + "8 False \n", + "21 False \n", + "34 False \n", + "47 False " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "electrodes_df = nwbfile.electrodes.to_dataframe()\n", + "nTrode1_electrodes = electrodes_df[electrodes_df['group_name'] == 'nTrode1']\n", + "electrode_indices = nTrode1_electrodes.index.values\n", + "nTrode1_electrodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get LFP index for nTrode1 and plotting_slice" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "lfp_electrodes_df = electrodes_df.iloc[nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].electrodes.data[:]]\n", + "nTrode1_lfp_index = np.where(lfp_electrodes_df[\"group_name\"] == \"nTrode1\")[0][0]\n", + "print(nTrode1_lfp_index)\n", + "\n", + "lfp_plotting_start_index = bisect_left(nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].timestamps, plotting_start_time)\n", + "lfp_plotting_stop_index = bisect_left(nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].timestamps, plotting_stop_time)\n", + "lfp_plotting_slice = slice(lfp_plotting_start_index, lfp_plotting_stop_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get Units for nTrode1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nTrodeunitIndglobalIDnWaveformswaveformFWHMwaveformPeakMinusTroughspike_timeselectrode_group
id
5911nTrode1_unit135850.2380711136[325.090733333333, 325.6376, 325.677866666667,...nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...
6012nTrode1_unit2107450.2962611103[271.756933333333, 272.3406, 272.451366666667,...nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...
6113nTrode1_unit3109750.287069745[2452.31, 2452.7101, 2457.16183333333, 2463.77...nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...
6214nTrode1_unit42950.2542801482[357.468366666667, 357.480533333333, 396.89313...nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\...
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" + ], + "text/plain": [ + " nTrode unitInd globalID nWaveforms waveformFWHM \\\n", + "id \n", + "59 1 1 nTrode1_unit1 3585 0.238071 \n", + "60 1 2 nTrode1_unit2 10745 0.296261 \n", + "61 1 3 nTrode1_unit3 10975 0.287069 \n", + "62 1 4 nTrode1_unit4 295 0.254280 \n", + "\n", + " waveformPeakMinusTrough \\\n", + "id \n", + "59 1136 \n", + "60 1103 \n", + "61 745 \n", + "62 1482 \n", + "\n", + " spike_times \\\n", + "id \n", + "59 [325.090733333333, 325.6376, 325.677866666667,... \n", + "60 [271.756933333333, 272.3406, 272.451366666667,... \n", + "61 [2452.31, 2452.7101, 2457.16183333333, 2463.77... \n", + "62 [357.468366666667, 357.480533333333, 396.89313... \n", + "\n", + " electrode_group \n", + "id \n", + "59 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... \n", + "60 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... \n", + "61 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... \n", + "62 nTrode1 abc.NwbElectrodeGroup at 0x5966900608\\... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "units_df = nwbfile.units.to_dataframe()\n", + "nTrode1_units = units_df[units_df['nTrode'] == 1]\n", + "nTrode1_units" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve Ephys Data for nTrode1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Ephys\n", + "electrical_series = np.asarray(nwbfile.acquisition[\"ElectricalSeries\"].data[plotting_slice, electrode_indices])\n", + "raw_to_uV = nwbfile.acquisition[\"ElectricalSeries\"].conversion * 1e6\n", + "electrical_series_in_uV = electrical_series * raw_to_uV\n", + "electrical_series_timestamps = np.asarray(nwbfile.acquisition[\"ElectricalSeries\"].timestamps[plotting_slice])\n", + "lfp = np.asarray(nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].data[lfp_plotting_slice, nTrode1_lfp_index])\n", + "lfp_conversion = nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].conversion * 1e6\n", + "lfp_in_uV = lfp * lfp_conversion\n", + "lfp_timestamps = np.asarray(nwbfile.processing[\"ecephys\"].data_interfaces[\"LFP\"].electrical_series[\"ElectricalSeriesLFP\"].timestamps[lfp_plotting_slice])\n", + "\n", + "# Sorted Units\n", + "spike_times = []\n", + "for i, row in nTrode1_units.iterrows():\n", + " plotting_spike_time_mask = (row[\"spike_times\"] >= plotting_start_time) & (row[\"spike_times\"] < plotting_stop_time)\n", + " plotting_spike_times = row[\"spike_times\"][plotting_spike_time_mask]\n", + " spike_times.append(plotting_spike_times)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Example Tetrode" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time (s)')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3, 1, figsize=(15, 10), sharex=True)\n", + "ax[0].plot(electrical_series_timestamps, electrical_series_in_uV)\n", + "ax[0].set_ylabel(\"Voltage (uV)\")\n", + "ax[0].set_title(\"Ephys Data (nTrode1)\")\n", + "ax[0].legend([channel_id for channel_id in nTrode1_electrodes['chID'].values])\n", + "colors = ['red', 'blue', 'green', 'orange']\n", + "for i, (idx, row) in enumerate(nTrode1_units.iterrows()):\n", + " plotting_spike_time_mask = (row[\"spike_times\"] >= plotting_start_time) & (row[\"spike_times\"] < plotting_stop_time)\n", + " plotting_spike_times = row[\"spike_times\"][plotting_spike_time_mask]\n", + " unit_ind = row[\"unitInd\"]\n", + " ax[1].eventplot(plotting_spike_times, lineoffsets=i, colors=colors[i], label=f\"Unit {unit_ind}\")\n", + "ax[1].set_yticks([])\n", + "ax[1].set_ylabel(\"Units\")\n", + "ax[1].set_title(\"Spike Times (nTrode1)\")\n", + "ax[1].legend()\n", + "\n", + "ax[2].plot(lfp_timestamps, lfp_in_uV, color='black')\n", + "ax[2].set_ylabel(\"Voltage (uV)\")\n", + "ax[2].set_title(\"LFP Data (nTrode1)\")\n", + "ax[2].set_xlabel(\"Time (s)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get Behavior Data for second epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "event_names = [\n", + " \"reward_well_1\",\n", + " \"reward_well_2\",\n", + " \"reward_well_3\",\n", + " \"reward_well_4\",\n", + " \"reward_well_5\",\n", + " \"reward_well_6\",\n", + " \"reward_well_7\",\n", + " \"reward_well_8\",\n", + " \"reward_pump_1\",\n", + " \"reward_pump_2\",\n", + " \"reward_pump_3\",\n", + " \"reward_pump_4\",\n", + " \"reward_pump_5\",\n", + "]\n", + "behavioral_events = nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_events\"].time_series\n", + "name_to_timestamps = {name: behavioral_events[name].timestamps[:] for name in event_names}\n", + "plotting_start_time = epochs_df.start_time.iloc[1]\n", + "plotting_stop_time = epochs_df.stop_time.iloc[1]\n", + "name_to_timestamps_plotting = {\n", + " name: timestamps[(timestamps >= plotting_start_time) & (timestamps < plotting_stop_time)]\n", + " for name, timestamps in name_to_timestamps.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Behavior Data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "well_colors = ['red', 'blue', 'green', 'orange', 'purple', 'brown', 'pink', 'gray']\n", + "\n", + "fig, axs = plt.subplots(2, 1, figsize=(15, 10), sharex=True)\n", + "\n", + "# Plot reward well pokes\n", + "for i in range(1, 9):\n", + " well_name = f\"reward_well_{i}\"\n", + " if name_to_timestamps_plotting[well_name].size == 0:\n", + " continue\n", + " axs[0].stem(name_to_timestamps_plotting[well_name], \n", + " np.ones_like(name_to_timestamps_plotting[well_name]) * i, \n", + " linefmt=f'{well_colors[i-1]}', \n", + " markerfmt=f'{well_colors[i-1]}', \n", + " basefmt=' ', \n", + " label=well_name)\n", + "\n", + "# Plot reward pumps\n", + "for i in range(1, 6):\n", + " pump_name = f\"reward_pump_{i}\"\n", + " if name_to_timestamps_plotting[pump_name].size == 0:\n", + " continue\n", + " axs[1].stem(name_to_timestamps_plotting[pump_name], \n", + " np.ones_like(name_to_timestamps_plotting[pump_name]) * i, \n", + " linefmt=f'{well_colors[i-1]}', \n", + " markerfmt=f'{well_colors[i-1]}', \n", + " basefmt=' ', \n", + " label=pump_name)\n", + " \n", + "# Add axis labels and titles\n", + "axs[0].set_ylabel(\"Well #\")\n", + "axs[0].set_title(\"Reward Well Pokes\")\n", + "\n", + "axs[1].set_ylabel(\"Pump #\")\n", + "axs[1].set_title(\"Reward Pumps\")\n", + "axs[1].set_xlabel(\"Time (s)\")\n", + "\n", + "# Show legends\n", + "for ax in axs:\n", + " ax.legend(loc=\"upper right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get DLC data for the second epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pose_estimation_series = nwbfile.processing[\"behavior\"].data_interfaces[\"PoseEstimation_S02_F01_Home+4_HomeAltVisitAll\"].pose_estimation_series\n", + "name_to_data = {name: series.data[:] for name, series in pose_estimation_series.items()}\n", + "pes_timestamps = pose_estimation_series[\"PoseEstimationSeriesBaseoftail\"].timestamps[:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot DLC data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15, 10))\n", + "all_x = np.array([data[:, 0] for data in name_to_data.values()])\n", + "all_y = np.array([data[:, 1] for data in name_to_data.values()])\n", + "\n", + "x = np.nanmean(all_x, axis=0)\n", + "y = np.nanmean(all_y, axis=0)\n", + "\n", + "sc = plt.scatter(x, y, c=pes_timestamps, cmap='viridis', s=1)\n", + "plt.xlabel('X Position')\n", + "plt.ylabel('Y Position')\n", + "plt.title('Average Position Trajectory')\n", + "plt.colorbar(sc, label='Time (s)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jadhav_notebook_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/001343/stream_nwbfile.py b/001343/stream_nwbfile.py new file mode 100644 index 0000000..02bff64 --- /dev/null +++ b/001343/stream_nwbfile.py @@ -0,0 +1,35 @@ +from pynwb import NWBHDF5IO +import remfile +import h5py +from dandi.dandiapi import DandiAPIClient + +def stream_nwbfile(DANDISET_ID, file_path): + '''Stream NWB file from DANDI archive. + + Parameters + ---------- + DANDISET_ID : str + Dandiset ID + file_path : str + Path to NWB file in DANDI archive + + Returns + ------- + nwbfile : NWBFile + NWB file + io : NWBHDF5IO + NWB IO object (for closing) + + Notes + ----- + The io object must be closed after use. + ''' + with DandiAPIClient() as client: + client.dandi_authenticate() + asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path) + s3_url = asset.get_content_url(follow_redirects=1, strip_query=False) + file_system = remfile.File(s3_url) + file = h5py.File(file_system, mode="r") + io = NWBHDF5IO(file=file, load_namespaces=True) + nwbfile = io.read() + return nwbfile, io \ No newline at end of file