From e716bd68233e2e5e4c2bdc3f7c57ac6c9c1cd63e Mon Sep 17 00:00:00 2001 From: weiglszonja Date: Thu, 4 Apr 2024 22:42:13 +0200 Subject: [PATCH] add visualizations --- .../TurnerLab/public_demo/000947_demo.ipynb | 10634 +--------------- 1 file changed, 307 insertions(+), 10327 deletions(-) diff --git a/000947/TurnerLab/public_demo/000947_demo.ipynb b/000947/TurnerLab/public_demo/000947_demo.ipynb index 722cf78..4c2be67 100644 --- a/000947/TurnerLab/public_demo/000947_demo.ipynb +++ b/000947/TurnerLab/public_demo/000947_demo.ipynb @@ -46,7 +46,6 @@ "source": [ "from dandi.dandiapi import DandiAPIClient\n", "\n", - "\n", "client = DandiAPIClient.for_dandi_instance(\"dandi\")\n", "\n", "dandiset_id = \"000947\"\n", @@ -62,8 +61,7 @@ "id": "538c499f-3bd6-4ea4-8c1d-1745a4c39102", "metadata": {}, "source": [ - "We will use `remfile` for streaming the file. You can read more about `remfile` at [this tutorial section](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html#method-3-remfile).\n", - "You can install `remfile` with pip:" + "We will use `remfile` for streaming the file. You can read more about `remfile` at [this tutorial section](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html#method-3-remfile)." ] }, { @@ -111,595 +109,19 @@ " });\n", " });\n", " \n", - "

root (NWBFile)

session_description: This session contains raw and high-pass filtered (200 Hz) extracellular data from globus pallidus-internus (GPi)\n", + "

root (NWBFile)

session_description: This session contains raw and high-pass filtered (200 Hz) extracellular data from globus pallidus-internus (GPi)\n", "in parkinsonism monkeys performing a choice reaction time reaching task.\n", - "
identifier: a67d909d-f704-4173-9377-d13b364beec8
session_start_time2019-01-20 14:51:30-08:00
timestamps_reference_time2019-01-20 14:51:30-08:00
file_create_date
02024-03-29 14:29:36.104389+01:00
experimenter('Kase, Daisuke', 'Zimnik, Andrew')
related_publications('https://doi.org/10.1101/2023.09.08.556120',)
acquisition
ElectricalSeries
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: Acquisition traces for the ElectricalSeries.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
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", - " \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", - " \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", - "
locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
keywords
processing
ecephys
description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP.
data_interfaces
Processed
electrical_series
ElectricalSeriesProcessedGPi
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
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", - " \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", - " \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", - "
locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
epoch_tagsset()
electrodes
description: metadata about extracellular electrodes
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locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
electrode_groups
Group GPi
description: Group vp16 electrodes.
location: GPi
device
description: TDT recording
manufacturer: Tucker-Davis Technologies (TDT)
devices
DeviceEcephys
description: TDT recording
manufacturer: Tucker-Davis Technologies (TDT)
intervals
trials
description: experimental trials
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start_timestop_timeerror_onset_timereward_start_timereward_stop_timemovement_start_timemovement_stop_timereturn_start_timereturn_stop_timecue_onset_timetarget
id
050.99262060.075663NaN53.93264754.21223953.70232853.91929358.72607260.07566353.478851Left
160.08664170.747259NaN63.63664463.96207163.34705763.62239069.58198870.74725963.103672Right
270.75729487.668572NaN75.16667975.44234074.93947475.14435685.59743087.66857274.664960Left
387.680328102.365225NaN90.64943690.92739190.40736290.63718993.556941102.36522590.165125Left

... and 82 more rows.

subject
age__reference: birth
description: USDA registration number RQ7513
sex: F
species: Macaca mulatta
subject_id: Isis
date_of_birth2005-06-14 00:00:00-07:00
lab_meta_data
MPTPMetaData
MPTP_status: post_MPTP
trials
description: experimental trials
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start_timestop_timeerror_onset_timereward_start_timereward_stop_timemovement_start_timemovement_stop_timereturn_start_timereturn_stop_timecue_onset_timetarget
id
050.99262060.075663NaN53.93264754.21223953.70232853.91929358.72607260.07566353.478851Left
160.08664170.747259NaN63.63664463.96207163.34705763.62239069.58198870.74725963.103672Right
270.75729487.668572NaN75.16667975.44234074.93947475.14435685.59743087.66857274.664960Left
387.680328102.365225NaN90.64943690.92739190.40736290.63718993.556941102.36522590.165125Left

... and 82 more rows.

units
description: The curated single-units from the Plexon Offline Sorter v3, selected based on the quality of spike sorting.
waveform_unit: volts
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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
spike_timesunit_namesort_labellocationchannel_idsunit_quality_post_sorting
id
0[2.70639104, 9.90437376, 10.7485184, 25.95905536, 32.83980288, 35.08396032, 35.29662464, 35.52210944, 36.69393408, 37.01583872, 38.19077632, 39.07383296, 39.3914368, 39.93300992, 40.15898624, 41.47580928, 41.5619072, 41.9633152, 43.5089408, 46.93188608, 47.98267392, 48.22069248, 49.05426944, 49.52121344, 49.8327552, 50.36019712, 50.70405632, 51.05463296, 51.1660032, 51.19455232, 51.32029952, 52.01391616, 55.67819776, 56.54417408, 56.65349632, 58.96105984, 59.06096128, 59.06538496, 59.65074432, 59.773952, 59.91477248, 60.18473984, 60.22868992, 60.31081472, 60.39416832, 61.0531328, 61.22381312, 61.79459072, 61.89142016, 62.50590208, 62.52089344, 62.75272704, 64.4968448, 64.85630976, 65.33189632, 65.61615872, 65.78782208, 65.82075392, 65.88604416, 66.87256576, 68.52087808, 68.57363456, 69.18520832, 69.23583488, 69.51145472, 69.54426368, 69.61287168, 69.62118656, 69.70789888, 70.04012544, 70.09263616, 70.3000576, 70.44161536, 70.52419072, 70.6064384, 70.61950464, 70.684672, 70.95435264, 70.9910528, 71.00485632, 71.01714432, 71.0234112, 71.0897664, 71.1309312, 71.15476992, 71.1704576, 71.19106048, 71.43735296, 71.53942528, 71.58263808, 71.91113728, 71.92551424, 72.0265216, 72.0494592, 72.0545792, 72.11134976, 72.18610176, 72.282112, 72.31787008, 72.47020032, ...]01.0GPi20F
1[0.02125824, 0.09744384, 0.17870848, 0.20815872, 0.23252992, 0.28860416, 0.37056512, 0.41070592, 0.44269568, 0.46821376, 0.495616, 0.52641792, 0.55898112, 0.58535936, 0.61304832, 0.64688128, 0.69103616, 0.7323648, 0.77234176, 0.82857984, 0.87543808, 0.9209856, 1.00442112, 1.07831296, 1.12738304, 1.1644928, 1.3326336, 1.37265152, 1.40783616, 1.44310272, 1.47808256, 1.508352, 1.55222016, 1.5867904, 1.62578432, 1.67632896, 1.73146112, 1.76566272, 1.80768768, 1.84582144, 1.88903424, 1.92602112, 1.980416, 2.03583488, 2.2022144, 2.24382976, 2.60943872, 2.76230144, 2.80477696, 2.87367168, 2.94047744, 2.96464384, 2.99388928, 3.04922624, 3.11083008, 3.13987072, 3.16489728, 3.1973376, 3.23313664, 3.28183808, 3.31173888, 3.33545472, 3.35691776, 3.38960384, 3.44113152, 3.47492352, 3.50633984, 3.54455552, 3.5837952, 3.6306944, 3.66718976, 3.71027968, 3.74816768, 3.78052608, 3.82582784, 3.9337984, 4.03095552, 4.08449024, 4.2246144, 4.28609536, 4.3194368, 4.35920896, 4.40098816, 4.42834944, 4.46550016, 4.49675264, 4.5242368, 4.55319552, 4.63765504, 4.73874432, 4.76573696, 4.8113664, 4.84642816, 4.89222144, 4.93154304, 4.97467392, 5.01137408, 5.04123392, 5.0796544, 5.218304, ...]11.0GPi22excellent
2[0.01036288, 0.055296, 0.09613312, 0.1349632, 0.17596416, 0.23576576, 0.28045312, 0.37912576, 0.44838912, 0.503808, 0.56111104, 0.61771776, 0.6627328, 0.72359936, 0.79826944, 0.8445952, 0.894976, 0.94531584, 0.99536896, 1.05754624, 1.09490176, 1.15683328, 1.21450496, 1.26349312, 1.3156352, 1.35299072, 1.39841536, 1.4516224, 1.48852736, 1.53124864, 1.57966336, 1.62770944, 1.69005056, 1.73244416, 1.76713728, 1.81280768, 1.8470912, 1.88952576, 1.93089536, 1.97623808, 2.023424, 2.0668416, 2.11996672, 2.22162944, 2.2839296, 2.3199744, 2.38645248, 2.43273728, 2.4793088, 2.53759488, 2.5894912, 2.64531968, 2.68972032, 2.73297408, 2.78392832, 2.83045888, 2.8725248, 2.92335616, 2.97897984, 3.0181376, 3.07576832, 3.11808, 3.17108224, 3.22441216, 3.26324224, 3.3032192, 3.3429504, 3.4000896, 3.45636864, 3.49822976, 3.53005568, 3.53247232, 3.58424576, 3.63646976, 3.69057792, 3.73665792, 3.78208256, 3.83483904, 3.89431296, 3.93949184, 3.98176256, 4.03525632, 4.08887296, 4.1365504, 4.19352576, 4.22572032, 4.27655168, 4.32345088, 4.36768768, 4.4177408, 4.46406656, 4.50777088, 4.55983104, 4.6020608, 4.6409728, 4.67542016, 4.7620096, 4.81087488, 4.86260736, 4.90717184, ...]22.0GPi22good
3[0.10190848, 0.13770752, 0.1859584, 0.26558464, 0.31346688, 0.35794944, 0.40185856, 0.44355584, 0.4763648, 0.50765824, 0.54874112, 0.58089472, 0.62255104, 0.65466368, 0.69230592, 0.74010624, 0.78680064, 0.8368128, 0.87740416, 0.91971584, 0.96407552, 1.0092544, 1.0334208, 1.08896256, 1.12652288, 1.18480896, 1.24203008, 1.27758336, 1.32653056, 1.36327168, 1.44769024, 1.48504576, 1.52334336, 1.58461952, 1.62041856, 1.654784, 1.68665088, 1.7383424, 1.77205248, 1.80109312, 1.83291904, 1.87076608, 1.963008, 1.99090176, 2.02878976, 2.08089088, 2.1426176, 2.17358336, 2.23023104, 2.2573056, 2.27016704, 2.3103488, 2.35692032, 2.49024512, 2.5294848, 2.56765952, 2.61824512, 2.64720384, 2.6847232, 2.724864, 2.74837504, 2.79093248, 2.81903104, 2.85364224, 2.88989184, 2.92900864, 2.98774528, 3.03026176, 3.06884608, 3.11308288, 3.15588608, 3.18824448, 3.22502656, 3.25595136, 3.26651904, 3.31472896, 3.37051648, 3.40811776, 3.44547328, 3.4973696, 3.56667392, 3.60660992, 3.69860608, 3.7564416, 3.83832064, 3.89062656, 3.93039872, 3.97266944, 3.99818752, 4.02960384, 4.06806528, 4.0998912, 4.13175808, 4.16264192, 4.19590144, 4.22232064, 4.25934848, 4.288512, 4.32635904, 4.37387264, ...]33.0GPi22good

... and 4 more rows.

experiment_description: This dataset contains recordings of single-unit activity from globus pallidus-internus (GPi) in MPTP\n", + "
identifier: a67d909d-f704-4173-9377-d13b364beec8
session_start_time: 2019-01-20 14:51:30-08:00
timestamps_reference_time: 2019-01-20 14:51:30-08:00
file_create_date
2024-03-29 14:29:36.104389+01:00
experimenter: ('Kase, Daisuke', 'Zimnik, Andrew')
related_publications: ('https://doi.org/10.1101/2023.09.08.556120',)
acquisition (1)
ElectricalSeries
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: Acquisition traces for the ElectricalSeries.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
id
colnames: ('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')
columns: (, , , , , , , , , , , , , , )
keywords
processing (1)
ecephys
description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP.
data_interfaces (1)
Processed
electrical_series (1)
ElectricalSeriesProcessedGPi
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
id
colnames: ('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')
columns: (, , , , , , , , , , , , , , )
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
epoch_tags: set()
electrodes
description: metadata about extracellular electrodes
id
colnames: ('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')
columns: (, , , , , , , , , , , , , , )
electrode_groups (1)
Group GPi
description: Group vp16 electrodes.
location: GPi
device
description: TDT recording
manufacturer: Tucker-Davis Technologies (TDT)
devices (1)
DeviceEcephys
description: TDT recording
manufacturer: Tucker-Davis Technologies (TDT)
intervals (1)
trials
description: experimental trials
id
colnames: ('start_time', 'stop_time', 'error_onset_time', 'reward_start_time', 'reward_stop_time', 'movement_start_time', 'movement_stop_time', 'return_start_time', 'return_stop_time', 'cue_onset_time', 'target')
columns: (, , , , , , , , , , )
subject
age__reference: birth
description: USDA registration number RQ7513
sex: F
species: Macaca mulatta
subject_id: Isis
date_of_birth: 2005-06-14 00:00:00-07:00
lab_meta_data (1)
MPTPMetaData
MPTP_status: post_MPTP
trials
description: experimental trials
id
colnames: ('start_time', 'stop_time', 'error_onset_time', 'reward_start_time', 'reward_stop_time', 'movement_start_time', 'movement_stop_time', 'return_start_time', 'return_stop_time', 'cue_onset_time', 'target')
columns: (, , , , , , , , , , )
units
description: The curated single-units from the Plexon Offline Sorter v3, selected based on the quality of spike sorting.
id
colnames: ('spike_times', 'unit_name', 'sort_label', 'location', 'channel_ids', 'unit_quality_post_sorting')
columns: (, , , , , , )
waveform_unit: volts
experiment_description: This dataset contains recordings of single-unit activity from globus pallidus-internus (GPi) in MPTP\n", "(1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced monkeys performing a choice reaction time reaching task.\n", "The neuronal activity was recorded using 16-contact linear probes (0.5–1.0 MΩ, V-probe, Plexon) or glass-insulated\n", "tungsten microelectrodes (0.5–1.5 MΩ, Alpha Omega). The neuronal data were amplified (4×, 2 Hz–7.5 kHz) and\n", "digitized at 24.414 kHz (approx., 16-bit resolution; Tucker Davis Technologies).\n", "The neuronal data were high-pass filtered (Fpass: 200 Hz, Matlab FIRPM) and thresholded, and candidate action\n", "potentials were sorted into clusters in principal components space (Off-line Sorter, Plexon).\n", - "
session_id: post-MPTP-I-190118-6
lab: Turner
institution: University of Pittsburgh
pharmacology: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; the chemical compound that induces parkinsonism) administration date 2017-10-27 (ICA; internal carotid artery injection), 2017-11-28 (IM; intramuscular injection), 2017-12-01 (IM), 2018-06-08 (ICA), 2018-07-05 (IM), 2018-07-19 (IM), 2018-08-27 (IM)
" + "
session_id: post-MPTP-I-190118-6
lab: Turner
institution: University of Pittsburgh
pharmacology: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; the chemical compound that induces parkinsonism) administration date 2017-10-27 (ICA; internal carotid artery injection), 2017-11-28 (IM; intramuscular injection), 2017-12-01 (IM), 2018-06-08 (ICA), 2018-07-05 (IM), 2018-07-19 (IM), 2018-08-27 (IM)
" ], "text/plain": [ - "root pynwb.file.NWBFile at 0x5095624976\n", + "root pynwb.file.NWBFile at 0x5009103760\n", "Fields:\n", " acquisition: {\n", " ElectricalSeries \n", @@ -741,7 +163,7 @@ "\n", " session_id: post-MPTP-I-190118-6\n", " session_start_time: 2019-01-20 14:51:30-08:00\n", - " subject: subject pynwb.file.Subject at 0x4826556176\n", + " subject: subject pynwb.file.Subject at 0x5009444304\n", "Fields:\n", " age__reference: birth\n", " date_of_birth: 2005-06-14 00:00:00-07:00\n", @@ -788,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 183, "id": "e5a3c3bb-8c9a-41b6-b3d0-dd0a6581b030", "metadata": {}, "outputs": [ @@ -831,10 +253,10 @@ " });\n", " });\n", " \n", - "

subject (Subject)

age__reference: birth
description: USDA registration number RQ7513
sex: F
species: Macaca mulatta
subject_id: Isis
date_of_birth2005-06-14 00:00:00-07:00
" + "

subject (Subject)

age__reference: birth
description: USDA registration number RQ7513
sex: F
species: Macaca mulatta
subject_id: Isis
date_of_birth: 2005-06-14 00:00:00-07:00
" ], "text/plain": [ - "subject pynwb.file.Subject at 0x4826556176\n", + "subject pynwb.file.Subject at 0x5009444304\n", "Fields:\n", " age__reference: birth\n", " date_of_birth: 2005-06-14 00:00:00-07:00\n", @@ -844,7 +266,7 @@ " subject_id: Isis" ] }, - "execution_count": 3, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -863,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 184, "id": "8e8c9994-6aad-45b2-a647-fef07440e211", "metadata": {}, "outputs": [ @@ -906,15 +328,15 @@ " });\n", " });\n", " \n", - "

MPTPMetaData (TurnerLabMetaData)

MPTP_status: post_MPTP
" + "

MPTPMetaData (TurnerLabMetaData)

MPTP_status: post_MPTP
" ], "text/plain": [ - "MPTPMetaData abc.TurnerLabMetaData at 0x5095572944\n", + "MPTPMetaData abc.TurnerLabMetaData at 0x5013298256\n", "Fields:\n", " MPTP_status: post_MPTP" ] }, - "execution_count": 4, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -938,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 185, "id": "67073433-ae57-4459-900a-6b79e55b7795", "metadata": {}, "outputs": [ @@ -1172,9640 +594,144 @@ "5 126.400512 133.235917 121.009603 Right \n", "6 146.674319 148.448256 135.913800 Left \n", "7 162.919014 165.340938 152.366694 Right \n", - "8 173.979689 180.914258 168.523612 Left \n", - "9 194.873057 197.386732 184.101397 Right " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trials = nwbfile.trials.to_dataframe()\n", - "\n", - "trials.head(10)" - ] - }, - { - "cell_type": "markdown", - "id": "f29abc49-b06c-48bb-8cfa-8f4ce3200ae0", - "metadata": {}, - "source": [ - "# Access Recording \n", - "\n", - "This section demonstrates how to access the raw `ElectricalSeries` data.\n", - "\n", - "`NWB` organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:\n", - "\n", - "- `acquisition`: raw, acquired data that should never change\n", - "- `processing`: processed data, typically the results of preprocessing algorithms and could change\n", - "\n", - "## Raw ElectricalSeries\n", - "\n", - "The raw ElectricalSeries data is stored in an [pynwb.ecephys.ElectricalSeries](https://pynwb.readthedocs.io/en/stable/pynwb.ecephys.html#pynwb.ecephys.ElectricalSeries) object which is added to `nwbfile.acquisition`. The data can be accessed as `nwbfile.acquisition[\"ElectricalSeries\"]`.\n", - "\n", - "The data in `ElectricalSeries` is stored as a two dimensional array: the first dimension is time, the second dimension represents electrodes/channels.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "60c80beb-9785-425c-8f6a-705f1795d707", - "metadata": {}, - "outputs": [], - "source": [ - "electrical_series = nwbfile.acquisition[\"ElectricalSeries\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f3c486f2-0f1d-4d41-b756-e03b042dd8ec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "displayModeBar": true, - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "electrodes=Conx 20
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "data = electrical_series.data[:1000, :]\n", - "df = pd.DataFrame(data)\n", - "df[\"Time (s)\"] = np.arange(0, data.shape[0])\n", - "df.set_index(\"Time (s)\", inplace=True)\n", - "df.columns.name = \"electrodes\"\n", - "channel_name_mapper = dict(zip(df.columns, electrical_series.electrodes[\"channel_name\"][:]))\n", - "df.rename(channel_name_mapper, axis=1, inplace=True)\n", - "\n", - "import plotly.express as px\n", - "\n", - "fig = px.line(df, facet_row=\"electrodes\", facet_row_spacing=0.01)\n", - "\n", - "# hide and lock down axes\n", - "fig.update_xaxes(visible=True, fixedrange=False)\n", - "fig.update_yaxes(visible=False, fixedrange=False)\n", - "\n", - "# remove facet/subplot labels\n", - "fig.update_layout(annotations=[], overwrite=True)\n", - "\n", - "# strip down the rest of the plot\n", - "fig.update_layout(\n", - " showlegend=True,\n", - " plot_bgcolor=\"white\",\n", - " margin=dict(t=10, l=10, b=10, r=10)\n", - ")\n", - "\n", - "fig.show(config=dict(displayModeBar=True))\n" - ] - }, - { - "cell_type": "markdown", - "id": "540565a6-abad-4f63-a6a1-b06b656f6b3a", - "metadata": {}, - "source": [ - "The electrodes table describe the electrodes that generated this data. Extracellular electrodes are stored in an \"electrodes\" table, which is a [DynamicTable](https://hdmf.readthedocs.io/en/stable/hdmf.common.table.html#hdmf.common.table.DynamicTable) and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.electrodes.to_dataframe()`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d4a56a82-e135-4f41-941c-d25b2b6fc5ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
\n", - "
" - ], - "text/plain": [ - " location group group_name \\\n", - "id \n", - "0 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", - "1 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", - "2 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", - "3 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", - "\n", - " channel_name gain_to_uV offset_to_uV channel_ids electrode_type \\\n", - "id \n", - "0 Conx 20 1.0 0.0 20 vp16 \n", - "1 Conx 22 1.0 0.0 22 vp16 \n", - "2 Conx 23 1.0 0.0 23 vp16 \n", - "3 Conx 26 1.0 0.0 26 vp16 \n", - "\n", - " chamber_type chamber_x chamber_y chamber_z acx_x acx_y acx_z \n", - "id \n", - "0 Sagittal -1.0 5.0 45.30 -8.710117 -4.341093 0.092948 \n", - "1 Sagittal -1.0 5.0 45.60 -8.693499 -4.499198 -0.161466 \n", - "2 Sagittal -1.0 5.0 45.75 -8.685190 -4.578251 -0.288674 \n", - "3 Sagittal -1.0 5.0 44.55 -8.751660 -3.945831 0.728984 " + "8 173.979689 180.914258 168.523612 Left \n", + "9 194.873057 197.386732 184.101397 Right " ] }, - "execution_count": 8, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nwbfile.electrodes.to_dataframe()" + "trials = nwbfile.trials.to_dataframe()\n", + "\n", + "trials.head(10)" ] }, { "cell_type": "markdown", - "id": "c4e898a7-8920-45d1-aa1e-a86fbfc3d13b", + "id": "f29abc49-b06c-48bb-8cfa-8f4ce3200ae0", "metadata": {}, "source": [ - "## Filtered ElectricalSeries\n", + "# Access Recording \n", "\n", + "This section demonstrates how to access the raw `ElectricalSeries` data.\n", "\n", - "The processed ecephys data is stored in \"processing/ecephys\" which can be accessed as `nwbfile.processing[\"ecephys\"]`.\n", - "Within this processing module we can access the container of filtered traces as `nwbfile.processing[\"ecephys\"][\"Processed\"]` which can hold multiple processed `ElectricalSeries` objects.\n" + "`NWB` organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:\n", + "\n", + "- `acquisition`: raw, acquired data that should never change\n", + "- `processing`: processed data, typically the results of preprocessing algorithms and could change\n", + "\n", + "## Raw ElectricalSeries\n", + "\n", + "The raw ElectricalSeries data is stored in an [pynwb.ecephys.ElectricalSeries](https://pynwb.readthedocs.io/en/stable/pynwb.ecephys.html#pynwb.ecephys.ElectricalSeries) object which is added to `nwbfile.acquisition`. The data can be accessed as `nwbfile.acquisition[\"ElectricalSeries\"]`.\n", + "\n", + "The data in `ElectricalSeries` is stored as a two dimensional array: the first dimension is time, the second dimension represents electrodes/channels.\n" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "21f92c95-f391-43d9-a4e1-9bab82c4d499", + "execution_count": 186, + "id": "60c80beb-9785-425c-8f6a-705f1795d707", "metadata": {}, + "outputs": [], + "source": [ + "electrical_series = nwbfile.acquisition[\"ElectricalSeries\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "f3c486f2-0f1d-4d41-b756-e03b042dd8ec", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - "

ecephys (ProcessingModule)

description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP.
data_interfaces
Processed
electrical_series
ElectricalSeriesProcessedGPi
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
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locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
" - ], + "image/png": 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"text/plain": [ - "ecephys pynwb.base.ProcessingModule at 0x5095551760\n", - "Fields:\n", - " data_interfaces: {\n", - " Processed \n", - " }\n", - " description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP." + "
" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "nwbfile.processing[\"ecephys\"]" + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# Prepare data for plotting\n", + "data = electrical_series.data[:1000, :]\n", + "df = pd.DataFrame(data)\n", + "df[\"Time (s)\"] = np.arange(0, data.shape[0])\n", + "df.set_index(\"Time (s)\", inplace=True)\n", + "df.columns.name = \"electrodes\"\n", + "channel_name_mapper = dict(zip(df.columns, electrical_series.electrodes[\"channel_name\"][:]))\n", + "df.rename(channel_name_mapper, axis=1, inplace=True)\n", + "\n", + "fig, axes = plt.subplots(nrows=len(df.columns), sharex=True, sharey=True, dpi=200)\n", + "lines = df.plot(subplots=True, ax=axes, legend=False)\n", + "\n", + "# Hide y-axis labels\n", + "for ax in axes:\n", + " ax.yaxis.set_visible(False)\n", + "\n", + "# Remove box around the plots\n", + "for ax in axes:\n", + " ax.set_frame_on(False)\n", + "\n", + "# Get handles and labels for all lines\n", + "handles, labels = [], []\n", + "for line in lines:\n", + " h, l = line.get_legend_handles_labels()\n", + " handles.extend(h)\n", + " labels.extend(l)\n", + "\n", + "# Create a single legend box\n", + "fig.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.2, 0.8), frameon=False)\n", + "plt.xlabel('Time (s)')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "540565a6-abad-4f63-a6a1-b06b656f6b3a", + "metadata": {}, + "source": [ + "The electrodes table describe the electrodes that generated this data. Extracellular electrodes are stored in an \"electrodes\" table, which is a [DynamicTable](https://hdmf.readthedocs.io/en/stable/hdmf.common.table.html#hdmf.common.table.DynamicTable) and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.electrodes.to_dataframe()`." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "e855f083-c6c9-44e8-ae0a-efa50f43175b", + "execution_count": 187, + "id": "d4a56a82-e135-4f41-941c-d25b2b6fc5ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ + "
\n", + "\n", - " \n", - " \n", - "

Processed (FilteredEphys)

electrical_series
ElectricalSeriesProcessedGPi
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
table\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "\n", + "
\n", " \n", " \n", " \n", @@ -10848,7 +774,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10866,7 +792,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10884,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10902,7 +828,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10918,28 +844,121 @@ " \n", " \n", " \n", - "
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 201.0
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 221.0
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 231.0
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPi pynwb.ecephys.ElectrodeGroup at 0x50...Group GPiConx 261.00.728984
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
" + "\n", + "
" + ], + "text/plain": [ + " location group group_name \\\n", + "id \n", + "0 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", + "1 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", + "2 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", + "3 GPi Group GPi pynwb.ecephys.ElectrodeGroup at 0x50... Group GPi \n", + "\n", + " channel_name gain_to_uV offset_to_uV channel_ids electrode_type \\\n", + "id \n", + "0 Conx 20 1.0 0.0 20 vp16 \n", + "1 Conx 22 1.0 0.0 22 vp16 \n", + "2 Conx 23 1.0 0.0 23 vp16 \n", + "3 Conx 26 1.0 0.0 26 vp16 \n", + "\n", + " chamber_type chamber_x chamber_y chamber_z acx_x acx_y acx_z \n", + "id \n", + "0 Sagittal -1.0 5.0 45.30 -8.710117 -4.341093 0.092948 \n", + "1 Sagittal -1.0 5.0 45.60 -8.693499 -4.499198 -0.161466 \n", + "2 Sagittal -1.0 5.0 45.75 -8.685190 -4.578251 -0.288674 \n", + "3 Sagittal -1.0 5.0 44.55 -8.751660 -3.945831 0.728984 " + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.electrodes.to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "c4e898a7-8920-45d1-aa1e-a86fbfc3d13b", + "metadata": {}, + "source": [ + "## Filtered ElectricalSeries\n", + "\n", + "\n", + "The processed ecephys data is stored in \"processing/ecephys\" which can be accessed as `nwbfile.processing[\"ecephys\"]`.\n", + "Within this processing module we can access the container of filtered traces as `nwbfile.processing[\"ecephys\"][\"Processed\"]` which can hold multiple processed `ElectricalSeries` objects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "21f92c95-f391-43d9-a4e1-9bab82c4d499", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

ecephys (ProcessingModule)

description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP.
data_interfaces (1)
Processed
electrical_series (1)
ElectricalSeriesProcessedGPi
starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
id
colnames: ('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')
columns: (, , , , , , , , , , , , , , )
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
" ], "text/plain": [ - "Processed pynwb.ecephys.FilteredEphys at 0x5095580048\n", + "ecephys pynwb.base.ProcessingModule at 0x5083282896\n", "Fields:\n", - " electrical_series: {\n", - " ElectricalSeriesProcessedGPi \n", - " }" + " data_interfaces: {\n", + " Processed \n", + " }\n", + " description: Intermediate data from extracellular electrophysiology recordings, e.g., LFP." ] }, - "execution_count": 10, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nwbfile.processing[\"ecephys\"][\"Processed\"]" + "nwbfile.processing[\"ecephys\"]" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 192, "id": "92063ea2-4d07-4bed-b0c8-6c52c63d8aa5", "metadata": {}, "outputs": [ @@ -10982,123 +1001,10 @@ " });\n", " });\n", " \n", - "

ElectricalSeriesProcessedGPi (ElectricalSeries)

starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
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", - " \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", - " \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", - "
locationgroupgroup_namechannel_namegain_to_uVoffset_to_uVchannel_idselectrode_typechamber_typechamber_xchamber_ychamber_zacx_xacx_yacx_z
id
0GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 201.00.020vp16Sagittal-1.05.045.30-8.710117-4.3410930.092948
1GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 221.00.022vp16Sagittal-1.05.045.60-8.693499-4.499198-0.161466
2GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 231.00.023vp16Sagittal-1.05.045.75-8.685190-4.578251-0.288674
3GPiGroup GPi pynwb.ecephys.ElectrodeGroup at 0x5095624080\\nFields:\\n description: Group vp16 electrodes.\\n device: DeviceEcephys pynwb.device.Device at 0x5095636880\\nFields:\\n description: TDT recording\\n manufacturer: Tucker-Davis Technologies (TDT)\\n\\n location: GPi\\nGroup GPiConx 261.00.026vp16Sagittal-1.05.044.55-8.751660-3.9458310.728984
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
" + "

ElectricalSeriesProcessedGPi (ElectricalSeries)

starting_time: 9.5367431640625e-07
rate: 24414.0625
resolution: -1.0
comments: no comments
description: High-pass filtered traces (200 Hz) from GPi region.
conversion: 1e-06
offset: 0.0
unit: volts
data
starting_time_unit: seconds
electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
id
colnames: ('location', 'group', 'group_name', 'channel_name', 'gain_to_uV', 'offset_to_uV', 'channel_ids', 'electrode_type', 'chamber_type', 'chamber_x', 'chamber_y', 'chamber_z', 'acx_x', 'acx_y', 'acx_z')
columns: (, , , , , , , , , , , , , , )
filtering: Equiripple High-pass filter designed using the FIRPM function in MATLAB with a stop-band frequency of 200 Hz, a pass-band frequency of 300 Hz, a stop-band attenuation of 0.0001 dB, a pass-band ripple of 0.057501127785 dB, and a density factor of 20.
" ], "text/plain": [ - "ElectricalSeriesProcessedGPi pynwb.ecephys.ElectricalSeries at 0x5095571536\n", + "ElectricalSeriesProcessedGPi pynwb.ecephys.ElectricalSeries at 0x5010966480\n", "Fields:\n", " comments: no comments\n", " conversion: 1e-06\n", @@ -11114,13 +1020,16 @@ " unit: volts" ] }, - "execution_count": 11, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nwbfile.processing[\"ecephys\"][\"Processed\"][\"ElectricalSeriesProcessedGPi\"]" + "processed_ecephys = nwbfile.processing[\"ecephys\"][\"Processed\"]\n", + "\n", + "filtered_electrical_series = processed_ecephys[\"ElectricalSeriesProcessedGPi\"]\n", + "filtered_electrical_series" ] }, { @@ -11130,17 +1039,12 @@ "source": [ "# Access Units \n", "\n", - "Spike times are stored in the `Units` table, which is a DynamicTable and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.units.to_dataframe()`.\n", - "\n", - "The spike_times and other metadata that are stored in `nwbfile.units` is extracted from the .mat file that contains the \"units\" structure.\n", - "\n", - "_Note_:\n", - "The spike times from the Plexon files are added to \"processing/ecephys\" processing module and can be accessed as `nwbfile.processing[\"ecephys\"][\"units\"]`." + "Spike times are stored in the `Units` table, which is a DynamicTable and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.units.to_dataframe()`.\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 191, "id": "35d72c09-8b23-4949-a1f3-28d3e50365a8", "metadata": {}, "outputs": [ @@ -11283,7 +1187,7 @@ "7 GPi 26 excellent " ] }, - "execution_count": 12, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -11294,10 +1198,86 @@ }, { "cell_type": "markdown", - "id": "7e486ac3-66cb-443f-ae38-696a1993f6ea", + "id": "28ba02f3-f725-492f-9258-d867499c0e6c", + "metadata": {}, + "source": [ + "### Interactive visualization of spike times\n", + "\n", + "![Showing a raster plot in Neurosift](images/neurosift_units.png)\n", + "\n", + "Visit [Neurosift](https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/2bf73c66-8534-4d9a-bee5-a42025dea7a2/download/&dandisetId=000947&dandisetVersion=draft&tab=view:DirectRasterPlot|/units&tab-time=146.82820800814233,157.46102593869216,undefined)" + ] + }, + { + "cell_type": "markdown", + "id": "e4068f8f-eeea-475e-abe8-a60a04632b1e", "metadata": {}, "source": [ - "We also use [Neurosift](https://github.com/flatironinstitute/neurosift), a platform for the visualization of neuroscience data in the web browser." + "### PSTH\n", + "\n", + "In this example we will show the activity of one single unit sampled from GPi and aligned to the time of movement onset.\n", + "\n", + "We will use [Pynapple](https://pynapple-org.github.io/pynapple/) to compute and visualize the PSTH using the `pynapple.compute_perievent()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "54b5e5c9-3f64-40c9-ad8f-2d7208204132", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "import pynapple as nap\n", + "\n", + "movement_onset_ts = nap.Ts(nwbfile.trials[\"movement_start_time\"][:], time_units=\"s\")\n", + "spike_times_group = nap.TsGroup({unit_ind: nap.Ts(spikes) for unit_ind, spikes in enumerate(nwbfile.units[\"spike_times\"])})\n", + "\n", + "perievent_histogram = nap.compute_perievent(\n", + " data=spike_times_group,\n", + " tref=movement_onset_ts,\n", + " minmax=(-0.5, 1),\n", + " time_unit=\"s\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 4), dpi=200)\n", + "\n", + "ax.plot(\n", + " perievent_histogram[4].to_tsd(),\n", + " \"|\",\n", + " color=\"black\",\n", + " markersize=3,\n", + ")\n", + "plt.xlabel(\"Time from Movement onset (s)\")\n", + "plt.ylabel(\"Trials\")\n", + "plt.xlim(-0.5, 1)\n", + "plt.axvline(0.0)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "75b472b7-8daf-4ebc-87a1-171844aea5d8", + "metadata": {}, + "source": [ + "We can also use [Neurosift](https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/2bf73c66-8534-4d9a-bee5-a42025dea7a2/download/&dandisetId=000947&dandisetVersion=draft&tab=view:PSTH|/intervals/trials&tab-time=undefined,undefined,undefined) for this visualization:\n", + "\n", + "![Showing PSTH in Neurosift](images/neurosift_psth.png)\n" ] } ], @@ -11317,7 +1297,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.5" } }, "nbformat": 4,