From 42e999557332c62a2fdcfa897d1b665b39099b85 Mon Sep 17 00:00:00 2001 From: ehennestad Date: Tue, 3 Dec 2024 13:13:29 +0100 Subject: [PATCH] Add support for NWB schemas v2.8.0 (#630) * Add nwb 2.8.0 schemas and regenerated types * Update requirements to use pynwb and nwbinspector from github@dev * ... * Fix typo of name for dataset in bands table in ecephys tutorial * debug unexpected pynwb version * Remove hdmf-zarr dependency, as it is not used * Fix: Make inherited read-only datasets in schema read-only properties in matnwb * Add debug flag for printing installed python packages in PynwbTutorialTest * Add more detailed device example to ecephys tutorial * Add more detailed device example to ophys tutorial --- +tests/+unit/PynwbTutorialTest.m | 20 +- +tests/requirements.txt | 4 +- +types/+core/Device.m | 85 +- +types/+core/EventWaveform.m | 2 +- +types/+core/ImageMaskSeries.m | 2 +- +types/+core/NWBFile.m | 34 +- +types/+core/SpikeEventSeries.m | 2 +- +types/+core/Units.m | 6 +- .github/workflows/run_tests.yml | 1 + nwb-schema/2.8.0/core/nwb.base.yaml | 264 +++ nwb-schema/2.8.0/core/nwb.behavior.yaml | 124 ++ nwb-schema/2.8.0/core/nwb.device.yaml | 26 + nwb-schema/2.8.0/core/nwb.ecephys.yaml | 332 ++++ nwb-schema/2.8.0/core/nwb.epoch.yaml | 31 + nwb-schema/2.8.0/core/nwb.file.yaml | 524 +++++ nwb-schema/2.8.0/core/nwb.icephys.yaml | 430 ++++ nwb-schema/2.8.0/core/nwb.image.yaml | 222 +++ nwb-schema/2.8.0/core/nwb.misc.yaml | 331 ++++ nwb-schema/2.8.0/core/nwb.namespace.yaml | 60 + nwb-schema/2.8.0/core/nwb.ogen.yaml | 48 + nwb-schema/2.8.0/core/nwb.ophys.yaml | 360 ++++ nwb-schema/2.8.0/core/nwb.retinotopy.yaml | 234 +++ .../2.8.0/hdmf-common-schema/common/base.yaml | 21 + .../common/experimental.yaml | 14 + .../hdmf-common-schema/common/namespace.yaml | 51 + .../hdmf-common-schema/common/resources.yaml | 105 + .../hdmf-common-schema/common/sparse.yaml | 35 + .../hdmf-common-schema/common/table.yaml | 168 ++ tutorials/ecephys.mlx | Bin 360227 -> 360517 bytes tutorials/html/ecephys.html | 1730 +++++++++-------- tutorials/html/ophys.html | 33 +- tutorials/ophys.mlx | Bin 173820 -> 173300 bytes tutorials/private/mcode/ecephys.m | 23 +- tutorials/private/mcode/ophys.m | 20 +- 34 files changed, 4453 insertions(+), 889 deletions(-) create mode 100644 nwb-schema/2.8.0/core/nwb.base.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.behavior.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.device.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.ecephys.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.epoch.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.file.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.icephys.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.image.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.misc.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.namespace.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.ogen.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.ophys.yaml create mode 100644 nwb-schema/2.8.0/core/nwb.retinotopy.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/base.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/experimental.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/namespace.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/resources.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/sparse.yaml create mode 100644 nwb-schema/2.8.0/hdmf-common-schema/common/table.yaml diff --git a/+tests/+unit/PynwbTutorialTest.m b/+tests/+unit/PynwbTutorialTest.m index 93113a56..ee6e9807 100644 --- a/+tests/+unit/PynwbTutorialTest.m +++ b/+tests/+unit/PynwbTutorialTest.m @@ -25,6 +25,7 @@ 'streaming.py', ... % Requires that HDF5 library is installed with the ROS3 driver enabled which is not a given 'object_id.py', ... % Does not export nwb file 'plot_configurator.py', ... % Does not export nwb file + 'plot_zarr_io', ... % Does not export nwb file in nwb format 'brain_observatory.py', ... % Requires allen sdk 'extensions.py'}; % Discrepancy between tutorial and schema: https://github.com/NeurodataWithoutBorders/pynwb/issues/1952 @@ -32,12 +33,14 @@ SkippedFiles = {'family_nwb_file_0.nwb'} % requires family driver from h5py % PythonDependencies - Package dependencies for running pynwb tutorials - PythonDependencies = {'hdmf-zarr', 'dataframe-image', 'matplotlib'} + PythonDependencies = {'dataframe-image', 'matplotlib'} end properties (Access = private) PythonEnvironment % Stores the value of the environment variable % "PYTHONPATH" to restore when test is finished. + + Debug (1,1) logical = false end methods (TestClassSetup) @@ -66,6 +69,12 @@ function setupClass(testCase) L = dir('temp_venv/lib/python*/site-*'); % Find the site-packages folder pythonPath = fullfile(L.folder, L.name); setenv('PYTHONPATH', pythonPath) + + pythonPath = tests.util.getPythonPath(); + + if testCase.Debug + [~, m] = system(sprintf('%s -m pip list', pythonPath)); disp(m) + end end end @@ -172,7 +181,12 @@ function installPythonDependencies(testCase) for i = 1:numel(testCase.PythonDependencies) iName = testCase.PythonDependencies{i}; installCmdStr = sprintf('%s install %s', pipExecutable, iName); - evalc( "system(installCmdStr)" ); % Install without command window output + + if testCase.Debug + [~, m] = system(installCmdStr); disp(m) + else + evalc( "system(installCmdStr)" ); % Install without command window output + end end end end @@ -208,7 +222,7 @@ function installPythonDependencies(testCase) end function pynwbFolder = downloadPynwb() - githubUrl = 'https://github.com/NeurodataWithoutBorders/pynwb/archive/refs/heads/master.zip'; + githubUrl = 'https://github.com/NeurodataWithoutBorders/pynwb/archive/refs/heads/dev.zip'; pynwbFolder = downloadZippedGithubRepo(githubUrl, '.'); % Download in current directory end diff --git a/+tests/requirements.txt b/+tests/requirements.txt index cc9afd91..12070c83 100644 --- a/+tests/requirements.txt +++ b/+tests/requirements.txt @@ -1,3 +1,3 @@ -pynwb hdf5plugin -nwbinspector +git+https://github.com/NeurodataWithoutBorders/nwbinspector.git@dev +git+https://github.com/NeurodataWithoutBorders/pynwb.git@dev \ No newline at end of file diff --git a/+types/+core/Device.m b/+types/+core/Device.m index a626863b..426a9bd5 100644 --- a/+types/+core/Device.m +++ b/+types/+core/Device.m @@ -4,8 +4,11 @@ % OPTIONAL PROPERTIES properties - description; % (char) Description of the device (e.g., model, firmware version, processing software version, etc.) as free-form text. - manufacturer; % (char) The name of the manufacturer of the device. + description; % (char) Description of the device as free-form text. If there is any software/firmware associated with the device, the names and versions of those can be added to NWBFile.was_generated_by. + manufacturer; % (char) The name of the manufacturer of the device, e.g., Imec, Plexon, Thorlabs. + model_name; % (char) The model name of the device, e.g., Neuropixels 1.0, V-Probe, Bergamo III. + model_number; % (char) The model number (or part/product number) of the device, e.g., PRB_1_4_0480_1, PLX-VP-32-15SE(75)-(260-80)(460-10)-300-(1)CON/32m-V, BERGAMO. + serial_number; % (char) The serial number of the device. end methods @@ -20,9 +23,15 @@ p.StructExpand = false; addParameter(p, 'description',[]); addParameter(p, 'manufacturer',[]); + addParameter(p, 'model_name',[]); + addParameter(p, 'model_number',[]); + addParameter(p, 'serial_number',[]); misc.parseSkipInvalidName(p, varargin); obj.description = p.Results.description; obj.manufacturer = p.Results.manufacturer; + obj.model_name = p.Results.model_name; + obj.model_number = p.Results.model_number; + obj.serial_number = p.Results.serial_number; if strcmp(class(obj), 'types.core.Device') cellStringArguments = convertContainedStringsToChars(varargin(1:2:end)); types.util.checkUnset(obj, unique(cellStringArguments)); @@ -35,6 +44,15 @@ function set.manufacturer(obj, val) obj.manufacturer = obj.validate_manufacturer(val); end + function set.model_name(obj, val) + obj.model_name = obj.validate_model_name(val); + end + function set.model_number(obj, val) + obj.model_number = obj.validate_model_number(val); + end + function set.serial_number(obj, val) + obj.serial_number = obj.validate_serial_number(val); + end %% VALIDATORS function val = validate_description(obj, val) @@ -73,6 +91,60 @@ validshapes = {[1]}; types.util.checkDims(valsz, validshapes); end + function val = validate_model_name(obj, val) + val = types.util.checkDtype('model_name', 'char', val); + if isa(val, 'types.untyped.DataStub') + if 1 == val.ndims + valsz = [val.dims 1]; + else + valsz = val.dims; + end + elseif istable(val) + valsz = [height(val) 1]; + elseif ischar(val) + valsz = [size(val, 1) 1]; + else + valsz = size(val); + end + validshapes = {[1]}; + types.util.checkDims(valsz, validshapes); + end + function val = validate_model_number(obj, val) + val = types.util.checkDtype('model_number', 'char', val); + if isa(val, 'types.untyped.DataStub') + if 1 == val.ndims + valsz = [val.dims 1]; + else + valsz = val.dims; + end + elseif istable(val) + valsz = [height(val) 1]; + elseif ischar(val) + valsz = [size(val, 1) 1]; + else + valsz = size(val); + end + validshapes = {[1]}; + types.util.checkDims(valsz, validshapes); + end + function val = validate_serial_number(obj, val) + val = types.util.checkDtype('serial_number', 'char', val); + if isa(val, 'types.untyped.DataStub') + if 1 == val.ndims + valsz = [val.dims 1]; + else + valsz = val.dims; + end + elseif istable(val) + valsz = [height(val) 1]; + elseif ischar(val) + valsz = [size(val, 1) 1]; + else + valsz = size(val); + end + validshapes = {[1]}; + types.util.checkDims(valsz, validshapes); + end %% EXPORT function refs = export(obj, fid, fullpath, refs) refs = export@types.core.NWBContainer(obj, fid, fullpath, refs); @@ -85,6 +157,15 @@ if ~isempty(obj.manufacturer) io.writeAttribute(fid, [fullpath '/manufacturer'], obj.manufacturer); end + if ~isempty(obj.model_name) + io.writeAttribute(fid, [fullpath '/model_name'], obj.model_name); + end + if ~isempty(obj.model_number) + io.writeAttribute(fid, [fullpath '/model_number'], obj.model_number); + end + if ~isempty(obj.serial_number) + io.writeAttribute(fid, [fullpath '/serial_number'], obj.serial_number); + end end end diff --git a/+types/+core/EventWaveform.m b/+types/+core/EventWaveform.m index 375c758c..3915db7c 100644 --- a/+types/+core/EventWaveform.m +++ b/+types/+core/EventWaveform.m @@ -1,5 +1,5 @@ classdef EventWaveform < types.core.NWBDataInterface & types.untyped.GroupClass -% EVENTWAVEFORM Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition. +% EVENTWAVEFORM DEPRECATED. Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition. % OPTIONAL PROPERTIES diff --git a/+types/+core/ImageMaskSeries.m b/+types/+core/ImageMaskSeries.m index 7514ac8b..ae2c1710 100644 --- a/+types/+core/ImageMaskSeries.m +++ b/+types/+core/ImageMaskSeries.m @@ -1,5 +1,5 @@ classdef ImageMaskSeries < types.core.ImageSeries & types.untyped.GroupClass -% IMAGEMASKSERIES An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed. +% IMAGEMASKSERIES DEPRECATED. An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed. % OPTIONAL PROPERTIES diff --git a/+types/+core/NWBFile.m b/+types/+core/NWBFile.m index 9b6a1417..2b05d58e 100644 --- a/+types/+core/NWBFile.m +++ b/+types/+core/NWBFile.m @@ -50,6 +50,7 @@ general_subject; % (Subject) Information about the animal or person from which the data was measured. general_surgery; % (char) Narrative description about surgery/surgeries, including date(s) and who performed surgery. general_virus; % (char) Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc. + general_was_generated_by; % (char) Name and version of software package(s) used to generate data contained in this NWB File. For each software package or library, include the name of the software as the first value and the version as the second value. intervals; % (TimeIntervals) Optional additional table(s) for describing other experimental time intervals. intervals_epochs; % (TimeIntervals) Divisions in time marking experimental stages or sub-divisions of a single recording session. intervals_invalid_times; % (TimeIntervals) Time intervals that should be removed from analysis. @@ -64,7 +65,7 @@ methods function obj = NWBFile(varargin) % NWBFILE Constructor for NWBFile - varargin = [{'nwb_version' '2.7.0'} varargin]; + varargin = [{'nwb_version' '2.8.0'} varargin]; obj = obj@types.core.NWBContainer(varargin{:}); @@ -107,6 +108,7 @@ addParameter(p, 'general_subject',[]); addParameter(p, 'general_surgery',[]); addParameter(p, 'general_virus',[]); + addParameter(p, 'general_was_generated_by',[]); addParameter(p, 'identifier',[]); addParameter(p, 'intervals',types.untyped.Set()); addParameter(p, 'intervals_epochs',[]); @@ -157,6 +159,7 @@ obj.general_subject = p.Results.general_subject; obj.general_surgery = p.Results.general_surgery; obj.general_virus = p.Results.general_virus; + obj.general_was_generated_by = p.Results.general_was_generated_by; obj.identifier = p.Results.identifier; obj.intervals = p.Results.intervals; obj.intervals_epochs = p.Results.intervals_epochs; @@ -282,6 +285,9 @@ function set.general_virus(obj, val) obj.general_virus = obj.validate_general_virus(val); end + function set.general_was_generated_by(obj, val) + obj.general_was_generated_by = obj.validate_general_was_generated_by(val); + end function set.identifier(obj, val) obj.identifier = obj.validate_identifier(val); end @@ -727,6 +733,24 @@ validshapes = {[1]}; types.util.checkDims(valsz, validshapes); end + function val = validate_general_was_generated_by(obj, val) + val = types.util.checkDtype('general_was_generated_by', 'char', val); + if isa(val, 'types.untyped.DataStub') + if 1 == val.ndims + valsz = [val.dims 1]; + else + valsz = val.dims; + end + elseif istable(val) + valsz = [height(val) 1]; + elseif ischar(val) + valsz = [size(val, 1) 1]; + else + valsz = size(val); + end + validshapes = {[2,Inf]}; + types.util.checkDims(valsz, validshapes); + end function val = validate_identifier(obj, val) val = types.util.checkDtype('identifier', 'char', val); if isa(val, 'types.untyped.DataStub') @@ -1041,6 +1065,14 @@ io.writeDataset(fid, [fullpath '/general/virus'], obj.general_virus); end end + io.writeGroup(fid, [fullpath '/general']); + if ~isempty(obj.general_was_generated_by) + if startsWith(class(obj.general_was_generated_by), 'types.untyped.') + refs = obj.general_was_generated_by.export(fid, [fullpath '/general/was_generated_by'], refs); + elseif ~isempty(obj.general_was_generated_by) + io.writeDataset(fid, [fullpath '/general/was_generated_by'], obj.general_was_generated_by, 'forceArray'); + end + end if startsWith(class(obj.identifier), 'types.untyped.') refs = obj.identifier.export(fid, [fullpath '/identifier'], refs); elseif ~isempty(obj.identifier) diff --git a/+types/+core/SpikeEventSeries.m b/+types/+core/SpikeEventSeries.m index e397634b..95b53c18 100644 --- a/+types/+core/SpikeEventSeries.m +++ b/+types/+core/SpikeEventSeries.m @@ -1,5 +1,5 @@ classdef SpikeEventSeries < types.core.ElectricalSeries & types.untyped.GroupClass -% SPIKEEVENTSERIES Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode). +% SPIKEEVENTSERIES Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode). diff --git a/+types/+core/Units.m b/+types/+core/Units.m index e84b982a..75f2f708 100644 --- a/+types/+core/Units.m +++ b/+types/+core/Units.m @@ -13,9 +13,9 @@ spike_times_index; % (VectorIndex) Index into the spike_times dataset. waveform_mean; % (VectorData) Spike waveform mean for each spike unit. waveform_sd; % (VectorData) Spike waveform standard deviation for each spike unit. - waveforms; % (VectorData) Individual waveforms for each spike on each electrode. This is a doubly indexed column. The 'waveforms_index' column indexes which waveforms in this column belong to the same spike event for a given unit, where each waveform was recorded from a different electrode. The 'waveforms_index_index' column indexes the 'waveforms_index' column to indicate which spike events belong to a given unit. For example, if the 'waveforms_index_index' column has values [2, 5, 6], then the first 2 elements of the 'waveforms_index' column correspond to the 2 spike events of the first unit, the next 3 elements of the 'waveforms_index' column correspond to the 3 spike events of the second unit, and the next 1 element of the 'waveforms_index' column corresponds to the 1 spike event of the third unit. If the 'waveforms_index' column has values [3, 6, 8, 10, 12, 13], then the first 3 elements of the 'waveforms' column contain the 3 spike waveforms that were recorded from 3 different electrodes for the first spike time of the first unit. See https://nwb-schema.readthedocs.io/en/stable/format_description.html#doubly-ragged-arrays for a graphical representation of this example. When there is only one electrode for each unit (i.e., each spike time is associated with a single waveform), then the 'waveforms_index' column will have values 1, 2, ..., N, where N is the number of spike events. The number of electrodes for each spike event should be the same within a given unit. The 'electrodes' column should be used to indicate which electrodes are associated with each unit, and the order of the waveforms within a given unit x spike event should be in the same order as the electrodes referenced in the 'electrodes' column of this table. The number of samples for each waveform must be the same. - waveforms_index; % (VectorIndex) Index into the waveforms dataset. One value for every spike event. See 'waveforms' for more detail. - waveforms_index_index; % (VectorIndex) Index into the waveforms_index dataset. One value for every unit (row in the table). See 'waveforms' for more detail. + waveforms; % (VectorData) Individual waveforms for each spike on each electrode. This is a doubly indexed column. The 'waveforms_index' column indexes which waveforms in this column belong to the same spike event for a given unit, where each waveform was recorded from a different electrode. The 'waveforms_index_index' column indexes the 'waveforms_index' column to indicate which spike events belong to a given unit. For example, if the 'waveforms_index_index' column has values [2, 5, 6], then the first 2 elements of the 'waveforms_index' column correspond to the 2 spike events of the first unit, the next 3 elements of the 'waveforms_index' column correspond to the 3 spike events of the second unit, and the next 1 element of the 'waveforms_index' column corresponds to the 1 spike event of the third unit. If the 'waveforms_index' column has values [3, 6, 8, 10, 12, 13], then the first 3 elements of the 'waveforms' column contain the 3 spike waveforms that were recorded from 3 different electrodes for the first spike time of the first unit. See https://nwb-schema.readthedocs.io/en/stable/format_description.html#doubly-ragged-arrays for a graphical representation of this example. When there is only one electrode for each unit (i.e., each spike time is associated with a single waveform), then the 'waveforms_index' column will have values 1, 2, ..., N, where N is the number of spike events. The number of electrodes for each spike event should be the same within a given unit. The 'electrodes' column should be used to indicate which electrodes are associated with each unit, and the order of the waveforms within a given unit x spike event should be the same as the order of the electrodes referenced in the 'electrodes' column of this table. The number of samples for each waveform must be the same. + waveforms_index; % (VectorIndex) Index into the 'waveforms' dataset. One value for every spike event. See 'waveforms' for more detail. + waveforms_index_index; % (VectorIndex) Index into the 'waveforms_index' dataset. One value for every unit (row in the table). See 'waveforms' for more detail. end methods diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index ad7aee46..89877a0f 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -28,6 +28,7 @@ jobs: run: | python -m pip install -U pip pip install -r +tests/requirements.txt + python -m pip list echo "HDF5_PLUGIN_PATH=$(python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)")" >> "$GITHUB_ENV" echo $( python -m pip show nwbinspector | grep ^Location: | awk '{print $2}' ) - name: Install MATLAB diff --git a/nwb-schema/2.8.0/core/nwb.base.yaml b/nwb-schema/2.8.0/core/nwb.base.yaml new file mode 100644 index 00000000..859e9045 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.base.yaml @@ -0,0 +1,264 @@ +datasets: +- neurodata_type_def: NWBData + neurodata_type_inc: Data + doc: An abstract data type for a dataset. + +- neurodata_type_def: TimeSeriesReferenceVectorData + neurodata_type_inc: VectorData + default_name: timeseries + dtype: + - name: idx_start + dtype: int32 + doc: Start index into the TimeSeries 'data' and 'timestamp' datasets of the referenced + TimeSeries. The first dimension of those arrays is always time. + - name: count + dtype: int32 + doc: Number of data samples available in this time series, during this epoch + - name: timeseries + dtype: + target_type: TimeSeries + reftype: object + doc: The TimeSeries that this index applies to + doc: Column storing references to a TimeSeries (rows). For each TimeSeries this + VectorData column stores the start_index and count to indicate the range in time + to be selected as well as an object reference to the TimeSeries. + +- neurodata_type_def: Image + neurodata_type_inc: NWBData + dtype: numeric + dims: + - - x + - y + - - x + - y + - r, g, b + - - x + - y + - r, g, b, a + shape: + - - null + - null + - - null + - null + - 3 + - - null + - null + - 4 + doc: An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the + third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or + (x, y, (r, g, b, a)). + attributes: + - name: resolution + dtype: float32 + doc: Pixel resolution of the image, in pixels per centimeter. + required: false + - name: description + dtype: text + doc: Description of the image. + required: false + +- neurodata_type_def: ImageReferences + neurodata_type_inc: NWBData + dtype: + target_type: Image + reftype: object + dims: + - num_images + shape: + - null + doc: Ordered dataset of references to Image objects. + +groups: +- neurodata_type_def: NWBContainer + neurodata_type_inc: Container + doc: An abstract data type for a generic container storing collections of data and + metadata. Base type for all data and metadata containers. + +- neurodata_type_def: NWBDataInterface + neurodata_type_inc: NWBContainer + doc: An abstract data type for a generic container storing collections of data, + as opposed to metadata. + +- neurodata_type_def: TimeSeries + neurodata_type_inc: NWBDataInterface + doc: General purpose time series. + attributes: + - name: description + dtype: text + default_value: no description + doc: Description of the time series. + required: false + - name: comments + dtype: text + default_value: no comments + doc: Human-readable comments about the TimeSeries. This second descriptive field + can be used to store additional information, or descriptive information if the + primary description field is populated with a computer-readable string. + required: false + datasets: + - name: data + dims: + - - num_times + - - num_times + - num_DIM2 + - - num_times + - num_DIM2 + - num_DIM3 + - - num_times + - num_DIM2 + - num_DIM3 + - num_DIM4 + shape: + - - null + - - null + - null + - - null + - null + - null + - - null + - null + - null + - null + doc: Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension + should always represent time. This can also be used to store binary data + (e.g., image frames). This can also be a link to data stored in an external file. + attributes: + - name: conversion + dtype: float32 + default_value: 1.0 + doc: Scalar to multiply each element in data to convert it to the specified 'unit'. + If the data are stored in acquisition system units or other units + that require a conversion to be interpretable, multiply the data by 'conversion' + to convert the data to the specified 'unit'. e.g. if the data acquisition system + stores values in this object as signed 16-bit integers (int16 range + -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data + acquisition system gain is 8000X, then the 'conversion' multiplier to get from + raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9. + required: false + - name: offset + dtype: float32 + default_value: 0.0 + doc: Scalar to add to the data after scaling by 'conversion' to finalize its coercion + to the specified 'unit'. Two common examples of this include (a) data stored in an + unsigned type that requires a shift after scaling to re-center the data, + and (b) specialized recording devices that naturally cause a scalar offset with + respect to the true units. + required: false + - name: resolution + dtype: float32 + default_value: -1.0 + doc: Smallest meaningful difference between values in data, stored in the specified + by unit, e.g., the change in value of the least significant bit, or a larger + number if signal noise is known to be present. If unknown, use -1.0. + required: false + - name: unit + dtype: text + doc: Base unit of measurement for working with the data. Actual stored values are + not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + - name: continuity + dtype: text + doc: Optionally describe the continuity of the data. Can be "continuous", "instantaneous", or + "step". For example, a voltage trace would be "continuous", because samples + are recorded from a continuous process. An array of lick times would be "instantaneous", + because the data represents distinct moments in time. Times of image presentations would be + "step" because the picture remains the same until the next timepoint. This field is optional, + but is useful in providing information about the underlying data. It may inform the way this + data is interpreted, the way it is visualized, and what analysis methods are applicable. + required: false + - name: starting_time + dtype: float64 + doc: Timestamp of the first sample in seconds. When timestamps are uniformly + spaced, the timestamp of the first sample can be specified and all subsequent + ones calculated from the sampling rate attribute. + quantity: '?' + attributes: + - name: rate + dtype: float32 + doc: Sampling rate, in Hz. + - name: unit + dtype: text + value: seconds + doc: Unit of measurement for time, which is fixed to 'seconds'. + - name: timestamps + dtype: float64 + dims: + - num_times + shape: + - null + doc: Timestamps for samples stored in data, in seconds, relative to the + common experiment master-clock stored in NWBFile.timestamps_reference_time. + quantity: '?' + attributes: + - name: interval + dtype: int32 + value: 1 + doc: Value is '1' + - name: unit + dtype: text + value: seconds + doc: Unit of measurement for timestamps, which is fixed to 'seconds'. + - name: control + dtype: uint8 + dims: + - num_times + shape: + - null + doc: Numerical labels that apply to each time point in data for the purpose of + querying and slicing data by these values. If present, the length of this + array should be the same size as the first dimension of data. + quantity: '?' + - name: control_description + dtype: text + dims: + - num_control_values + shape: + - null + doc: Description of each control value. Must be present if control is present. + If present, control_description[0] should describe time points where control == 0. + quantity: '?' + groups: + - name: sync + doc: Lab-specific time and sync information as provided directly from hardware + devices and that is necessary for aligning all acquired time information to + a common timebase. The timestamp array stores time in the common timebase. + This group will usually only be populated in TimeSeries that are + stored external to the NWB file, in files storing raw data. Once timestamp + data is calculated, the contents of 'sync' are mostly for archival purposes. + quantity: '?' + +- neurodata_type_def: ProcessingModule + neurodata_type_inc: NWBContainer + doc: A collection of processed data. + attributes: + - name: description + dtype: text + doc: Description of this collection of processed data. + groups: + - neurodata_type_inc: NWBDataInterface + doc: Data objects stored in this collection. + quantity: '*' + - neurodata_type_inc: DynamicTable + doc: Tables stored in this collection. + quantity: '*' + +- neurodata_type_def: Images + neurodata_type_inc: NWBDataInterface + default_name: Images + doc: A collection of images with an optional way to specify the order of the images + using the "order_of_images" dataset. An order must be specified if the images are + referenced by index, e.g., from an IndexSeries. + attributes: + - name: description + dtype: text + doc: Description of this collection of images. + datasets: + - neurodata_type_inc: Image + doc: Images stored in this collection. + quantity: '+' + - name: order_of_images + neurodata_type_inc: ImageReferences + doc: Ordered dataset of references to Image objects stored in the parent group. + Each Image object in the Images group should be stored once and only once, so + the dataset should have the same length as the number of images. + quantity: '?' diff --git a/nwb-schema/2.8.0/core/nwb.behavior.yaml b/nwb-schema/2.8.0/core/nwb.behavior.yaml new file mode 100644 index 00000000..1a951b2f --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.behavior.yaml @@ -0,0 +1,124 @@ +groups: +- neurodata_type_def: SpatialSeries + neurodata_type_inc: TimeSeries + doc: "Direction, e.g., of gaze or travel, or position. The TimeSeries::data field\ + \ is a 2D array storing position or direction relative to some reference frame.\ + \ Array structure: [num measurements] [num dimensions]. Each SpatialSeries has\ + \ a text dataset reference_frame that indicates the zero-position, or the zero-axes\ + \ for direction. For example, if representing gaze direction, 'straight-ahead'\ + \ might be a specific pixel on the monitor, or some other point in space. For\ + \ position data, the 0,0 point might be the top-left corner of an enclosure, as\ + \ viewed from the tracking camera. The unit of data will indicate how to interpret\ + \ SpatialSeries values." + datasets: + - name: data + dtype: numeric + dims: + - - num_times + - - num_times + - x + - - num_times + - x,y + - - num_times + - x,y,z + shape: + - - null + - - null + - 1 + - - null + - 2 + - - null + - 3 + doc: 1-D or 2-D array storing position or direction relative to some reference frame. + attributes: + - name: unit + dtype: text + default_value: meters + doc: Base unit of measurement for working with the data. The default value + is 'meters'. Actual stored values are not necessarily stored in these units. + To access the data in these units, multiply 'data' by 'conversion' and add 'offset'. + required: false + - name: reference_frame + dtype: text + doc: Description defining what exactly 'straight-ahead' means. + quantity: '?' + +- neurodata_type_def: BehavioralEpochs + neurodata_type_inc: NWBDataInterface + default_name: BehavioralEpochs + doc: TimeSeries for storing behavioral epochs. The objective of this and the other + two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is + to provide generic hooks for software tools/scripts. This allows a tool/script + to take the output one specific interface (e.g., UnitTimes) and plot that data + relative to another data modality (e.g., behavioral events) without having to + define all possible modalities in advance. Declaring one of these interfaces means + that one or more TimeSeries of the specified type is published. These TimeSeries + should reside in a group having the same name as the interface. For example, if + a BehavioralTimeSeries interface is declared, the module will have one or more + TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs + should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries + is for continuous data. + groups: + - neurodata_type_inc: IntervalSeries + doc: IntervalSeries object containing start and stop times of epochs. + quantity: '*' + +- neurodata_type_def: BehavioralEvents + neurodata_type_inc: NWBDataInterface + default_name: BehavioralEvents + doc: TimeSeries for storing behavioral events. See description of BehavioralEpochs + for more details. + groups: + - neurodata_type_inc: TimeSeries + doc: TimeSeries object containing behavioral events. + quantity: '*' + +- neurodata_type_def: BehavioralTimeSeries + neurodata_type_inc: NWBDataInterface + default_name: BehavioralTimeSeries + doc: TimeSeries for storing Behavoioral time series data. See description of BehavioralEpochs + for more details. + groups: + - neurodata_type_inc: TimeSeries + doc: TimeSeries object containing continuous behavioral data. + quantity: '*' + +- neurodata_type_def: PupilTracking + neurodata_type_inc: NWBDataInterface + default_name: PupilTracking + doc: Eye-tracking data, representing pupil size. + groups: + - neurodata_type_inc: TimeSeries + doc: TimeSeries object containing time series data on pupil size. + quantity: '+' + +- neurodata_type_def: EyeTracking + neurodata_type_inc: NWBDataInterface + default_name: EyeTracking + doc: Eye-tracking data, representing direction of gaze. + groups: + - neurodata_type_inc: SpatialSeries + doc: SpatialSeries object containing data measuring direction of gaze. + quantity: '*' + +- neurodata_type_def: CompassDirection + neurodata_type_inc: NWBDataInterface + default_name: CompassDirection + doc: With a CompassDirection interface, a module publishes a SpatialSeries object + representing a floating point value for theta. The SpatialSeries::reference_frame + field should indicate what direction corresponds to 0 and which is the direction + of rotation (this should be clockwise). The si_unit for the SpatialSeries should + be radians or degrees. + groups: + - neurodata_type_inc: SpatialSeries + doc: SpatialSeries object containing direction of gaze travel. + quantity: '*' + +- neurodata_type_def: Position + neurodata_type_inc: NWBDataInterface + default_name: Position + doc: Position data, whether along the x, x/y or x/y/z axis. + groups: + - neurodata_type_inc: SpatialSeries + doc: SpatialSeries object containing position data. + quantity: '+' diff --git a/nwb-schema/2.8.0/core/nwb.device.yaml b/nwb-schema/2.8.0/core/nwb.device.yaml new file mode 100644 index 00000000..5f1a993d --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.device.yaml @@ -0,0 +1,26 @@ +groups: +- neurodata_type_def: Device + neurodata_type_inc: NWBContainer + doc: Metadata about a data acquisition device, e.g., recording system, electrode, microscope. + attributes: + - name: description + dtype: text + doc: Description of the device as free-form text. If there is any software/firmware associated + with the device, the names and versions of those can be added to NWBFile.was_generated_by. + required: false + - name: manufacturer + dtype: text + doc: The name of the manufacturer of the device, e.g., Imec, Plexon, Thorlabs. + required: false + - name: model_number + dtype: text + doc: The model number (or part/product number) of the device, e.g., PRB_1_4_0480_1, PLX-VP-32-15SE(75)-(260-80)(460-10)-300-(1)CON/32m-V, BERGAMO. + required: false + - name: model_name + dtype: text + doc: The model name of the device, e.g., Neuropixels 1.0, V-Probe, Bergamo III. + required: false + - name: serial_number + dtype: text + doc: The serial number of the device. + required: false diff --git a/nwb-schema/2.8.0/core/nwb.ecephys.yaml b/nwb-schema/2.8.0/core/nwb.ecephys.yaml new file mode 100644 index 00000000..69a66639 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.ecephys.yaml @@ -0,0 +1,332 @@ +groups: +- neurodata_type_def: ElectricalSeries + neurodata_type_inc: TimeSeries + doc: A time series of acquired voltage data from extracellular recordings. + The data field is an int or float array storing data in volts. The first + dimension should always represent time. The second dimension, if present, + should represent channels. + attributes: + - name: filtering + dtype: text + doc: Filtering applied to all channels of the data. For example, if this ElectricalSeries represents + high-pass-filtered data (also known as AP Band), then this value could be "High-pass 4-pole Bessel filter + at 500 Hz". If this ElectricalSeries represents low-pass-filtered LFP data and the type of filter is unknown, + then this value could be "Low-pass filter at 300 Hz". If a non-standard filter type is used, provide as much + detail about the filter properties as possible. + required: false + datasets: + - name: data + dtype: numeric + dims: + - - num_times + - - num_times + - num_channels + - - num_times + - num_channels + - num_samples + shape: + - - null + - - null + - null + - - null + - null + - null + doc: Recorded voltage data. + attributes: + - name: unit + dtype: text + value: volts + doc: Base unit of measurement for working with the data. This value is fixed to + 'volts'. Actual stored values are not necessarily stored in these units. To + access the data in these units, multiply 'data' by 'conversion', followed by + 'channel_conversion' (if present), and then add 'offset'. + - name: electrodes + neurodata_type_inc: DynamicTableRegion + doc: DynamicTableRegion pointer to the electrodes that this time series was generated from. + - name: channel_conversion + dtype: float32 + dims: + - num_channels + shape: + - null + doc: Channel-specific conversion factor. Multiply the data in the 'data' dataset by these + values along the channel axis (as indicated by axis attribute) AND by the global + conversion factor in the 'conversion' attribute of 'data' to get the data values in + Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This + approach allows for both global and per-channel data conversion factors needed + to support the storage of electrical recordings as native values generated by data + acquisition systems. If this dataset is not present, then there is no channel-specific + conversion factor, i.e. it is 1 for all channels. + quantity: '?' + attributes: + - name: axis + dtype: int32 + value: 1 + doc: The zero-indexed axis of the 'data' dataset that the channel-specific conversion + factor corresponds to. This value is fixed to 1. + +- neurodata_type_def: SpikeEventSeries + neurodata_type_inc: ElectricalSeries + doc: "Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This + may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description + field should describe how events were detected. All events span the same recording + channels and store + snapshots of equal duration. TimeSeries::data array structure: [num events] + [num channels] [num samples] (or [num events] [num samples] for single electrode)." + datasets: + - name: data + dtype: numeric + dims: + - - num_events + - num_samples + - - num_events + - num_channels + - num_samples + shape: + - - null + - null + - - null + - null + - null + doc: Spike waveforms. + attributes: + - name: unit + dtype: text + value: volts + doc: Unit of measurement for waveforms, which is fixed to 'volts'. + - name: timestamps + dtype: float64 + dims: + - num_times + shape: + - null + doc: Timestamps for samples stored in data, in seconds, relative to the + common experiment master-clock stored in NWBFile.timestamps_reference_time. + Timestamps are required for the events. Unlike for TimeSeries, timestamps are + required for SpikeEventSeries and are thus re-specified here. + attributes: + - name: interval + dtype: int32 + value: 1 + doc: Value is '1' + - name: unit + dtype: text + value: seconds + doc: Unit of measurement for timestamps, which is fixed to 'seconds'. + +- neurodata_type_def: FeatureExtraction + neurodata_type_inc: NWBDataInterface + default_name: FeatureExtraction + doc: Features, such as PC1 and PC2, that are extracted from signals stored in a + SpikeEventSeries or other source. + datasets: + - name: description + dtype: text + dims: + - num_features + shape: + - null + doc: Description of features (eg, ''PC1'') for each of the extracted features. + - name: features + dtype: float32 + dims: + - num_events + - num_channels + - num_features + shape: + - null + - null + - null + doc: Multi-dimensional array of features extracted from each event. + - name: times + dtype: float64 + dims: + - num_events + shape: + - null + doc: Times of events that features correspond to (can be a link). + - name: electrodes + neurodata_type_inc: DynamicTableRegion + doc: DynamicTableRegion pointer to the electrodes that this time series was generated from. + +- neurodata_type_def: EventDetection + neurodata_type_inc: NWBDataInterface + default_name: EventDetection + doc: Detected spike events from voltage trace(s). + datasets: + - name: detection_method + dtype: text + doc: Description of how events were detected, such as voltage threshold, or dV/dT + threshold, as well as relevant values. + - name: source_idx + dtype: int32 + dims: + - num_events + shape: + - null + doc: Indices (zero-based) into source ElectricalSeries::data array corresponding + to time of event. ''description'' should define what is meant by time of + event (e.g., .25 ms before action potential peak, zero-crossing time, etc). + The index points to each event from the raw data. + - name: times + dtype: float64 + dims: + - num_events + shape: + - null + doc: Timestamps of events, in seconds. + attributes: + - name: unit + dtype: text + value: seconds + doc: Unit of measurement for event times, which is fixed to 'seconds'. + links: + - name: source_electricalseries + target_type: ElectricalSeries + doc: Link to the ElectricalSeries that this data was calculated from. Metadata + about electrodes and their position can be read from that ElectricalSeries so + it's not necessary to include that information here. + +- neurodata_type_def: EventWaveform + neurodata_type_inc: NWBDataInterface + default_name: EventWaveform + doc: DEPRECATED. Represents either the waveforms of detected events, as extracted from a raw + data trace in /acquisition, or the event waveforms that were stored during experiment + acquisition. + groups: + - neurodata_type_inc: SpikeEventSeries + doc: SpikeEventSeries object(s) containing detected spike event waveforms. + quantity: '*' + +- neurodata_type_def: FilteredEphys + neurodata_type_inc: NWBDataInterface + default_name: FilteredEphys + doc: Electrophysiology data from one or more channels that has been subjected to filtering. + Examples of filtered data include Theta and Gamma (LFP has its own interface). + FilteredEphys modules publish an ElectricalSeries for each filtered channel or + set of channels. The name of each ElectricalSeries is arbitrary but should be + informative. The source of the filtered data, whether this is from analysis of + another time series or as acquired by hardware, should be noted in each's TimeSeries::description + field. There is no assumed 1::1 correspondence between filtered ephys signals + and electrodes, as a single signal can apply to many nearby electrodes, and one + electrode may have different filtered (e.g., theta and/or gamma) signals represented. + Filter properties should be noted in the ElectricalSeries 'filtering' attribute. + groups: + - neurodata_type_inc: ElectricalSeries + doc: ElectricalSeries object(s) containing filtered electrophysiology data. + quantity: '+' + +- neurodata_type_def: LFP + neurodata_type_inc: NWBDataInterface + default_name: LFP + doc: LFP data from one or more channels. The electrode map in each published ElectricalSeries + will identify which channels are providing LFP data. Filter properties should + be noted in the ElectricalSeries 'filtering' attribute. + groups: + - neurodata_type_inc: ElectricalSeries + doc: ElectricalSeries object(s) containing LFP data for one or more channels. + quantity: '+' + +- neurodata_type_def: ElectrodeGroup + neurodata_type_inc: NWBContainer + doc: A physical grouping of electrodes, e.g. a shank of an array. + attributes: + - name: description + dtype: text + doc: Description of this electrode group. + - name: location + dtype: text + doc: Location of electrode group. Specify the area, layer, comments on estimation + of area/layer, etc. Use standard atlas names for anatomical regions when possible. + datasets: + - name: position + dtype: + - name: x + dtype: float32 + doc: x coordinate + - name: y + dtype: float32 + doc: y coordinate + - name: z + dtype: float32 + doc: z coordinate + doc: stereotaxic or common framework coordinates + quantity: '?' + links: + - name: device + target_type: Device + doc: Link to the device that was used to record from this electrode group. + +# The types below have been deprecated +- neurodata_type_def: ClusterWaveforms + neurodata_type_inc: NWBDataInterface + default_name: ClusterWaveforms + doc: DEPRECATED The mean waveform shape, including standard deviation, of the different + clusters. Ideally, the waveform analysis should be performed on data that is only + high-pass filtered. This is a separate module because it is expected to require + updating. For example, IMEC probes may require different storage requirements + to store/display mean waveforms, requiring a new interface or an extension of + this one. + datasets: + - name: waveform_filtering + dtype: text + doc: Filtering applied to data before generating mean/sd + - name: waveform_mean + dtype: float32 + dims: + - num_clusters + - num_samples + shape: + - null + - null + doc: The mean waveform for each cluster, using the same indices for each wave + as cluster numbers in the associated Clustering module (i.e, cluster 3 is in + array slot [3]). Waveforms corresponding to gaps in cluster sequence should + be empty (e.g., zero- filled) + - name: waveform_sd + dtype: float32 + dims: + - num_clusters + - num_samples + shape: + - null + - null + doc: Stdev of waveforms for each cluster, using the same indices as in mean + links: + - name: clustering_interface + target_type: Clustering + doc: Link to Clustering interface that was the source of the clustered data + +- neurodata_type_def: Clustering + neurodata_type_inc: NWBDataInterface + default_name: Clustering + doc: DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., + klustakwik) or as a result of manual sorting. + datasets: + - name: description + dtype: text + doc: Description of clusters or clustering, (e.g. cluster 0 is noise, clusters + curated using Klusters, etc) + - name: num + dtype: int32 + dims: + - num_events + shape: + - null + doc: Cluster number of each event + - name: peak_over_rms + dtype: float32 + dims: + - num_clusters + shape: + - null + doc: Maximum ratio of waveform peak to RMS on any channel in the cluster (provides + a basic clustering metric). + - name: times + dtype: float64 + dims: + - num_events + shape: + - null + doc: Times of clustered events, in seconds. This may be a link to times field + in associated FeatureExtraction module. diff --git a/nwb-schema/2.8.0/core/nwb.epoch.yaml b/nwb-schema/2.8.0/core/nwb.epoch.yaml new file mode 100644 index 00000000..a4c06a90 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.epoch.yaml @@ -0,0 +1,31 @@ +groups: +- neurodata_type_def: TimeIntervals + neurodata_type_inc: DynamicTable + doc: A container for aggregating epoch data and the TimeSeries that each epoch applies + to. + datasets: + - name: start_time + neurodata_type_inc: VectorData + dtype: float32 + doc: Start time of epoch, in seconds. + - name: stop_time + neurodata_type_inc: VectorData + dtype: float32 + doc: Stop time of epoch, in seconds. + - name: tags + neurodata_type_inc: VectorData + dtype: text + doc: User-defined tags that identify or categorize events. + quantity: '?' + - name: tags_index + neurodata_type_inc: VectorIndex + doc: Index for tags. + quantity: '?' + - name: timeseries + neurodata_type_inc: TimeSeriesReferenceVectorData + doc: An index into a TimeSeries object. + quantity: '?' + - name: timeseries_index + neurodata_type_inc: VectorIndex + doc: Index for timeseries. + quantity: '?' diff --git a/nwb-schema/2.8.0/core/nwb.file.yaml b/nwb-schema/2.8.0/core/nwb.file.yaml new file mode 100644 index 00000000..e9d146e7 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.file.yaml @@ -0,0 +1,524 @@ +groups: +- neurodata_type_def: NWBFile + neurodata_type_inc: NWBContainer + name: root + doc: An NWB file storing cellular-based neurophysiology data from a single + experimental session. + attributes: + - name: nwb_version + dtype: text + value: "2.8.0" + doc: File version string. Use semantic versioning, e.g. 1.2.1. This will be the + name of the format with trailing major, minor and patch numbers. + datasets: + - name: file_create_date + dtype: isodatetime + dims: + - num_modifications + shape: + - null + doc: 'A record of the date the file was created and of subsequent modifications. + The date is stored in UTC with local timezone offset as ISO 8601 + extended formatted strings: 2018-09-28T14:43:54.123+02:00. Dates stored in + UTC end in "Z" with no timezone offset. Date accuracy is up to milliseconds. + The file can be created after the experiment was run, so this may differ from + the experiment start time. Each modification to the nwb file adds a new entry + to the array.' + - name: identifier + dtype: text + doc: A unique text identifier for the file. For example, concatenated lab name, + file creation date/time and experimentalist, or a hash of these and/or other + values. The goal is that the string should be unique to all other files. + - name: session_description + dtype: text + doc: A description of the experimental session and data in the file. + - name: session_start_time + dtype: isodatetime + doc: 'Date and time of the experiment/session start. The date is stored + in UTC with local timezone offset as ISO 8601 extended formatted string: + 2018-09-28T14:43:54.123+02:00. + Dates stored in UTC end in "Z" with no timezone offset. Date accuracy is + up to milliseconds.' + - name: timestamps_reference_time + dtype: isodatetime + doc: 'Date and time corresponding to time zero of all timestamps. The + date is stored in UTC with local timezone offset as ISO 8601 extended formatted + string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in "Z" with + no timezone offset. Date accuracy is up to milliseconds. All times stored + in the file use this time as reference (i.e., time zero).' + groups: + - name: acquisition + doc: Data streams recorded from the system, including ephys, ophys, tracking, + etc. This group should be read-only after the experiment is completed and + timestamps are corrected to a common timebase. The data stored here may be links + to raw data stored in external NWB files. This will allow keeping bulky raw + data out of the file while preserving the option of keeping some/all in the + file. Acquired data includes tracking and experimental data streams + (i.e., everything measured from the system). If bulky data is stored in the /acquisition + group, the data can exist in a separate NWB file that is linked to by the file + being used for processing and analysis. + groups: + - neurodata_type_inc: NWBDataInterface + doc: Acquired, raw data. + quantity: '*' + - neurodata_type_inc: DynamicTable + doc: Tabular data that is relevant to acquisition + quantity: '*' + - name: analysis + doc: Lab-specific and custom scientific analysis of data. There is no defined + format for the content of this group - the format is up to the individual user/lab. + To facilitate sharing analysis data between labs, the contents here + should be stored in standard types (e.g., neurodata_types) and appropriately documented. + The file can store lab-specific and custom data analysis without + restriction on its form or schema, reducing data formatting restrictions on + end users. Such data should be placed in the analysis group. The analysis data + should be documented so that it could be shared with other labs. + groups: + - neurodata_type_inc: NWBContainer + doc: Custom analysis results. + quantity: '*' + - neurodata_type_inc: DynamicTable + doc: Tabular data that is relevant to data stored in analysis + quantity: '*' + - name: scratch + doc: 'A place to store one-off analysis results. Data placed here is not intended for + sharing. By placing data here, users acknowledge that there is no guarantee that + their data meets any standard.' + quantity: '?' + groups: + - neurodata_type_inc: NWBContainer + doc: Any one-off containers + quantity: '*' + - neurodata_type_inc: DynamicTable + doc: Any one-off tables + quantity: '*' + datasets: + - neurodata_type_inc: ScratchData + doc: Any one-off datasets + quantity: '*' + - name: processing + doc: "The home for ProcessingModules. These modules perform intermediate analysis\ + \ of data that is necessary to perform before scientific analysis. Examples\ + \ include spike clustering, extracting position from tracking data, stitching\ + \ together image slices. ProcessingModules can be large\ + \ and express many data sets from relatively complex analysis (e.g., spike detection\ + \ and clustering) or small, representing extraction of position information\ + \ from tracking video, or even binary lick/no-lick decisions. Common software\ + \ tools (e.g., klustakwik, MClust) are expected to read/write data here. \ + \ 'Processing' refers to intermediate analysis of the acquired data to make\ + \ it more amenable to scientific analysis." + groups: + - neurodata_type_inc: ProcessingModule + doc: Intermediate analysis of acquired data. + quantity: '*' + - name: stimulus + doc: 'Data pushed into the system (eg, video stimulus, sound, voltage, etc) and + secondary representations of that data (eg, measurements of something used as + a stimulus). This group should be made read-only after experiment complete and timestamps + are corrected to common timebase. Stores both presented stimuli and stimulus + templates, the latter in case the same stimulus is presented multiple times, + or is pulled from an external stimulus library. Stimuli are here + defined as any signal that is pushed into the system as part of the experiment + (eg, sound, video, voltage, etc). Many different experiments can use the same + stimuli, and stimuli can be re-used during an experiment. The stimulus group + is organized so that one version of template stimuli can be stored and these + be used multiple times. These templates can exist in the present file or can + be linked to a remote library file.' + groups: + - name: presentation + doc: Stimuli presented during the experiment. + groups: + - neurodata_type_inc: TimeSeries + doc: TimeSeries objects containing data of presented stimuli. + quantity: '*' + - neurodata_type_inc: NWBDataInterface + doc: 'Generic NWB data interfaces, usually from an extension, + containing data of presented stimuli.' + quantity: '*' + - neurodata_type_inc: DynamicTable + doc: DynamicTable objects containing data of presented stimuli. + quantity: '*' + # even though TimeSeries is a child type of NWBDataInterface, we do not remove TimeSeries + # in order to maintain backwards compatibility in the APIs that + # use the neurodata_type_inc from the schema to set the variable name + - name: templates + doc: 'Template stimuli. Timestamps in templates are based on stimulus + design and are relative to the beginning of the stimulus. When templates are + used, the stimulus instances must convert presentation times to the experiment`s + time reference frame.' + groups: + - neurodata_type_inc: TimeSeries + doc: TimeSeries objects containing template data of presented stimuli. + quantity: '*' + - neurodata_type_inc: Images + doc: Images objects containing images of presented stimuli. + quantity: '*' + - name: general + doc: "Experimental metadata, including protocol, notes and description of hardware\ + \ device(s). The metadata stored in this section should be used to\ + \ describe the experiment. Metadata necessary for interpreting the data is stored\ + \ with the data. General experimental metadata, including animal\ + \ strain, experimental protocols, experimenter, devices, etc, are stored under\ + \ 'general'. Core metadata (e.g., that required to interpret data fields) is\ + \ stored with the data itself, and implicitly defined by the file specification\ + \ (e.g., time is in seconds). The strategy used here for storing non-core metadata\ + \ is to use free-form text fields, such as would appear in sentences or paragraphs\ + \ from a Methods section. Metadata fields are text to enable them to be more\ + \ general, for example to represent ranges instead of numerical values. Machine-readable\ + \ metadata is stored as attributes to these free-form datasets. All entries\ + \ in the below table are to be included when data is present. Unused groups\ + \ (e.g., intracellular_ephys in an optophysiology experiment) should not be\ + \ created unless there is data to store within them." + datasets: + - name: data_collection + dtype: text + doc: Notes about data collection and analysis. + quantity: '?' + - name: experiment_description + dtype: text + doc: General description of the experiment. + quantity: '?' + - name: experimenter + dtype: text + doc: Name of person(s) who performed the experiment. Can also specify roles + of different people involved. + quantity: '?' + dims: + - num_experimenters + shape: + - null + - name: institution + dtype: text + doc: Institution(s) where experiment was performed. + quantity: '?' + - name: keywords + dtype: text + dims: + - num_keywords + shape: + - null + doc: Terms to search over. + quantity: '?' + - name: lab + dtype: text + doc: Laboratory where experiment was performed. + quantity: '?' + - name: notes + dtype: text + doc: Notes about the experiment. + quantity: '?' + - name: pharmacology + dtype: text + doc: Description of drugs used, including how and when they were administered. + Anesthesia(s), painkiller(s), etc., plus dosage, concentration, etc. + quantity: '?' + - name: protocol + dtype: text + doc: Experimental protocol, if applicable. e.g., include IACUC protocol number. + quantity: '?' + - name: related_publications + dtype: text + doc: Publication information. PMID, DOI, URL, etc. + dims: + - num_publications + shape: + - null + quantity: '?' + - name: session_id + dtype: text + doc: Lab-specific ID for the session. + quantity: '?' + - name: slices + dtype: text + doc: Description of slices, including information about preparation thickness, + orientation, temperature, and bath solution. + quantity: '?' + - name: source_script + dtype: text + doc: Script file or link to public source code used to create this NWB file. + quantity: '?' + attributes: + - name: file_name + dtype: text + doc: Name of script file. + - name: was_generated_by + dtype: text + doc: Name and version of software package(s) used to generate data contained in + this NWB File. For each software package or library, include the name of the + software as the first value and the version as the second value. + dims: + - num_sources + - name, version + shape: + - null + - 2 + quantity: '?' + - name: stimulus + dtype: text + doc: Notes about stimuli, such as how and where they were presented. + quantity: '?' + - name: surgery + dtype: text + doc: Narrative description about surgery/surgeries, including date(s) and who + performed surgery. + quantity: '?' + - name: virus + dtype: text + doc: Information about virus(es) used in experiments, including virus ID, source, + date made, injection location, volume, etc. + quantity: '?' + groups: + - neurodata_type_inc: LabMetaData + doc: Place-holder than can be extended so that lab-specific meta-data can be + placed in /general. + quantity: '*' + - name: devices + doc: Description of hardware devices used during experiment, e.g., monitors, + ADC boards, microscopes, etc. + quantity: '?' + groups: + - neurodata_type_inc: Device + doc: Data acquisition devices. + quantity: '*' + - name: subject + neurodata_type_inc: Subject + doc: Information about the animal or person from which the data was measured. + quantity: '?' + - name: extracellular_ephys + doc: Metadata related to extracellular electrophysiology. + quantity: '?' + groups: + - neurodata_type_inc: ElectrodeGroup + doc: Physical group of electrodes. + quantity: '*' + - name: electrodes + neurodata_type_inc: DynamicTable + doc: A table of all electrodes (i.e. channels) used for recording. + quantity: '?' + datasets: + - name: x + neurodata_type_inc: VectorData + dtype: float32 + doc: x coordinate of the channel location in the brain (+x is posterior). + quantity: '?' + - name: y + neurodata_type_inc: VectorData + dtype: float32 + doc: y coordinate of the channel location in the brain (+y is inferior). + quantity: '?' + - name: z + neurodata_type_inc: VectorData + dtype: float32 + doc: z coordinate of the channel location in the brain (+z is right). + quantity: '?' + - name: imp + neurodata_type_inc: VectorData + dtype: float32 + doc: Impedance of the channel, in ohms. + quantity: '?' + - name: location + neurodata_type_inc: VectorData + dtype: text + doc: Location of the electrode (channel). Specify the area, layer, comments + on estimation of area/layer, stereotaxic coordinates if in vivo, etc. Use + standard atlas names for anatomical regions when possible. + - name: filtering + neurodata_type_inc: VectorData + dtype: text + doc: Description of hardware filtering, including the filter name and frequency cutoffs. + quantity: '?' + - name: group + neurodata_type_inc: VectorData + dtype: + target_type: ElectrodeGroup + reftype: object + doc: Reference to the ElectrodeGroup this electrode is a part of. + - name: group_name + neurodata_type_inc: VectorData + dtype: text + doc: Name of the ElectrodeGroup this electrode is a part of. + - name: rel_x + neurodata_type_inc: VectorData + dtype: float32 + doc: x coordinate in electrode group + quantity: '?' + - name: rel_y + neurodata_type_inc: VectorData + dtype: float32 + doc: y coordinate in electrode group + quantity: '?' + - name: rel_z + neurodata_type_inc: VectorData + dtype: float32 + doc: z coordinate in electrode group + quantity: '?' + - name: reference + neurodata_type_inc: VectorData + dtype: text + doc: Description of the reference electrode and/or reference scheme used for this electrode, e.g., + "stainless steel skull screw" or "online common average referencing". + quantity: '?' + - name: intracellular_ephys + doc: Metadata related to intracellular electrophysiology. + quantity: '?' + datasets: + - name: filtering + dtype: text + doc: '[DEPRECATED] Use IntracellularElectrode.filtering instead. Description + of filtering used. Includes filtering type and parameters, frequency fall-off, + etc. If this changes between TimeSeries, filter description should be stored + as a text attribute for each TimeSeries.' + quantity: '?' + groups: + - neurodata_type_inc: IntracellularElectrode + doc: An intracellular electrode. + quantity: '*' + - name: sweep_table + neurodata_type_inc: SweepTable + doc: '[DEPRECATED] Table used to group different PatchClampSeries. SweepTable + is being replaced by IntracellularRecordingsTable and SimultaneousRecordingsTable + tables. Additional SequentialRecordingsTable, RepetitionsTable and + ExperimentalConditions tables provide enhanced support for experiment metadata.' + quantity: '?' + - name: intracellular_recordings + neurodata_type_inc: IntracellularRecordingsTable + doc: A table to group together a stimulus and response from a single electrode + and a single simultaneous recording. Each row in the table represents a + single recording consisting typically of a stimulus and a corresponding + response. In some cases, however, only a stimulus or a response are recorded + as as part of an experiment. In this case both, the stimulus and response + will point to the same TimeSeries while the idx_start and count of the invalid + column will be set to -1, thus, indicating that no values have been recorded + for the stimulus or response, respectively. Note, a recording MUST contain + at least a stimulus or a response. Typically the stimulus and response are + PatchClampSeries. However, the use of AD/DA channels that are not associated + to an electrode is also common in intracellular electrophysiology, in which + case other TimeSeries may be used. + quantity: '?' + - name: simultaneous_recordings + neurodata_type_inc: SimultaneousRecordingsTable + doc: A table for grouping different intracellular recordings from the IntracellularRecordingsTable + table together that were recorded simultaneously from different electrodes + quantity: '?' + - name: sequential_recordings + neurodata_type_inc: SequentialRecordingsTable + doc: A table for grouping different sequential recordings from the SimultaneousRecordingsTable + table together. This is typically used to group together sequential recordings + where the a sequence of stimuli of the same type with varying parameters + have been presented in a sequence. + quantity: '?' + - name: repetitions + neurodata_type_inc: RepetitionsTable + doc: A table for grouping different sequential intracellular recordings together. + With each SequentialRecording typically representing a particular type of + stimulus, the RepetitionsTable table is typically used to group sets of + stimuli applied in sequence. + quantity: '?' + - name: experimental_conditions + neurodata_type_inc: ExperimentalConditionsTable + doc: A table for grouping different intracellular recording repetitions together + that belong to the same experimental experimental_conditions. + quantity: '?' + - name: optogenetics + doc: Metadata describing optogenetic stimuluation. + quantity: '?' + groups: + - neurodata_type_inc: OptogeneticStimulusSite + doc: An optogenetic stimulation site. + quantity: '*' + - name: optophysiology + doc: Metadata related to optophysiology. + quantity: '?' + groups: + - neurodata_type_inc: ImagingPlane + doc: An imaging plane. + quantity: '*' + - name: intervals + doc: Experimental intervals, whether that be logically distinct sub-experiments + having a particular scientific goal, trials (see trials subgroup) during an + experiment, or epochs (see epochs subgroup) deriving from analysis of data. + quantity: '?' + groups: + - name: epochs + neurodata_type_inc: TimeIntervals + doc: Divisions in time marking experimental stages or sub-divisions of a single + recording session. + quantity: '?' + - name: trials + neurodata_type_inc: TimeIntervals + doc: Repeated experimental events that have a logical grouping. + quantity: '?' + - name: invalid_times + neurodata_type_inc: TimeIntervals + doc: Time intervals that should be removed from analysis. + quantity: '?' + - neurodata_type_inc: TimeIntervals + doc: Optional additional table(s) for describing other experimental time intervals. + quantity: '*' + - name: units + neurodata_type_inc: Units + doc: Data about sorted spike units. + quantity: '?' + +- neurodata_type_def: LabMetaData + neurodata_type_inc: NWBContainer + doc: Lab-specific meta-data. + +- neurodata_type_def: Subject + neurodata_type_inc: NWBContainer + doc: Information about the animal or person from which the data was measured. + datasets: + - name: age + dtype: text + doc: Age of subject. Can be supplied instead of 'date_of_birth'. + quantity: '?' + attributes: + - name: reference + doc: "Age is with reference to this event. Can be 'birth' or + 'gestational'. If reference is omitted, 'birth' is implied." + dtype: text + required: false + default_value: birth + - name: date_of_birth + dtype: isodatetime + doc: Date of birth of subject. Can be supplied instead of 'age'. + quantity: '?' + - name: description + dtype: text + doc: Description of subject and where subject came from (e.g., breeder, if + animal). + quantity: '?' + - name: genotype + dtype: text + doc: Genetic strain. If absent, assume Wild Type (WT). + quantity: '?' + - name: sex + dtype: text + doc: Gender of subject. + quantity: '?' + - name: species + dtype: text + doc: Species of subject. + quantity: '?' + - name: strain + dtype: text + doc: Strain of subject. + quantity: '?' + - name: subject_id + dtype: text + doc: ID of animal/person used/participating in experiment (lab convention). + quantity: '?' + - name: weight + dtype: text + doc: Weight at time of experiment, at time of surgery and at other important + times. + quantity: '?' + +datasets: +- neurodata_type_def: ScratchData + neurodata_type_inc: NWBData + doc: Any one-off datasets + attributes: + - name: notes + doc: 'Any notes the user has about the dataset being stored' + dtype: text diff --git a/nwb-schema/2.8.0/core/nwb.icephys.yaml b/nwb-schema/2.8.0/core/nwb.icephys.yaml new file mode 100644 index 00000000..62c1e46b --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.icephys.yaml @@ -0,0 +1,430 @@ +groups: +- neurodata_type_def: PatchClampSeries + neurodata_type_inc: TimeSeries + doc: An abstract base class for patch-clamp data - stimulus or response, + current or voltage. + attributes: + - name: stimulus_description + dtype: text + doc: Protocol/stimulus name for this patch-clamp dataset. + - name: sweep_number + dtype: uint32 + doc: Sweep number, allows to group different PatchClampSeries together. + required: false + datasets: + - name: data + dtype: numeric + dims: + - num_times + shape: + - null + doc: Recorded voltage or current. + attributes: + - name: unit + dtype: text + doc: Base unit of measurement for working with the data. Actual stored values are + not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + - name: gain + dtype: float32 + doc: Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp). + quantity: '?' + links: + - name: electrode + target_type: IntracellularElectrode + doc: Link to IntracellularElectrode object that describes the electrode that was + used to apply or record this data. + +- neurodata_type_def: CurrentClampSeries + neurodata_type_inc: PatchClampSeries + doc: Voltage data from an intracellular current-clamp recording. A + corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is + used to store the current injected. + datasets: + - name: data + doc: Recorded voltage. + attributes: + - name: unit + dtype: text + value: volts + doc: Base unit of measurement for working with the data. which is fixed to 'volts'. + Actual stored values are not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + - name: bias_current + dtype: float32 + doc: Bias current, in amps. + quantity: '?' + - name: bridge_balance + dtype: float32 + doc: Bridge balance, in ohms. + quantity: '?' + - name: capacitance_compensation + dtype: float32 + doc: Capacitance compensation, in farads. + quantity: '?' + +- neurodata_type_def: IZeroClampSeries + neurodata_type_inc: CurrentClampSeries + doc: Voltage data from an intracellular recording when all current + and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). + There is no CurrentClampStimulusSeries associated with an IZero series because + the amplifier is disconnected and no stimulus can reach the cell. + attributes: + - name: stimulus_description + dtype: text + doc: An IZeroClampSeries has no stimulus, so this attribute is automatically set to "N/A" + value: N/A + datasets: + - name: bias_current + dtype: float32 + value: 0.0 + doc: Bias current, in amps, fixed to 0.0. + - name: bridge_balance + dtype: float32 + value: 0.0 + doc: Bridge balance, in ohms, fixed to 0.0. + - name: capacitance_compensation + dtype: float32 + value: 0.0 + doc: Capacitance compensation, in farads, fixed to 0.0. + +- neurodata_type_def: CurrentClampStimulusSeries + neurodata_type_inc: PatchClampSeries + doc: Stimulus current applied during current clamp recording. + datasets: + - name: data + doc: Stimulus current applied. + attributes: + - name: unit + dtype: text + value: amperes + doc: Base unit of measurement for working with the data. which is fixed to 'amperes'. + Actual stored values are not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + +- neurodata_type_def: VoltageClampSeries + neurodata_type_inc: PatchClampSeries + doc: Current data from an intracellular voltage-clamp recording. A + corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is + used to store the voltage injected. + datasets: + - name: data + doc: Recorded current. + attributes: + - name: unit + dtype: text + value: amperes + doc: Base unit of measurement for working with the data. which is fixed to 'amperes'. + Actual stored values are not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + - name: capacitance_fast + dtype: float32 + doc: Fast capacitance, in farads. + quantity: '?' + attributes: + - name: unit + dtype: text + value: farads + doc: Unit of measurement for capacitance_fast, which is fixed to 'farads'. + - name: capacitance_slow + dtype: float32 + doc: Slow capacitance, in farads. + quantity: '?' + attributes: + - name: unit + dtype: text + value: farads + doc: Unit of measurement for capacitance_fast, which is fixed to 'farads'. + - name: resistance_comp_bandwidth + dtype: float32 + doc: Resistance compensation bandwidth, in hertz. + quantity: '?' + attributes: + - name: unit + dtype: text + value: hertz + doc: Unit of measurement for resistance_comp_bandwidth, which is fixed to 'hertz'. + - name: resistance_comp_correction + dtype: float32 + doc: Resistance compensation correction, in percent. + quantity: '?' + attributes: + - name: unit + dtype: text + value: percent + doc: Unit of measurement for resistance_comp_correction, which is fixed to 'percent'. + - name: resistance_comp_prediction + dtype: float32 + doc: Resistance compensation prediction, in percent. + quantity: '?' + attributes: + - name: unit + dtype: text + value: percent + doc: Unit of measurement for resistance_comp_prediction, which is fixed to 'percent'. + - name: whole_cell_capacitance_comp + dtype: float32 + doc: Whole cell capacitance compensation, in farads. + quantity: '?' + attributes: + - name: unit + dtype: text + value: farads + doc: Unit of measurement for whole_cell_capacitance_comp, which is fixed to 'farads'. + - name: whole_cell_series_resistance_comp + dtype: float32 + doc: Whole cell series resistance compensation, in ohms. + quantity: '?' + attributes: + - name: unit + dtype: text + value: ohms + doc: Unit of measurement for whole_cell_series_resistance_comp, which is fixed to 'ohms'. + +- neurodata_type_def: VoltageClampStimulusSeries + neurodata_type_inc: PatchClampSeries + doc: Stimulus voltage applied during a voltage clamp recording. + datasets: + - name: data + doc: Stimulus voltage applied. + attributes: + - name: unit + dtype: text + value: volts + doc: Base unit of measurement for working with the data. which is fixed to 'volts'. + Actual stored values are not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion' and add 'offset'. + +- neurodata_type_def: IntracellularElectrode + neurodata_type_inc: NWBContainer + doc: An intracellular electrode and its metadata. + datasets: + - name: cell_id + dtype: text + doc: unique ID of the cell + quantity: '?' + - name: description + dtype: text + doc: Description of electrode (e.g., whole-cell, sharp, etc.). + - name: filtering + dtype: text + doc: Electrode specific filtering. + quantity: '?' + - name: initial_access_resistance + dtype: text + doc: Initial access resistance. + quantity: '?' + - name: location + dtype: text + doc: Location of the electrode. Specify the area, layer, comments on estimation + of area/layer, stereotaxic coordinates if in vivo, etc. Use standard atlas + names for anatomical regions when possible. + quantity: '?' + - name: resistance + dtype: text + doc: Electrode resistance, in ohms. + quantity: '?' + - name: seal + dtype: text + doc: Information about seal used for recording. + quantity: '?' + - name: slice + dtype: text + doc: Information about slice used for recording. + quantity: '?' + links: + - name: device + target_type: Device + doc: Device that was used to record from this electrode. + +- neurodata_type_def: SweepTable + neurodata_type_inc: DynamicTable + doc: '[DEPRECATED] Table used to group different PatchClampSeries. SweepTable + is being replaced by IntracellularRecordingsTable and SimultaneousRecordingsTable + tables. Additional SequentialRecordingsTable, RepetitionsTable, and + ExperimentalConditions tables provide enhanced support for experiment metadata.' + datasets: + - name: sweep_number + neurodata_type_inc: VectorData + dtype: uint32 + doc: Sweep number of the PatchClampSeries in that row. + - name: series + neurodata_type_inc: VectorData + dtype: + target_type: PatchClampSeries + reftype: object + doc: The PatchClampSeries with the sweep number in that row. + - name: series_index + neurodata_type_inc: VectorIndex + doc: Index for series. + +- neurodata_type_def: IntracellularElectrodesTable + neurodata_type_inc: DynamicTable + doc: Table for storing intracellular electrode related metadata. + attributes: + - name: description + dtype: text + value: Table for storing intracellular electrode related metadata. + doc: Description of what is in this dynamic table. + datasets: + - name: electrode + neurodata_type_inc: VectorData + dtype: + target_type: IntracellularElectrode + reftype: object + doc: Column for storing the reference to the intracellular electrode. + +- neurodata_type_def: IntracellularStimuliTable + neurodata_type_inc: DynamicTable + doc: Table for storing intracellular stimulus related metadata. + attributes: + - name: description + dtype: text + value: Table for storing intracellular stimulus related metadata. + doc: Description of what is in this dynamic table. + datasets: + - name: stimulus + neurodata_type_inc: TimeSeriesReferenceVectorData + doc: Column storing the reference to the recorded stimulus for the recording (rows). + - name: stimulus_template + neurodata_type_inc: TimeSeriesReferenceVectorData + doc: Column storing the reference to the stimulus template for the recording (rows). + quantity: '?' + +- neurodata_type_def: IntracellularResponsesTable + neurodata_type_inc: DynamicTable + doc: Table for storing intracellular response related metadata. + attributes: + - name: description + dtype: text + value: Table for storing intracellular response related metadata. + doc: Description of what is in this dynamic table. + datasets: + - name: response + neurodata_type_inc: TimeSeriesReferenceVectorData + doc: Column storing the reference to the recorded response for the recording (rows) + +- neurodata_type_def: IntracellularRecordingsTable + neurodata_type_inc: AlignedDynamicTable + name: intracellular_recordings + doc: A table to group together a stimulus and response from a single electrode and + a single simultaneous recording. Each row in the table represents a single recording + consisting typically of a stimulus and a corresponding response. In some cases, + however, only a stimulus or a response is recorded as part of an experiment. + In this case, both the stimulus and response will point to the same TimeSeries + while the idx_start and count of the invalid column will be set to -1, thus, indicating + that no values have been recorded for the stimulus or response, respectively. + Note, a recording MUST contain at least a stimulus or a response. Typically the + stimulus and response are PatchClampSeries. However, the use of AD/DA channels + that are not associated to an electrode is also common in intracellular electrophysiology, + in which case other TimeSeries may be used. + attributes: + - name: description + dtype: text + value: A table to group together a stimulus and response from a single electrode + and a single simultaneous recording and for storing metadata about the intracellular + recording. + doc: Description of the contents of this table. Inherited from AlignedDynamicTable + and overwritten here to fix the value of the attribute. + groups: + - name: electrodes + neurodata_type_inc: IntracellularElectrodesTable + doc: Table for storing intracellular electrode related metadata. + - name: stimuli + neurodata_type_inc: IntracellularStimuliTable + doc: Table for storing intracellular stimulus related metadata. + - name: responses + neurodata_type_inc: IntracellularResponsesTable + doc: Table for storing intracellular response related metadata. + +- neurodata_type_def: SimultaneousRecordingsTable + neurodata_type_inc: DynamicTable + name: simultaneous_recordings + doc: A table for grouping different intracellular recordings from the IntracellularRecordingsTable + table together that were recorded simultaneously from different electrodes. + datasets: + - name: recordings + neurodata_type_inc: DynamicTableRegion + doc: A reference to one or more rows in the IntracellularRecordingsTable table. + attributes: + - name: table + dtype: + target_type: IntracellularRecordingsTable + reftype: object + doc: Reference to the IntracellularRecordingsTable table that this table region + applies to. This specializes the attribute inherited from DynamicTableRegion + to fix the type of table that can be referenced here. + - name: recordings_index + neurodata_type_inc: VectorIndex + doc: Index dataset for the recordings column. + +- neurodata_type_def: SequentialRecordingsTable + neurodata_type_inc: DynamicTable + name: sequential_recordings + doc: A table for grouping different sequential recordings from the SimultaneousRecordingsTable + table together. This is typically used to group together sequential recordings + where a sequence of stimuli of the same type with varying parameters have + been presented in a sequence. + datasets: + - name: simultaneous_recordings + neurodata_type_inc: DynamicTableRegion + doc: A reference to one or more rows in the SimultaneousRecordingsTable table. + attributes: + - name: table + dtype: + target_type: SimultaneousRecordingsTable + reftype: object + doc: Reference to the SimultaneousRecordingsTable table that this table region + applies to. This specializes the attribute inherited from DynamicTableRegion + to fix the type of table that can be referenced here. + - name: simultaneous_recordings_index + neurodata_type_inc: VectorIndex + doc: Index dataset for the simultaneous_recordings column. + - name: stimulus_type + neurodata_type_inc: VectorData + dtype: text + doc: The type of stimulus used for the sequential recording. + +- neurodata_type_def: RepetitionsTable + neurodata_type_inc: DynamicTable + name: repetitions + doc: A table for grouping different sequential intracellular recordings together. + With each SequentialRecording typically representing a particular type of stimulus, + the RepetitionsTable table is typically used to group sets of stimuli applied + in sequence. + datasets: + - name: sequential_recordings + neurodata_type_inc: DynamicTableRegion + doc: A reference to one or more rows in the SequentialRecordingsTable table. + attributes: + - name: table + dtype: + target_type: SequentialRecordingsTable + reftype: object + doc: Reference to the SequentialRecordingsTable table that this table region + applies to. This specializes the attribute inherited from DynamicTableRegion + to fix the type of table that can be referenced here. + - name: sequential_recordings_index + neurodata_type_inc: VectorIndex + doc: Index dataset for the sequential_recordings column. + +- neurodata_type_def: ExperimentalConditionsTable + neurodata_type_inc: DynamicTable + name: experimental_conditions + doc: A table for grouping different intracellular recording repetitions together + that belong to the same experimental condition. + datasets: + - name: repetitions + neurodata_type_inc: DynamicTableRegion + doc: A reference to one or more rows in the RepetitionsTable table. + attributes: + - name: table + dtype: + target_type: RepetitionsTable + reftype: object + doc: Reference to the RepetitionsTable table that this table region applies + to. This specializes the attribute inherited from DynamicTableRegion to fix + the type of table that can be referenced here. + - name: repetitions_index + neurodata_type_inc: VectorIndex + doc: Index dataset for the repetitions column. diff --git a/nwb-schema/2.8.0/core/nwb.image.yaml b/nwb-schema/2.8.0/core/nwb.image.yaml new file mode 100644 index 00000000..3f782a63 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.image.yaml @@ -0,0 +1,222 @@ +datasets: +- neurodata_type_def: GrayscaleImage + neurodata_type_inc: Image + dims: + - x + - y + shape: + - null + - null + doc: A grayscale image. + dtype: numeric + +- neurodata_type_def: RGBImage + neurodata_type_inc: Image + dims: + - x + - y + - r, g, b + shape: + - null + - null + - 3 + doc: A color image. + dtype: numeric + +- neurodata_type_def: RGBAImage + neurodata_type_inc: Image + dims: + - x + - y + - r, g, b, a + shape: + - null + - null + - 4 + doc: A color image with transparency. + dtype: numeric + +groups: +- neurodata_type_def: ImageSeries + neurodata_type_inc: TimeSeries + doc: General image data that is common between acquisition and stimulus time series. + Sometimes the image data is stored in the file in a raw format while other + times it will be stored as a series of external image files in the host file system. + The data field will either be binary data, if the data is stored in the NWB file, or + empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z]. + datasets: + - name: data + dtype: numeric + dims: + - - frame + - x + - y + - - frame + - x + - y + - z + shape: + - - null + - null + - null + - - null + - null + - null + - null + doc: Binary data representing images across frames. If data are stored in an external + file, this should be an empty 3D array. + - name: dimension + dtype: int32 + dims: + - rank + shape: + - null + doc: Number of pixels on x, y, (and z) axes. + quantity: '?' + - name: external_file + dtype: text + dims: + - num_files + shape: + - null + doc: Paths to one or more external file(s). The field is only present if format='external'. + This is only relevant if the image series is stored in the file system as one + or more image file(s). This field should NOT be used if the image is stored + in another NWB file and that file is linked to this file. + quantity: '?' + attributes: + - name: starting_frame + dtype: int32 + dims: + - num_files + shape: + - null + doc: Each external image may contain one or more consecutive frames of the full + ImageSeries. This attribute serves as an index to indicate which frames each file + contains, to facilitate random access. The 'starting_frame' attribute, hence, + contains a list of frame numbers within the full ImageSeries of the first frame + of each file listed in the parent 'external_file' dataset. Zero-based indexing is + used (hence, the first element will always be zero). For example, if the + 'external_file' dataset has three paths to files and the first file has 5 frames, + the second file has 10 frames, and the third file has 20 frames, then this + attribute will have values [0, 5, 15]. If there is a single external file that + holds all of the frames of the ImageSeries (and so there is a single element in + the 'external_file' dataset), then this attribute should have value [0]. + - name: format + dtype: text + default_value: raw + doc: Format of image. If this is 'external', then the attribute 'external_file' + contains the path information to the image files. If this is 'raw', then the raw + (single-channel) binary data is stored in the 'data' dataset. If this attribute + is not present, then the default format='raw' case is assumed. + quantity: '?' + links: + - name: device + target_type: Device + doc: Link to the Device object that was used to capture these images. + quantity: '?' + +- neurodata_type_def: ImageMaskSeries + neurodata_type_inc: ImageSeries + doc: DEPRECATED. An alpha mask that is applied to a presented visual stimulus. The 'data' array + contains an array of mask values that are applied to the displayed image. Mask + values are stored as RGBA. Mask can vary with time. The timestamps array indicates + the starting time of a mask, and that mask pattern continues until it's explicitly + changed. + links: + - name: masked_imageseries + target_type: ImageSeries + doc: Link to ImageSeries object that this image mask is applied to. + +- neurodata_type_def: OpticalSeries + neurodata_type_inc: ImageSeries + doc: Image data that is presented or recorded. A stimulus template movie will be + stored only as an image. When the image is presented as stimulus, additional data + is required, such as field of view (e.g., how much of the visual field the image + covers, or how what is the area of the target being imaged). If the OpticalSeries + represents acquired imaging data, orientation is also important. + datasets: + - name: distance + dtype: float32 + doc: Distance from camera/monitor to target/eye. + quantity: '?' + - name: field_of_view + dtype: float32 + dims: + - - width, height + - - width, height, depth + shape: + - - 2 + - - 3 + doc: Width, height and depth of image, or imaged area, in meters. + quantity: '?' + - name: data + dtype: numeric + dims: + - - frame + - x + - y + - - frame + - x + - y + - r, g, b + shape: + - - null + - null + - null + - - null + - null + - null + - 3 + doc: Images presented to subject, either grayscale or RGB + - name: orientation + dtype: text + doc: Description of image relative to some reference frame (e.g., which way is + up). Must also specify frame of reference. + quantity: '?' + +- neurodata_type_def: IndexSeries + neurodata_type_inc: TimeSeries + doc: Stores indices to image frames stored in an ImageSeries. The purpose of the + IndexSeries is to allow a static image stack to be stored in an Images + object, and the images in the stack to be referenced out-of-order. This can be for + the display of individual images, or of movie segments (as a movie is simply a + series of images). The data field stores the index of the frame in the referenced + Images object, and the timestamps array indicates when that image + was displayed. + datasets: + - name: data + dtype: uint32 + dims: + - num_times + shape: + - null + doc: Index of the image (using zero-indexing) in the linked Images object. + attributes: + - name: conversion + dtype: float32 + doc: This field is unused by IndexSeries. + required: false + - name: resolution + dtype: float32 + doc: This field is unused by IndexSeries. + required: false + - name: offset + dtype: float32 + doc: This field is unused by IndexSeries. + required: false + - name: unit + dtype: text + value: N/A + doc: This field is unused by IndexSeries and has the value N/A. + links: + - name: indexed_timeseries + target_type: ImageSeries + doc: Link to ImageSeries object containing images that are indexed. Use of this link + is discouraged and will be deprecated. Link to an Images type instead. + quantity: '?' + - name: indexed_images + target_type: Images + doc: Link to Images object containing an ordered set of images that are indexed. The Images object + must contain a 'ordered_images' dataset specifying the order of the images in the Images type. + quantity: '?' diff --git a/nwb-schema/2.8.0/core/nwb.misc.yaml b/nwb-schema/2.8.0/core/nwb.misc.yaml new file mode 100644 index 00000000..2e4d3f78 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.misc.yaml @@ -0,0 +1,331 @@ +groups: +- neurodata_type_def: AbstractFeatureSeries + neurodata_type_inc: TimeSeries + doc: Abstract features, such as quantitative descriptions of sensory stimuli. The + TimeSeries::data field is a 2D array, storing those features (e.g., for visual + grating stimulus this might be orientation, spatial frequency and contrast). Null + stimuli (eg, uniform gray) can be marked as being an independent feature (eg, + 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, + or through use of the TimeSeries::control fields. A set of features is considered + to persist until the next set of features is defined. The final set of features + stored should be the null set. This is useful when storing the raw stimulus + is impractical. + datasets: + - name: data + dtype: numeric + dims: + - - num_times + - - num_times + - num_features + shape: + - - null + - - null + - null + doc: Values of each feature at each time. + attributes: + - name: unit + dtype: text + default_value: see 'feature_units' + doc: Since there can be different units for different features, store the units + in 'feature_units'. The default value for this attribute is "see 'feature_units'". + required: false + - name: feature_units + dtype: text + dims: + - num_features + shape: + - null + doc: Units of each feature. + quantity: '?' + - name: features + dtype: text + dims: + - num_features + shape: + - null + doc: Description of the features represented in TimeSeries::data. + +- neurodata_type_def: AnnotationSeries + neurodata_type_inc: TimeSeries + doc: Stores user annotations made during an experiment. The data[] + field stores a text array, and timestamps are stored for each annotation (ie, + interval=1). This is largely an alias to a standard TimeSeries storing a text + array but that is identifiable as storing annotations in a machine-readable way. + datasets: + - name: data + dtype: text + dims: + - num_times + shape: + - null + doc: Annotations made during an experiment. + attributes: + - name: resolution + dtype: float32 + value: -1.0 + doc: Smallest meaningful difference between values in data. Annotations have + no units, so the value is fixed to -1.0. + - name: unit + dtype: text + value: n/a + doc: Base unit of measurement for working with the data. Annotations have + no units, so the value is fixed to 'n/a'. + +- neurodata_type_def: IntervalSeries + neurodata_type_inc: TimeSeries + doc: Stores intervals of data. The timestamps field stores the beginning and end + of intervals. The data field stores whether the interval just started (>0 value) + or ended (<0 value). Different interval types can be represented in the same series + by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature + C, etc). The field data stores an 8-bit integer. This is largely an alias of a + standard TimeSeries but that is identifiable as representing time intervals in + a machine-readable way. + datasets: + - name: data + dtype: int8 + dims: + - num_times + shape: + - null + doc: Use values >0 if interval started, <0 if interval ended. + attributes: + - name: resolution + dtype: float32 + value: -1.0 + doc: Smallest meaningful difference between values in data. Annotations have + no units, so the value is fixed to -1.0. + - name: unit + dtype: text + value: n/a + doc: Base unit of measurement for working with the data. Annotations have + no units, so the value is fixed to 'n/a'. + +- neurodata_type_def: DecompositionSeries + neurodata_type_inc: TimeSeries + doc: Spectral analysis of a time series, e.g. of an LFP or a speech signal. + datasets: + - name: data + dtype: numeric + dims: + - num_times + - num_channels + - num_bands + shape: + - null + - null + - null + doc: Data decomposed into frequency bands. + attributes: + - name: unit + dtype: text + default_value: no unit + doc: Base unit of measurement for working with the data. Actual stored values are + not necessarily stored in these units. To access the data in these units, + multiply 'data' by 'conversion'. + - name: metric + dtype: text + doc: The metric used, e.g. phase, amplitude, power. + - name: source_channels + neurodata_type_inc: DynamicTableRegion + doc: DynamicTableRegion pointer to the channels that this decomposition series was generated from. + quantity: '?' + groups: + - name: bands + neurodata_type_inc: DynamicTable + doc: Table for describing the bands that this series was generated from. There + should be one row in this table for each band. + datasets: + - name: band_name + neurodata_type_inc: VectorData + dtype: text + doc: Name of the band, e.g. theta. + - name: band_limits + neurodata_type_inc: VectorData + dtype: float32 + dims: + - num_bands + - low, high + shape: + - null + - 2 + doc: Low and high limit of each band in Hz. If it is a Gaussian filter, use + 2 SD on either side of the center. + - name: band_mean + neurodata_type_inc: VectorData + dtype: float32 + dims: + - num_bands + shape: + - null + doc: The mean Gaussian filters, in Hz. + quantity: '?' + - name: band_stdev + neurodata_type_inc: VectorData + dtype: float32 + dims: + - num_bands + shape: + - null + doc: The standard deviation of Gaussian filters, in Hz. + quantity: '?' + links: + - name: source_timeseries + target_type: TimeSeries + doc: Link to TimeSeries object that this data was calculated from. Metadata about + electrodes and their position can be read from that ElectricalSeries so it is + not necessary to store that information here. + quantity: '?' + +- neurodata_type_def: Units + neurodata_type_inc: DynamicTable + default_name: Units + doc: Data about spiking units. Event times of observed units (e.g. cell, synapse, + etc.) should be concatenated and stored in spike_times. + datasets: + - name: spike_times_index + neurodata_type_inc: VectorIndex + doc: Index into the spike_times dataset. + quantity: '?' + - name: spike_times + neurodata_type_inc: VectorData + dtype: float64 + doc: Spike times for each unit in seconds. + quantity: '?' + attributes: + - name: resolution + dtype: float64 + doc: The smallest possible difference between two spike times. Usually 1 divided by the acquisition sampling rate + from which spike times were extracted, but could be larger if the acquisition time series was downsampled or + smaller if the acquisition time series was smoothed/interpolated and it is possible for the spike time to be + between samples. + required: false + - name: obs_intervals_index + neurodata_type_inc: VectorIndex + doc: Index into the obs_intervals dataset. + quantity: '?' + - name: obs_intervals + neurodata_type_inc: VectorData + dtype: float64 + dims: + - num_intervals + - start|end + shape: + - null + - 2 + doc: Observation intervals for each unit. + quantity: '?' + - name: electrodes_index + neurodata_type_inc: VectorIndex + doc: Index into electrodes. + quantity: '?' + - name: electrodes + neurodata_type_inc: DynamicTableRegion + doc: Electrode that each spike unit came from, specified using a DynamicTableRegion. + quantity: '?' + - name: electrode_group + neurodata_type_inc: VectorData + dtype: + target_type: ElectrodeGroup + reftype: object + doc: Electrode group that each spike unit came from. + quantity: '?' + - name: waveform_mean + neurodata_type_inc: VectorData + dtype: float32 + dims: + - - num_units + - num_samples + - - num_units + - num_samples + - num_electrodes + shape: + - - null + - null + - - null + - null + - null + doc: Spike waveform mean for each spike unit. + quantity: '?' + attributes: + - name: sampling_rate + dtype: float32 + doc: Sampling rate, in hertz. + required: false + - name: unit + dtype: text + value: volts + doc: Unit of measurement. This value is fixed to 'volts'. + required: false + - name: waveform_sd + neurodata_type_inc: VectorData + dtype: float32 + dims: + - - num_units + - num_samples + - - num_units + - num_samples + - num_electrodes + shape: + - - null + - null + - - null + - null + - null + doc: Spike waveform standard deviation for each spike unit. + quantity: '?' + attributes: + - name: sampling_rate + dtype: float32 + doc: Sampling rate, in hertz. + required: false + - name: unit + dtype: text + value: volts + doc: Unit of measurement. This value is fixed to 'volts'. + required: false + - name: waveforms + neurodata_type_inc: VectorData + dtype: numeric + dims: + - num_waveforms + - num_samples + shape: + - null + - null + doc: "Individual waveforms for each spike on each electrode. This is a doubly indexed column. The 'waveforms_index' + column indexes which waveforms in this column belong to the same spike event for a given unit, where each waveform + was recorded from a different electrode. The 'waveforms_index_index' column indexes the 'waveforms_index' column + to indicate which spike events belong to a given unit. For example, if the + 'waveforms_index_index' column has values [2, 5, 6], then the first 2 elements of the 'waveforms_index' column + correspond to the 2 spike events of the first unit, the next 3 elements of the 'waveforms_index' column correspond + to the 3 spike events of the second unit, and the next 1 element of the 'waveforms_index' column corresponds to + the 1 spike event of the third unit. If the 'waveforms_index' column has values [3, 6, 8, 10, 12, 13], then + the first 3 elements of the 'waveforms' column contain the 3 spike waveforms that were recorded from 3 different + electrodes for the first spike time of the first unit. See + https://nwb-schema.readthedocs.io/en/stable/format_description.html#doubly-ragged-arrays for a graphical + representation of this example. When there is only one electrode for each unit (i.e., each spike time is + associated with a single waveform), then the 'waveforms_index' column will have values 1, 2, ..., N, where N is + the number of spike events. The number of electrodes for each spike event should be the same within a given unit. + The 'electrodes' column should be used to indicate which electrodes are associated with each unit, and the order + of the waveforms within a given unit x spike event should be the same as the order of the electrodes referenced in + the 'electrodes' column of this table. The number of samples for each waveform must be the same." + quantity: '?' + attributes: + - name: sampling_rate + dtype: float32 + doc: Sampling rate, in hertz. + required: false + - name: unit + dtype: text + value: volts + doc: Unit of measurement. This value is fixed to 'volts'. + required: false + - name: waveforms_index + neurodata_type_inc: VectorIndex + doc: Index into the 'waveforms' dataset. One value for every spike event. See 'waveforms' for more detail. + quantity: '?' + - name: waveforms_index_index + neurodata_type_inc: VectorIndex + doc: Index into the 'waveforms_index' dataset. One value for every unit (row in the table). See 'waveforms' for more + detail. + quantity: '?' diff --git a/nwb-schema/2.8.0/core/nwb.namespace.yaml b/nwb-schema/2.8.0/core/nwb.namespace.yaml new file mode 100644 index 00000000..dd967efe --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.namespace.yaml @@ -0,0 +1,60 @@ +namespaces: +- name: core + doc: NWB namespace + author: + - Andrew Tritt + - Oliver Ruebel + - Ryan Ly + - Ben Dichter + - Keith Godfrey + - Jeff Teeters + contact: + - ajtritt@lbl.gov + - oruebel@lbl.gov + - rly@lbl.gov + - bdichter@lbl.gov + - keithg@alleninstitute.org + - jteeters@berkeley.edu + full_name: NWB core + schema: + - namespace: hdmf-common + - doc: This source module contains base data types used throughout the NWB data + format. + source: nwb.base.yaml + title: Base data types + - doc: This source module contains neurodata_types for device data. + source: nwb.device.yaml + title: Devices + - doc: This source module contains neurodata_types for epoch data. + source: nwb.epoch.yaml + title: Epochs + - doc: This source module contains neurodata_types for image data. + source: nwb.image.yaml + title: Image data + - doc: Main NWB file specification. + source: nwb.file.yaml + title: NWB file + - doc: Miscellaneous types. + source: nwb.misc.yaml + title: Miscellaneous neurodata_types. + - doc: This source module contains neurodata_types for behavior data. + source: nwb.behavior.yaml + title: Behavior + - doc: This source module contains neurodata_types for extracellular electrophysiology + data. + source: nwb.ecephys.yaml + title: Extracellular electrophysiology + - doc: This source module contains neurodata_types for intracellular electrophysiology + data. + source: nwb.icephys.yaml + title: Intracellular electrophysiology + - doc: This source module contains neurodata_types for opto-genetics data. + source: nwb.ogen.yaml + title: Optogenetics + - doc: This source module contains neurodata_types for optical physiology data. + source: nwb.ophys.yaml + title: Optical physiology + - doc: This source module contains neurodata_type for retinotopy data. + source: nwb.retinotopy.yaml + title: Retinotopy + version: "2.8.0" diff --git a/nwb-schema/2.8.0/core/nwb.ogen.yaml b/nwb-schema/2.8.0/core/nwb.ogen.yaml new file mode 100644 index 00000000..419d6190 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.ogen.yaml @@ -0,0 +1,48 @@ +groups: +- neurodata_type_def: OptogeneticSeries + neurodata_type_inc: TimeSeries + doc: An optogenetic stimulus. + datasets: + - name: data + dtype: numeric + dims: + - - num_times + - - num_times + - num_rois + shape: + - - null + - - null + - null + doc: Applied power for optogenetic stimulus, in watts. Shape can be 1D or 2D. + 2D data is meant to be used in an extension of OptogeneticSeries that + defines what the second dimension represents. + attributes: + - name: unit + dtype: text + value: watts + doc: Unit of measurement for data, which is fixed to 'watts'. + links: + - name: site + target_type: OptogeneticStimulusSite + doc: Link to OptogeneticStimulusSite object that describes the site to which this + stimulus was applied. + +- neurodata_type_def: OptogeneticStimulusSite + neurodata_type_inc: NWBContainer + doc: A site of optogenetic stimulation. + datasets: + - name: description + dtype: text + doc: Description of stimulation site. + - name: excitation_lambda + dtype: float32 + doc: Excitation wavelength, in nm. + - name: location + dtype: text + doc: Location of the stimulation site. Specify the area, layer, comments on estimation + of area/layer, stereotaxic coordinates if in vivo, etc. Use standard atlas + names for anatomical regions when possible. + links: + - name: device + target_type: Device + doc: Device that generated the stimulus. diff --git a/nwb-schema/2.8.0/core/nwb.ophys.yaml b/nwb-schema/2.8.0/core/nwb.ophys.yaml new file mode 100644 index 00000000..7fd4b217 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.ophys.yaml @@ -0,0 +1,360 @@ +groups: +- neurodata_type_def: OnePhotonSeries + neurodata_type_inc: ImageSeries + doc: Image stack recorded over time from 1-photon microscope. + attributes: + - name: pmt_gain + dtype: float32 + doc: Photomultiplier gain. + required: false + - name: scan_line_rate + dtype: float32 + doc: Lines imaged per second. This is also stored in /general/optophysiology but + is kept here as it is useful information for analysis, and so good to be stored + w/ the actual data. + required: false + - name: exposure_time + dtype: float32 + doc: Exposure time of the sample; often the inverse of the frequency. + required: false + - name: binning + dtype: uint8 + doc: Amount of pixels combined into 'bins'; could be 1, 2, 4, 8, etc. + required: false + - name: power + dtype: float32 + doc: Power of the excitation in mW, if known. + required: false + - name: intensity + dtype: float32 + doc: Intensity of the excitation in mW/mm^2, if known. + required: false + links: + - name: imaging_plane + target_type: ImagingPlane + doc: Link to ImagingPlane object from which this TimeSeries data was generated. + +- neurodata_type_def: TwoPhotonSeries + neurodata_type_inc: ImageSeries + doc: Image stack recorded over time from 2-photon microscope. + attributes: + - name: pmt_gain + dtype: float32 + doc: Photomultiplier gain. + required: false + - name: scan_line_rate + dtype: float32 + doc: Lines imaged per second. This is also stored in /general/optophysiology but + is kept here as it is useful information for analysis, and so good to be stored + w/ the actual data. + required: false + datasets: + - name: field_of_view + dtype: float32 + dims: + - - width|height + - - width|height|depth + shape: + - - 2 + - - 3 + doc: Width, height and depth of image, or imaged area, in meters. + quantity: '?' + links: + - name: imaging_plane + target_type: ImagingPlane + doc: Link to ImagingPlane object from which this TimeSeries data was generated. + +- neurodata_type_def: RoiResponseSeries + neurodata_type_inc: TimeSeries + doc: ROI responses over an imaging plane. The first dimension represents time. + The second dimension, if present, represents ROIs. + datasets: + - name: data + dtype: numeric + dims: + - - num_times + - - num_times + - num_ROIs + shape: + - - null + - - null + - null + doc: Signals from ROIs. + - name: rois + neurodata_type_inc: DynamicTableRegion + doc: DynamicTableRegion referencing into an ROITable containing information on the ROIs + stored in this timeseries. + +- neurodata_type_def: DfOverF + neurodata_type_inc: NWBDataInterface + default_name: DfOverF + doc: dF/F information about a region of interest (ROI). Storage hierarchy of dF/F + should be the same as for segmentation (i.e., same names for ROIs and for image + planes). + groups: + - neurodata_type_inc: RoiResponseSeries + doc: RoiResponseSeries object(s) containing dF/F for a ROI. + quantity: '+' + +- neurodata_type_def: Fluorescence + neurodata_type_inc: NWBDataInterface + default_name: Fluorescence + doc: Fluorescence information about a region of interest (ROI). Storage hierarchy + of fluorescence should be the same as for segmentation (ie, same names for ROIs + and for image planes). + groups: + - neurodata_type_inc: RoiResponseSeries + doc: RoiResponseSeries object(s) containing fluorescence data for a ROI. + quantity: '+' + +- neurodata_type_def: ImageSegmentation + neurodata_type_inc: NWBDataInterface + default_name: ImageSegmentation + doc: Stores pixels in an image that represent different regions of interest (ROIs) + or masks. All segmentation for a given imaging plane is stored together, with + storage for multiple imaging planes (masks) supported. Each ROI is stored in its + own subgroup, with the ROI group containing both a 2D mask and a list of pixels + that make up this mask. Segments can also be used for masking neuropil. If segmentation + is allowed to change with time, a new imaging plane (or module) is required and + ROI names should remain consistent between them. + groups: + - neurodata_type_inc: PlaneSegmentation + doc: Results from image segmentation of a specific imaging plane. + quantity: '+' + +- neurodata_type_def: PlaneSegmentation + neurodata_type_inc: DynamicTable + doc: Results from image segmentation of a specific imaging plane. + datasets: + - name: image_mask + neurodata_type_inc: VectorData + dims: + - - num_roi + - num_x + - num_y + - - num_roi + - num_x + - num_y + - num_z + shape: + - - null + - null + - null + - - null + - null + - null + - null + doc: ROI masks for each ROI. Each image mask is the size of the original imaging + plane (or volume) and members of the ROI are finite non-zero. + quantity: '?' + - name: pixel_mask_index + neurodata_type_inc: VectorIndex + doc: Index into pixel_mask. + quantity: '?' + - name: pixel_mask + neurodata_type_inc: VectorData + dtype: + - name: x + dtype: uint32 + doc: Pixel x-coordinate. + - name: y + dtype: uint32 + doc: Pixel y-coordinate. + - name: weight + dtype: float32 + doc: Weight of the pixel. + doc: 'Pixel masks for each ROI: a list of indices and weights for the ROI. Pixel + masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation' + quantity: '?' + - name: voxel_mask_index + neurodata_type_inc: VectorIndex + doc: Index into voxel_mask. + quantity: '?' + - name: voxel_mask + neurodata_type_inc: VectorData + dtype: + - name: x + dtype: uint32 + doc: Voxel x-coordinate. + - name: y + dtype: uint32 + doc: Voxel y-coordinate. + - name: z + dtype: uint32 + doc: Voxel z-coordinate. + - name: weight + dtype: float32 + doc: Weight of the voxel. + doc: 'Voxel masks for each ROI: a list of indices and weights for the ROI. Voxel + masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation' + quantity: '?' + groups: + - name: reference_images + doc: Image stacks that the segmentation masks apply to. + groups: + - neurodata_type_inc: ImageSeries + doc: One or more image stacks that the masks apply to (can be one-element + stack). + quantity: '*' + links: + - name: imaging_plane + target_type: ImagingPlane + doc: Link to ImagingPlane object from which this data was generated. + +- neurodata_type_def: ImagingPlane + neurodata_type_inc: NWBContainer + doc: An imaging plane and its metadata. + datasets: + - name: description + dtype: text + doc: Description of the imaging plane. + quantity: '?' + - name: excitation_lambda + dtype: float32 + doc: Excitation wavelength, in nm. + - name: imaging_rate + dtype: float32 + doc: Rate that images are acquired, in Hz. If the corresponding TimeSeries is present, the rate should be stored + there instead. + quantity: '?' + - name: indicator + dtype: text + doc: Calcium indicator. + - name: location + dtype: text + doc: Location of the imaging plane. Specify the area, layer, comments on estimation + of area/layer, stereotaxic coordinates if in vivo, etc. Use standard atlas + names for anatomical regions when possible. + - name: manifold + dtype: float32 + dims: + - - height + - width + - x, y, z + - - height + - width + - depth + - x, y, z + shape: + - - null + - null + - 3 + - - null + - null + - null + - 3 + doc: "DEPRECATED Physical position of each pixel. 'xyz' represents the position\ + \ of the pixel relative to the defined coordinate space. Deprecated in favor of origin_coords and grid_spacing." + quantity: '?' + attributes: + - name: conversion + dtype: float32 + default_value: 1.0 + doc: Scalar to multiply each element in data to convert it to the specified 'unit'. + If the data are stored in acquisition system units or other units + that require a conversion to be interpretable, multiply the data by 'conversion' + to convert the data to the specified 'unit'. e.g. if the data acquisition system + stores values in this object as pixels from x = -500 to 499, y = -500 to 499 + that correspond to a 2 m x 2 m range, then the 'conversion' multiplier to get + from raw data acquisition pixel units to meters is 2/1000. + required: false + - name: unit + dtype: text + default_value: meters + doc: Base unit of measurement for working with the data. The default value is 'meters'. + required: false + - name: origin_coords + dtype: float32 + dims: + - - x, y + - - x, y, z + shape: + - - 2 + - - 3 + doc: Physical location of the first element of the imaging plane (0, 0) for 2-D data or (0, 0, 0) for 3-D data. + See also reference_frame for what the physical location is relative to (e.g., bregma). + quantity: '?' + attributes: + - name: unit + dtype: text + default_value: meters + doc: Measurement units for origin_coords. The default value is 'meters'. + - name: grid_spacing + dtype: float32 + dims: + - - x, y + - - x, y, z + shape: + - - 2 + - - 3 + doc: Space between pixels in (x, y) or voxels in (x, y, z) directions, in the specified unit. + Assumes imaging plane is a regular grid. See also reference_frame to interpret the grid. + quantity: '?' + attributes: + - name: unit + dtype: text + default_value: meters + doc: Measurement units for grid_spacing. The default value is 'meters'. + - name: reference_frame + dtype: text + doc: Describes reference frame of origin_coords and grid_spacing. + For example, this can be a text description of the anatomical location and orientation of the grid + defined by origin_coords and grid_spacing or the vectors needed to transform or rotate the grid to + a common anatomical axis (e.g., AP/DV/ML). This field is necessary to interpret origin_coords and grid_spacing. + If origin_coords and grid_spacing are not present, then this field is not required. + For example, if the microscope takes 10 x 10 x 2 images, where the first value of the data matrix + (index (0, 0, 0)) corresponds to (-1.2, -0.6, -2) mm relative to bregma, the spacing between pixels is 0.2 mm in + x, 0.2 mm in y and 0.5 mm in z, and larger numbers in x means more anterior, larger numbers in y means more + rightward, and larger numbers in z means more ventral, then enter the following -- + origin_coords = (-1.2, -0.6, -2) + grid_spacing = (0.2, 0.2, 0.5) + reference_frame = "Origin coordinates are relative to bregma. First dimension corresponds to anterior-posterior + axis (larger index = more anterior). Second dimension corresponds to medial-lateral axis (larger index = more + rightward). Third dimension corresponds to dorsal-ventral axis (larger index = more ventral)." + quantity: '?' + groups: + - neurodata_type_inc: OpticalChannel + doc: An optical channel used to record from an imaging plane. + quantity: '+' + links: + - name: device + target_type: Device + doc: Link to the Device object that was used to record from this electrode. + +- neurodata_type_def: OpticalChannel + neurodata_type_inc: NWBContainer + doc: An optical channel used to record from an imaging plane. + datasets: + - name: description + dtype: text + doc: Description or other notes about the channel. + - name: emission_lambda + dtype: float32 + doc: Emission wavelength for channel, in nm. + +- neurodata_type_def: MotionCorrection + neurodata_type_inc: NWBDataInterface + default_name: MotionCorrection + doc: 'An image stack where all frames are shifted (registered) to a common coordinate + system, to account for movement and drift between frames. Note: each frame at + each point in time is assumed to be 2-D (has only x & y dimensions).' + groups: + - neurodata_type_inc: CorrectedImageStack + doc: Results from motion correction of an image stack. + quantity: '+' + +- neurodata_type_def: CorrectedImageStack + neurodata_type_inc: NWBDataInterface + doc: Results from motion correction of an image stack. + groups: + - name: corrected + neurodata_type_inc: ImageSeries + doc: Image stack with frames shifted to the common coordinates. + - name: xy_translation + neurodata_type_inc: TimeSeries + doc: Stores the x,y delta necessary to align each frame to the common coordinates, + for example, to align each frame to a reference image. + links: + - name: original + target_type: ImageSeries + doc: Link to ImageSeries object that is being registered. diff --git a/nwb-schema/2.8.0/core/nwb.retinotopy.yaml b/nwb-schema/2.8.0/core/nwb.retinotopy.yaml new file mode 100644 index 00000000..1cf98457 --- /dev/null +++ b/nwb-schema/2.8.0/core/nwb.retinotopy.yaml @@ -0,0 +1,234 @@ +groups: +- neurodata_type_def: ImagingRetinotopy + neurodata_type_inc: NWBDataInterface + default_name: ImagingRetinotopy + doc: 'DEPRECATED. Intrinsic signal optical imaging or widefield imaging for measuring + retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses + to specific stimuli and a combined polarity map from which to identify visual areas. + This group does not store the raw responses imaged during retinotopic mapping or the + stimuli presented, but rather the resulting phase and power maps after applying a Fourier + transform on the averaged responses. + Note: for data consistency, all images and arrays are stored in the format [row][column] + and [row, col], which equates to [y][x]. Field of view and dimension arrays may + appear backward (i.e., y before x).' + datasets: + - name: axis_1_phase_map + dtype: float32 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: Phase response to stimulus on the first measured axis. + attributes: + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: unit + dtype: text + doc: Unit that axis data is stored in (e.g., degrees). + - name: axis_1_power_map + dtype: float32 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: Power response on the first measured axis. Response is scaled so 0.0 is no + power in the response and 1.0 is maximum relative power. + quantity: '?' + attributes: + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: unit + dtype: text + doc: Unit that axis data is stored in (e.g., degrees). + - name: axis_2_phase_map + dtype: float32 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: Phase response to stimulus on the second measured axis. + attributes: + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: unit + dtype: text + doc: Unit that axis data is stored in (e.g., degrees). + - name: axis_2_power_map + dtype: float32 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: Power response on the second measured axis. Response is scaled so 0.0 is + no power in the response and 1.0 is maximum relative power. + quantity: '?' + attributes: + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: unit + dtype: text + doc: Unit that axis data is stored in (e.g., degrees). + - name: axis_descriptions + dtype: text + dims: + - axis_1, axis_2 + shape: + - 2 + doc: Two-element array describing the contents of the two response axis fields. + Description should be something like ['altitude', 'azimuth'] or '['radius', + 'theta']. + - name: focal_depth_image + dtype: uint16 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: 'Gray-scale image taken with same settings/parameters (e.g., focal depth, + wavelength) as data collection. Array format: [rows][columns].' + quantity: '?' + attributes: + - name: bits_per_pixel + dtype: int32 + doc: Number of bits used to represent each value. This is necessary to determine + maximum (white) pixel value. + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: focal_depth + dtype: float32 + doc: Focal depth offset, in meters. + - name: format + dtype: text + doc: Format of image. Right now only 'raw' is supported. + - name: sign_map + dtype: float32 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: Sine of the angle between the direction of the gradient in axis_1 and axis_2. + quantity: '?' + attributes: + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: vasculature_image + dtype: uint16 + dims: + - num_rows + - num_cols + shape: + - null + - null + doc: 'Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]' + attributes: + - name: bits_per_pixel + dtype: int32 + doc: Number of bits used to represent each value. This is necessary to determine + maximum (white) pixel value + - name: dimension + dtype: int32 + dims: + - num_rows, num_cols + shape: + - 2 + doc: 'Number of rows and columns in the image. NOTE: row, column representation + is equivalent to height, width.' + - name: field_of_view + dtype: float32 + dims: + - height, width + shape: + - 2 + doc: Size of viewing area, in meters. + - name: format + dtype: text + doc: Format of image. Right now only 'raw' is supported. diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/base.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/base.yaml new file mode 100644 index 00000000..1e8cbc00 --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/base.yaml @@ -0,0 +1,21 @@ +# hdmf-schema-language=2.0.2 +datasets: +- data_type_def: Data + doc: An abstract data type for a dataset. + +groups: +- data_type_def: Container + doc: An abstract data type for a group storing collections of data and + metadata. Base type for all data and metadata containers. + +- data_type_def: SimpleMultiContainer + data_type_inc: Container + doc: A simple Container for holding onto multiple containers. + datasets: + - data_type_inc: Data + quantity: '*' + doc: Data objects held within this SimpleMultiContainer. + groups: + - data_type_inc: Container + quantity: '*' + doc: Container objects held within this SimpleMultiContainer. diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/experimental.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/experimental.yaml new file mode 100644 index 00000000..0cb6ba45 --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/experimental.yaml @@ -0,0 +1,14 @@ +groups: [] +datasets: +- data_type_def: EnumData + data_type_inc: VectorData + dtype: uint8 + doc: Data that come from a fixed set of values. A data value of i corresponds + to the i-th value in the VectorData referenced by the 'elements' attribute. + attributes: + - name: elements + dtype: + target_type: VectorData + reftype: object + doc: Reference to the VectorData object that contains the enumerable elements + diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/namespace.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/namespace.yaml new file mode 100644 index 00000000..b174003f --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/namespace.yaml @@ -0,0 +1,51 @@ +# hdmf-schema-language=2.0.2 +namespaces: +- name: hdmf-common + doc: Common data structures provided by HDMF + author: + - Andrew Tritt + - Oliver Ruebel + - Ryan Ly + - Ben Dichter + contact: + - ajtritt@lbl.gov + - oruebel@lbl.gov + - rly@lbl.gov + - bdichter@lbl.gov + full_name: HDMF Common + schema: + - doc: base data types + source: base.yaml + title: Base data types + - doc: data types for a column-based table + source: table.yaml + title: Table data types + - doc: data types for different types of sparse matrices + source: sparse.yaml + title: Sparse data types + version: 1.8.0 + +- name: hdmf-experimental + doc: Experimental data structures provided by HDMF. These are not guaranteed to be available in the future. + author: + - Andrew Tritt + - Oliver Ruebel + - Ryan Ly + - Ben Dichter + - Matthew Avaylon + contact: + - ajtritt@lbl.gov + - oruebel@lbl.gov + - rly@lbl.gov + - bdichter@lbl.gov + - mavaylon@lbl.gov + full_name: HDMF Experimental + schema: + - namespace: hdmf-common + - doc: Experimental data types + source: experimental.yaml + title: Experimental data types + - doc: data types for storing references to web accessible resources + source: resources.yaml + title: Resource reference data types + version: 0.5.0 diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/resources.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/resources.yaml new file mode 100644 index 00000000..5e091c77 --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/resources.yaml @@ -0,0 +1,105 @@ +# hdmf-schema-language=2.0.2 +groups: +- data_type_def: HERD + data_type_inc: Container + doc: "HDMF External Resources Data Structure. A set of six tables for tracking external resource references in a file or across multiple files." + datasets: + - data_type_inc: Data + name: keys + doc: A table for storing user terms that are used to refer to external resources. + dtype: + - name: key + dtype: text + doc: The user term that maps to one or more resources in the `resources` table, e.g., "human". + dims: + - num_rows + shape: + - null + + - data_type_inc: Data + name: files + doc: A table for storing object ids of files used in external resources. + dtype: + - name: file_object_id + dtype: text + doc: The object id (UUID) of a file that contains objects that refers to external resources. + dims: + - num_rows + shape: + - null + + - data_type_inc: Data + name: entities + doc: A table for mapping user terms (i.e., keys) to resource entities. + dtype: + - name: entity_id + dtype: text + doc: "The compact uniform resource identifier (CURIE) of the entity, in the form + [prefix]:[unique local identifier], e.g., 'NCBI_TAXON:9606'." + - name: entity_uri + dtype: text + doc: "The URI for the entity this reference applies to. This can be an empty string. + e.g., https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=info&id=9606" + dims: + - num_rows + shape: + - null + + - data_type_inc: Data + name: objects + doc: A table for identifying which objects in a file contain references to external resources. + dtype: + - name: files_idx + dtype: uint + doc: The row index to the file in the `files` table containing the object. + - name: object_id + dtype: text + doc: The object id (UUID) of the object. + - name: object_type + dtype: text + doc: The data type of the object. + - name: relative_path + dtype: text + doc: The relative path from the data object with the `object_id` to the dataset or attribute + with the value(s) that is associated with an external resource. This can be an empty + string if the object is a dataset that contains the value(s) that is associated + with an external resource. + - name: field + dtype: text + doc: The field within the compound data type using an external resource. This is used only if + the dataset or attribute is a compound data type; otherwise this should be an empty + string. + dims: + - num_rows + shape: + - null + + - data_type_inc: Data + name: object_keys + doc: A table for identifying which objects use which keys. + dtype: + - name: objects_idx + dtype: uint + doc: The row index to the object in the `objects` table that holds the key + - name: keys_idx + dtype: uint + doc: The row index to the key in the `keys` table. + dims: + - num_rows + shape: + - null + + - data_type_inc: Data + name: entity_keys + doc: A table for identifying which keys use which entity. + dtype: + - name: entities_idx + dtype: uint + doc: The row index to the entity in the `entities` table. + - name: keys_idx + dtype: uint + doc: The row index to the key in the `keys` table. + dims: + - num_rows + shape: + - null diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/sparse.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/sparse.yaml new file mode 100644 index 00000000..09de8177 --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/sparse.yaml @@ -0,0 +1,35 @@ +# hdmf-schema-language=2.0.2 +groups: +- data_type_def: CSRMatrix + data_type_inc: Container + doc: 'A compressed sparse row matrix. Data are stored in the standard CSR format, where column indices for row i are + stored in indices[indptr[i]:indptr[i+1]] and their corresponding values are stored in data[indptr[i]:indptr[i+1]].' + attributes: + - name: shape + dtype: uint + dims: + - number of rows, number of columns + shape: + - 2 + doc: The shape (number of rows, number of columns) of this sparse matrix. + datasets: + - name: indices + dtype: uint + dims: + - number of non-zero values + shape: + - null + doc: The column indices. + - name: indptr + dtype: uint + dims: + - number of rows in the matrix + 1 + shape: + - null + doc: The row index pointer. + - name: data + dims: + - number of non-zero values + shape: + - null + doc: The non-zero values in the matrix. diff --git a/nwb-schema/2.8.0/hdmf-common-schema/common/table.yaml b/nwb-schema/2.8.0/hdmf-common-schema/common/table.yaml new file mode 100644 index 00000000..2bacf8b3 --- /dev/null +++ b/nwb-schema/2.8.0/hdmf-common-schema/common/table.yaml @@ -0,0 +1,168 @@ +# hdmf-schema-language=2.0.2 +datasets: + +- data_type_def: VectorData + data_type_inc: Data + doc: An n-dimensional dataset representing a column of a DynamicTable. + If used without an accompanying VectorIndex, first dimension is + along the rows of the DynamicTable and each step along the first + dimension is a cell of the larger table. VectorData can also be + used to represent a ragged array if paired with a VectorIndex. + This allows for storing arrays of varying length in a single cell + of the DynamicTable by indexing into this VectorData. The first + vector is at VectorData[0:VectorIndex[0]]. The second vector is at + VectorData[VectorIndex[0]:VectorIndex[1]], and so on. + dims: + - - dim0 + - - dim0 + - dim1 + - - dim0 + - dim1 + - dim2 + - - dim0 + - dim1 + - dim2 + - dim3 + shape: + - - null + - - null + - null + - - null + - null + - null + - - null + - null + - null + - null + attributes: + - name: description + dtype: text + doc: Description of what these vectors represent. + +- data_type_def: VectorIndex + data_type_inc: VectorData + dtype: uint8 + doc: Used with VectorData to encode a ragged array. An array of indices + into the first dimension of the target VectorData, and forming a map + between the rows of a DynamicTable and the indices of the VectorData. + The name of the VectorIndex is expected to be the name of the target + VectorData object followed by "_index". + dims: + - num_rows + shape: + - null + attributes: + - name: target + dtype: + target_type: VectorData + reftype: object + doc: Reference to the target dataset that this index applies to. + +- data_type_def: ElementIdentifiers + data_type_inc: Data + default_name: element_id + dtype: int + dims: + - num_elements + shape: + - null + doc: A list of unique identifiers for values within a dataset, e.g. rows of a DynamicTable. + +- data_type_def: DynamicTableRegion + data_type_inc: VectorData + dtype: int + doc: DynamicTableRegion provides a link from one table to an index or region of another. The `table` attribute is a + link to another `DynamicTable`, indicating which table is referenced, and the data is int(s) indicating the row(s) + (0-indexed) of the target array. `DynamicTableRegion`s can be used to associate rows with repeated meta-data without + data duplication. They can also be used to create hierarchical relationships between multiple `DynamicTable`s. + `DynamicTableRegion` objects may be paired with a `VectorIndex` object to create ragged references, so a single cell + of a `DynamicTable` can reference many rows of another `DynamicTable`. + dims: + - num_rows + shape: + - null + attributes: + - name: table + dtype: + target_type: DynamicTable + reftype: object + doc: Reference to the DynamicTable object that this region applies to. + - name: description + dtype: text + doc: Description of what this table region points to. + +groups: + +- data_type_def: DynamicTable + data_type_inc: Container + doc: A group containing multiple datasets that are aligned on the first dimension + (Currently, this requirement if left up to APIs to check and enforce). These datasets + represent different columns in the table. Apart from a column that contains unique + identifiers for each row, there are no other required datasets. Users are free to add + any number of custom VectorData objects (columns) here. DynamicTable also supports + ragged array columns, where each element can be of a different size. To add a ragged + array column, use a VectorIndex type to index the corresponding VectorData type. + See documentation for VectorData and VectorIndex for more details. + Unlike a compound data type, which is analogous to storing an + array-of-structs, a DynamicTable can be thought of as a struct-of-arrays. This provides + an alternative structure to choose from when optimizing storage for anticipated access + patterns. Additionally, this type provides a way of creating a table without having to + define a compound type up front. Although this convenience may be attractive, users + should think carefully about how data will be accessed. DynamicTable is more appropriate + for column-centric access, whereas a dataset with a compound type would be more + appropriate for row-centric access. Finally, data size should also be taken into account. + For small tables, performance loss may be an acceptable trade-off for the flexibility of + a DynamicTable. + attributes: + - name: colnames + dtype: text + dims: + - num_columns + shape: + - null + doc: The names of the columns in this table. This should be used to specify + an order to the columns. + - name: description + dtype: text + doc: Description of what is in this dynamic table. + datasets: + - name: id + data_type_inc: ElementIdentifiers + dtype: int + dims: + - num_rows + shape: + - null + doc: Array of unique identifiers for the rows of this dynamic table. + - data_type_inc: VectorData + doc: Vector columns, including index columns, of this dynamic table. + quantity: '*' + +- data_type_def: AlignedDynamicTable + data_type_inc: DynamicTable + doc: DynamicTable container that supports storing a collection of sub-tables. Each + sub-table is a DynamicTable itself that is aligned with the main table by row + index. I.e., all DynamicTables stored in this group MUST have the same number + of rows. This type effectively defines a 2-level table in which the main data + is stored in the main table implemented by this type and additional columns of + the table are grouped into categories, with each category being represented by + a separate DynamicTable stored within the group. + attributes: + - name: categories + dtype: text + dims: + - num_categories + shape: + - null + doc: The names of the categories in this AlignedDynamicTable. Each category is + represented by one DynamicTable stored in the parent group. This attribute should + be used to specify an order of categories and the category names must match + the names of the corresponding DynamicTable in the group. + groups: + - data_type_inc: DynamicTable + doc: A DynamicTable representing a particular category for columns in the AlignedDynamicTable + parent container. The table MUST be aligned with (i.e., have the same number + of rows) as all other DynamicTables stored in the AlignedDynamicTable parent + container. The name of the category is given by the name of the DynamicTable + and its description by the description attribute of the DynamicTable. + quantity: '*' diff --git a/tutorials/ecephys.mlx b/tutorials/ecephys.mlx index ca95380dd00de75ced024335e7a18af8dcefeabb..f5b4a2ef0520bd8c7186fd774130aeda5d668cbe 100644 GIT binary patch delta 21853 zcmV(;K-<5g{uRZ57O>$13a^Q2F8d<@0D<0<>jN8q+j85;mGAnBj#6GyiNu>oT9V^Q zQIwcbm1jm}Yi2g(sj13G?8ENwyKXV|9I0oI@$m0H-DL(*}*WGIDRak zy&s)_&0g=1<9K>jtwzJKGqIvdFm-%*cN>HgD~3PAQFRuC_B0HJP830R6R+B+)!NmG z<@)>B zSe(P$0V?A1Qyf}D$MX`;3dN=84C64Ej^_X`FBr|mO%ey8Yk4@Gm?o8((xq?bj$EZ> zPljh5FLpxTik)uY$0>X#!y*925t8ZAFqllO*d4f@8_x;I`_y-L9JoVAymkINaYHA6 zdcFU)QLCR=1pLU3_eTT#5N@)#wv` zkuC4v=9?e7-#Qke+{1g#|HyskH}bFeo2B_>Hl?bjaNj)Y56@scpE{xE`X8Z*l=Hk42e%fu+^o4`! zMi9Bkn{a9X8^RL-9|ocqg0=xDJQwGdZ*Pl|JIAMu_RCg=lHX0;kB+!;Cjc=RdJ%>0 zP$AeNtS&p3FYC?o3_ZUK!Wj04(3&YR74ua>3f(Fi8d2}5gtzD)r1n(ap8 zqTMsc{?U&f2ZA-xqSd`TZe-P&3nvDOLCV=S2Glx@&SkT6oWTH8pn#k6`g@KQC!mRo 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Neurodata Without Borders Extracellular Electrophysiology Tutorial

Table of Contents
Neurodata Without Borders Extracellular Electrophysiology Tutorial - About This Tutorial - Before You Begin - Setting up the NWB File - Electrode Information - Electrodes Table - Links - Recorded Extracellular Signals - Referencing Electrodes - Raw Voltage Data - Processed Extracellular Electrical Signals - LFP - Other Types of Filtered Electrical Signals - Decomposition of LFP Data into Frequency Bands - Spike Times and Extracellular Events - Sorted Spike Times - Ragged Arrays - Unsorted Spike Times - Detected Events - Storing Spike Features (e.g Principal Components) - Choosing NWB-Types for Electrophysiology Data (A Summary) - Writing the NWB File - Reading NWB Data - Accessing Data Regions -Learn more! - See the API documentation to learn what data types are available. - MATLAB tutorials - Python tutorials

About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: - - nwb_version: '2.7.0' +.S22 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left; }

Neurodata Without Borders Extracellular Electrophysiology Tutorial

Table of Contents
Neurodata Without Borders Extracellular Electrophysiology Tutorial + About This Tutorial + Before You Begin + Setting up the NWB File + Electrode Information + Electrodes Table + Links + Recorded Extracellular Signals + Referencing Electrodes + Raw Voltage Data + Processed Extracellular Electrical Signals + LFP + Other Types of Filtered Electrical Signals + Decomposition of LFP Data into Frequency Bands + Spike Times and Extracellular Events + Sorted Spike Times + Ragged Arrays + Unsorted Spike Times + Detected Events + Storing Spike Features (e.g Principal Components) + Choosing NWB-Types for Electrophysiology Data (A Summary) + Writing the NWB File + Reading NWB Data + Accessing Data Regions +Learn more! + See the API documentation to learn what data types are available. + MATLAB tutorials + Python tutorials

About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: + + nwb_version: '2.8.0' file_create_date: [] identifier: 'Mouse5_Day3' session_description: 'mouse in open exploration' @@ -146,6 +146,7 @@ general_subject: [] general_surgery: '' general_virus: '' + general_was_generated_by: '' intervals: [0×1 types.untyped.Set] intervals_epochs: [] intervals_invalid_times: [] @@ -155,900 +156,900 @@ stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] units: [] -

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
device = types.core.Device(...
'description', 'the best array', ...
'manufacturer', 'Probe Company 9000' ...
);
nwb.general_devices.set('array', device);
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Decomposition of LFP Data into Frequency Bands

In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a DecompositionSeries object. This object allows you to include metadata about the frequency bands and metric used (e.g., power, phase, amplitude), as well as link the decomposed data to the original TimeSeries signal the data was derived from.
In this tutorial, the examples for FilteredEphys and DecompositionSeries may appear similar. However, the key difference is that DecompositionSeries is specialized for storing the results of spectral analyses of timeseries data in general, whereas FilteredEphys is defined specifically as a container for filtered electrical signals.
Note: When adding data to a DecompositionSeries, the data argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the dimensionMapNoDataPipes tutorial)
% Define the frequency bands of interest (in Hz):
band_names = {'theta'; 'beta'; 'gamma'};
band_mean = [8; 21; 55];
band_stdev = [2; 4.5; 12.5];
band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
 
% The bands should be added to the DecompositionSeries as a dynamic table
bands = table(band_names, band_mean, band_stdev, band_limits, ...
'VariableNames', {'band_name', 'band_mean', 'band_stdev', 'band_limits'})
bands = 3×4 table
 band_nameband_meanband_stdevband_limits
12
1'theta'82412
2'beta'214.50001230
3'gamma'5512.50003080
 
bands = util.table2nwb( bands );
 
% Generate random phase data for the demonstration.
phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
 
decomp_series = types.core.DecompositionSeries(...
'data', phase_data, ...
'bands', bands, ...
'metric', 'phase', ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000.0, ... % Hz
'source_channels', electrode_table_region, ...
'source_timeseries', lfp_electrical_series);
 
% Add decomposition series to ecephys module
ecephys_module.nwbdatainterface.set('theta', decomp_series);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×24 double1×19 double1×20 double1×25 double1×22 double1×20 double1×22 double1×17 double1×21 double1×25 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1124×1 double
2219×1 double
3320×1 double
4425×1 double
5522×1 double
6620×1 double
7722×1 double
8817×1 double
9921×1 double
101025×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = - - 0.3732 0.7791 0.5937 - 0.7586 0.8887 0.6501 - 0.3051 0.4772 0.4288 - 0.4012 0.5913 0.3957 - 0.3939 0.3286 0.1415 - 0.5662 0.5443 0.2179 - 0.9869 0.4951 0.4386 - 0.7861 0.9518 0.4311 - 0.6751 0.4070 0.1629 - 0.4426 0.5435 0.8580 - 0.0396 0.6455 0.9119 - 0.9309 0.4391 0.6281 - 0.2086 0.5926 0.6285 - 0.9404 0.2170 0.2241 - 0.5310 0.3074 0.9547 - 0.3391 0.8501 0.5889 - 0.0462 0.7716 0.8651 - 0.0262 0.6235 0.9419 - 0.8849 0.0555 0.4975 - 0.4348 0.8429 0.5563 - 0.3528 0.4937 0.8508 - 0.5342 0.9424 0.9996 - 0.5746 0.0628 0.8755 - 0.4744 0.8553 0.9244 - 0.7230 0.6936 0.2125 - 0.4087 0.2393 0.7200 - 0.2982 0.4357 0.2804 - 0.1524 0.7107 0.3372 - 0.9375 0.9103 0.1958 - 0.7425 0.0687 0.4879 - 0.2087 0.0370 0.0393 - 0.1920 0.2053 0.4417 - 0.1422 0.4232 0.1870 - 0.8810 0.7692 0.7563 - 0.1456 0.9029 0.5702 - 0.2523 0.1514 0.7324 - 0.4195 0.4896 0.2667 - 0.3307 0.6090 0.6230 - 0.1701 0.9241 0.5880 - 0.5319 0.0420 0.6809 +

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.
The electrodes table references a required ElectrodeGroup, which is used to represent a group of electrodes. Before creating an ElectrodeGroup, you must define a Device object. The fields description, manufacturer, model_number, model_name, and serial_number are optional, but recommended.
device = types.core.Device(...
'description', 'A 12-channel array with 4 shanks and 3 channels per shank', ...
'manufacturer', 'Array Technologies', ...
'model_number', 'PRB_1_4_0480_123', ...
'model_name', 'Neurovoxels 0.99', ...
'serial_number', '1234567890' ...
);
 
% Add device to nwb object
nwb.general_devices.set('array', device);

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Decomposition of LFP Data into Frequency Bands

In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a DecompositionSeries object. This object allows you to include metadata about the frequency bands and metric used (e.g., power, phase, amplitude), as well as link the decomposed data to the original TimeSeries signal the data was derived from.
In this tutorial, the examples for FilteredEphys and DecompositionSeries may appear similar. However, the key difference is that DecompositionSeries is specialized for storing the results of spectral analyses of timeseries data in general, whereas FilteredEphys is defined specifically as a container for filtered electrical signals.
Note: When adding data to a DecompositionSeries, the data argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the dimensionMapNoDataPipes tutorial)
% Define the frequency bands of interest (in Hz):
band_names = {'theta'; 'beta'; 'gamma'};
band_mean = [8; 21; 55];
band_stdev = [2; 4.5; 12.5];
band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
 
% The bands should be added to the DecompositionSeries as a dynamic table
bands = table(band_names, band_mean, band_stdev, band_limits, ...
'VariableNames', {'band_name', 'band_mean', 'band_stdev', 'band_limits'})
bands = 3×4 table
 band_nameband_meanband_stdevband_limits
12
1'theta'82412
2'beta'214.50001230
3'gamma'5512.50003080
 
bands = util.table2nwb( bands );
 
% Generate random phase data for the demonstration.
phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
 
decomp_series = types.core.DecompositionSeries(...
'data', phase_data, ...
'bands', bands, ...
'metric', 'phase', ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000.0, ... % Hz
'source_channels', electrode_table_region, ...
'source_timeseries', lfp_electrical_series);
 
% Add decomposition series to ecephys module
ecephys_module.nwbdatainterface.set('theta', decomp_series);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×18 double1×24 double1×22 double1×19 double1×16 double1×28 double1×19 double1×27 double1×26 double1×19 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1118×1 double
2224×1 double
3322×1 double
4419×1 double
5516×1 double
6628×1 double
7719×1 double
8827×1 double
9926×1 double
101019×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = + + 0.8840 0.4104 0.0773 + 0.2895 0.4434 0.7276 + 0.7282 0.2321 0.1961 + 0.8443 0.0524 0.5851 + 0.3713 0.0241 0.4969 + 0.5494 0.6839 0.7355 + 0.1517 0.7949 0.5979 + 0.6635 0.3582 0.3362 + 0.2304 0.5858 0.8594 + 0.3852 0.2620 0.3264 + 0.3118 0.3305 0.5870 + 0.4677 0.4784 0.8121 + 0.5636 0.3811 0.2998 + 0.5963 0.1882 0.1086 + 0.6762 0.3520 0.0824 + 0.0599 0.0252 0.1052 + 0.1278 0.7587 0.7405 + 0.0422 0.7312 0.6667 + 0.2422 0.8595 0.1119 + 0.6675 0.0296 0.8458 + 0.2531 0.6969 0.2860 + 0.8549 0.6434 0.0730 + 0.9224 0.1372 0.5922 + 0.8046 0.2026 0.3796 + 0.0283 0.4356 0.2686 + 0.0220 0.7392 0.6113 + 0.4983 0.7065 0.8138 + 0.1084 0.7089 0.0159 + 0.4491 0.8796 0.8570 + 0.2666 0.8124 0.1619 + 0.8874 0.7056 0.0604 + 0.7425 0.2605 0.2477 + 0.4856 0.2008 0.1097 + 0.1616 0.0121 0.0490 + 0.1388 0.9127 0.5684 + 0.1459 0.9058 0.1248 + 0.1711 0.1007 0.3990 + 0.8393 0.6710 0.8213 + 0.6889 0.1315 0.7412 + 0.3700 0.8719 0.8248 spike_snippets(:,:,2) = - 0.6946 0.9867 0.1621 - 0.2072 0.7996 0.6578 - 0.7396 0.9594 0.2076 - 0.5369 0.8403 0.3706 - 0.3963 0.8120 0.9353 - 0.0532 0.0624 0.7060 - 0.1420 0.1284 0.4933 - 0.9286 0.5055 0.6835 - 0.3156 0.7733 0.3820 - 0.4400 0.2803 0.6084 - 0.2611 0.6750 0.0324 - 0.0603 0.6302 0.9327 - 0.3091 0.1783 0.3724 - 0.5451 0.6656 0.4110 - 0.1974 0.1752 0.6761 - 0.9598 0.3204 0.7998 - 0.7463 0.9291 0.1296 - 0.1828 0.4812 0.6270 - 0.7885 0.1908 0.7519 - 0.1154 0.7749 0.6845 - 0.5216 0.3994 0.9655 - 0.2219 0.3672 0.2583 - 0.4704 0.8092 0.4067 - 0.9942 0.0751 0.1858 - 0.2373 0.5807 0.9973 - 0.6321 0.7136 0.1707 - 0.4648 0.7071 0.1290 - 0.6359 0.6416 0.3091 - 0.1669 0.8553 0.1380 - 0.7639 0.3221 0.2149 - 0.0145 0.3226 0.4020 - 0.5560 0.0633 0.2573 - 0.6934 0.7846 0.9890 - 0.5472 0.7723 0.0297 - 0.7413 0.4124 0.4747 - 0.9513 0.8615 0.3798 - 0.8774 0.0074 0.0822 - 0.2707 0.2008 0.6769 - 0.9066 0.2487 0.3473 - 0.9031 0.5947 0.2998 + 0.3587 0.9811 0.2971 + 0.7277 0.8138 0.2456 + 0.7136 0.7200 0.9779 + 0.8198 0.6446 0.2123 + 0.7078 0.3514 0.5493 + 0.8665 0.6074 0.3950 + 0.9498 0.9263 0.6231 + 0.4755 0.7082 0.7975 + 0.1605 0.2079 0.8562 + 0.0512 0.8677 0.3511 + 0.1897 0.9574 0.7471 + 0.6744 0.6732 0.0225 + 0.6024 0.5067 0.0387 + 0.8992 0.5272 0.5283 + 0.5722 0.9761 0.7628 + 0.7082 0.5364 0.8801 + 0.1330 0.8471 0.4737 + 0.6811 0.2800 0.9775 + 0.5363 0.9029 0.5870 + 0.7977 0.0398 0.4918 + 0.4657 0.2871 0.0126 + 0.9780 0.3657 0.7575 + 0.8543 0.7694 0.6129 + 0.3676 0.3227 0.3590 + 0.4902 0.7841 0.7379 + 0.7595 0.7320 0.6265 + 0.2180 0.4572 0.1383 + 0.3310 0.0001 0.5000 + 0.0245 0.9370 0.0438 + 0.7992 0.6612 0.7073 + 0.3443 0.9668 0.3462 + 0.9261 0.6006 0.5526 + 0.4018 0.0765 0.4504 + 0.0286 0.3908 0.4233 + 0.6364 0.7242 0.6814 + 0.4236 0.0721 0.5432 + 0.5599 0.7267 0.6644 + 0.1459 0.2461 0.6893 + 0.2536 0.3381 0.0641 + 0.6326 0.7257 0.8844 spike_snippets(:,:,3) = - 0.5451 0.8070 0.3206 - 0.6928 0.4280 0.8321 - 0.6805 0.9535 0.2361 - 0.2666 0.1868 0.4845 - 0.9815 0.7233 0.8900 - 0.7260 0.8210 0.3314 - 0.3164 0.7107 0.6713 - 0.4181 0.4670 0.0010 - 0.9741 0.2856 0.7074 - 0.4238 0.8375 0.8412 - 0.9819 0.8191 0.0949 - 0.4364 0.9326 0.4354 - 0.7310 0.9098 0.4869 - 0.4943 0.9199 0.3313 - 0.6452 0.6932 0.7494 - 0.0505 0.9546 0.9591 - 0.1264 0.4607 0.1279 - 0.9333 0.0234 0.7867 - 0.3341 0.3857 0.6957 - 0.7642 0.1095 0.0429 - 0.9580 0.4884 0.6948 - 0.6084 0.5523 0.8079 - 0.5066 0.6660 0.3920 - 0.0327 0.7744 0.8886 - 0.9240 0.5647 0.0658 - 0.5356 0.0641 0.7811 - 0.4054 0.5640 0.4154 - 0.9789 0.5963 0.8608 - 0.5668 0.1509 0.3670 - 0.7025 0.9988 0.9933 - 0.8613 0.4405 0.1464 - 0.1041 0.7715 0.3392 - 0.3661 0.3882 0.5087 - 0.7465 0.0436 0.0087 - 0.3572 0.4768 0.0233 - 0.9081 0.2854 0.6945 - 0.2392 0.3414 0.6361 - 0.6355 0.0123 0.6029 - 0.8906 0.9561 0.0998 - 0.8724 0.4231 0.8791 + 0.1362 0.1425 0.3644 + 0.2234 0.6658 0.9637 + 0.4727 0.6631 0.2876 + 0.0085 0.0553 0.4585 + 0.9502 0.0977 0.6954 + 0.2312 0.1565 0.5087 + 0.2705 0.3535 0.5659 + 0.2368 0.0900 0.8627 + 0.1081 0.7041 0.3725 + 0.6482 0.0005 0.1544 + 0.2362 0.6108 0.6114 + 0.8803 0.9088 0.2091 + 0.0856 0.8648 0.1002 + 0.3896 0.2870 0.4510 + 0.0470 0.1266 0.1895 + 0.9096 0.4791 0.3934 + 0.8571 0.5306 0.4322 + 0.5716 0.3844 0.8254 + 0.8413 0.8704 0.3305 + 0.5318 0.7753 0.7935 + 0.4743 0.8884 0.4022 + 0.8342 0.4738 0.8089 + 0.8143 0.2582 0.8370 + 0.8134 0.4044 0.0324 + 0.2955 0.2959 0.2695 + 0.6966 0.4155 0.6720 + 0.4360 0.3938 0.8744 + 0.4421 0.9780 0.2194 + 0.2874 0.1777 0.7541 + 0.8277 0.5363 0.0101 + 0.1075 0.8672 0.3565 + 0.5107 0.4739 0.3066 + 0.9965 0.3262 0.0391 + 0.6357 0.1903 0.3097 + 0.2875 0.9710 0.5432 + 0.2359 0.5163 0.9214 + 0.8104 0.6590 0.3723 + 0.2666 0.0595 0.3578 + 0.8609 0.1882 0.3043 + 0.6504 0.3113 0.0771 spike_snippets(:,:,4) = - 0.4854 0.2021 0.4178 - 0.1042 0.0572 0.5235 - 0.7787 0.8715 0.3109 - 0.0947 0.2513 0.3742 - 0.4724 0.0945 0.4348 - 0.7312 0.1811 0.7789 - 0.1908 0.4436 0.7879 - 0.3579 0.8552 0.1141 - 0.4405 0.4878 0.4577 - 0.7250 0.3002 0.0478 - 0.4961 0.2143 0.2460 - 0.3935 0.5856 0.3734 - 0.4304 0.8539 0.6590 - 0.4677 0.2888 0.9005 - 0.6995 0.0156 0.3576 - 0.8153 0.7794 0.9313 - 0.6333 0.6757 0.7350 - 0.5318 0.6517 0.2361 - 0.8009 0.7342 0.3832 - 0.2961 0.9837 0.3576 - 0.2021 0.9876 0.7297 - 0.9424 0.2764 0.0504 - 0.1479 0.1211 0.1844 - 0.4527 0.4286 0.2950 - 0.4787 0.1957 0.8230 - 0.4661 0.1299 0.0478 - 0.4762 0.0754 0.6567 - 0.4106 0.8626 0.7855 - 0.2155 0.7685 0.4191 - 0.8953 0.0073 0.6560 - 0.2029 0.2695 0.3976 - 0.1809 0.4246 0.5822 - 0.9363 0.5071 0.9302 - 0.3073 0.1328 0.5443 - 0.6552 0.0491 0.8480 - 0.3227 0.3559 0.1201 - 0.0361 0.0332 0.5380 - 0.0322 0.5357 0.4574 - 0.8864 0.4393 0.4757 - 0.3848 0.2086 0.7510 + 0.8050 0.2022 0.5860 + 0.5796 0.4442 0.5489 + 0.3618 0.1478 0.7466 + 0.9093 0.7029 0.2356 + 0.0460 0.1836 0.8784 + 0.2005 0.4046 0.1156 + 0.4469 0.5336 0.6318 + 0.9771 0.7806 0.7635 + 0.4451 0.4810 0.1803 + 0.1741 0.2258 0.4179 + 0.7091 0.6490 0.2747 + 0.4820 0.6451 0.5042 + 0.6605 0.6045 0.9469 + 0.0161 0.7395 0.0059 + 0.4782 0.2793 0.8853 + 0.4091 0.9862 0.4804 + 0.8455 0.2913 0.5672 + 0.0649 0.6610 0.3551 + 0.9321 0.3362 0.8347 + 0.4598 0.4687 0.1846 + 0.7372 0.5780 0.8394 + 0.3125 0.0901 0.3703 + 0.4837 0.2241 0.0313 + 0.0275 0.7825 0.1160 + 0.6096 0.8158 0.9808 + 0.4574 0.8434 0.0143 + 0.1786 0.6356 0.4819 + 0.8691 0.1741 0.4058 + 0.0887 0.9107 0.3661 + 0.9826 0.7998 0.8946 + 0.9549 0.2801 0.6857 + 0.9784 0.3684 0.1300 + 0.6258 0.8026 0.6570 + 0.5787 0.1236 0.1830 + 0.5000 0.9966 0.2871 + 0.0470 0.1272 0.4818 + 0.8635 0.1213 0.7935 + 0.8270 0.6987 0.0980 + 0.0250 0.9571 0.9466 + 0.4383 0.4128 0.4984 spike_snippets(:,:,5) = - 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0.4796 0.1469 0.7166 - 0.7013 0.1920 0.6477 - 0.3507 0.7289 0.3165 - 0.8040 0.2512 0.8675 - 0.8645 0.4048 0.6909 - 0.6119 0.1603 0.0853 - 0.3848 0.3168 0.8677 - 0.7272 0.9591 0.4262 - 0.4190 0.5329 0.7629 - 0.8615 0.3420 0.3994 - 0.6660 0.7825 0.5770 - 0.0337 0.5107 0.3806 - 0.2774 0.0544 0.5483 - 0.3664 0.7206 0.4609 - 0.3950 0.5821 0.2499 - 0.8709 0.9255 0.2459 - 0.4484 0.3779 0.7865 - 0.5256 0.7493 0.3957 - 0.3331 0.4078 0.6649 - 0.0315 0.3368 0.5578 - 0.8973 0.6093 0.0103 - 0.8446 0.0363 0.2517 - 0.8092 0.1453 0.8943 - 0.7750 0.4485 0.2392 - 0.8051 0.5641 0.9237 - 0.5442 0.3667 0.5998 - 0.7352 0.7777 0.9312 - 0.4753 0.1060 0.7647 - 0.6985 0.3146 0.1770 - 0.9186 0.0215 0.0437 - 0.8929 0.5599 0.3737 - 0.6774 0.9312 0.5492 - 0.2597 0.3554 0.3777 - 0.3778 0.6063 0.0516 - 0.3313 0.3930 0.4723 - 0.3727 0.7249 0.9840 - 0.7936 0.9136 0.4857 - 0.4638 0.5821 0.0701 - 0.8432 0.2344 0.4754 - 0.5643 0.0735 0.2353 + 0.0843 0.0789 0.0190 + 0.9539 0.2201 0.2193 + 0.3115 0.9336 0.0849 + 0.3433 0.4676 0.2098 + 0.2704 0.2124 0.2048 + 0.1612 0.4393 0.1003 + 0.8375 0.4583 0.4274 + 0.0563 0.4216 0.9778 + 0.4400 0.4879 0.2062 + 0.4732 0.5376 0.3918 + 0.9230 0.5727 0.4353 + 0.7881 0.6609 0.1073 + 0.9234 0.2811 0.0459 + 0.5888 0.4915 0.8911 + 0.2365 0.1278 0.2981 + 0.1332 0.9264 0.4458 + 0.3365 0.7043 0.6547 + 0.8653 0.3776 0.0418 + 0.1819 0.6848 0.7250 + 0.0428 0.6453 0.1657 + 0.7494 0.2659 0.1569 + 0.3866 0.6769 0.7458 + 0.0382 0.7546 0.4898 + 0.7645 0.3238 0.0739 + 0.7457 0.2271 0.3200 + 0.9157 0.1533 0.4651 + 0.7660 0.2698 0.7659 + 0.9227 0.1591 0.3390 + 0.8408 0.8849 0.8486 + 0.1186 0.6178 0.3768 + 0.4187 0.6784 0.4346 + 0.3279 0.7755 0.4404 + 0.8957 0.8531 0.9597 + 0.8631 0.8959 0.0828 + 0.1697 0.9542 0.9433 + 0.2106 0.2864 0.0747 + 0.1872 0.6363 0.1239 + 0.0568 0.2628 0.8335 + 0.9199 0.8350 0.3247 + 0.3773 0.3289 0.0652 spike_snippets(:,:,20) = - 0.6873 0.0605 0.9140 - 0.9657 0.0721 0.8518 - 0.8899 0.1328 0.6940 - 0.0387 0.6798 0.9621 - 0.4656 0.5908 0.2731 - 0.7438 0.5670 0.2736 - 0.3971 0.0360 0.3144 - 0.8050 0.7448 0.6957 - 0.0030 0.4603 0.2473 - 0.2465 0.6805 0.3513 - 0.8086 0.2980 0.2490 - 0.2352 0.3977 0.5605 - 0.2713 0.7731 0.5679 - 0.8310 0.7215 0.5309 - 0.7490 0.9698 0.8578 - 0.5483 0.1271 0.5879 - 0.3070 0.0921 0.7794 - 0.1199 0.8039 0.5579 - 0.9437 0.8100 0.8285 - 0.7219 0.4346 0.4720 - 0.7465 0.6658 0.1583 - 0.5633 0.8219 0.5985 - 0.3870 0.4728 0.3190 - 0.4641 0.8891 0.5325 - 0.8391 0.2668 0.5287 - 0.2010 0.5825 0.9167 - 0.9513 0.0738 0.1681 - 0.2306 0.2030 0.1066 - 0.9499 0.1480 0.6067 - 0.9601 0.6199 0.4368 - 0.9094 0.2456 0.3924 - 0.9030 0.7869 0.9830 - 0.1268 0.3279 0.8036 - 0.4003 0.7065 0.6491 - 0.6768 0.2883 0.4791 - 0.1409 0.1552 0.0926 - 0.7783 0.5634 0.3639 - 0.7050 0.2093 0.0664 - 0.2874 0.9361 0.8809 - 0.5040 0.0051 0.3799 -
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]); % reverse dimension order for matnwb
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
0.9651 -0.8883 -0.0094 0.8173 -1.0690 0.1127 -1.4322 -1.0934 -1.5671 1.0822 - -0.6077 0.1766 0.1769 0.3961 -0.4618 -0.6057 0.4036 -0.8789 -1.2575 0.5712 - -0.1929 0.5733 -1.4192 -0.2441 1.7385 -0.8977 -0.4516 -0.9052 -0.1145 0.3583 - 0.2470 -0.4401 1.6814 -0.3924 -0.1789 0.1862 -0.4147 -0.1626 -0.4556 -0.1766 - 0.2099 -0.4356 -0.8001 -0.3095 -1.3299 0.1646 0.5666 1.0098 -0.4388 0.7080 -
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 24×1
0.3390 - 0.8821 - 0.0155 - 0.9190 - 0.3082 - 0.1643 - 0.0970 - 0.8547 - 0.1262 - 0.3271 -

Learn more!

See the API documentation to learn what data types are available.

MATLAB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics:
+ 0.3211 0.6437 0.8537 + 0.1520 0.5627 0.1590 + 0.9570 0.1282 0.0198 + 0.5171 0.9608 0.5398 + 0.5639 0.0015 0.5351 + 0.7871 0.4368 0.6525 + 0.0547 0.4123 0.4774 + 0.2071 0.3338 0.3417 + 0.9039 0.8778 0.9688 + 0.5819 0.2111 0.0526 + 0.7195 0.6208 0.2210 + 0.8373 0.6037 0.2933 + 0.5257 0.4083 0.5324 + 0.3001 0.5219 0.6432 + 0.6423 0.4282 0.2968 + 0.0757 0.3441 0.9642 + 0.8420 0.1641 0.5281 + 0.2294 0.1050 0.7394 + 0.4970 0.7150 0.6435 + 0.2402 0.3646 0.3062 + 0.8428 0.1960 0.4764 + 0.4495 0.6721 0.6090 + 0.3322 0.9081 0.6180 + 0.3987 0.8989 0.3979 + 0.4572 0.4035 0.3269 + 0.7665 0.9586 0.1624 + 0.4178 0.9179 0.0901 + 0.3773 0.9761 0.4298 + 0.8494 0.8965 0.7878 + 0.8920 0.3539 0.2586 + 0.0978 0.6594 0.7246 + 0.3655 0.8161 0.2763 + 0.5166 0.0215 0.4210 + 0.8319 0.7026 0.3662 + 0.1834 0.9958 0.9970 + 0.9425 0.3698 0.1860 + 0.5619 0.5043 0.2714 + 0.1227 0.7896 0.3180 + 0.3038 0.2416 0.8651 + 0.0602 0.9943 0.4567 +
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]); % reverse dimension order for matnwb
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
-3.5690 0.9190 1.1629 0.1770 -1.1149 -0.0227 1.7513 0.0439 0.3346 -0.6137 + -0.8413 1.7329 0.4917 0.5800 -2.9249 0.5906 0.0099 0.5686 1.5835 -0.2693 + -0.0354 0.5030 -0.1769 0.9896 -0.5627 -2.1664 0.5403 -0.6677 0.4199 -0.2109 + 0.7809 -0.3773 -0.7599 -0.6833 -1.2777 -1.6061 0.4971 0.1317 0.0585 -0.8233 + 0.2562 0.5045 0.0044 0.2529 0.9348 2.2901 -0.0942 0.2287 -0.5769 -0.1247 +
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 18×1
0.0591 + 0.2652 + 0.3253 + 0.6815 + 0.7179 + 0.4488 + 0.0672 + 0.6978 + 0.4125 + 0.7288 +

Learn more!

See the API documentation to learn what data types are available.

MATLAB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics: