core_nwb_misc#

pydantic model ConfiguredBaseModel#

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field hdf5_path: str | None = None#

The absolute path that this object is stored in an NWB file

pydantic model LinkML_Meta#

Bases: BaseModel

Extra LinkML Metadata stored as a class attribute

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:
field tree_root: bool = False#
pydantic model AbstractFeatureSeries#

Bases: TimeSeries

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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

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.

field control: List[int] | None [Optional]#

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.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: AbstractFeatureSeriesData [Required]#

Values of each feature at each time.

field description: str | None = None#

Description of the time series.

field feature_units: List[str] | None [Optional]#

Units of each feature.

field features: List[str] [Optional]#

Description of the features represented in TimeSeries::data.

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model AbstractFeatureSeriesData#

Bases: ConfiguredBaseModel

Values of each feature at each time.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* num_times'], Number] | NDArray[Shape['* num_times, * num_features'], Number] | None = None#
field name: Literal['data'] = 'data'#
field unit: str | None = None#

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’”.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model AnnotationSeries#

Bases: TimeSeries

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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

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.

field control: List[int] | None [Optional]#

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.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: List[str] [Optional]#

Annotations made during an experiment.

field description: str | None = None#

Description of the time series.

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model IntervalSeries#

Bases: TimeSeries

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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

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.

field control: List[int] | None [Optional]#

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.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: List[int] [Optional]#

Use values >0 if interval started, <0 if interval ended.

field description: str | None = None#

Description of the time series.

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model DecompositionSeries#

Bases: TimeSeries

Spectral analysis of a time series, e.g. of an LFP or a speech signal.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field bands: DecompositionSeriesBands [Required]#

Table for describing the bands that this series was generated from. There should be one row in this table for each band.

field comments: str | None = None#

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.

field control: List[int] | None [Optional]#

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.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: DecompositionSeriesData [Required]#

Data decomposed into frequency bands.

field description: str | None = None#

Description of the time series.

field metric: str [Required]#

The metric used, e.g. phase, amplitude, power.

field name: str [Required]#
field source_channels: DecompositionSeriesSourceChannels | None = None#

DynamicTableRegion pointer to the channels that this decomposition series was generated from.

field starting_time: TimeSeriesStartingTime | None = None#

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.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model DecompositionSeriesData#

Bases: ConfiguredBaseModel

Data decomposed into frequency bands.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* num_times, * num_channels, * num_bands'], Number] | None = None#
field name: Literal['data'] = 'data'#
field unit: str | None = None#

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’.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model DecompositionSeriesSourceChannels#

Bases: DynamicTableRegion

DynamicTableRegion pointer to the channels that this decomposition series was generated from.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['source_channels'] = 'source_channels'#
field table: DynamicTable | None = None#

Reference to the DynamicTable object that this region applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model DecompositionSeriesBands#

Bases: DynamicTable

Table for describing the bands that this series was generated from. There should be one row in this table for each band.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field band_limits: DecompositionSeriesBandsBandLimits [Required]#

Low and high limit of each band in Hz. If it is a Gaussian filter, use 2 SD on either side of the center.

field band_mean: List[float] [Optional]#

The mean Gaussian filters, in Hz.

field band_name: List[str] | None [Optional]#

Name of the band, e.g. theta.

field band_stdev: List[float] [Optional]#

The standard deviation of Gaussian filters, in Hz.

field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['bands'] = 'bands'#
field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model DecompositionSeriesBandsBandLimits#

Bases: VectorData

Low and high limit of each band in Hz. If it is a Gaussian filter, use 2 SD on either side of the center.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['band_limits'] = 'band_limits'#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model Units#

Bases: DynamicTable

Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field electrode_group: List[ElectrodeGroup] | None [Optional]#

Electrode group that each spike unit came from.

field electrodes: UnitsElectrodes | None = None#

Electrode that each spike unit came from, specified using a DynamicTableRegion.

field electrodes_index: UnitsElectrodesIndex | None = None#

Index into electrodes.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field obs_intervals: UnitsObsIntervals | None = None#

Observation intervals for each unit.

field obs_intervals_index: UnitsObsIntervalsIndex | None = None#

Index into the obs_intervals dataset.

field spike_times: UnitsSpikeTimes | None = None#

Spike times for each unit in seconds.

field spike_times_index: UnitsSpikeTimesIndex | None = None#

Index into the spike_times dataset.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

field waveform_mean: UnitsWaveformMean | None = None#

Spike waveform mean for each spike unit.

field waveform_sd: UnitsWaveformSd | None = None#

Spike waveform standard deviation for each spike unit.

field waveforms: UnitsWaveforms | None = None#

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.

field waveforms_index: UnitsWaveformsIndex | None = None#

Index into the waveforms dataset. One value for every spike event. See ‘waveforms’ for more detail.

field waveforms_index_index: UnitsWaveformsIndexIndex | None = None#

Index into the waveforms_index dataset. One value for every unit (row in the table). See ‘waveforms’ for more detail.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model UnitsSpikeTimesIndex#

Bases: VectorIndex

Index into the spike_times dataset.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['spike_times_index'] = 'spike_times_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsSpikeTimes#

Bases: VectorData

Spike times for each unit in seconds.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['spike_times'] = 'spike_times'#
field resolution: float | None = None#

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.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsObsIntervalsIndex#

Bases: VectorIndex

Index into the obs_intervals dataset.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['obs_intervals_index'] = 'obs_intervals_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsObsIntervals#

Bases: VectorData

Observation intervals for each unit.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['obs_intervals'] = 'obs_intervals'#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsElectrodesIndex#

Bases: VectorIndex

Index into electrodes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['electrodes_index'] = 'electrodes_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsElectrodes#

Bases: DynamicTableRegion

Electrode that each spike unit came from, specified using a DynamicTableRegion.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['electrodes'] = 'electrodes'#
field table: DynamicTable | None = None#

Reference to the DynamicTable object that this region applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsWaveformMean#

Bases: VectorData

Spike waveform mean for each spike unit.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['waveform_mean'] = 'waveform_mean'#
field sampling_rate: float | None = None#

Sampling rate, in hertz.

field unit: str | None = None#

Unit of measurement. This value is fixed to ‘volts’.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsWaveformSd#

Bases: VectorData

Spike waveform standard deviation for each spike unit.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['waveform_sd'] = 'waveform_sd'#
field sampling_rate: float | None = None#

Sampling rate, in hertz.

field unit: str | None = None#

Unit of measurement. This value is fixed to ‘volts’.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsWaveforms#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['waveforms'] = 'waveforms'#
field sampling_rate: float | None = None#

Sampling rate, in hertz.

field unit: str | None = None#

Unit of measurement. This value is fixed to ‘volts’.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsWaveformsIndex#

Bases: VectorIndex

Index into the waveforms dataset. One value for every spike event. See ‘waveforms’ for more detail.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['waveforms_index'] = 'waveforms_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model UnitsWaveformsIndexIndex#

Bases: VectorIndex

Index into the waveforms_index dataset. One value for every unit (row in the table). See ‘waveforms’ for more detail.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['waveforms_index_index'] = 'waveforms_index_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#