core_nwb_epoch

class ConfiguredBaseModel(*, hdf5_path: str | None = None, object_id: str | None = None)

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.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

hdf5_path: str | None

object_id: str | None

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class LinkMLMeta(root: RootModelRootType = PydanticUndefined)

Bases: RootModel

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.

self is explicitly positional-only to allow self as a field name.

root: Dict[str, Any]

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_fields: ClassVar[dict[str, FieldInfo]] = {'root': FieldInfo(annotation=Dict[str, Any], required=False, default={})}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class TimeIntervalsTimeseries(*, hdf5_path: str | None = None, object_id: str | None = None, name: ~typing.Literal['timeseries'] = 'timeseries', description: str | None = None, array: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* dim0], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* dim0, * dim1], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* dim0, * dim1, * dim2], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* dim0, * dim1, * dim2, * dim3], ~typing.Any] | None = None, idx_start: ~numpy.int32 | None = None, count: ~numpy.int32 | None = None, timeseries: ~nwb_linkml.models.pydantic.core.v2_2_4.core_nwb_base.TimeSeries | None = None)

Bases: VectorData

An index into a TimeSeries object.

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.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.epoch'})

name: Literal['timeseries']

idx_start: np.int32 | None

count: np.int32 | None

timeseries: TimeSeries | None

description: str | None

array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'array': FieldInfo(annotation=Union[NDArray, NDArray, NDArray, NDArray, NoneType], required=False, default=None), 'count': FieldInfo(annotation=Union[int32, NoneType], required=False, default=None, description='Number of data samples available in this time series, during this epoch.'), 'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Description of what these vectors represent.'), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'idx_start': FieldInfo(annotation=Union[int32, NoneType], required=False, default=None, description="Start index into the TimeSeries 'data' and 'timestamp' datasets of the referenced TimeSeries. The first dimension of those arrays is always time."), 'name': FieldInfo(annotation=Literal['timeseries'], required=False, default='timeseries', json_schema_extra={'linkml_meta': {'equals_string': 'timeseries', 'ifabsent': 'string(timeseries)'}}), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object'), 'timeseries': FieldInfo(annotation=Union[TimeSeries, NoneType], required=False, default=None, description='the TimeSeries that this index applies to.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.