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: Literal['timeseries'] = 'timeseries', description: str | None = None, idx_start: int32 | None = None, count: int32 | None = None, timeseries: 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

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]] = {'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.