core_nwb_retinotopy

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:

• hdf5_path (str | None)

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:

• tree_root (bool)

field tree_root: bool = False

pydantic model ImagingRetinotopy

Bases: NWBDataInterface

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).

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:

• axis_1_phase_map (ImagingRetinotopyAxis1PhaseMap)

• axis_1_power_map (Optional[ImagingRetinotopyAxis1PowerMap])

• axis_2_phase_map (ImagingRetinotopyAxis2PhaseMap)

• axis_2_power_map (Optional[ImagingRetinotopyAxis2PowerMap])

• axis_descriptions (List[str])

• focal_depth_image (Optional[ImagingRetinotopyFocalDepthImage])

• name (str)

• sign_map (Optional[ImagingRetinotopySignMap])

• vasculature_image (ImagingRetinotopyVasculatureImage)

field axis_1_phase_map: ImagingRetinotopyAxis1PhaseMap [Required]

Phase response to stimulus on the first measured axis.

field axis_1_power_map: ImagingRetinotopyAxis1PowerMap | None = None

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.

field axis_2_phase_map: ImagingRetinotopyAxis2PhaseMap [Required]

Phase response to stimulus on the second measured axis.

field axis_2_power_map: ImagingRetinotopyAxis2PowerMap | None = None

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.

field axis_descriptions: List[str] [Optional]

Two-element array describing the contents of the two response axis fields. Description should be something like [‘altitude’, ‘azimuth’] or ‘[‘radius’, ‘theta’].

field focal_depth_image: ImagingRetinotopyFocalDepthImage | None = None

Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].

field name: str [Required]

field sign_map: ImagingRetinotopySignMap | None = None

Sine of the angle between the direction of the gradient in axis_1 and axis_2.

field vasculature_image: ImagingRetinotopyVasculatureImage [Required]

Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]

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

pydantic model ImagingRetinotopyAxis1PhaseMap

Bases: ConfiguredBaseModel

Phase response to stimulus on the first measured axis.

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], Float32]])

• dimension (Optional[int])

• field_of_view (Optional[float])

• name (Literal['axis_1_phase_map'])

• unit (Optional[str])

field array: NDArray[Shape['* num_rows, * num_cols'], Float32] | None = None

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field name: Literal['axis_1_phase_map'] = 'axis_1_phase_map'

field unit: str | None = None

Unit that axis data is stored in (e.g., degrees).

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

pydantic model ImagingRetinotopyAxis1PowerMap

Bases: ConfiguredBaseModel

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.

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], Float32]])

• dimension (Optional[int])

• field_of_view (Optional[float])

• name (Literal['axis_1_power_map'])

• unit (Optional[str])

field array: NDArray[Shape['* num_rows, * num_cols'], Float32] | None = None

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field name: Literal['axis_1_power_map'] = 'axis_1_power_map'

field unit: str | None = None

Unit that axis data is stored in (e.g., degrees).

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

pydantic model ImagingRetinotopyAxis2PhaseMap

Bases: ConfiguredBaseModel

Phase response to stimulus on the second measured axis.

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], Float32]])

• dimension (Optional[int])

• field_of_view (Optional[float])

• name (Literal['axis_2_phase_map'])

• unit (Optional[str])

field array: NDArray[Shape['* num_rows, * num_cols'], Float32] | None = None

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field name: Literal['axis_2_phase_map'] = 'axis_2_phase_map'

field unit: str | None = None

Unit that axis data is stored in (e.g., degrees).

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

pydantic model ImagingRetinotopyAxis2PowerMap

Bases: ConfiguredBaseModel

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.

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], Float32]])

• dimension (Optional[int])

• field_of_view (Optional[float])

• name (Literal['axis_2_power_map'])

• unit (Optional[str])

field array: NDArray[Shape['* num_rows, * num_cols'], Float32] | None = None

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field name: Literal['axis_2_power_map'] = 'axis_2_power_map'

field unit: str | None = None

Unit that axis data is stored in (e.g., degrees).

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

pydantic model ImagingRetinotopyFocalDepthImage

Bases: ConfiguredBaseModel

Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], UInt16]])

• bits_per_pixel (Optional[int])

• dimension (Optional[int])

• field_of_view (Optional[float])

• focal_depth (Optional[float])

• format (Optional[str])

• name (Literal['focal_depth_image'])

field array: NDArray[Shape['* num_rows, * num_cols'], UInt16] | None = None

field bits_per_pixel: int | None = None

Number of bits used to represent each value. This is necessary to determine maximum (white) pixel value.

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field focal_depth: float | None = None

Focal depth offset, in meters.

field format: str | None = None

Format of image. Right now only ‘raw’ is supported.

field name: Literal['focal_depth_image'] = 'focal_depth_image'

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

pydantic model ImagingRetinotopySignMap

Bases: ConfiguredBaseModel

Sine of the angle between the direction of the gradient in axis_1 and axis_2.

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], Float32]])

• dimension (Optional[int])

• field_of_view (Optional[float])

• name (Literal['sign_map'])

field array: NDArray[Shape['* num_rows, * num_cols'], Float32] | None = None

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field name: Literal['sign_map'] = 'sign_map'

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

pydantic model ImagingRetinotopyVasculatureImage

Bases: ConfiguredBaseModel

Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]

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:

• array (Optional[NDArray[Shape['* num_rows, * num_cols'], UInt16]])

• bits_per_pixel (Optional[int])

• dimension (Optional[int])

• field_of_view (Optional[float])

• format (Optional[str])

• name (Literal['vasculature_image'])

field array: NDArray[Shape['* num_rows, * num_cols'], UInt16] | None = None

field bits_per_pixel: int | None = None

Number of bits used to represent each value. This is necessary to determine maximum (white) pixel value

field dimension: int | None = None

Number of rows and columns in the image. NOTE: row, column representation is equivalent to height, width.

field field_of_view: float | None = None

Size of viewing area, in meters.

field format: str | None = None

Format of image. Right now only ‘raw’ is supported.

field name: Literal['vasculature_image'] = 'vasculature_image'

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