mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-09-19 23:44:28 +00:00
sneakers-the-rat
be21325123
- split off generated subclasses into "include" files, not sure if that's good, but in any case we have them separable now. - more work on cleanly un-nesting scalar and 1D-vector data into attributes and lists, respectively - brought the pydantic generator in-repo to do a bunch of overrides
63 lines
3.7 KiB
Python
63 lines
3.7 KiB
Python
from __future__ import annotations
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from datetime import datetime, date
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from enum import Enum
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from typing import List, Dict, Optional, Any, Union
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from pydantic import BaseModel as BaseModel, Field
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from nptyping import NDArray, Shape, Float, Float32, Double, Float64, LongLong, Int64, Int, Int32, Int16, Short, Int8, UInt, UInt32, UInt16, UInt8, UInt64, Number, String, Unicode, Unicode, Unicode, String, Bool, Datetime64
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import sys
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if sys.version_info >= (3, 8):
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from typing import Literal
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else:
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from typing_extensions import Literal
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from .core_nwb_retinotopy_include import (
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ImagingRetinotopyAxisDescriptions,
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ImagingRetinotopyFocalDepthImage,
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ImagingRetinotopyAxis2PowerMap,
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ImagingRetinotopyVasculatureImage,
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ImagingRetinotopyAxis2PhaseMap,
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ImagingRetinotopyAxis1PhaseMap,
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ImagingRetinotopyAxis1PowerMap,
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ImagingRetinotopySignMap
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)
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from .core_nwb_base import (
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NWBDataInterface
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)
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metamodel_version = "None"
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version = "None"
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class WeakRefShimBaseModel(BaseModel):
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__slots__ = '__weakref__'
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class ConfiguredBaseModel(WeakRefShimBaseModel,
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validate_assignment = True,
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validate_all = True,
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underscore_attrs_are_private = True,
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extra = 'forbid',
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arbitrary_types_allowed = True,
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use_enum_values = True):
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pass
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class ImagingRetinotopy(NWBDataInterface):
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"""
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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).
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"""
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axis_1_phase_map: ImagingRetinotopyAxis1PhaseMap = Field(..., description="""Phase response to stimulus on the first measured axis.""")
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axis_1_power_map: Optional[ImagingRetinotopyAxis1PowerMap] = Field(None, description="""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.""")
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axis_2_phase_map: ImagingRetinotopyAxis2PhaseMap = Field(..., description="""Phase response to stimulus on the second measured axis.""")
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axis_2_power_map: Optional[ImagingRetinotopyAxis2PowerMap] = Field(None, description="""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.""")
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axis_descriptions: ImagingRetinotopyAxisDescriptions = Field(..., description="""Two-element array describing the contents of the two response axis fields. Description should be something like ['altitude', 'azimuth'] or '['radius', 'theta'].""")
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focal_depth_image: Optional[ImagingRetinotopyFocalDepthImage] = Field(None, description="""Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].""")
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sign_map: Optional[ImagingRetinotopySignMap] = Field(None, description="""Sine of the angle between the direction of the gradient in axis_1 and axis_2.""")
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vasculature_image: ImagingRetinotopyVasculatureImage = Field(..., description="""Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]""")
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# Update forward refs
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# see https://pydantic-docs.helpmanual.io/usage/postponed_annotations/
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ImagingRetinotopy.update_forward_refs()
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