siibra.features.tabular.cell_density_profile

Classes

CellDensityProfile	Represents a 1-dimensional profile of measurements along cortical depth,
PolyLine	Simple polyline representation which allows equidistant sampling.

Functions

cell_reader(bytes_buffer)
layer_reader(bytes_buffer)
poly_rev(poly)
poly_srt(poly)

Module Contents

class siibra.features.tabular.cell_density_profile.CellDensityProfile(section: int, patch: int, url: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)

Represents a 1-dimensional profile of measurements along cortical depth, measured at relative depths between 0 representing the pial surface, and 1 corresponding to the gray/white matter boundary.

Mandatory attributes are the list of depth coordinates and the list of corresponding measurement values, which have to be of equal length, as well as a unit and description of the measurements.

Optionally, the depth coordinates of layer boundaries can be specified.

Most attributes are modelled as properties, so dervide classes are able to implement lazy loading instead of direct initialization.

boundary_annotation(boundary: Tuple[int, int]) → numpy.ndarray

Returns the annotation of a specific layer boundary.

layer_annotation(layer: int) → numpy.ndarray

BIGBRAIN_VOLUMETRIC_SHRINKAGE_FACTOR = 1.931

DESCRIPTION = 'Cortical profile of estimated densities of detected cell bodies (in detected cells per 0.1 cube...

property boundary_positions

property cells: pandas.DataFrame

property density_image: numpy.ndarray

property depth_image: numpy.ndarray

Cortical depth image from layer boundary polygons by equidistant sampling.

property layer_mask: numpy.ndarray

Generates a layer mask from boundary annotations.

property layers: pandas.DataFrame

property location

patch

section

property shape

(y,x)

class siibra.features.tabular.cell_density_profile.PolyLine(pts)

Simple polyline representation which allows equidistant sampling.

length()

sample(d: Iterable[float] | numpy.ndarray | float)

lengths

pts

siibra.features.tabular.cell_density_profile.cell_reader(bytes_buffer: bytes)

siibra.features.tabular.cell_density_profile.layer_reader(bytes_buffer: bytes)

siibra.features.tabular.cell_density_profile.poly_rev(poly)

siibra.features.tabular.cell_density_profile.poly_srt(poly)