siibra.features.tabular.layerwise_cell_density

Classes

LayerwiseCellDensity

Represents a table of different measures anchored to a brain location.

Module Contents

class siibra.features.tabular.layerwise_cell_density.LayerwiseCellDensity(segmentfiles: list, layerfiles: list, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)

Inheritance diagram of siibra.features.tabular.layerwise_cell_density.LayerwiseCellDensity

Represents a table of different measures anchored to a brain location.

Columns represent different types of values, while rows represent different samples. The number of columns might thus be interpreted as the feature dimension.

As an example, receptor fingerprints use rows to represent different neurotransmitter receptors, and separate columns for the mean and standard deviations measure across multiple tissue samples.

plot(*args, backend='matplotlib', **kwargs)

Create a bar plot of a columns of the data. :param backend: “matplotlib”, “plotly”, or others supported by pandas DataFrame

plotting backend.

Parameters:: **kwargs – takes Matplotlib.pyplot keyword arguments

BIGBRAIN_VOLUMETRIC_SHRINKAGE_FACTOR = 1.931

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

property data

unit = '# detected cells / $0.1mm^3$'