siibra.features.tabular

Multimodal data features in tabular formats.

Submodules

Classes

`BigBrainIntensityProfile`	Represents a 1-dimensional profile of measurements along cortical depth,
`CellDensityProfile`	Represents a 1-dimensional profile of measurements along cortical depth,
`GeneExpressions`	A set gene expressions for different candidate genes
`LayerwiseBigBrainIntensities`	Represents a table of different measures anchored to a brain location.
`LocalFieldPotential`	Local field potential recording anchored to Waxholm coordinates and potentially a region.
`ReceptorDensityFingerprint`	Represents a table of different measures anchored to a brain location.
`ReceptorDensityProfile`	Represents a 1-dimensional profile of measurements along cortical depth,
`RegionalBOLD`	Blood-oxygen-level-dependent (BOLD) signals per region.
`RegionalLocalFieldPotential`	Local field potential recording anchored to Waxholm region and a preselected spectrum type.

Functions

`cell_reader`(bytes_buffer)
`layer_reader`(bytes_buffer)

Package Contents

class siibra.features.tabular.BigBrainIntensityProfile(anchor: siibra.features.anchor.AnatomicalAnchor, depths: list, values: list, boundaries: list)

Inheritance diagram of siibra.features.tabular.BigBrainIntensityProfile

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.

DESCRIPTION = "Cortical profiles of BigBrain staining intensities computed by Konrad Wagstyl, as described in...

property location

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

Inheritance diagram of siibra.features.tabular.CellDensityProfile

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.GeneExpressions(levels: List[float], z_scores: List[float], genes: List[str], additional_columns: dict, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: List = [DATASET])

Inheritance diagram of siibra.features.tabular.GeneExpressions

A set gene expressions for different candidate genes measured inside a brain structure.

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

Create a box plot per gene.

Parameters:

backend (str) – “matplotlib”, “plotly”, or others supported by pandas DataFrame plotting backend.
**kwargs – Keyword arguments are passed on to the plot command.

ALLEN_ATLAS_NOTIFICATION = Multiline-String

Show Value

"""
    For retrieving microarray data, siibra connects to the web API of
    the Allen Brain Atlas (© 2015 Allen Institute for Brain Science),
    available from https://brain-map.org/api/index.html. Any use of the
    microarray data needs to be in accordance with their terms of use,
    as specified at https://alleninstitute.org/legal/terms-use/.
    """

DATASET

DESCRIPTION = Multiline-String

Show Value

"""
    Gene expressions extracted from microarray data in the Allen Atlas.
    """

unit = 'expression level'

class siibra.features.tabular.LayerwiseBigBrainIntensities(anchor: siibra.features.anchor.AnatomicalAnchor, means: list, stds: list)

Inheritance diagram of siibra.features.tabular.LayerwiseBigBrainIntensities

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.

DESCRIPTION = "Layerwise averages and standard deviations of of BigBrain staining intensities computed by...

class siibra.features.tabular.LocalFieldPotential(anchor: siibra.features.anchor.AnatomicalAnchor, db_entry: Dict)

Inheritance diagram of siibra.features.tabular.LocalFieldPotential

Local field potential recording anchored to Waxholm coordinates and potentially a region.

get_lfp_file()

Fetch the local field potential data file.

Returns:: Open file handle returned by the HTTP request.
Return type:: h5-file-like
Raises:: MissingFileException – If no local field potential file is available for this entry.

get_motion_file()

Fetch the motion data file.

Returns:: Open file handle returned by the HTTP request.
Return type:: h5-file-like
Raises:: MissingFileException – If no motion file is available for this entry.

get_psd_file()

Fetch the power spectral density file.

Returns:: Open file handle returned by the HTTP request.
Return type:: h5-file-like
Raises:: MissingFileException – If no power spectral density file is available for this entry.

classmethod get_spectra(lfps: List[LocalFieldPotential])

Compute an average spectrum from local field potential features.

Parameters:: lfps (list of LocalFieldPotential) – Local field potential features to include in the spectrum.
Returns:: Average spectrum indexed by frequency.
Return type:: pandas.DataFrame

classmethod plot_spectra(lfps: List[LocalFieldPotential], backend='matplotlib', **kwargs)

Plot an average spectrum from local field potential features.

Parameters:

lfps (list of LocalFieldPotential) – Local field potential features to include in the spectrum.
backend (str, optional) – Plotting backend passed to pandas.DataFrame.plot(). The default is "matplotlib".
**kwargs – Additional keyword arguments passed to pandas.DataFrame.plot().

Returns:

Plot object returned by the selected pandas plotting backend.

Return type:

object

ID_TEMPLATE = '41673110-f3eb-43cd-9d9c-c845c6f0573c--{index}'

property coordinates

property name: Returns a short human-readable name of this feature.

property pathology

property pharmacology

property session

property signal_quality

property subject

class siibra.features.tabular.ReceptorDensityFingerprint(tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)

Inheritance diagram of siibra.features.tabular.ReceptorDensityFingerprint

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.

classmethod parse_tsv_data(data: dict)

plot(*args, receptors: List[str] = None, backend: str = 'matplotlib', **kwargs)

Create a bar plot of receptor density fingerprint.

Parameters:

receptors (List[str], optional) – Plot a subset of receptors.
backend (str) – “matplotlib”, “plotly”, or others supported by pandas DataFrame plotting backend.
**kwargs – takes Matplotlib.pyplot keyword arguments

polar_plot(*args, backend='matplotlib', **kwargs): Create a polar plot of the fingerprint. backend: str

“matplotlib” or “plotly”

DESCRIPTION = 'Fingerprint of densities (in fmol/mg protein) of receptors for classical neurotransmitters...

property data

property neurotransmitters: List[str]

property receptors: List[str]

property unit: str

class siibra.features.tabular.ReceptorDensityProfile(receptor: str, tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)

Inheritance diagram of siibra.features.tabular.ReceptorDensityProfile

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.

classmethod parse_tsv_data(data)

DESCRIPTION = 'Cortical profile of densities (in fmol/mg protein) of receptors for classical neurotransmitters...

property key

property neurotransmitter

receptor

property receptor_fullname

property unit: Optionally overridden in derived classes.

class siibra.features.tabular.RegionalBOLD(paradigm: str, **kwargs)

Inheritance diagram of siibra.features.tabular.RegionalBOLD

Blood-oxygen-level-dependent (BOLD) signals per region.

property name: Returns a short human-readable name of this feature.

paradigm

class siibra.features.tabular.RegionalLocalFieldPotential(anchor: siibra.features.anchor.AnatomicalAnchor, db_entries: pandas.DataFrame, pathology: str | None = None, pharmacology: str | None = None, signal_quality: str | None = None)

Inheritance diagram of siibra.features.tabular.RegionalLocalFieldPotential

Local field potential recording anchored to Waxholm region and a preselected spectrum type.

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

Plot the average local field potential spectrum.

Parameters:

*args – Positional arguments passed to the parent plotting method.
backend (str, optional) – Plotting backend passed to the parent plotting method. The default is "matplotlib".
**kwargs – Additional keyword arguments passed to the parent plotting method.

Returns:

Plot object returned by the selected backend.

Return type:

object

ID_TEMPLATE = '41673110-f3eb-43cd-9d9c-c845c6f0573c--{indices_as_hex}'

property coordinates

property data

Compute the smoothed median spectra represented by this feature.

Returns:: Average spectra by type and indexed by frequency.
Return type:: pandas.DataFrame

Notes

Accessing this property downloads the required power spectral density files and recomputes the spectrum.

property name: Returns a short human-readable name of this feature.

pathology = None

pharmacology = None

property session

signal_quality = None

property subjects

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

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