GreyOrdinates

Import in python using from mcot.core.greyordinate import GreyOrdinates

Documentation

class mcot.core.greyordinate.GreyOrdinates(data, brain_model_axis: nibabel.cifti2.cifti2_axes.BrainModelAxis, other_axes: Sequence[nibabel.cifti2.cifti2_axes.Axis] = None, parent_file=None)[source]

Represents data on voxels or vertices

__init__(data, brain_model_axis: nibabel.cifti2.cifti2_axes.BrainModelAxis, other_axes: Sequence[nibabel.cifti2.cifti2_axes.Axis] = None, parent_file=None)[source]

Defines a new dataset in greyordinate space

Parameters:
  • data – (…, N) array for N greyordinates
  • brain_model_axis – CIFTI axis describing the greyordinate space
  • other_axes – sequence of CIFTI axes describing the other dimensions
  • parent_file – file in which the dataset has been stored

Methods

as_dask([chunks, name]) Returns the greyordinates as a dask array
empty(filename, axes[, dtype]) Creates an empty file to store the greyordinates with the type determined by the extension:
empty_cifti(filename, axes[, dtype]) Creates an empty greyordinate object based on the axes
empty_hdf5(filename, axes[, dtype]) Creates an empty greyordinate object based on the axes
empty_zarr(filename, axes[, dtype]) Creates an empty greyordinate object based on the axes
from_cifti(filename[, writable]) Creates new greyordinate object from dense CIFTI file
from_filename(filename[, mask_values, writable]) Reads greyordinate data from the given file
from_gifti(filename[, mask_values]) Creates a new greyordinate object from a GIFTI file
from_hdf5(group) Retrieves data from HDF5 group
from_nifti(filename[, mask_values]) Creates a new greyordinate object from a NIFTI file
surface(anatomy[, fill, partial]) Gets a specific surface
to_cifti([other_axes]) Create a CIFTI image from the data
to_filename(filename) Stores the greyordinate data to the given filename.
to_hdf5(group[, compression]) Stores the image in the HDF5 group
transpose() Transposes a dense connectome
volume() Get the volumetric data as a Nifti1Image