# labs.datasets.volumes.volume_img¶

## Module: labs.datasets.volumes.volume_img¶

Inheritance diagram for nipy.labs.datasets.volumes.volume_img:

An image that stores the data as an (x, y, z, …) array, with an affine mapping to the world space

## VolumeImg¶

class nipy.labs.datasets.volumes.volume_img.VolumeImg(data, affine, world_space, metadata=None, interpolation='continuous')

A regularly-spaced image for embedding data in an x, y, z 3D world, for neuroimaging.

This object is an ndarray representing a volume, with the first 3 dimensions being spatial, and mapped to a named world space using an affine (4x4 matrix).

Notes

The data is stored in an undefined way: prescalings might need to be applied to it before using it, or the data might be loaded on demand. The best practice to access the data is not to access the _data attribute, but to use the get_data method.

Attributes

 affine (4x4 ndarray) Affine mapping from indices to world coordinates. world_space (string) Name of the world space the data is embedded in. For instance mni152. metadata (dictionnary) Optional, user-defined, dictionnary used to carry around extra information about the data as it goes through transformations. The consistency of this information may not be maintained as the data is modified. interpolation (‘continuous’ or ‘nearest’) String giving the interpolation logic used when calculating values in different world spaces _data : Private pointer to the data.
__init__(data, affine, world_space, metadata=None, interpolation='continuous')

Creates a new neuroimaging image with an affine mapping.

Parameters: data : ndarray ndarray representing the data. affine : 4x4 ndarray affine transformation to the reference world space world_space : string name of the reference world space. metadata : dictionnary dictionnary of user-specified information to store with the image.
affine = array([[ 1., 0., 0., 0.], [ 0., 1., 0., 0.], [ 0., 0., 1., 0.], [ 0., 0., 0., 1.]])
as_volume_img(affine=None, shape=None, interpolation=None, copy=True)

Resample the image to be an image with the data points lying on a regular grid with an affine mapping to the word space (a nipy VolumeImg).

Parameters: affine: 4x4 or 3x3 ndarray, optional Affine of the new voxel grid or transform object pointing to the new voxel coordinate grid. If a 3x3 ndarray is given, it is considered to be the rotation part of the affine, and the best possible bounding box is calculated, in this case, the shape argument is not used. If None is given, a default affine is provided by the image. shape: (n_x, n_y, n_z), tuple of integers, optional The shape of the grid used for sampling, if None is given, a default affine is provided by the image. interpolation : None, ‘continuous’ or ‘nearest’, optional Interpolation type used when calculating values in different word spaces. If None, the image’s interpolation logic is used. resampled_image : nipy VolumeImg New nipy VolumeImg with the data sampled on the grid defined by the affine and shape.

Notes

The coordinate system of the image is not changed: the returned image points to the same world space.

get_affine()
get_transform()

Returns the transform object associated with the volumetric structure which is a general description of the mapping from the values to the world space.

Returns: transform : nipy.datasets.Transform object
like_from_data(data)

Returns an volumetric data structure with the same relationship between data and world space, and same metadata, but different data.

Parameters: data: ndarray
resampled_to_img(target_image, interpolation=None)

Resample the data to be on the same voxel grid than the target volume structure.

Parameters: target_image : nipy image Nipy image onto the voxel grid of which the data will be resampled. This can be any kind of img understood by Nipy (datasets, pynifti objects, nibabel object) or a string giving the path to a nifti of analyse image. interpolation : None, ‘continuous’ or ‘nearest’, optional Interpolation type used when calculating values in different word spaces. If None, the image’s interpolation logic is used. resampled_image : nipy_image New nipy image with the data resampled.

Notes

Both the target image and the original image should be embedded in the same world space.

xyz_ordered(resample=False, copy=True)

Returns an image with the affine diagonal and positive in the world space it is embedded in.

Parameters: resample: boolean, optional If resample is False, no resampling is performed, the axis are only permuted. If it is impossible to get xyz ordering by permuting the axis, a ‘CompositionError’ is raised. copy: boolean, optional If copy is True, a deep copy of the image (including the data) is made.