labs.datasets.volumes.volume_grid¶
Module: labs.datasets.volumes.volume_grid¶
Inheritance diagram for nipy.labs.datasets.volumes.volume_grid:
The volume grid class.
This class represents data lying on a (non rigid, non regular) grid embedded in a 3D world represented as a 3+D array.
VolumeGrid¶
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class
nipy.labs.datasets.volumes.volume_grid.VolumeGrid(data, transform, metadata=None, interpolation='continuous')[source]¶ Bases:
nipy.labs.datasets.volumes.volume_data.VolumeDataA class representing data stored in a 3+D array embedded in a 3D world.
This object has data stored in an array-like multidimensional indexable objects, with the 3 first dimensions corresponding to spatial axis and defining a 3D grid that may be non-regular or non-rigid.
The object knows how the data is mapped to a 3D “real-world space”, and how it can change real-world coordinate system. The transform mapping it to world is arbitrary, and thus the grid can be warped: in the world space, the grid may not be regular or orthogonal.
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.
If the transform associated with the image has no inverse mapping, data corresponding to a given world space position cannot be calulated. If it has no forward mapping, it is impossible to resample another dataset on the same support.
Attributes
world_space: string
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 is not maintained as the data is modified.
_data:
Private pointer to the data.
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__init__(data, transform, metadata=None, interpolation='continuous')[source]¶ The base image containing data.
- Parameters
data: ndarray
n dimensional array giving the embedded data, with the 3 first dimensions being spatial.
transform: nipy transform object
The transformation from voxel to world.
metadata : dictionnary, optional
Dictionnary of user-specified information to store with the image.
interpolation : ‘continuous’ or ‘nearest’, optional
Interpolation type used when calculating values in different word spaces.
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world_space= ''¶
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metadata= {}¶
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interpolation= 'continuous'¶
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as_volume_img(affine=None, shape=None, interpolation=None, copy=True)[source]¶ 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.
- Returns
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.
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get_world_coords()[source]¶ Return the data points coordinates in the world space.
- Returns
x: ndarray
x coordinates of the data points in world space
y: ndarray
y coordinates of the data points in world space
z: ndarray
z coordinates of the data points in world space
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like_from_data(data)[source]¶ Returns an volumetric data structure with the same relationship between data and world space, and same metadata, but different data.
- Parameters
data: ndarray
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get_transform()[source]¶ 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
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values_in_world(x, y, z, interpolation=None)[source]¶ Return the values of the data at the world-space positions given by x, y, z
- Parameters
x : number or ndarray
x positions in world space, in other words milimeters
y : number or ndarray
y positions in world space, in other words milimeters. The shape of y should match the shape of x
z : number or ndarray
z positions in world space, in other words milimeters. The shape of z should match the shape of x
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.
- Returns
values : number or ndarray
Data values interpolated at the given world position. This is a number or an ndarray, depending on the shape of the input coordinate.
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composed_with_transform(w2w_transform)¶ Return a new image embedding the same data in a different word space using the given world to world transform.
- Parameters
w2w_transform : transform object
The transform object giving the mapping between the current world space of the image, and the new word space.
- Returns
remapped_image : nipy image
An image containing the same data, expressed in the new world space.
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get_data()¶ Return data as a numpy array.
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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.
- Returns
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.
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