algorithms.resample

Module: algorithms.resample

Some simple examples and utility functions for resampling.

Functions

nipy.algorithms.resample.resample(image, target, mapping, shape, order=3)

Resample an image to a target CoordinateMap with a “world-to-world” mapping and spline interpolation of a given order.

Here, “world-to-world” refers to the fact that mapping should be a callable that takes a physical coordinate in “target” and gives a physical coordinate in “image”.

Parameters:

image : Image instance that is to be resampled

target :target CoordinateMap for output image :

mapping : transformation from target.function_range

to image.coordmap.function_range, i.e. ‘world-to-world mapping’ Can be specified in three ways: a callable, a tuple (A, b) representing the mapping y=dot(A,x)+b or a representation of this in homogeneous coordinates.

shape : shape of output array, in target.function_domain

order : what order of interpolation to use in scipy.ndimage

Returns:

output : Image instance with interpolated data and output.coordmap == target

nipy.algorithms.resample.resample_img2img(source, target, order=3)

Resample source image to space of target image

This wraps the resample function to resample one image onto another the output of the function will give an image with shape of the target and data from the source

Parameters:

source : Image

Image instance that is to be resampled

target : Image

Image instance to which source is resampled The output image will have the same shape as the target, and the same coordmap

order : int, optional

What order of interpolation to use in scipy.ndimage

Returns:

output : Image

Image with interpolated data and output.coordmap == target.coordmap

Examples

>>> from nipy.testing import funcfile, anatfile
>>> from nipy.io.api import load_image
>>> aimg_source = load_image(anatfile)
>>> aimg_target = aimg_source
>>> # in this case, we resample aimg to itself
>>> resimg = resample_img2img(aimg_source, aimg_target)
>>>