API changes

Here we provide information about functions or classes that have been removed, renamed or are deprecated (not recommended) during different release circles.

Dipy 0.10 Changes

** New visualization module**

fvtk.slicer input parameters have changed. Now the slicer function is more powerfull and supports RGB images too. See tutorial viz_slice.py for more information.

Interpolation The default behavior of the function core.sphere.interp_rbf has changed. The default smoothing parameter is now set to 0.1 (previously 0). In addition, the default norm is now angle (was previously euclidean_norm). Note that the use of euclidean_norm is discouraged, and this norm will be deprecated in the 0.11 release cycle.

Registration

The following utilty functions from vector_fields module were renamed:

warp_2d_affine is now transform_2d_affine warp_2d_affine_nn is now transform_2d_affine_nn warp_3d_affine is now transform_3d_affine warp_3d_affine_nn is now transform_3d_affine_nn

Dipy 0.9 Changes

GQI integration length

Calculation of integration length in GQI2 now matches the calculation in the ‘standard’ method. Using values of 1-1.3 for either is recommended (see docs and references therein).

Dipy 0.8 Changes

Peaks

The module peaks is now available from dipy.direction and it can still be accessed from dipy.reconst but it will be completelly removed in version 0.10.

Resample

The function resample from dipy.align.aniso2iso is deprecated. Please, use instead reslice from dipy.align.reslice. The module aniso2iso will be completely removed in version 0.10.

Changes between 0.7.1 and 0.6

Peaks_from_model

The function peaks_from_model is now available from dipy.reconst.peaks . Please replace all imports like

from dipy.reconst.odf import peaks_from_model

with

from dipy.reconst.peaks import peaks_from_model

Target

The function target from dipy.tracking.utils now takes an affine transform instead of a voxel sizes array. Please update all code using target in a way similar to this

img = nib.load(anat)
voxel_dim = img.get_header()['pixdim'][1:4]
streamlines = utils.target(streamlines, img.get_data(), voxel_dim)

to something similar to

img = nib.load(anat)
streamlines = utils.target(streamlines, img.get_data(), img.get_affine())