Various tools related to creating and working with streamlines
This module provides tools for targeting streamlines using ROIs, for making connectivity matrices from whole brain fiber tracking and some other tools that allow streamlines to interact with image data.
Some functions in this module use an affine matrix to represent the coordinate system associated with the points of a streamline. Dipy uses a similar convention to nifti files when interpreting this affine matrix. This convention is that the point at the center of voxel [i, j, k`]` is represented by the point ``[x, y, z] where [x, y, z, 1] = affine * [i, j, k, 1]. Also when the phrase “voxel coordinates” is used, it is understood to be the same as affine = eye(4).
As an example, lets take a 2d image where the affine is ``[[1., 0., 0.],
[0., 2., 0.], [0., 0., 1.]]``:
A———— | | | | | C | | | | | | | —-B——– | | | | | | | | | | | | ————- | | | | | | | | | | | | ————D
A = [-.5, -1.] B = [ .5, 1.] C = [ 0., 0.] D = [ 2.5, 5.]