[Neuroimaging] [dipy] connectivity_matrix

[Jan Schreiber]

Hi Dipy Experts,

the function "connectivity_matrix()" provides the option to produce a
symmetric connectivity matrix.
The computation is done in line 202 in
https://github.com/nipy/dipy/blob/master/dipy/tracking/utils.py

201  if symmetric:
202      matrix = np.maximum(matrix, matrix.T)

The test of this function defines the expected matrix as

100  expected = expected + expected.T

I think the test might not be correct and it should be

100  expected = expected + expected.T - np.diag(np.diag(expected))

This does pop up as an error because the test does not include a
connection with start and end point in the same ROI. A within-ROI
connection produces an entry on the diagonal and would be counted twice
with "expected + expected.T".


A patch for an extended test is attached. I also added some more
combinations of possible connections.

What keeps puzzling me is that the function that computes the symmetry as
  matrix = np.maximum(matrix, matrix.T)
gives the same result as
  expected = expected + expected.T - np.diag(np.diag(expected))

Cheers,
Jan

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