def test_dyad():
"""
Test the dyadic tensor and coherence based on the dyadic tensor
"""
eigs1 = [np.eye(3), np.eye(3)]
dyad = ozb.dyadic_tensor(eigs1, average=True)
npt.assert_equal(ozb.dyad_coherence(dyad), 1.0)
# For this collection of eigenvectors:
eigs2 = np.array([[[1,0,0],[0,2,0],[0,0,3]],[[1,0,0],[0,2,0],[0,0,3]]])
dyad_dist = ozb.dyadic_tensor(eigs2, average=False)
# The dispersion should be null:
npt.assert_equal(ozb.dyad_dispersion(dyad_dist), 0)
def test_dyad():
"""
Test the dyadic tensor and coherence based on the dyadic tensor
"""
eigs1 = [np.eye(3), np.eye(3)]
dyad = ozb.dyadic_tensor(eigs1, average=True)
npt.assert_equal(ozb.dyad_coherence(dyad), 1.0)
# For this collection of eigenvectors:
eigs2 = np.array([[[1, 0, 0], [0, 2, 0], [0, 0, 3]],
[[1, 0, 0], [0, 2, 0], [0, 0, 3]]])
dyad_dist = ozb.dyadic_tensor(eigs2, average=False)
# The dispersion should be null:
npt.assert_equal(ozb.dyad_dispersion(dyad_dist), 0)
def _dyad_stats(tensor_model_list, mask=None, dyad_stat=boot.dyad_coherence,
average=True):
"""
Helper function that does most of the work on calcualting dyad statistics
"""
if mask is None:
mask = np.ones(tensor_model_list[0].shape[:3])
# flatten the eigenvectors:
tensor_model_flat=np.array([this.evecs[np.where(mask)] for this in
tensor_model_list])
out_flat = np.empty(tensor_model_flat[0].shape[0])
# Loop over voxels
for idx in xrange(tensor_model_flat.shape[1]):
dyad = boot.dyadic_tensor(tensor_model_flat[:,idx,:,:],
average=average)
out_flat[idx] = dyad_stat(dyad)
out = ozu.nans(tensor_model_list[0].shape[:3])
out[np.where(mask)] = out_flat
return out
def _dyad_stats(tensor_model_list,
mask=None,
dyad_stat=boot.dyad_coherence,
average=True):
"""
Helper function that does most of the work on calcualting dyad statistics
"""
if mask is None:
mask = np.ones(tensor_model_list[0].shape[:3])
# flatten the eigenvectors:
tensor_model_flat = np.array(
[this.evecs[np.where(mask)] for this in tensor_model_list])
out_flat = np.empty(tensor_model_flat[0].shape[0])
# Loop over voxels
for idx in xrange(tensor_model_flat.shape[1]):
dyad = boot.dyadic_tensor(tensor_model_flat[:, idx, :, :],
average=average)
out_flat[idx] = dyad_stat(dyad)
out = ozu.nans(tensor_model_list[0].shape[:3])
out[np.where(mask)] = out_flat
return out