def test_gaussian_weights():
# Some bogus x,y,z coordinates
x = np.arange(10)
y = np.arange(10)
z = np.arange(10)
# This has the wrong shape (2, 3, 10):
bundle = np.array([[x, y, z], [x, y, z]])
pytest.raises(ValueError, seg.gaussian_weights, bundle)
# Reallocate with the right shape. This time, we're going to create a
# distribution for which we can predict the weights we would expect
# to get:
bundle = np.array([np.array([x, y, z]).T + 1,
np.array([x, y, z]).T - 1])
# In this case, all nodes receives an equal weight of 0.5:
w = seg.gaussian_weights(bundle)
npt.assert_equal(w, np.ones(bundle.shape[:-1]) * 0.5)
# Here, some nodes are twice as far from the mean as others
bundle = np.array([np.array([x, y, z]).T + 2,
np.array([x, y, z]).T + 1,
np.array([x, y, z]).T - 1,
np.array([x, y, z]).T - 2])
w = seg.gaussian_weights(bundle)
# And their weights should be halved
# XXX Need to check how to transform this through the
# Gaussian distribution!
npt.assert_almost_equal(w[0], w[1] / 2)
def test_gaussian_weights():
# Some bogus x,y,z coordinates
x = np.arange(10)
y = np.arange(10)
z = np.arange(10)
# Create a distribution for which we can predict the weights we would
# expect to get:
bundle = np.array([np.array([x, y, z]).T + 1, np.array([x, y, z]).T - 1])
# In this case, all nodes receives an equal weight of 0.5:
w = seg.gaussian_weights(bundle)
npt.assert_equal(w, np.ones(bundle.shape[:-1]) * 0.5)
# Here, some nodes are twice as far from the mean as others
bundle = np.array([
np.array([x, y, z]).T + 2,
np.array([x, y, z]).T + 1,
np.array([x, y, z]).T - 1,
np.array([x, y, z]).T - 2
])
w = seg.gaussian_weights(bundle)
# And their weights should be halved
# XXX Need to check how to transform this through the
# Gaussian distribution!
npt.assert_almost_equal(w[0], w[1] / 2)
print("Segmenting fiber groups...")
fiber_groups = seg.segment(hardi_fdata,
hardi_fbval,
hardi_fbvec,
streamlines,
bundles,
reg_template=MNI_T2_img,
mapping=mapping,
as_generator=False,
affine=img.affine)
print("Cleaning fiber groups...")
for bundle in bundles:
fiber_groups[bundle] = seg.clean_fiber_group(fiber_groups[bundle])
FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_data()
print("Extracting tract profiles...")
for bundle in bundles:
fig, ax = plt.subplots(1)
weights = seg.gaussian_weights(fiber_groups[bundle])
profile = seg.calculate_tract_profile(FA_data, fiber_groups[bundle],
weights=weights)
ax.plot(profile)
ax.set_title(bundle)
plt.show()
prob_threshold=5)
print("Getting tract profiles")
n_points = 100
dfs = []
for bundle in fiber_groups:
print("Getting profile for: %s" % bundle)
if len(fiber_groups[bundle]) > 0:
bundle_df = pd.DataFrame(data={
'tractID': [bundle] * n_points,
'nodeID': np.arange(1, n_points + 1)})
for stat_key in dti_params.keys():
if stat_key == 'params':
pass
else:
stat_data = nib.load(dti_params[stat_key]).get_data()
fgarray = seg._resample_bundle(fiber_groups[bundle], n_points)
weights = seg.gaussian_weights(fgarray)
profile = seg.calculate_tract_profile(stat_data,
fgarray,
weights=weights)
bundle_df[stat_key] = profile
dfs.append(bundle_df)
else:
print("There are no fibers in %s" % bundle)
result = pd.concat(dfs)
MNI_T2_img = dpd.read_mni_template()
if not op.exists('mapping.nii.gz'):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)
mapping = reg.syn_register_dwi(hardi_fdata, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
print("Segmenting fiber groups...")
fiber_groups = seg.segment(hardi_fdata,
hardi_fbval,
hardi_fbvec,
streamlines,
bundles,
reg_template=MNI_T2_img,
mapping=mapping,
as_generator=False)
FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_data()
print("Extracting tract profiles...")
for bundle in bundles:
fig, ax = plt.subplots(1)
w = seg.gaussian_weights(fiber_groups[bundle])
profile = seg.calculate_tract_profile(FA_data, fiber_groups[bundle],
weights=w)
ax.plot(profile)
ax.set_title(bundle)
