def test_predict_dti():
with nbtmp.InTemporaryDirectory() as tmpdir:
fbval = op.join(tmpdir, 'dti.bval')
fbvec = op.join(tmpdir, 'dti.bvec')
fdata = op.join(tmpdir, 'dti.nii.gz')
make_dti_data(fbval, fbvec, fdata)
file_dict = dti.fit_dti(fdata, fbval, fbvec, out_dir=tmpdir)
params_file = file_dict['params']
gtab = dpg.gradient_table(fbval, fbvec)
predict_fname = dti.predict(params_file,
gtab,
S0_file=fdata,
out_dir=tmpdir)
prediction = nib.load(predict_fname).get_fdata()
npt.assert_almost_equal(prediction, nib.load(fdata).get_fdata())
# If you have a mask into the volume, you will predict only that
# part of the volume:
mask = np.zeros(prediction.shape[:3], dtype=bool)
mask[2:4, 2:4, 2:4] = 1
file_dict = dti.fit_dti(fdata, fbval, fbvec, mask=mask, out_dir=tmpdir)
params_file = file_dict['params']
predict_fname = dti.predict(params_file,
gtab,
S0_file=fdata,
out_dir=tmpdir)
prediction = nib.load(predict_fname).get_fdata()
npt.assert_almost_equal(prediction[mask],
nib.load(fdata).get_fdata()[mask])
def test_pft_tracking():
for fname in [fit_dti(fdata, fbval, fbvec)['params'],
fit_csd(fdata, fbval, fbvec,
response=((0.0015, 0.0003, 0.0003), 100),
sh_order=8, lambda_=1, tau=0.1, mask=None,
out_dir=tmpdir.name)]:
img = nib.load(fdata)
data_shape = img.shape
data_affine = img.affine
pve_wm_data = nib.Nifti1Image(np.ones(data_shape[:3]), img.affine)
pve_gm_data = nib.Nifti1Image(np.zeros(data_shape[:3]), img.affine)
pve_csf_data = nib.Nifti1Image(np.zeros(data_shape[:3]), img.affine)
stop_mask = (pve_wm_data, pve_gm_data, pve_csf_data)
for directions in ["det", "prob"]:
for stop_threshold in ["ACT", "CMC"]:
sl = track(
fname,
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
stop_mask=stop_mask,
stop_threshold=stop_threshold,
n_seeds=1,
step_size=step_size,
min_length=min_length,
tracker="pft").streamlines
npt.assert_(len(sl[0]) >= min_length * step_size)
# Test error handling:
with pytest.raises(RuntimeError):
track(
fname,
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
stop_mask=0, # Stop mask needs to be a tuple!
stop_threshold=stop_threshold,
n_seeds=1,
step_size=step_size,
min_length=min_length,
tracker="pft")
with pytest.raises(RuntimeError):
track(
fname,
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
stop_mask=stop_mask,
stop_threshold=None, # Stop threshold needs to be a string!
n_seeds=1,
step_size=step_size,
min_length=min_length,
tracker="pft")
def test_fit_dti():
# Let's see whether we can pass a list of files for each one:
fdata1, fbval1, fbvec1 = dpd.get_fnames('small_101D')
fdata2, fbval2, fbvec2 = dpd.get_fnames('small_101D')
with nbtmp.InTemporaryDirectory() as tmpdir:
file_dict = dti.fit_dti([fdata1, fdata2], [fbval1, fbval2],
[fbvec1, fbvec2],
out_dir=tmpdir)
for f in file_dict.values():
npt.assert_(op.exists(f))
def test_dti_local_tracking():
fdict = fit_dti(fdata, fbval, fbvec)
for directions in ["det", "prob"]:
sl = track(
fdict['params'],
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
n_seeds=1,
step_size=step_size,
min_length=min_length,
tracker="local").streamlines
npt.assert_(len(sl[0]) >= min_length * step_size)
dpd.fetch_stanford_hardi()
hardi_dir = op.join(fetcher.dipy_home, "stanford_hardi")
hardi_fdata = op.join(hardi_dir, "HARDI150.nii.gz")
hardi_fbval = op.join(hardi_dir, "HARDI150.bval")
hardi_fbvec = op.join(hardi_dir, "HARDI150.bvec")
img = nib.load(hardi_fdata)
##########################################################################
# Calculate DTI:
# -------------------------
print("Calculating DTI...")
if not op.exists(op.join(working_dir, 'dti_FA.nii.gz')):
dti_params = dti.fit_dti(hardi_fdata,
hardi_fbval,
hardi_fbvec,
out_dir=working_dir)
else:
dti_params = {
'FA': op.join(working_dir, 'dti_FA.nii.gz'),
'params': op.join(working_dir, 'dti_params.nii.gz')
}
FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_fdata()
##########################################################################
# Register the individual data to a template:
# -------------------------------------------
# For the purpose of bundle segmentation, the individual brain is registered to
# the MNI T2 template. The waypoint ROIs used in segmentation are then each
