def test_csd_tracking():
for sh_order in [4, 8, 10]:
fname = 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)
for directions in ["det", "prob"]:
sl_serial = track(fname, directions,
max_angle=30., sphere=None,
seed_mask=None,
seeds=seeds,
stop_mask=None,
stop_threshold=0.2,
step_size=0.5,
n_jobs=1)
npt.assert_equal(sl_serial[0].shape[-1], 3)
for engine in ["dask"]:
for backend in ["threading"]:
sl_parallel = track(fname, directions,
max_angle=30., sphere=None,
seed_mask=None,
seeds=seeds,
stop_mask=None,
stop_threshold=0.2,
step_size=0.5,
n_jobs=2,
engine=engine,
backend=backend)
npt.assert_equal(sl_parallel[0].shape[-1], 3)
if directions == 'det':
npt.assert_almost_equal(sl_parallel[0], sl_serial[0])
def test_fit_csd():
fdata, fbval, fbvec = dpd.get_data('small_64D')
with nbtmp.InTemporaryDirectory() as tmpdir:
# Convert from npy to txt:
bvals = np.load(fbval)
bvecs = np.load(fbvec)
np.savetxt(op.join(tmpdir, 'bvals.txt'), bvals)
np.savetxt(op.join(tmpdir, 'bvecs.txt'), bvecs)
for sh_order in [4, 6]:
fname = csd.fit_csd(fdata, op.join(tmpdir, 'bvals.txt'),
op.join(tmpdir, 'bvecs.txt'),
out_dir=tmpdir, sh_order=sh_order)
npt.assert_(op.exists(fname))
sh_coeffs_img = nib.load(fname)
npt.assert_equal(sh_order,
calculate_max_order(sh_coeffs_img.shape[-1]))
def test_csd_tracking():
for sh_order in [4, 8, 10]:
fname = 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)
for directions in ["det", "prob"]:
sl = track(fname, directions,
max_angle=30.,
sphere=None,
seed_mask=None,
seeds=seeds,
stop_mask=None,
step_size=0.5)
npt.assert_(len(sl[0]) > 10)
def test_csd_tracking():
for sh_order in [4, 8, 10]:
fname = 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)
for directions in ["det", "prob"]:
sl = track(fname, directions,
max_angle=30.,
sphere=None,
seed_mask=None,
n_seeds=seeds,
stop_mask=None,
step_size=0.5,
min_length=10)
npt.assert_(len(sl[0]) > 10)
def test_fit_csd():
fdata, fbval, fbvec = dpd.get_data('small_64D')
with nbtmp.InTemporaryDirectory() as tmpdir:
# Convert from npy to txt:
bvals = np.load(fbval)
bvecs = np.load(fbvec)
np.savetxt(op.join(tmpdir, 'bvals.txt'), bvals)
np.savetxt(op.join(tmpdir, 'bvecs.txt'), bvecs)
for sh_order in [4, 6]:
fname = csd.fit_csd(fdata,
op.join(tmpdir, 'bvals.txt'),
op.join(tmpdir, 'bvecs.txt'),
out_dir=tmpdir,
sh_order=sh_order)
npt.assert_(op.exists(fname))
sh_coeffs_img = nib.load(fname)
npt.assert_equal(sh_order,
calculate_max_order(sh_coeffs_img.shape[-1]))
def test_csd_tracking():
for sh_order in [4, 8, 10]:
fname = 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)
for directions in ["det", "prob"]:
sl_serial = track(fname,
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
seeds=seeds,
stop_mask=None,
stop_threshold=0.2,
step_size=0.5,
n_jobs=1,
engine="serial")
npt.assert_equal(sl_serial[0].shape[-1], 3)
for engine in ["dask", "joblib"]:
for backend in ["threading"]:
sl_parallel = track(fname,
directions,
max_angle=30.,
sphere=None,
seed_mask=None,
seeds=seeds,
stop_mask=None,
stop_threshold=0.2,
step_size=0.5,
n_jobs=2,
engine=engine,
backend=backend)
npt.assert_equal(sl_parallel[0].shape[-1], 3)
if directions == 'det':
npt.assert_almost_equal(sl_parallel[0], sl_serial[0])
