def test_parfor():
my_array = np.arange(100).reshape(10, 10)
i, j = np.random.randint(0, 9, 2)
my_list = list(my_array.ravel())
for engine in ["joblib", "dask", "serial"]:
for backend in ["threading", "multiprocessing"]:
npt.assert_equal(
para.parfor(power_it,
my_list,
engine=engine,
backend=backend,
out_shape=my_array.shape)[i, j],
power_it(my_array[i, j]))
# If it's not reshaped, the first item should be the item 0, 0:
npt.assert_equal(
para.parfor(power_it, my_list, engine=engine,
backend=backend)[0], power_it(my_array[0, 0]))
def test_parfor():
my_array = np.arange(100).reshape(10, 10)
i, j = np.random.randint(0, 9, 2)
my_list = list(my_array.ravel())
for engine in ["joblib", "dask", "serial"]:
for backend in ["threading", "multiprocessing"]:
npt.assert_equal(para.parfor(power_it,
my_list,
engine=engine,
backend=backend,
out_shape=my_array.shape)[i, j],
power_it(my_array[i, j]))
# If it's not reshaped, the first item should be the item 0, 0:
npt.assert_equal(para.parfor(power_it,
my_list,
engine=engine,
backend=backend)[0],
power_it(my_array[0, 0]))
def track(params_file,
directions="det",
max_angle=30.,
sphere=None,
seed_mask=None,
seeds=2,
stop_mask=None,
stop_threshold=0.2,
step_size=0.5,
n_jobs=-1,
n_chunks=100,
backend="threading",
engine="dask"):
"""
Tractography
Parameters
----------
params_file : str, nibabel img.
Full path to a nifti file containing CSD spherical harmonic
coefficients, or nibabel img with model params.
directions : str
How tracking directions are determined.
One of: {"deterministic" | "probablistic"}
max_angle : float, optional.
The maximum turning angle in each step. Default: 30
sphere : Sphere object, optional.
The discretization of direction getting. default:
dipy.data.default_sphere.
seed_mask : array, optional.
Binary mask describing the ROI within which we seed for tracking.
Default to the entire volume.
seed : int or 2D array, optional.
The seeding density: if this is an int, it is is how many seeds in each
voxel on each dimension (for example, 2 => [2, 2, 2]). If this is a 2D
array, these are the coordinates of the seeds.
stop_mask : array, optional.
A floating point value that determines a stopping criterion (e.g. FA).
Default to no stopping (all ones).
stop_threshold : float, optional.
A value of the stop_mask below which tracking is terminated. Default to
0.2.
step_size : float, optional.
Returns
-------
LocalTracking object.
"""
if isinstance(params_file, str):
params_img = nib.load(params_file)
else:
params_img = params_file
model_params = params_img.get_data()
affine = params_img.get_affine()
if isinstance(seeds, int):
if seed_mask is None:
seed_mask = np.ones(params_img.shape[:3])
seeds = dtu.seeds_from_mask(seed_mask, density=seeds, affine=affine)
if sphere is None:
sphere = dpd.default_sphere
if directions == "det":
dg = DeterministicMaximumDirectionGetter
elif directions == "prob":
dg = ProbabilisticDirectionGetter
# These are models that have ODFs (there might be others in the future...)
if model_params.shape[-1] == 12 or model_params.shape[-1] == 27:
model = "ODF"
# Could this be an SHM model? If the max order is a whole even number, it
# might be:
elif shm.calculate_max_order(model_params.shape[-1]) % 2 == 0:
model = "SHM"
if model == "SHM":
dg = dg.from_shcoeff(model_params, max_angle=max_angle, sphere=sphere)
elif model == "ODF":
evals = model_params[..., :3]
evecs = model_params[..., 3:12].reshape(params_img.shape[:3] + (3, 3))
odf = tensor_odf(evals, evecs, sphere)
dg = dg.from_pmf(odf, max_angle=max_angle, sphere=sphere)
if stop_mask is None:
stop_mask = np.ones(params_img.shape[:3])
threshold_classifier = ThresholdTissueClassifier(stop_mask, stop_threshold)
if engine == "serial":
return _local_tracking(seeds,
dg,
threshold_classifier,
affine,
step_size=step_size)
else:
if n_chunks < seeds.shape[0]:
seeds_list = []
i2 = 0
seeds_per_chunk = seeds.shape[0] // n_chunks
for chunk in range(n_chunks - 1):
i1 = i2
i2 = seeds_per_chunk * (chunk + 1)
seeds_list.append(seeds[i1:i2])
else:
seeds_list = seeds
ll = parfor(_local_tracking,
seeds_list,
n_jobs=n_jobs,
engine=engine,
backend=backend,
func_args=[dg, threshold_classifier, affine],
func_kwargs=dict(step_size=step_size))
return (list(chain(*ll)))
def generate_streamlines(self):
streamlines = parfor(self._track, self.seeds, n_jobs=self.n_jobs,
backend=self.backend, engine=self.engine)
streamlines = list(chain(*streamlines))
return dtu.move_streamlines(streamlines,
self.affine)
