def test_tractogram_to_world(self):
tractogram = DATA['lazy_tractogram'].copy()
affine = np.random.RandomState(1234).randn(4, 4)
affine[-1] = [0, 0, 0, 1] # Remove perspective projection.
# Apply the affine to the streamlines, then bring them back
# to world space in a lazy manner.
transformed_tractogram = tractogram.apply_affine(affine)
assert_array_equal(transformed_tractogram.affine_to_rasmm,
np.linalg.inv(affine))
tractogram_world = transformed_tractogram.to_world()
assert_true(tractogram_world is not transformed_tractogram)
assert_array_almost_equal(tractogram_world.affine_to_rasmm,
np.eye(4))
for s1, s2 in zip(tractogram_world.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Calling to_world twice should do nothing.
tractogram_world = tractogram_world.to_world()
assert_array_almost_equal(tractogram_world.affine_to_rasmm, np.eye(4))
for s1, s2 in zip(tractogram_world.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Calling to_world when affine_to_rasmm is None should fail.
tractogram = DATA['lazy_tractogram'].copy()
tractogram.affine_to_rasmm = None
assert_raises(ValueError, tractogram.to_world)
def test_tractogram_to_world(self):
tractogram = DATA['lazy_tractogram'].copy()
affine = np.random.RandomState(1234).randn(4, 4)
affine[-1] = [0, 0, 0, 1] # Remove perspective projection.
# Apply the affine to the streamlines, then bring them back
# to world space in a lazy manner.
transformed_tractogram = tractogram.apply_affine(affine)
assert_array_equal(transformed_tractogram.affine_to_rasmm,
np.linalg.inv(affine))
tractogram_world = transformed_tractogram.to_world()
assert_true(tractogram_world is not transformed_tractogram)
assert_array_almost_equal(tractogram_world.affine_to_rasmm, np.eye(4))
for s1, s2 in zip(tractogram_world.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Calling to_world twice should do nothing.
tractogram_world = tractogram_world.to_world()
assert_array_almost_equal(tractogram_world.affine_to_rasmm, np.eye(4))
for s1, s2 in zip(tractogram_world.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Calling to_world when affine_to_rasmm is None should fail.
tractogram = DATA['lazy_tractogram'].copy()
tractogram.affine_to_rasmm = None
assert_raises(ValueError, tractogram.to_world)
def test_tractogram_apply_affine(self):
tractogram = DATA["tractogram"].copy()
affine = np.eye(4)
scaling = np.array((1, 2, 3), dtype=float)
affine[range(3), range(3)] = scaling
# Apply the affine to the streamline in a lazy manner.
transformed_tractogram = tractogram.apply_affine(affine, lazy=True)
assert_true(type(transformed_tractogram) is LazyTractogram)
check_tractogram(
transformed_tractogram,
streamlines=[s * scaling for s in DATA["streamlines"]],
data_per_streamline=DATA["data_per_streamline"],
data_per_point=DATA["data_per_point"],
)
assert_array_equal(transformed_tractogram.affine_to_rasmm, np.dot(np.eye(4), np.linalg.inv(affine)))
# Make sure streamlines of the original tractogram have not been
# modified.
assert_arrays_equal(tractogram.streamlines, DATA["streamlines"])
# Apply the affine to the streamlines in-place.
transformed_tractogram = tractogram.apply_affine(affine)
assert_true(transformed_tractogram is tractogram)
check_tractogram(
tractogram,
streamlines=[s * scaling for s in DATA["streamlines"]],
data_per_streamline=DATA["data_per_streamline"],
data_per_point=DATA["data_per_point"],
)
# Apply affine again and check the affine_to_rasmm.
transformed_tractogram = tractogram.apply_affine(affine)
assert_array_equal(
transformed_tractogram.affine_to_rasmm,
np.dot(np.eye(4), np.dot(np.linalg.inv(affine), np.linalg.inv(affine))),
)
# Check that applying an affine and its inverse give us back the
# original streamlines.
tractogram = DATA["tractogram"].copy()
affine = np.random.RandomState(1234).randn(4, 4)
affine[-1] = [0, 0, 0, 1] # Remove perspective projection.
tractogram.apply_affine(affine)
tractogram.apply_affine(np.linalg.inv(affine))
assert_array_almost_equal(tractogram.affine_to_rasmm, np.eye(4))
for s1, s2 in zip(tractogram.streamlines, DATA["streamlines"]):
assert_array_almost_equal(s1, s2)
# Test applying the identity transformation.
tractogram = DATA["tractogram"].copy()
tractogram.apply_affine(np.eye(4))
for s1, s2 in zip(tractogram.streamlines, DATA["streamlines"]):
assert_array_almost_equal(s1, s2)
# Test removing affine_to_rasmm
tractogram = DATA["tractogram"].copy()
tractogram.affine_to_rasmm = None
tractogram.apply_affine(affine)
assert_true(tractogram.affine_to_rasmm is None)
def test_tractogram_apply_affine(self):
tractogram = DATA['tractogram'].copy()
affine = np.eye(4)
scaling = np.array((1, 2, 3), dtype=float)
affine[range(3), range(3)] = scaling
# Apply the affine to the streamline in a lazy manner.
transformed_tractogram = tractogram.apply_affine(affine, lazy=True)
assert_true(type(transformed_tractogram) is LazyTractogram)
check_tractogram(
transformed_tractogram,
streamlines=[s * scaling for s in DATA['streamlines']],
data_per_streamline=DATA['data_per_streamline'],
data_per_point=DATA['data_per_point'])
assert_array_equal(transformed_tractogram.affine_to_rasmm,
np.dot(np.eye(4), np.linalg.inv(affine)))
# Make sure streamlines of the original tractogram have not been
# modified.
assert_arrays_equal(tractogram.streamlines, DATA['streamlines'])
# Apply the affine to the streamlines in-place.
transformed_tractogram = tractogram.apply_affine(affine)
assert_true(transformed_tractogram is tractogram)
check_tractogram(
tractogram,
streamlines=[s * scaling for s in DATA['streamlines']],
data_per_streamline=DATA['data_per_streamline'],
data_per_point=DATA['data_per_point'])
# Apply affine again and check the affine_to_rasmm.
transformed_tractogram = tractogram.apply_affine(affine)
assert_array_equal(
transformed_tractogram.affine_to_rasmm,
np.dot(np.eye(4),
np.dot(np.linalg.inv(affine), np.linalg.inv(affine))))
# Check that applying an affine and its inverse give us back the
# original streamlines.
tractogram = DATA['tractogram'].copy()
affine = np.random.RandomState(1234).randn(4, 4)
affine[-1] = [0, 0, 0, 1] # Remove perspective projection.
tractogram.apply_affine(affine)
tractogram.apply_affine(np.linalg.inv(affine))
assert_array_almost_equal(tractogram.affine_to_rasmm, np.eye(4))
for s1, s2 in zip(tractogram.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Test applying the identity transformation.
tractogram = DATA['tractogram'].copy()
tractogram.apply_affine(np.eye(4))
for s1, s2 in zip(tractogram.streamlines, DATA['streamlines']):
assert_array_almost_equal(s1, s2)
# Test removing affine_to_rasmm
tractogram = DATA['tractogram'].copy()
tractogram.affine_to_rasmm = None
tractogram.apply_affine(affine)
assert_true(tractogram.affine_to_rasmm is None)
def _data_gen():
for d in zip(*data):
data_for_points = {'fa': d[1],
'colors': d[2]}
data_for_streamline = {'mean_curvature': d[3],
'mean_torsion': d[4],
'mean_colors': d[5]}
yield TractogramItem(d[0],
data_for_streamline,
data_for_points)
def _data_gen():
for d in zip(*data):
data_for_points = {'fa': d[1], 'colors': d[2]}
data_for_streamline = {
'mean_curvature': d[3],
'mean_torsion': d[4],
'mean_colors': d[5]
}
yield TractogramItem(d[0], data_for_streamline,
data_for_points)
def bench_load_trk():
rng = np.random.RandomState(42)
dtype = 'float32'
NB_STREAMLINES = 5000
NB_POINTS = 1000
points = [
rng.rand(NB_POINTS, 3).astype(dtype) for i in range(NB_STREAMLINES)
]
scalars = [
rng.rand(NB_POINTS, 10).astype(dtype) for i in range(NB_STREAMLINES)
]
repeat = 10
with InTemporaryDirectory():
trk_file = "tmp.trk"
tractogram = Tractogram(points, affine_to_rasmm=np.eye(4))
TrkFile(tractogram).save(trk_file)
streamlines_old = [
d[0] - 0.5 for d in tv.read(trk_file, points_space="rasmm")[0]
]
mtime_old = measure('tv.read(trk_file, points_space="rasmm")', repeat)
print("Old: Loaded {:,} streamlines in {:6.2f}".format(
NB_STREAMLINES, mtime_old))
trk = nib.streamlines.load(trk_file, lazy_load=False)
streamlines_new = trk.streamlines
mtime_new = measure('nib.streamlines.load(trk_file, lazy_load=False)',
repeat)
print("\nNew: Loaded {:,} streamlines in {:6.2}".format(
NB_STREAMLINES, mtime_new))
print("Speedup of {:.2f}".format(mtime_old / mtime_new))
for s1, s2 in zip(streamlines_new, streamlines_old):
assert_array_equal(s1, s2)
# Points and scalars
with InTemporaryDirectory():
trk_file = "tmp.trk"
tractogram = Tractogram(points,
data_per_point={'scalars': scalars},
affine_to_rasmm=np.eye(4))
TrkFile(tractogram).save(trk_file)
streamlines_old = [
d[0] - 0.5 for d in tv.read(trk_file, points_space="rasmm")[0]
]
scalars_old = [
d[1] for d in tv.read(trk_file, points_space="rasmm")[0]
]
mtime_old = measure('tv.read(trk_file, points_space="rasmm")', repeat)
msg = "Old: Loaded {:,} streamlines with scalars in {:6.2f}"
print(msg.format(NB_STREAMLINES, mtime_old))
trk = nib.streamlines.load(trk_file, lazy_load=False)
scalars_new = trk.tractogram.data_per_point['scalars']
mtime_new = measure('nib.streamlines.load(trk_file, lazy_load=False)',
repeat)
msg = "New: Loaded {:,} streamlines with scalars in {:6.2f}"
print(msg.format(NB_STREAMLINES, mtime_new))
print("Speedup of {:2f}".format(mtime_old / mtime_new))
for s1, s2 in zip(scalars_new, scalars_old):
assert_array_equal(s1, s2)
def _data_gen():
for d in zip(*data):
data_for_points = {"fa": d[1], "colors": d[2]}
data_for_streamline = {"mean_curvature": d[3], "mean_torsion": d[4], "mean_colors": d[5]}
yield TractogramItem(d[0], data_for_streamline, data_for_points)