def test_peak_direction_tracker():
"""This tests that the Peaks And Metrics Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
peaks_values_lookup = np.array([[0., 0.],
[1., 0.],
[1., 0.],
[0.5, 0.5]])
peaks_indices_lookup = np.array([[-1, -1],
[0, -1],
[1, -1],
[0, 1]])
# PeaksAndMetricsDirectionGetter needs at 3 slices on each axis to work
simple_image = np.zeros([5, 6, 3], dtype=int)
simple_image[:, :, 1] = np.array([[0, 1, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.peak_values = peaks_values_lookup[simple_image]
dg.peak_indices = peaks_indices_lookup[simple_image]
dg.ang_thr = 90
mask = (simple_image >= 0).astype(float)
tc = ThresholdTissueClassifier(mask, 0.5)
seeds = [np.array([1., 1., 1.]),
np.array([2., 4., 1.]),
np.array([1., 3., 1.]),
np.array([4., 4., 1.])]
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
expected = [np.array([[0., 1., 1.],
[1., 1., 1.],
[2., 1., 1.],
[3., 1., 1.],
[4., 1., 1.]]),
np.array([[2., 0., 1.],
[2., 1., 1.],
[2., 2., 1.],
[2., 3., 1.],
[2., 4., 1.],
[2., 5., 1.]]),
np.array([[0., 3., 1.],
[1., 3., 1.],
[2., 3., 1.],
[2., 4., 1.],
[2., 5., 1.]]),
np.array([[4., 4., 1.]])]
for i, sl in enumerate(streamlines):
npt.assert_(np.allclose(sl, expected[i]))
def test_peak_direction_tracker():
"""This tests that the Peaks And Metrics Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
peaks_values_lookup = np.array([[0., 0.],
[1., 0.],
[1., 0.],
[0.5, 0.5]])
peaks_indices_lookup = np.array([[-1, -1],
[0, -1],
[1, -1],
[0, 1]])
# PeaksAndMetricsDirectionGetter needs at 3 slices on each axis to work
simple_image = np.zeros([5, 6, 3], dtype=int)
simple_image[:, :, 1] = np.array([[0, 1, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.peak_values = peaks_values_lookup[simple_image]
dg.peak_indices = peaks_indices_lookup[simple_image]
dg.ang_thr = 90
mask = (simple_image >= 0).astype(float)
tc = ThresholdTissueClassifier(mask, 0.5)
seeds = [np.array([1., 1., 1.]),
np.array([2., 4., 1.]),
np.array([1., 3., 1.]),
np.array([4., 4., 1.])]
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
expected = [np.array([[0., 1., 1.],
[1., 1., 1.],
[2., 1., 1.],
[3., 1., 1.],
[4., 1., 1.]]),
np.array([[2., 0., 1.],
[2., 1., 1.],
[2., 2., 1.],
[2., 3., 1.],
[2., 4., 1.],
[2., 5., 1.]]),
np.array([[0., 3., 1.],
[1., 3., 1.],
[2., 3., 1.],
[2., 4., 1.],
[2., 5., 1.]]),
np.array([[4., 4., 1.]])]
for i, sl in enumerate(streamlines):
npt.assert_(np.allclose(sl, expected[i]))