def test_radial_profile_cutoff(cutoff):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=cutoff)
assert unbin_dist.max() == cutoff
assert dist.max() < cutoff
def test_radial_profile_fail_pad(padding=30.0, max_distance=20.0):
'''
Cannot pad greater than max_distance
'''
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False,
max_distance=max_distance, pad_to_distance=padding)
def test_radial_profile_cutoff(cutoff):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=cutoff)
assert unbin_dist.max() == cutoff
assert dist.max() < cutoff
def test_radial_profile_fail_pad(padding=30.0, max_distance=20.0):
'''
Cannot pad greater than max_distance
'''
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False,
max_distance=max_distance, pad_to_distance=padding)
def test_radial_profile_output(theta):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=20)
params, errors, _, _, fail = \
gauss_model(dist, radprof, np.ones_like(dist), 1.0)
npt.assert_allclose(params[:-1], [1.0, 10.0, 0.0], atol=1e-1)
def test_radial_profile_output(theta):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=20)
params, errors, _, _, fail = \
gauss_model(dist, radprof, np.ones_like(dist), 1.0)
npt.assert_allclose(params[:-1], [1.0, 10.0, 0.0], atol=1e-1)
def test_radial_profile_padding(padding, max_distance=20.0):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False,
max_distance=max_distance, pad_to_distance=padding)
if padding <= max_distance:
assert unbin_dist.max() == max_distance
assert dist.max() < max_distance
else:
assert unbin_dist.max() == padding
assert dist.max() < padding
def test_radial_profile_padding(padding, max_distance=20.0):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False,
max_distance=max_distance, pad_to_distance=padding)
if padding <= max_distance:
assert unbin_dist.max() == max_distance
assert dist.max() < max_distance
else:
assert unbin_dist.max() == padding
assert dist.max() < padding
def test_radial_profile_autocut_plateau():
'''
Test auto-cutting with a plateau and a second fall.
'''
model, skeleton = generate_filament_model(shape=160,
width=10.0,
amplitude=10.0,
background=5.0)
# Create a second drop-off profile 40 pixels from the center on each side.
for i, row in enumerate(model[120:].T):
model[120:, i] = generate_gaussian_profile(np.arange(row.size),
width=5.0,
amplitude=5.0,
background=0.0)
for i, row in enumerate(model[:40].T):
model[:40, i] = generate_gaussian_profile(np.arange(row.size),
width=5.0,
amplitude=5.0,
background=0.0)[::-1]
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=True,
max_distance=60.0, auto_cut_kwargs={'smooth_size': 3.0,
'pad_cut': 0,
'interp_factor': 1})
# By-eye, this should be 18-19
npt.assert_almost_equal(dist.max(), 38.201, decimal=3)
def test_radial_profile_autocut():
'''
Test auto-cutting with a secondary offset peak.
'''
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
model += np.roll(model, -30, axis=0).copy()
model += np.roll(model, +30, axis=0).copy()
# all_skeleton += np.roll(skeleton, -30, axis=0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=True,
max_distance=50.0, auto_cut_kwargs={'smooth_size': 3.0,
'pad_cut': 0})
npt.assert_equal(dist.max(), 19.25)
def test_radial_profile_autocut():
'''
Test auto-cutting with a secondary offset peak.
'''
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
model += np.roll(model, -30, axis=0).copy()
model += np.roll(model, +30, axis=0).copy()
# all_skeleton += np.roll(skeleton, -30, axis=0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=True,
max_distance=50.0, auto_cut_kwargs={'smooth_size': 3.0,
'pad_cut': 0})
npt.assert_equal(dist.max(), 19.25)
def test_radial_profile_autocut_plateau():
'''
Test auto-cutting with a plateau and a second fall.
'''
model, skeleton = generate_filament_model(shape=160, width=10.0,
amplitude=10.0, background=5.0)
# Create a second drop-off profile 40 pixels from the center on each side.
for i, row in enumerate(model[120:].T):
model[120:, i] = generate_gaussian_profile(np.arange(row.size),
width=5.0,
amplitude=5.0,
background=0.0)
for i, row in enumerate(model[:40].T):
model[:40, i] = generate_gaussian_profile(np.arange(row.size),
width=5.0,
amplitude=5.0,
background=0.0)[::-1]
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=True,
max_distance=60.0, auto_cut_kwargs={'smooth_size': 3.0,
'pad_cut': 0,
'interp_factor': 1})
# By-eye, this should be 18-19
npt.assert_almost_equal(dist.max(), 38.201, decimal=3)
