def test_unique_edges():
faces = np.array([[0, 1, 2], [1, 2, 0]])
e = array_to_set([[1, 2], [0, 1], [0, 2]])
u = unique_edges(faces)
nt.assert_equal(e, array_to_set(u))
u, m = unique_edges(faces, return_mapping=True)
nt.assert_equal(e, array_to_set(u))
edges = [[[0, 1], [1, 2], [2, 0]], [[1, 2], [2, 0], [0, 1]]]
nt.assert_equal(np.sort(u[m], -1), np.sort(edges, -1))
def test_unique_edges():
faces = np.array([[0, 1, 2],
[1, 2, 0]])
e = array_to_set([[1, 2],
[0, 1],
[0, 2]])
u = unique_edges(faces)
nt.assert_equal(e, array_to_set(u))
u, m = unique_edges(faces, return_mapping=True)
nt.assert_equal(e, array_to_set(u))
edges = [[[0, 1], [1, 2], [2, 0]],
[[1, 2], [2, 0], [0, 1]]]
nt.assert_equal(np.sort(u[m], -1), np.sort(edges, -1))
def test_dni_eit():
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
data, descr = sim_data(bvals, bvecs)
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
#create the sphere
odf_sphere = (vertices, faces)
dn = DiffusionNablaModel(bvals, bvecs, odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit = dn.fit(data)
print('DiffusionNablaModel')
for i, d in enumerate(data):
print(descr[i], np.sum(dnfit.peak_values[i] > 0))
ei = EquatorialInversionModel(bvals, bvecs, odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data, return_odf=True)
print('EquatorialInversionModel')
for i, d in enumerate(data):
print(descr[i], np.sum(eifit.peak_values[i] > 0))
assert_equal(descr[i][1], np.sum(eifit.peak_values[i] > 0))
def test_local_maxima():
sphere = get_sphere('symmetric724')
vertices, faces = sphere.vertices, sphere.faces
edges = unique_edges(faces)
odf = abs(vertices.sum(-1))
odf[1] = 10.
odf[143] = 143.
odf[505] = 505
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [505, 143, 10])
npt.assert_array_equal(peak_index, [505, 143, 1])
hemisphere = HemiSphere(xyz=vertices, faces=faces)
vertices_half, edges_half = hemisphere.vertices, hemisphere.edges
odf = abs(vertices_half.sum(-1))
odf[1] = 10.
odf[143] = 143.
peak_value, peak_index = local_maxima(odf, edges_half)
npt.assert_array_equal(peak_value, [143, 10])
npt.assert_array_equal(peak_index, [143, 1])
odf[20] = np.nan
npt.assert_raises(ValueError, local_maxima, odf, edges_half)
def test_dni_eit():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data,descr=sim_data(bvals,bvecs)
#load odf sphere
vertices,faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
#create the sphere
odf_sphere=(vertices,faces)
dn=DiffusionNablaModel(bvals,bvecs,odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit=dn.fit(data)
print('DiffusionNablaModel')
for i,d in enumerate(data):
print(descr[i], np.sum(dnfit.peak_values[i]>0))
ei=EquatorialInversionModel(bvals,bvecs,odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data,return_odf=True)
print('EquatorialInversionModel')
for i,d in enumerate(data):
print(descr[i], np.sum(eifit.peak_values[i]>0))
assert_equal(descr[i][1], np.sum(eifit.peak_values[i]>0))
def bench_local_maxima():
repeat = 10000
sphere = get_sphere('symmetric724')
vertices, faces = sphere.vertices, sphere.faces
odf = abs(vertices.sum(-1))
edges = unique_edges(faces)
print('Timing peak finding')
timed0 = measure("local_maxima(odf, edges)", repeat)
print('Actual sphere: %0.2f' % timed0)
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.
odf[143] = 143.
odf[505] = 505.
timed1 = measure("local_maxima(odf, edges)", repeat)
print('Few-peak sphere: %0.2f' % timed1)
def bench_local_maxima():
repeat = 10000
sphere = get_sphere("symmetric724")
vertices, faces = sphere.vertices, sphere.faces
odf = abs(vertices.sum(-1))
edges = unique_edges(faces)
print("Timing peak finding")
timed0 = measure("local_maxima(odf, edges)", repeat)
print("Actual sphere: %0.2f" % timed0)
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.0
odf[143] = 143.0
odf[505] = 505.0
timed1 = measure("local_maxima(odf, edges)", repeat)
print("Few-peak sphere: %0.2f" % timed1)
def test_local_maxima():
sphere = get_sphere('symmetric724')
vertices, faces = sphere.vertices, sphere.faces
edges = unique_edges(faces)
# Check that the first peak is == max(odf)
odf = abs(vertices.sum(-1))
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_equal(max(odf), peak_values[0])
npt.assert_equal(max(odf), odf[peak_index[0]])
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.
odf[143] = 143.
odf[505] = 505.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [505, 143, 1])
npt.assert_array_equal(peak_index, [505, 143, 1])
# Check that neighboring points can both be peaks
odf = np.zeros(len(vertices))
point1, point2 = edges[0]
odf[[point1, point2]] = 1.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [1., 1.])
npt.assert_(point1 in peak_index)
npt.assert_(point2 in peak_index)
# Repeat with a hemisphere
hemisphere = HemiSphere(xyz=vertices, faces=faces)
vertices, edges = hemisphere.vertices, hemisphere.edges
# Check that the first peak is == max(odf)
odf = abs(vertices.sum(-1))
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_equal(max(odf), peak_values[0])
npt.assert_equal(max(odf), odf[peak_index[0]])
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.
odf[143] = 143.
odf[300] = 300.
peak_value, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_value, [300, 143, 1])
npt.assert_array_equal(peak_index, [300, 143, 1])
# Check that neighboring points can both be peaks
odf = np.zeros(len(vertices))
point1, point2 = edges[0]
odf[[point1, point2]] = 1.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [1., 1.])
npt.assert_(point1 in peak_index)
npt.assert_(point2 in peak_index)
# Should raise an error if odf has nans
odf[20] = np.nan
npt.assert_raises(ValueError, local_maxima, odf, edges)
# Should raise an error if edge values are too large to index odf
edges[0, 0] = 9999
odf[20] = 0
npt.assert_raises(IndexError, local_maxima, odf, edges)
def test_local_maxima():
sphere = default_sphere
vertices, faces = sphere.vertices, sphere.faces
edges = unique_edges(faces)
# Check that the first peak is == max(odf)
odf = abs(vertices.sum(-1))
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_equal(max(odf), peak_values[0])
npt.assert_equal(max(odf), odf[peak_index[0]])
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.
odf[143] = 143.
odf[361] = 361.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [361, 143, 1])
npt.assert_array_equal(peak_index, [361, 143, 1])
# Check that neighboring points can both be peaks
odf = np.zeros(len(vertices))
point1, point2 = edges[0]
odf[[point1, point2]] = 1.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [1., 1.])
npt.assert_(point1 in peak_index)
npt.assert_(point2 in peak_index)
# Repeat with a hemisphere
hemisphere = HemiSphere(xyz=vertices, faces=faces)
vertices, edges = hemisphere.vertices, hemisphere.edges
# Check that the first peak is == max(odf)
odf = abs(vertices.sum(-1))
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_equal(max(odf), peak_values[0])
npt.assert_equal(max(odf), odf[peak_index[0]])
# Create an artificial odf with a few peaks
odf = np.zeros(len(vertices))
odf[1] = 1.
odf[143] = 143.
odf[300] = 300.
peak_value, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_value, [300, 143, 1])
npt.assert_array_equal(peak_index, [300, 143, 1])
# Check that neighboring points can both be peaks
odf = np.zeros(len(vertices))
point1, point2 = edges[0]
odf[[point1, point2]] = 1.
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [1., 1.])
npt.assert_(point1 in peak_index)
npt.assert_(point2 in peak_index)
# Should raise an error if odf has nans
odf[20] = np.nan
npt.assert_raises(ValueError, local_maxima, odf, edges)
# Should raise an error if edge values are too large to index odf
edges[0, 0] = 9999
odf[20] = 0
npt.assert_raises(IndexError, local_maxima, odf, edges)
