def test_real_sh_descoteaux2():
vertices = hemi_icosahedron.subdivide(2).vertices
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(vertices, mevals, angles, [50, 50])
mevals = np.array([[0.0015, 0.0003, 0.0003]])
angles = [(0, 0)]
odf2 = multi_tensor_odf(-vertices, mevals, angles, [100])
sphere = Sphere(xyz=np.vstack((vertices, -vertices)))
# Asymmetric spherical function with 162 coefficients
sf = np.append(odf, odf2)
# In order for our approximation to be precise enough, we
# will use a SH basis of orders up to 10 (121 coefficients)
with warnings.catch_warnings(record=True) as w:
B, m, n = real_sh_descoteaux(10,
sphere.theta,
sphere.phi,
full_basis=True)
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[0].category, PendingDeprecationWarning))
npt.assert_(descoteaux07_legacy_msg in str(w[0].message))
invB = smooth_pinv(B, L=np.zeros_like(n))
sh_coefs = np.dot(invB, sf)
sf_approx = np.dot(B, sh_coefs)
assert_array_almost_equal(sf_approx, sf, 2)
def test_csd_predict():
"""
Test prediction API
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = small_sphere
multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
# Predicting from a fit should give the same result as predicting from a
# model, S0 is 1 by default
prediction1 = csd_fit.predict()
prediction2 = csd.predict(csd_fit.shm_coeff)
npt.assert_array_equal(prediction1, prediction2)
npt.assert_array_equal(prediction1[..., gtab.b0s_mask], 1.)
# Same with a different S0
prediction1 = csd_fit.predict(S0=123.)
prediction2 = csd.predict(csd_fit.shm_coeff, S0=123.)
npt.assert_array_equal(prediction1, prediction2)
npt.assert_array_equal(prediction1[..., gtab.b0s_mask], 123.)
# For "well behaved" coefficients, the model should be able to find the
# coefficients from the predicted signal.
coeff = np.random.random(csd_fit.shm_coeff.shape) - .5
coeff[..., 0] = 10.
S = csd.predict(coeff)
csd_fit = csd.fit(S)
npt.assert_array_almost_equal(coeff, csd_fit.shm_coeff)
# Test predict on nd-data set
S_nd = np.zeros((2, 3, 4, S.size))
S_nd[:] = S
fit = csd.fit(S_nd)
predict1 = fit.predict()
predict2 = csd.predict(fit.shm_coeff)
npt.assert_array_almost_equal(predict1, predict2)
def test_csd_predict():
"""
Test prediction API
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = small_sphere
multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
# Predicting from a fit should give the same result as predicting from a
# model, S0 is 1 by default
prediction1 = csd_fit.predict()
prediction2 = csd.predict(csd_fit.shm_coeff)
npt.assert_array_equal(prediction1, prediction2)
npt.assert_array_equal(prediction1[..., gtab.b0s_mask], 1.)
# Same with a different S0
prediction1 = csd_fit.predict(S0=123.)
prediction2 = csd.predict(csd_fit.shm_coeff, S0=123.)
npt.assert_array_equal(prediction1, prediction2)
npt.assert_array_equal(prediction1[..., gtab.b0s_mask], 123.)
# For "well behaved" coefficients, the model should be able to find the
# coefficients from the predicted signal.
coeff = np.random.random(csd_fit.shm_coeff.shape) - .5
coeff[..., 0] = 10.
S = csd.predict(coeff)
csd_fit = csd.fit(S)
npt.assert_array_almost_equal(coeff, csd_fit.shm_coeff)
# Test predict on nd-data set
S_nd = np.zeros((2, 3, 4, S.size))
S_nd[:] = S
fit = csd.fit(S_nd)
predict1 = fit.predict()
predict2 = csd.predict(fit.shm_coeff)
npt.assert_array_almost_equal(predict1, predict2)
def test_sf_to_sh():
# Subdividing a hemi_icosahedron twice produces 81 unique points, which
# is more than enough to fit a order 8 (45 coefficients) spherical harmonic
sphere = hemi_icosahedron.subdivide(2)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
odf = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
# 1D case with the 3 bases functions
odf_sh = sf_to_sh(odf, sphere, 8)
odf2 = sh_to_sf(odf_sh, sphere, 8)
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "mrtrix")
odf2 = sh_to_sf(odf_sh, sphere, 8, "mrtrix")
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "fibernav")
odf2 = sh_to_sf(odf_sh, sphere, 8, "fibernav")
assert_array_almost_equal(odf, odf2, 2)
# 2D case
odf2d = np.vstack((odf2, odf))
odf2d_sh = sf_to_sh(odf2d, sphere, 8)
odf2d_sf = sh_to_sf(odf2d_sh, sphere, 8)
assert_array_almost_equal(odf2d, odf2d_sf, 2)
def test_sf_to_sh():
# Subdividing a hemi_icosahedron twice produces 81 unique points, which
# is more than enough to fit a order 8 (45 coefficients) spherical harmonic
sphere = hemi_icosahedron.subdivide(2)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])]
odf = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
# 1D case with the 3 bases functions
odf_sh = sf_to_sh(odf, sphere, 8)
odf2 = sh_to_sf(odf_sh, sphere, 8)
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "mrtrix")
odf2 = sh_to_sf(odf_sh, sphere, 8, "mrtrix")
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "fibernav")
odf2 = sh_to_sf(odf_sh, sphere, 8, "fibernav")
assert_array_almost_equal(odf, odf2, 2)
# 2D case
odf2d = np.vstack((odf2, odf))
odf2d_sh = sf_to_sh(odf2d, sphere, 8)
odf2d_sf = sh_to_sf(odf2d_sh, sphere, 8)
assert_array_almost_equal(odf2d, odf2d_sf, 2)
def test_csd_predict():
"""
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
prediction = csd_predict(csd_fit.shm_coeff, gtab, response=response, S0=S0)
npt.assert_equal(prediction.shape[0], S.shape[0])
model_prediction = csd.predict(csd_fit.shm_coeff)
assert_array_almost_equal(prediction, model_prediction)
# Roundtrip tests (quite inaccurate, because of regularization):
assert_array_almost_equal(csd_fit.predict(gtab, S0=S0),S,decimal=1)
assert_array_almost_equal(csd.predict(csd_fit.shm_coeff, S0=S0),S,decimal=1)
def test_r2_term_odf_sharp():
SNR = None
S0 = 1
angle = 75
_, fbvals, fbvecs = get_data('small_64D') #get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (angle, 0)],
fractions=[50, 50], snr=SNR)
mevecs = [all_tensor_evecs(sticks[0]).T,
all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None)
fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15.,
sh_order=8, lambda_=1., tau=0.1, r2_term=True)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
def test_minmax_normalize():
bvalue = 3000
S0 = 1
SNR = 100
sphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], sphere.vertices))
bvals = np.zeros(len(bvecs)) + bvalue
bvals[0] = 0
gtab = gradient_table(bvals, bvecs)
evals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
multi_tensor(gtab, evals, S0, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=SNR)
odf = multi_tensor_odf(sphere.vertices, evals, angles=[(0, 0), (90, 0)],
fractions=[50, 50])
odf2 = minmax_normalize(odf)
assert_equal(odf2.max(), 1)
assert_equal(odf2.min(), 0)
odf3 = np.empty(odf.shape)
odf3 = minmax_normalize(odf, odf3)
assert_equal(odf3.max(), 1)
assert_equal(odf3.min(), 0)
def test_peak_finding():
vertices, faces=get_sphere('symmetric724')
odf=np.zeros(len(vertices))
odf = np.abs(vertices.sum(-1))
odf[1] = 10.
odf[505] = 505.
odf[143] = 143.
peaks, inds=peak_finding(odf.astype('f8'), faces.astype('uint16'))
print peaks, inds
edges = unique_edges(faces)
peaks, inds = local_maxima(odf, edges)
print peaks, inds
vertices_half, edges_half, faces_half = reduce_antipodal(vertices, faces)
n = len(vertices_half)
peaks, inds = local_maxima(odf[:n], edges_half)
print peaks, inds
mevals=np.array(([0.0015,0.0003,0.0003],
[0.0015,0.0003,0.0003]))
e0=np.array([1,0,0.])
e1=np.array([0.,1,0])
mevecs=[all_tensor_evecs(e0),all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5,0.5], mevals, mevecs)
peaks, inds=peak_finding(odf, faces)
print peaks, inds
peaks2, inds2 = local_maxima(odf[:n], edges_half)
print peaks2, inds2
assert_equal(len(peaks), 2)
assert_equal(len(peaks2), 2)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def test_minmax_normalize():
bvalue = 3000
S0 = 1
SNR = 100
sphere = get_sphere("symmetric362")
bvecs = np.concatenate(([[0, 0, 0]], sphere.vertices))
bvals = np.zeros(len(bvecs)) + bvalue
bvals[0] = 0
gtab = gradient_table(bvals, bvecs)
evals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, evals, S0, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=SNR)
odf = multi_tensor_odf(sphere.vertices, evals, angles=[(0, 0), (90, 0)], fractions=[50, 50])
odf2 = minmax_normalize(odf)
assert_equal(odf2.max(), 1)
assert_equal(odf2.min(), 0)
odf3 = np.empty(odf.shape)
odf3 = minmax_normalize(odf, odf3)
assert_equal(odf3.max(), 1)
assert_equal(odf3.min(), 0)
def _create_mt_sim(mevals, angles, fractions, S0, SNR, half_sphere=False):
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=fractions,
snr=SNR)
sphere = get_sphere('symmetric724').subdivide(2)
if half_sphere:
sphere = HemiSphere.from_sphere(sphere)
odf_gt = multi_tensor_odf(sphere.vertices,
mevals,
angles=angles,
fractions=fractions)
return odf_gt, sticks, sphere
def test_sf_to_sh():
# Subdividing a hemi_icosahedron twice produces 81 unique points, which
# is more than enough to fit a order 8 (45 coefficients) spherical harmonic
hemisphere = hemi_icosahedron.subdivide(2)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(hemisphere.vertices, mevals, angles, [50, 50])
# 1D case with the 2 symmetric bases functions
odf_sh = sf_to_sh(odf, hemisphere, 8, "tournier07")
odf_reconst = sh_to_sf(odf_sh, hemisphere, 8, "tournier07")
assert_array_almost_equal(odf, odf_reconst, 2)
odf_sh = sf_to_sh(odf, hemisphere, 8, "descoteaux07")
odf_reconst = sh_to_sf(odf_sh, hemisphere, 8, "descoteaux07")
assert_array_almost_equal(odf, odf_reconst, 2)
# We now create an asymmetric signal
# to try out our full SH basis
mevals = np.array([[0.0015, 0.0003, 0.0003]])
angles = [(0, 0)]
odf2 = multi_tensor_odf(hemisphere.vertices, mevals, angles, [100])
# We simulate our asymmetric signal by using a different ODF
# per hemisphere. The sphere used is a concatenation of the
# vertices of our hemisphere, for a total of 162 vertices.
sphere = Sphere(xyz=np.vstack((hemisphere.vertices, -hemisphere.vertices)))
asym_odf = np.append(odf, odf2)
# Try out full bases with order 10 (121 coefficients)
odf_sh = sf_to_sh(asym_odf, sphere, 10, "tournier07_full")
odf_reconst = sh_to_sf(odf_sh, sphere, 10, "tournier07_full")
assert_array_almost_equal(odf_reconst, asym_odf, 2)
odf_sh = sf_to_sh(asym_odf, sphere, 10, "descoteaux07_full")
odf_reconst = sh_to_sf(odf_sh, sphere, 10, "descoteaux07_full")
assert_array_almost_equal(odf_reconst, asym_odf, 2)
# An invalid basis name should raise an error
assert_raises(ValueError, sh_to_sf, odf, hemisphere, basis_type="")
assert_raises(ValueError, sf_to_sh, odf_sh, hemisphere, basis_type="")
# 2D case
odf2d = np.vstack((odf, odf))
odf2d_sh = sf_to_sh(odf2d, hemisphere, 8)
odf2d_sf = sh_to_sf(odf2d_sh, hemisphere, 8)
assert_array_almost_equal(odf2d, odf2d_sf, 2)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def test_multi_tensor():
vertices, faces = get_sphere('symmetric724')
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
e0 = np.array([1, 0, 0.])
e1 = np.array([0., 1, 0])
mevecs = [all_tensor_evecs(e0), all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5, 0.5], mevals, mevecs)
assert odf.shape == (len(vertices), )
assert np.all(odf <= 1) & np.all(odf >= 0)
def test_multi_tensor():
vertices, faces = get_sphere('symmetric724')
mevals=np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
e0 = np.array([1, 0, 0.])
e1 = np.array([0., 1, 0])
mevecs=[all_tensor_evecs(e0), all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5,0.5], mevals, mevecs)
assert odf.shape == (len(vertices),)
assert np.all(odf <= 1) & np.all(odf >= 0)
def test_convert_sh_to_full_basis():
hemisphere = hemi_icosahedron.subdivide(2)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(hemisphere.vertices, mevals, angles, [50, 50])
sh_coeffs = sf_to_sh(odf, hemisphere, 8)
full_sh_coeffs = convert_sh_to_full_basis(sh_coeffs)
odf_reconst = sh_to_sf(full_sh_coeffs, hemisphere, 8, full_basis=True)
assert_array_almost_equal(odf, odf_reconst, 2)
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=[(0, 0), (60, 0)],
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric724')
mevecs = [all_tensor_evecs(sticks[0]).T, all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len(w) > 0, False)
def test_r2_term_odf_sharp():
SNR = None
S0 = 1
angle = 45 # 45 degrees is a very tight angle to disentangle
_, fbvals, fbvecs = get_data('small_64D') # get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (angle, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None)
fodf_sh = odf_sh_to_sharp(odfs_sh,
sphere,
basis=None,
ratio=3 / 15.,
sh_order=8,
lambda_=1.,
tau=0.1,
r2_term=True)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
# This should pass as well
sdt_model = ConstrainedSDTModel(gtab, ratio=3 / 15., sh_order=8)
sdt_fit = sdt_model.fit(S)
fodf = sdt_fit.odf(sphere)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
def test_real_full_sh_basis():
vertices = hemi_icosahedron.subdivide(2).vertices
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(vertices, mevals, angles, [50, 50])
mevals = np.array([[0.0015, 0.0003, 0.0003]])
angles = [(0, 0)]
odf2 = multi_tensor_odf(-vertices, mevals, angles, [100])
sphere = Sphere(xyz=np.vstack((vertices, -vertices)))
# Asymmetric spherical function with 162 coefficients
sf = np.append(odf, odf2)
# In order for our approximation to be precise enough, we
# will use a SH basis of orders up to 10 (121 coefficients)
B, m, n = real_full_sh_basis(10, sphere.theta, sphere.phi)
invB = smooth_pinv(B, L=np.zeros_like(n))
sh_coefs = np.dot(invB, sf)
sf_approx = np.dot(B, sh_coefs)
assert_array_almost_equal(sf_approx, sf, 2)
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (60, 0)],
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric724')
mevecs = [all_tensor_evecs(sticks[0]).T,
all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len(w) > 0, False)
def sim_tensor_2x(gtab, angle=90, sphere=None, S0=1., snr=None):
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
data, sticks = multi_tensor(gtab, mevals, S0,
angles=[(90 , 0), (90, angle)],
fractions=[50, 50], snr=snr)
mevecs = [all_tensor_evecs(sticks[0]).T,
all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
return data, sticks, odf_gt
def test_sf_to_sh():
# Subdividing a hemi_icosahedron twice produces 81 unique points, which
# is more than enough to fit a order 8 (45 coefficients) spherical harmonic
sphere = hemi_icosahedron.subdivide(2)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
odf = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
# 1D case with the 3 bases functions
odf_sh = sf_to_sh(odf, sphere, 8)
odf2 = sh_to_sf(odf_sh, sphere, 8)
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "tournier07")
odf2 = sh_to_sf(odf_sh, sphere, 8, "tournier07")
assert_array_almost_equal(odf, odf2, 2)
# Test the basis naming deprecation
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
odf_sh_mrtrix = sf_to_sh(odf, sphere, 8, "mrtrix")
odf2_mrtrix = sh_to_sf(odf_sh_mrtrix, sphere, 8, "mrtrix")
assert_array_almost_equal(odf, odf2_mrtrix, 2)
assert len(w) != 0
assert issubclass(w[-1].category, DeprecationWarning)
warnings.simplefilter("default", DeprecationWarning)
odf_sh = sf_to_sh(odf, sphere, 8, "descoteaux07")
odf2 = sh_to_sf(odf_sh, sphere, 8, "descoteaux07")
assert_array_almost_equal(odf, odf2, 2)
# Test the basis naming deprecation
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
odf_sh_fibernav = sf_to_sh(odf, sphere, 8, "fibernav")
odf2_fibernav = sh_to_sf(odf_sh_fibernav, sphere, 8, "fibernav")
assert_array_almost_equal(odf, odf2_fibernav, 2)
assert len(w) != 0
assert issubclass(w[-1].category, DeprecationWarning)
warnings.simplefilter("default", DeprecationWarning)
# 2D case
odf2d = np.vstack((odf2, odf))
odf2d_sh = sf_to_sh(odf2d, sphere, 8)
odf2d_sf = sh_to_sf(odf2d_sh, sphere, 8)
assert_array_almost_equal(odf2d, odf2d_sf, 2)
def test_sf_to_sh():
# Subdividing a hemi_icosahedron twice produces 81 unique points, which
# is more than enough to fit a order 8 (45 coefficients) spherical harmonic
sphere = hemi_icosahedron.subdivide(2)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
odf = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
# 1D case with the 3 bases functions
odf_sh = sf_to_sh(odf, sphere, 8)
odf2 = sh_to_sf(odf_sh, sphere, 8)
assert_array_almost_equal(odf, odf2, 2)
odf_sh = sf_to_sh(odf, sphere, 8, "tournier07")
odf2 = sh_to_sf(odf_sh, sphere, 8, "tournier07")
assert_array_almost_equal(odf, odf2, 2)
# Test the basis naming deprecation
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
odf_sh_mrtrix = sf_to_sh(odf, sphere, 8, "mrtrix")
odf2_mrtrix = sh_to_sf(odf_sh_mrtrix, sphere, 8, "mrtrix")
assert_array_almost_equal(odf, odf2_mrtrix, 2)
assert len(w) != 0
assert issubclass(w[-1].category, DeprecationWarning)
warnings.simplefilter("default", DeprecationWarning)
odf_sh = sf_to_sh(odf, sphere, 8, "descoteaux07")
odf2 = sh_to_sf(odf_sh, sphere, 8, "descoteaux07")
assert_array_almost_equal(odf, odf2, 2)
# Test the basis naming deprecation
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
odf_sh_fibernav = sf_to_sh(odf, sphere, 8, "fibernav")
odf2_fibernav = sh_to_sf(odf_sh_fibernav, sphere, 8, "fibernav")
assert_array_almost_equal(odf, odf2_fibernav, 2)
assert len(w) != 0
assert issubclass(w[-1].category, DeprecationWarning)
warnings.simplefilter("default", DeprecationWarning)
# 2D case
odf2d = np.vstack((odf2, odf))
odf2d_sh = sf_to_sh(odf2d, sphere, 8)
odf2d_sf = sh_to_sf(odf2d_sh, sphere, 8)
assert_array_almost_equal(odf2d, odf2d_sf, 2)
def test_convert_sh_to_legacy():
hemisphere = hemi_icosahedron.subdivide(2)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(hemisphere.vertices, mevals, angles, [50, 50])
sh_coeffs = sf_to_sh(odf, hemisphere, 8, legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs, 'descoteaux07')
expected_coeffs = sf_to_sh(odf, hemisphere, 8, legacy=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
sh_coeffs = sf_to_sh(odf,
hemisphere,
8,
basis_type='tournier07',
legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs, 'tournier07')
expected_coeffs = sf_to_sh(odf,
hemisphere,
8,
basis_type='tournier07',
legacy=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
# 2D case
odfs = np.array([odf, odf])
sh_coeffs = sf_to_sh(odfs,
hemisphere,
8,
basis_type='tournier07',
full_basis=True,
legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs,
'tournier07',
full_basis=True)
expected_coeffs = sf_to_sh(odfs,
hemisphere,
8,
basis_type='tournier07',
legacy=True,
full_basis=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
assert_raises(ValueError, convert_sh_to_legacy, sh_coeffs, '', True)
def test_r2_term_odf_sharp():
SNR = None
S0 = 1
angle = 45 #45 degrees is a very tight angle to disentangle
_, fbvals, fbvecs = get_data('small_64D') #get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (angle, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None)
fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15.,
sh_order=8, lambda_=1., tau=0.1, r2_term=True)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
# This should pass as well
sdt_model = ConstrainedSDTModel(gtab, ratio=3/15., sh_order=8)
sdt_fit = sdt_model.fit(S)
fodf = sdt_fit.odf(sphere)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
def _create_mt_sim(mevals, angles, fractions, S0, SNR, half_sphere=False):
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=fractions, snr=SNR)
sphere = get_sphere('symmetric724').subdivide(2)
if half_sphere:
sphere = HemiSphere.from_sphere(sphere)
odf_gt = multi_tensor_odf(sphere.vertices, mevals,
angles=angles, fractions=fractions)
return odf_gt, sticks, sphere
def test_convert_sh_to_full_basis():
hemisphere = hemi_icosahedron.subdivide(2)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(hemisphere.vertices, mevals, angles, [50, 50])
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
sh_coeffs = sf_to_sh(odf, hemisphere, 8)
full_sh_coeffs = convert_sh_to_full_basis(sh_coeffs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
odf_reconst = sh_to_sf(full_sh_coeffs, hemisphere, 8, full_basis=True)
assert_array_almost_equal(odf, odf_reconst, 2)
def synth_unimodal_odfs(qball_sphere,
sphere_vol,
directions,
cutoff=2 * np.pi,
const_width=3,
tightness=4.9):
verts = qball_sphere.vertices
l_labels = verts.shape[0]
# `const_width` unimodals for each entry of `directions`
val_base = 1e-6 * 290
vals = [tightness * val_base, val_base, val_base]
vecs = normalize(
np.array(
[[np.sin(phi * np.pi / 180.0),
np.cos(phi * np.pi / 180.0), 0] for phi in directions]).T).T
voxels = [
multi_tensor_odf(verts, np.array((vals, vals)), [v, v], [50, 50])
for v in vecs
]
if cutoff < 1.5 * np.pi:
# cut off support of distribution functions
for v, vox in zip(vecs, voxels):
for k in range(l_labels):
v_diff1 = verts[k] - v
v_diff2 = verts[k] + v
if np.einsum('n,n->', v_diff1, v_diff1) > cutoff**2 \
and np.einsum('n,n->', v_diff2, v_diff2) > cutoff**2:
vox[k] = 0
fin = np.zeros((l_labels, const_width * len(voxels)), order='C')
for i, vox in enumerate(voxels):
i1 = i * const_width
i2 = (i + 1) * const_width
fin[:, i1:i2] = np.tile(vox, (const_width, 1)).T
normalize_odf(fin, sphere_vol)
return fin
def test_convert_sh_to_legacy():
hemisphere = hemi_icosahedron.subdivide(2)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(hemisphere.vertices, mevals, angles, [50, 50])
sh_coeffs = sf_to_sh(odf, hemisphere, 8, legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs, 'descoteaux07')
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
expected_coeffs = sf_to_sh(odf, hemisphere, 8, legacy=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
sh_coeffs = sf_to_sh(odf,
hemisphere,
8,
basis_type='tournier07',
legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs, 'tournier07')
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=tournier07_legacy_msg,
category=PendingDeprecationWarning)
expected_coeffs = sf_to_sh(odf,
hemisphere,
8,
basis_type='tournier07',
legacy=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
# 2D case
odfs = np.array([odf, odf])
sh_coeffs = sf_to_sh(odfs,
hemisphere,
8,
basis_type='tournier07',
full_basis=True,
legacy=False)
converted_coeffs = convert_sh_to_legacy(sh_coeffs,
'tournier07',
full_basis=True)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=tournier07_legacy_msg,
category=PendingDeprecationWarning)
expected_coeffs = sf_to_sh(odfs,
hemisphere,
8,
basis_type='tournier07',
legacy=True,
full_basis=True)
assert_array_almost_equal(converted_coeffs, expected_coeffs, 2)
assert_raises(ValueError, convert_sh_to_legacy, sh_coeffs, '', True)
def test_sh():
from dipy.sims.voxel import multi_tensor, multi_tensor_odf, sticks_and_ball
from dipy.data import get_sphere, get_fnames
from dipy.core.gradients import gradient_table
from dipy.io.gradients import read_bvals_bvecs
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
d = 0.0015
S, sticks = sticks_and_ball(gtab,
d=d,
S0=1,
angles=[(0, 0), (30, 30)],
fractions=[60, 40],
snr=None)
print(S)
print(sticks)
mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (60, 0)]
fractions = [50, 50]
sphere = get_sphere('repulsion724')
sphere = sphere.subdivide(2)
odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)
print(odf)
ren = window.Scene()
odf_actor = actor.odf_slicer(odf[None, None, None, :],
sphere=sphere,
colormap='plasma')
# odf_actor.RotateX(90)
ren.add(odf_actor)
# window.show(ren)
odf_test_dec= \
"""
// Constants, see here: http://en.wikipedia.org/wiki/Table_of_spherical_harmonics
#define k01 0.2820947918 // sqrt( 1/PI)/2
#define k02 0.4886025119 // sqrt( 3/PI)/2
#define k03 1.0925484306 // sqrt( 15/PI)/2
#define k04 0.3153915652 // sqrt( 5/PI)/4
#define k05 0.5462742153 // sqrt( 15/PI)/4
#define k06 0.5900435860 // sqrt( 70/PI)/8
#define k07 2.8906114210 // sqrt(105/PI)/2
#define k08 0.4570214810 // sqrt( 42/PI)/8
#define k09 0.3731763300 // sqrt( 7/PI)/4
#define k10 1.4453057110 // sqrt(105/PI)/4
// unrolled version of the above
float SH_0_0( in vec3 s ) { vec3 n = s.zxy; return k01; }
float SH_1_0( in vec3 s ) { vec3 n = s.zxy; return -k02*n.y; }
float SH_1_1( in vec3 s ) { vec3 n = s.zxy; return k02*n.z; }
float SH_1_2( in vec3 s ) { vec3 n = s.zxy; return -k02*n.x; }
float SH_2_0( in vec3 s ) { vec3 n = s.zxy; return k03*n.x*n.y; }
float SH_2_1( in vec3 s ) { vec3 n = s.zxy; return -k03*n.y*n.z; }
float SH_2_2( in vec3 s ) { vec3 n = s.zxy; return k04*(3.0*n.z*n.z-1.0); }
float SH_2_3( in vec3 s ) { vec3 n = s.zxy; return -k03*n.x*n.z; }
float SH_2_4( in vec3 s ) { vec3 n = s.zxy; return k05*(n.x*n.x-n.y*n.y); }
float SH_3_0( in vec3 s ) { vec3 n = s.zxy; return -k06*n.y*(3.0*n.x*n.x-n.y*n.y); }
float SH_3_1( in vec3 s ) { vec3 n = s.zxy; return k07*n.z*n.y*n.x; }
float SH_3_2( in vec3 s ) { vec3 n = s.zxy; return -k08*n.y*(5.0*n.z*n.z-1.0); }
float SH_3_3( in vec3 s ) { vec3 n = s.zxy; return k09*n.z*(5.0*n.z*n.z-3.0); }
float SH_3_4( in vec3 s ) { vec3 n = s.zxy; return -k08*n.x*(5.0*n.z*n.z-1.0); }
float SH_3_5( in vec3 s ) { vec3 n = s.zxy; return k10*n.z*(n.x*n.x-n.y*n.y); }
float SH_3_6( in vec3 s ) { vec3 n = s.zxy; return -k06*n.x*(n.x*n.x-3.0*n.y*n.y); }
vec3 map( in vec3 p )
{
vec3 p00 = p - vec3( 0.00, 2.5,0.0);
vec3 p01 = p - vec3(-1.25, 1.0,0.0);
vec3 p02 = p - vec3( 0.00, 1.0,0.0);
vec3 p03 = p - vec3( 1.25, 1.0,0.0);
vec3 p04 = p - vec3(-2.50,-0.5,0.0);
vec3 p05 = p - vec3(-1.25,-0.5,0.0);
vec3 p06 = p - vec3( 0.00,-0.5,0.0);
vec3 p07 = p - vec3( 1.25,-0.5,0.0);
vec3 p08 = p - vec3( 2.50,-0.5,0.0);
vec3 p09 = p - vec3(-3.75,-2.0,0.0);
vec3 p10 = p - vec3(-2.50,-2.0,0.0);
vec3 p11 = p - vec3(-1.25,-2.0,0.0);
vec3 p12 = p - vec3( 0.00,-2.0,0.0);
vec3 p13 = p - vec3( 1.25,-2.0,0.0);
vec3 p14 = p - vec3( 2.50,-2.0,0.0);
vec3 p15 = p - vec3( 3.75,-2.0,0.0);
float r, d; vec3 n, s, res;
#ifdef SHOW_SPHERES
#define SHAPE (vec3(d-0.35, -1.0+2.0*clamp(0.5 + 16.0*r,0.0,1.0),d))
#else
#define SHAPE (vec3(d-abs(r), sign(r),d))
#endif
d=length(p00); n=p00/d; r = SH_0_0( n ); s = SHAPE; res = s;
d=length(p01); n=p01/d; r = SH_1_0( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p02); n=p02/d; r = SH_1_1( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p03); n=p03/d; r = SH_1_2( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p04); n=p04/d; r = SH_2_0( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p05); n=p05/d; r = SH_2_1( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p06); n=p06/d; r = SH_2_2( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p07); n=p07/d; r = SH_2_3( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p08); n=p08/d; r = SH_2_4( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p09); n=p09/d; r = SH_3_0( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p10); n=p10/d; r = SH_3_1( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p11); n=p11/d; r = SH_3_2( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p12); n=p12/d; r = SH_3_3( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p13); n=p13/d; r = SH_3_4( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p14); n=p14/d; r = SH_3_5( n ); s = SHAPE; if( s.x<res.x ) res=s;
d=length(p15); n=p15/d; r = SH_3_6( n ); s = SHAPE; if( s.x<res.x ) res=s;
return vec3( res.x, 0.5+0.5*res.y, res.z );
}
vec3 intersect( in vec3 ro, in vec3 rd )
{
vec3 res = vec3(1e10,-1.0, 1.0);
float maxd = 10.0;
float h = 1.0;
float t = 0.0;
vec2 m = vec2(-1.0);
for( int i=0; i<200; i++ )
{
if( h<0.001||t>maxd ) break;
vec3 res = map( ro+rd*t );
h = res.x;
m = res.yz;
t += h*0.3;
}
if( t<maxd && t<res.x ) res=vec3(t,m);
return res;
}
vec3 calcNormal( in vec3 pos )
{
vec3 eps = vec3(0.001,0.0,0.0);
return normalize( vec3(
map(pos+eps.xyy).x - map(pos-eps.xyy).x,
map(pos+eps.yxy).x - map(pos-eps.yxy).x,
map(pos+eps.yyx).x - map(pos-eps.yyx).x ) );
}
"""
odf_test_impl = \
"""
// camera matrix
vec3 ww = vec3(0.0,0.0,0.0); //MCDCMatrix (2);
vec3 uu = vec3(0.0,0.0,0.0); //MCDCMatrix (0);
vec3 vv = vec3(0.0,0.0,0.0); //MCDCMatrix (1);
vec3 tot = vec3(0.0);
vec2 p = (-vec2(0.5, 0.5) + (2.0*point,0)) / 0.5;
vec3 ro = vec3(0.0,0.0,0.0);
// create view ray
vec3 rd = normalize( p.x*uu + p.y*vv + 2.0*ww );
// background
vec3 col = vec3(0.3) * clamp(1.0-length(point)*0.5,0.0,1.0);
// raymarch
vec3 tmat = intersect(ro,rd);
if( tmat.y>-0.5 )
{
// geometry
vec3 pos = ro + tmat.x*rd;
vec3 nor = calcNormal(pos);
vec3 ref = reflect( rd, nor );
// material
vec3 mate = 0.5*mix( vec3(1.0,0.6,0.15), vec3(0.2,0.4,0.5), tmat.y );
float occ = clamp( 2.0*tmat.z, 0.0, 1.0 );
float sss = pow( clamp( 1.0 + dot(nor,rd), 0.0, 1.0 ), 1.0 );
// lights
vec3 lin = 2.5*occ*vec3(1.0,1.00,1.00)*(0.6+0.4*nor.y);
lin += 1.0*sss*vec3(1.0,0.95,0.70)*occ;
// surface-light interacion
col = mate.xyz * lin;
}
// gamma
col = pow( clamp(col,0.0,1.0), vec3(0.4545) );
tot += col;
fragOutput0 = vec4( tot, 1.0 );
"""
scene = window.Scene()
scene.background((0.8, 0.8, 0.8))
centers = np.array([[2, 0, 0], [0, 0, 0], [-2, 0, 0]])
# np.random.rand(3, 3) * 3
# colors = np.array([[255, 0, 0], [0, 255, 0], [0, 0, 255]])
colors = np.random.rand(3, 3) * 255
scale = 1 # np.random.rand(3) * 5
# https://www.shadertoy.com/view/MstXWS
# https://www.shadertoy.com/view/XsX3R4
fs_dec = \
"""
uniform mat4 MCDCMatrix;
uniform mat4 MCVCMatrix;
float sdRoundBox( vec3 p, vec3 b, float r )
{
vec3 q = abs(p) - b;
return length(max(q,0.0)) + min(max(q.x,max(q.y,q.z)),0.0) - r;
}
float sdEllipsoid( vec3 p, vec3 r )
{
float k0 = length(p/r);
float k1 = length(p/(r*r));
return k0*(k0-1.0)/k1;
}
float sdCylinder(vec3 p, float h, float r)
{
vec2 d = abs(vec2(length(p.xz),p.y)) - vec2(h,r);
return min(max(d.x,d.y),0.0) + length(max(d,0.0));
}
float sdSphere(vec3 pos, float r)
{
float d = length(pos) - r;
return d;
}
float map( in vec3 pos)
{
float d = sdSphere(pos-0.5, .2);
float d1 = sdCylinder(pos+0.5, 0.05, .5);
float d2 = sdEllipsoid(pos + vec3(-0.5,0.5,0), vec3(0.2,0.3,0.5));
float d3 = sdRoundBox(pos + vec3(0.5,-0.5,0), vec3(0.2,0.1,0.3), .05);
//.xy
return min(min(min(d, d1), d2), d3);
}
vec3 calcNormal( in vec3 pos )
{
vec2 e = vec2(0.0001,0.0);
return normalize( vec3(map(pos + e.xyy) - map(pos - e.xyy ),
map(pos + e.yxy) - map(pos - e.yxy),
map(pos + e.yyx) - map(pos - e.yyx)
)
);
}
float castRay(in vec3 ro, vec3 rd)
{
float t = 0.0;
for(int i=0; i < 100; i++)
{
vec3 pos = ro + t * rd;
vec3 nor = calcNormal(pos);
float h = map(pos);
if (h < 0.001) break;
t += h;
if (t > 20.0) break;
}
return t;
}
"""
fake_sphere = \
"""
vec3 uu = vec3(MCVCMatrix[0][0], MCVCMatrix[1][0], MCVCMatrix[2][0]); // camera right
vec3 vv = vec3(MCVCMatrix[0][1], MCVCMatrix[1][1], MCVCMatrix[2][1]); // camera up
vec3 ww = vec3(MCVCMatrix[0][2], MCVCMatrix[1][2], MCVCMatrix[2][2]); // camera direction
vec3 ro = MCVCMatrix[3].xyz * mat3(MCVCMatrix); // camera position
// create view ray
vec3 rd = normalize( point.x*-uu + point.y*-vv + 7*ww);
vec3 col = vec3(0.0);
float t = castRay(ro, rd);
if (t < 20.0)
{
vec3 pos = ro + t * rd;
vec3 nor = calcNormal(pos);
vec3 sun_dir = vec3(MCVCMatrix[0][2], MCVCMatrix[1][2], MCVCMatrix[2][2]); //normalize()
float dif = clamp( dot(nor, sun_dir), 0.0, 1.0);
//vec3 sun_dif = normalize()
col = color * dot(color,nor); // (color + diffuseColor + ambientColor + specularColor)*nor.zzz;//vec3(1.0);
fragOutput0 = vec4(col, 1.0);
}
else{
//fragOutput0 = vec4(0,1,0, 1.0);
discard;
}
/*float radius = 1.;
if(len > radius)
{discard;}
//err, lightColor0 vertexColorVSOutput normalVCVSOutput, ambientIntensity; diffuseIntensity;specularIntensity;specularColorUniform;
float c = len;
fragOutput0 = vec4(c,c,c, 1);
vec3 normalizedPoint = normalize(vec3(point.xy, sqrt(1. - len)));
vec3 direction = normalize(vec3(1., 1., 1.));
float df = max(0, dot(direction, normalizedPoint));
float sf = pow(df, 24);
fragOutput0 = vec4(max(df * color, sf * vec3(1)), 1);*/
"""
billboard_actor = actor.billboard(centers,
colors=colors.astype(np.uint8),
scale=scale,
fs_dec=fs_dec,
fs_impl=fake_sphere)
scene.add(billboard_actor)
scene.add(actor.axes())
scene.camera_info()
matrix = scene.camera().GetViewTransformMatrix()
mat = np.zeros((4, 4))
for i in range(4):
for j in range(4):
mat[i, j] = matrix.GetElement(i, j)
print(mat)
print(np.dot(-mat[:3, 3], mat[:3, :3])) # camera position
window.show(scene)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
sh_order = 8
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, sticks_cross = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)),
np.tile(S_single, (2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab, data, mask=None, sh_order=8,
peak_thr=0.01, init_fa=0.05,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs, b0_threshold=0)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
_ = ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_greater(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response(gtab, big_S, roi_center=(5, 5, 4),
roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1] / response[0][0])
auto_response(gtab, big_S, roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
_, _, nvoxels = auto_response(gtab, big_S, roi_center=(5, 5, 4),
roi_radius=30, fa_thr=0.5,
return_number_of_voxels=True)
assert_equal(nvoxels, 1000)
_, _, nvoxels = auto_response(gtab, big_S, roi_center=(5, 5, 4),
roi_radius=30, fa_thr=1,
return_number_of_voxels=True)
assert_equal(nvoxels, 0)
Let's create a multi tensor with 2 fiber directions at 60 degrees.
"""
evals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
directions = [(-30, 0), (30, 0)]
fractions = [50, 50]
signal, _ = multi_tensor(gtab, evals, 100, angles=directions,
fractions=fractions, snr=None)
sphere = get_sphere('symmetric724').subdivide(1)
odf_gt = multi_tensor_odf(sphere.vertices, evals, angles=directions,
fractions=fractions)
"""
Perform the reconstructions with standard DSI and DSI with deconvolution.
"""
dsi_model = DiffusionSpectrumModel(gtab)
dsi_odf = dsi_model.fit(signal).odf(sphere)
dsid_model = DiffusionSpectrumDeconvModel(gtab)
dsid_odf = dsid_model.fit(signal).odf(sphere)
"""
Finally, we can visualize the ground truth ODF, together with the DSI and DSI
def test_r2_term_odf_sharp():
SNR = None
S0 = 1
angle = 45 # 45 degrees is a very tight angle to disentangle
_, fbvals, fbvecs = get_fnames('small_64D') # get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
sphere = default_sphere
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (angle, 0)]
S, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf_sh = odf_sh_to_sharp(odfs_sh,
sphere,
basis=None,
ratio=3 / 15.,
sh_order=8,
lambda_=1.,
tau=0.1,
r2_term=True)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
# This should pass as well
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
sdt_model = ConstrainedSDTModel(gtab, ratio=3 / 15., sh_order=8)
sdt_fit = sdt_model.fit(S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = sdt_fit.odf(sphere)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
sphere = default_sphere
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)), np.tile(S_single,
(2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
response = recursive_response(gtab,
data,
mask=None,
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
with warnings.catch_warnings(record=True) as w:
sphere = Sphere(xyz=gtab.gradients[where_dwi])
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[0].category, UserWarning))
npt.assert_("Vertices are not on the unit sphere" in str(w[0].message))
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
sh_order = 8
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, sticks_cross = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)), np.tile(S_single,
(2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab,
data,
mask=None,
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
w_count = len(w)
# A warning is expected from the ConstrainedSDTModel constructor
# and additionnal warnings should be raised where legacy SH bases
# are used
assert_equal(w_count > 1, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
# Test that the warning from ConstrainedSDTModel
# constructor is no more raised
assert_equal(len(w) == w_count - 1, True)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, _ = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
'odf_coeffs_*.npz')))):
data = sph_io.load(os.path.basename(fn))
print "Dipy model..."
odf = m.evaluate_odf(data['signal'])
ODFs.append(odf)
separation_from_odf(odf)
print "Quadrature model..."
odf = kernel_reconstruct(coords['odf_theta'], coords['odf_phi'], data['beta'],
theta, phi,
kernel=even_kernel, N=data['kernel_N'])
odf[odf < 0] = 0
ODFs.append(odf)
print "Actual angle:", np.rad2deg(data['separation'])
separation_from_odf(odf)
ODFs.append(multi_tensor_odf(verts, data['weights'],
mevals=[[1700e-6, 300e-6, 300e-6]] * len(data['weights']),
mevecs=data['mevecs']))
N = 3
ODFs = np.array([[ODFs]]).reshape(len(ODFs) // N, 1, N, -1)
from dipy.viz import show_odfs
show_odfs(ODFs, (verts, faces), scale=2, radial_scale=True)
.. figure:: simulated_signal.png
:align: center
**Simulated MultiTensor signal**
"""
"""
For the ODF simulation we will need a sphere. Because we are interested in a
simulation of only a single voxel, we can use a sphere with very high
resolution. We generate that by subdividing the triangles of one of DIPY_'s
cached spheres, which we can read in the following way.
"""
sphere = get_sphere('symmetric724')
sphere = sphere.subdivide(2)
odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)
from dipy.viz import window, actor
# Enables/disables interactive visualization
interactive = False
ren = window.Renderer()
odf_actor = actor.odf_slicer(odf[None, None, None, :],
sphere=sphere,
colormap='plasma')
odf_actor.RotateX(90)
ren.add(odf_actor)
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len(w) > 0, False)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response(gtab, big_S, roi_center=(5, 5, 4), roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1]/response[0][0])
aresponse2, aratio2 = auto_response(gtab, big_S, roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len(w) > 0, False)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response(gtab, big_S, roi_center=(5, 5, 4), roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1]/response[0][0])
aresponse2, aratio2 = auto_response(gtab, big_S, roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
directions = [(-30, 0), (30, 0)]
fractions = [50, 50]
signal, _ = multi_tensor(gtab,
evals,
100,
angles=directions,
fractions=fractions,
snr=None)
sphere = get_sphere('symmetric724').subdivide(1)
odf_gt = multi_tensor_odf(sphere.vertices,
evals,
angles=directions,
fractions=fractions)
"""
Perform the reconstructions with standard DSI and DSI with deconvolution.
"""
dsi_model = DiffusionSpectrumModel(gtab)
dsi_odf = dsi_model.fit(signal).odf(sphere)
dsid_model = DiffusionSpectrumDeconvModel(gtab)
dsid_odf = dsid_model.fit(signal).odf(sphere)
"""
Finally, we can visualize the ground truth ODF, together with the DSI and DSI
with deconvolution ODFs and observe that with the deconvolved method it is
"""
import numpy as np
from gradients_spheres import sph
"""
We now need to create our initial signal. To do so, we will use our sphere's
vertices as the sampled points of our spherical function (SF). We will
use ``multi_tensor_odf`` to simulate an ODF. For more information on how to use
DIPY_ to simulate a signal and ODF, see :ref:`example_simulate_multi_tensor`.
"""
from dipy.sims.voxel import multi_tensor_odf
mevals = np.array([[0.0015, 0.00015, 0.00015], [0.0015, 0.00015, 0.00015]])
angles = [(0, 0), (60, 0)]
odf = multi_tensor_odf(sph.vertices, mevals, angles, [50, 50])
from dipy.viz import window, actor
# Enables/disables interactive visualization
interactive = False
scene = window.Scene()
scene.SetBackground(1, 1, 1)
odf_actor = actor.odf_slicer(odf[None, None, None, :], sphere=sph)
odf_actor.RotateX(90)
scene.add(odf_actor)
print('Saving illustration as symm_signal.png')
window.record(scene, out_path='symm_signal.png', size=(300, 300))
:align: center
**Simulated MultiTensor signal**
"""
"""
For the ODF simulation we will need a sphere. Because we are interested in a
simulation of only a single voxel, we can use a sphere with very high
resolution. We generate that by subdividing the triangles of one of dipy_'s
cached spheres, which we can read in the following way.
"""
sphere = get_sphere('symmetric724')
sphere = sphere.subdivide(2)
odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)
from dipy.viz import window, actor
# Enables/disables interactive visualization
interactive = False
ren = window.Renderer()
odf_actor = actor.odf_slicer(odf[None, None, None, :], sphere=sphere, colormap='plasma')
odf_actor.RotateX(90)
ren.add(odf_actor)
print('Saving illustration as multi_tensor_simulation')
window.record(ren, out_path='multi_tensor_simulation.png', size=(300, 300))
def compute_bounds(data, sph):
# get noise intervals from background pixels (using mask)
# determine background pixels
#_, mask = median_otsu(S_data, median_radius=3, numpass=1, dilate=2,
# vol_idx=np.where(gtab.bvals > 0)[0])
# for the moment using trivial bounds
b_sph = data['sph']
bvals = data['gtab'].bvals
S_data = data['S']
logging.debug("Getting bounds on S0 and Si's")
c = 0.05
S0_l = S_data[..., bvals == 0].clip(1.0).mean(-1) * (1.0 - c)
S0_u = S_data[..., bvals == 0].clip(1.0).mean(-1) * (1.0 + c)
S_l = S_data[..., bvals > 0] * (1.0 - c)
S_u = S_data[..., bvals > 0] * (1.0 + c)
assert ((S_u >= S_l).all())
assert ((S0_u >= S0_l).all())
logging.debug("Bounds for normalized data E = S/S_0 ...")
E_l = S_l / S0_u[..., None]
E_u = S_u / S0_l[..., None]
assert ((E_u >= E_l).all())
# E_l[:] = 0.0
# E_u[:] = 1.0
logging.debug("Bounds for the monotone decreasing log(-log) transform")
loglog_E_l = np.log(-np.log(E_u.clip(0.001, 0.999)))
loglog_E_u = np.log(-np.log(E_l.clip(0.001, 0.999)))
assert ((loglog_E_u >= loglog_E_l).all())
logging.debug("Bounds for the FRT")
FRT_op = FRT_linop(b_sph, sph)
rhs_l = np.einsum('kl,...l->...k', FRT_op, loglog_E_l)
rhs_u = np.einsum('kl,...l->...k', FRT_op, loglog_E_u)
assert ((rhs_u >= rhs_l).all())
c_shift = (2 - np.log(2)) / (4 * np.pi)
rhs_u, rhs_l = -rhs_l, -rhs_u
rhs_l += c_shift
rhs_u += c_shift
from tools import InverseLaplaceBeltrami, normalize_odf
Phi = InverseLaplaceBeltrami(sph, b_sph)
u_true = None
synthetic = False
if synthetic:
example = 2
if example == 1:
# uniform
u = np.ones(b_sph.mdims['l_labels'])
elif example == 2:
# bimodals
from dipy.sims.voxel import multi_tensor_odf
verts = b_sph.v.T
directions = [30, 90]
val_base = 1e-6 * 200
vals = [8.5 * val_base, val_base, val_base]
vecs = [
np.array([
np.sin(phi * np.pi / 180.0),
np.cos(phi * np.pi / 180.0), 0
]) for phi in directions
]
u = multi_tensor_odf(verts, np.array((vals, vals)), vecs, [50, 50])
normalize_odf(u, b_sph.b)
w = np.einsum('jk,k->j', Phi, u)
# print (w.mean())
c1 = 0.05
for i in range(rhs_u.shape[0]):
rhs_l[i, :] = w[:] * (1.0 - c1)
rhs_u[i, :] = w[:] * (1.0 + c1)
u_true = np.tile(u, (rhs_u.shape[0], 1)).T
assert ((rhs_u >= rhs_l).all())
return rhs_l, rhs_u, u_true
