def test_forecast_csd():
sphere = get_sphere('repulsion100')
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
dec_alg='CSD',
sphere=data.sphere,
lambda_csd=data.lambda_csd)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf_csd = f_fit.odf(sphere, clip_negative=False)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='WLS')
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf_wls = f_fit.odf(sphere, clip_negative=False)
value = fodf_wls[fodf_wls < 0].sum() < fodf_csd[fodf_csd < 0].sum()
assert_equal(value, 1)
def test_forecast_odf():
# check FORECAST fODF at different SH order
fm = ForecastModel(data.gtab,
sh_order=4,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = default_sphere
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab,
sh_order=6,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab,
sh_order=8,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
# stronger regularization is required for high order SH
fm = ForecastModel(data.gtab,
sh_order=10,
dec_alg='CSD',
sphere=sphere.vertices)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab,
sh_order=12,
dec_alg='CSD',
sphere=sphere.vertices)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
def test_forecast_indices():
# check anisotropic tensor
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=2,
lambda_lb=data.lambda_lb,
dec_alg='WLS')
f_fit = fm.fit(data.S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, data.mevals[0, 0], 5)
assert_almost_equal(d_perp, data.mevals[0, 1], 5)
gt_fa = np.sqrt(0.5 * (2 * (data.mevals[0, 0] - data.mevals[0, 1])**2) /
(data.mevals[0, 0]**2 + 2 * data.mevals[0, 1]**2))
gt_md = (data.mevals[0, 0] + 2 * data.mevals[0, 1]) / 3.0
assert_almost_equal(f_fit.fractional_anisotropy(), gt_fa, 2)
assert_almost_equal(f_fit.mean_diffusivity(), gt_md, 5)
# check isotropic tensor
mevals = np.array(([0.003, 0.003, 0.003], [0.003, 0.003, 0.003]))
data.angl = [(0, 0), (60, 0)]
S, sticks = multi_tensor(data.gtab,
mevals,
S0=100.0,
angles=data.angl,
fractions=[50, 50],
snr=None)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='WLS')
f_fit = fm.fit(S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, 3e-03, 5)
assert_almost_equal(d_perp, 3e-03, 5)
assert_almost_equal(f_fit.fractional_anisotropy(), 0.0, 5)
assert_almost_equal(f_fit.mean_diffusivity(), 3e-03, 10)
def test_forecast_csd():
sphere = get_sphere('repulsion100')
fm = ForecastModel(data.gtab, dec_alg='CSD',
sphere=data.sphere, lambda_csd=data.lambda_csd)
f_fit = fm.fit(data.S)
fodf_csd = f_fit.odf(sphere, clip_negative=False)
fm = ForecastModel(data.gtab, sh_order=data.sh_order,
lambda_lb=data.lambda_lb, dec_alg='WLS')
f_fit = fm.fit(data.S)
fodf_wls = f_fit.odf(sphere, clip_negative=False)
value = fodf_wls[fodf_wls < 0].sum() < fodf_csd[fodf_csd < 0].sum()
assert_equal(value, 1)
def test_forecast_csd():
sphere = get_sphere('repulsion100')
fm = ForecastModel(data.gtab, dec_alg='CSD',
sphere=data.sphere, lambda_csd=data.lambda_csd)
f_fit = fm.fit(data.S)
fodf_csd = f_fit.odf(sphere, clip_negative=False)
fm = ForecastModel(data.gtab, sh_order=data.sh_order,
lambda_lb=data.lambda_lb, dec_alg='WLS')
f_fit = fm.fit(data.S)
fodf_wls = f_fit.odf(sphere, clip_negative=False)
value = fodf_wls[fodf_wls < 0].sum() < fodf_csd[fodf_csd < 0].sum()
assert_equal(value, 1)
def test_forecast_odf():
# check FORECAST fODF at different SH order
fm = ForecastModel(data.gtab, sh_order=4,
dec_alg='CSD', sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = get_sphere('repulsion724')
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab, sh_order=6,
dec_alg='CSD', sphere=data.sphere)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab, sh_order=8,
dec_alg='CSD', sphere=data.sphere)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, data.sticks), 2, 1)
# stronger regularization is required for high order SH
fm = ForecastModel(data.gtab, sh_order=10,
dec_alg='CSD', sphere=sphere.vertices)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, data.sticks), 2, 1)
fm = ForecastModel(data.gtab, sh_order=12,
dec_alg='CSD', sphere=sphere.vertices)
f_fit = fm.fit(data.S)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, data.sticks), 2, 1)
def test_multivox_forecast():
gtab = get_3shell_gtab()
mevals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
angl1 = [(0, 0), (60, 0)]
angl2 = [(90, 0), (45, 90)]
angl3 = [(0, 0), (90, 0)]
S = np.zeros((3, 1, 1, len(gtab.bvals)))
S[0, 0, 0], _ = multi_tensor(gtab, mevals, S0=1.0, angles=angl1,
fractions=[50, 50], snr=None)
S[1, 0, 0], _ = multi_tensor(gtab, mevals, S0=1.0, angles=angl2,
fractions=[50, 50], snr=None)
S[2, 0, 0], _ = multi_tensor(gtab, mevals, S0=1.0, angles=angl3,
fractions=[50, 50], snr=None)
fm = ForecastModel(gtab, sh_order=8,
dec_alg='CSD')
f_fit = fm.fit(S)
S_predict = f_fit.predict()
assert_equal(S_predict.shape, S.shape)
mse1 = np.sum((S_predict[0, 0, 0]-S[0, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse1, 0.0, 3)
mse2 = np.sum((S_predict[1, 0, 0]-S[1, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse2, 0.0, 3)
mse3 = np.sum((S_predict[2, 0, 0]-S[2, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse3, 0.0, 3)
def test_forecast_indices():
# check anisotropic tensor
fm = ForecastModel(data.gtab,
sh_order=2,
lambda_lb=data.lambda_lb,
dec_alg='WLS')
f_fit = fm.fit(data.S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, data.mevals[0, 0], 5)
assert_almost_equal(d_perp, data.mevals[0, 1], 5)
gt_fa = np.sqrt(0.5 * (2 * (data.mevals[0, 0] - data.mevals[0, 1])**2) /
(data.mevals[0, 0]**2 + 2 * data.mevals[0, 1]**2))
gt_md = (data.mevals[0, 0] + 2 * data.mevals[0, 1]) / 3.0
assert_almost_equal(f_fit.fractional_anisotropy(), gt_fa, 2)
assert_almost_equal(f_fit.mean_diffusivity(), gt_md, 5)
# check isotropic tensor
mevals = np.array(([0.003, 0.003, 0.003], [0.003, 0.003, 0.003]))
data.angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(data.gtab,
mevals,
S0=100.0,
angles=data.angl,
fractions=[50, 50],
snr=None)
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='WLS')
f_fit = fm.fit(S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, 3e-03, 5)
assert_almost_equal(d_perp, 3e-03, 5)
assert_almost_equal(f_fit.fractional_anisotropy(), 0.0, 5)
assert_almost_equal(f_fit.mean_diffusivity(), 3e-03, 10)
def test_forecast_predict():
# check anisotropic tensor
fm = ForecastModel(data.gtab, sh_order=8,
dec_alg='CSD', sphere=data.sphere)
f_fit = fm.fit(data.S)
S = f_fit.predict(S0=1.0)
mse = np.sum((S-data.S/100.0)**2) / len(S)
assert_almost_equal(mse, 0.0, 3)
def test_forecast_predict():
# check anisotropic tensor
fm = ForecastModel(data.gtab, sh_order=8,
dec_alg='CSD', sphere=data.sphere)
f_fit = fm.fit(data.S)
S = f_fit.predict(S0=1.0)
mse = np.sum((S-data.S/100.0)**2) / len(S)
assert_almost_equal(mse, 0.0, 3)
def test_multivox_forecast():
gtab = get_3shell_gtab()
mevals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
angl1 = [(0, 0), (60, 0)]
angl2 = [(90, 0), (45, 90)]
angl3 = [(0, 0), (90, 0)]
S = np.zeros((3, 1, 1, len(gtab.bvals)))
S[0, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl1,
fractions=[50, 50],
snr=None)
S[1, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl2,
fractions=[50, 50],
snr=None)
S[2, 0, 0], _ = multi_tensor(gtab,
mevals,
S0=1.0,
angles=angl3,
fractions=[50, 50],
snr=None)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(gtab, sh_order=8, dec_alg='CSD')
f_fit = fm.fit(S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
S_predict = f_fit.predict()
assert_equal(S_predict.shape, S.shape)
mse1 = np.sum((S_predict[0, 0, 0] - S[0, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse1, 0.0, 3)
mse2 = np.sum((S_predict[1, 0, 0] - S[1, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse2, 0.0, 3)
mse3 = np.sum((S_predict[2, 0, 0] - S[2, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse3, 0.0, 3)
def test_forecast_indices():
# check anisotropic tensor
fm = ForecastModel(data.gtab, sh_order=2,
lambda_lb=data.lambda_lb, dec_alg='WLS')
f_fit = fm.fit(data.S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, data.mevals[0, 0], 5)
assert_almost_equal(d_perp, data.mevals[0, 1], 5)
gt_fa = np.sqrt(0.5 * (2*(data.mevals[0, 0] - data.mevals[0, 1])**2) / (
data.mevals[0, 0]**2 + 2*data.mevals[0, 1]**2))
gt_md = (data.mevals[0, 0] + 2*data.mevals[0, 1])/3.0
assert_almost_equal(f_fit.fractional_anisotropy(), gt_fa, 2)
assert_almost_equal(f_fit.mean_diffusivity(), gt_md, 5)
# check isotropic tensor
mevals = np.array(([0.003, 0.003, 0.003],
[0.003, 0.003, 0.003]))
data.angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(
data.gtab, mevals, S0=100.0, angles=data.angl,
fractions=[50, 50], snr=None)
fm = ForecastModel(data.gtab, sh_order=data.sh_order,
lambda_lb=data.lambda_lb, dec_alg='WLS')
f_fit = fm.fit(S)
d_par = f_fit.dpar
d_perp = f_fit.dperp
assert_almost_equal(d_par, 3e-03, 5)
assert_almost_equal(d_perp, 3e-03, 5)
assert_almost_equal(f_fit.fractional_anisotropy(), 0.0, 5)
assert_almost_equal(f_fit.mean_diffusivity(), 3e-03, 10)
def test_forecast_positive_constrain():
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='POS',
sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = get_sphere('repulsion100')
fodf = f_fit.odf(sphere, clip_negative=False)
assert_almost_equal(fodf[fodf < 0].sum(), 0, 2)
coeff = f_fit.sh_coeff
c0 = np.sqrt(1.0/(4*np.pi))
assert_almost_equal(coeff[0], c0, 5)
def test_forecast_positive_constrain():
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='POS',
sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = get_sphere('repulsion100')
fodf = f_fit.odf(sphere, clip_negative=False)
assert_almost_equal(fodf[fodf < 0].sum(), 0, 2)
coeff = f_fit.sh_coeff
c0 = np.sqrt(1.0/(4*np.pi))
assert_almost_equal(coeff[0], c0, 5)
def test_forecast_predict():
# check anisotropic tensor
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=8,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
S = f_fit.predict(S0=1.0)
mse = np.sum((S - data.S / 100.0)**2) / len(S)
assert_almost_equal(mse, 0.0, 3)
def test_forecast_positive_constrain():
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=data.sh_order,
lambda_lb=data.lambda_lb,
dec_alg='POS',
sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = get_sphere('repulsion100')
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere, clip_negative=False)
assert_almost_equal(fodf[fodf < 0].sum(), 0, 2)
coeff = f_fit.sh_coeff
c0 = np.sqrt(1.0 / (4 * np.pi))
assert_almost_equal(coeff[0], c0, 5)
def test_multivox_forecast():
gtab = get_3shell_gtab()
mevals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
angl1 = [(0, 0), (60, 0)]
angl2 = [(90, 0), (45, 90)]
angl3 = [(0, 0), (90, 0)]
S = np.zeros((3, 1, 1, len(gtab.bvals)))
S[0, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl1,
fractions=[50, 50], snr=None)
S[1, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl2,
fractions=[50, 50], snr=None)
S[2, 0, 0], sticks = MultiTensor(
gtab, mevals, S0=1.0, angles=angl3,
fractions=[50, 50], snr=None)
fm = ForecastModel(gtab, sh_order=8,
dec_alg='CSD')
f_fit = fm.fit(S)
S_predict = f_fit.predict()
assert_equal(S_predict.shape, S.shape)
mse1 = np.sum((S_predict[0, 0, 0]-S[0, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse1, 0.0, 3)
mse2 = np.sum((S_predict[1, 0, 0]-S[1, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse2, 0.0, 3)
mse3 = np.sum((S_predict[2, 0, 0]-S[2, 0, 0])**2) / len(gtab.bvals)
assert_almost_equal(mse3, 0.0, 3)
""" Instantiate the FORECAST Model. "sh_order" is the spherical harmonics order used for the fODF. dec_alg is the spherical deconvolution algorithm used for the FORECAST basis fitting, in this case we used the Constrained Spherical Deconvolution (CSD) algorithm. """ fm = ForecastModel(gtab, sh_order=6, dec_alg='CSD') """ Fit the FORECAST to the data """ f_fit = fm.fit(data_small, mask_small) """ Calculate the crossing invariant tensor indices [Kaden2016]_ : the parallel diffusivity, the perpendicular diffusivity, the fractional anisotropy and the mean diffusivity. """ d_par = f_fit.dpar d_perp = f_fit.dperp fa = f_fit.fractional_anisotropy() md = f_fit.mean_diffusivity() """ Show the indices and save them in FORECAST_indices.png. """
mask_small = data_small[..., 0] > 1000 """ Instantiate the FORECAST Model. "sh_order" is the spherical harmonics order used for the fODF. dec_alg is the spherical deconvolution algorithm used for the FORECAST basis fitting, in this case we used the Constrained Spherical Deconvolution (CSD) algorithm. """ fm = ForecastModel(gtab, sh_order=6, dec_alg='CSD') """ Fit the FORECAST to the data """ f_fit = fm.fit(data_small, mask_small) """ Calculate the crossing invariant tensor indices [Kaden2016]_ : the parallel diffusivity, the perpendicular diffusivity, the fractional anisotropy and the mean diffusivity. """ d_par = f_fit.dpar d_perp = f_fit.dperp fa = f_fit.fractional_anisotropy() md = f_fit.mean_diffusivity() """ Show the indices and save them in FORECAST_indices.png. """ fig = plt.figure(figsize=(6, 6))
def test_forecast_odf():
# check FORECAST fODF at different SH order
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=4,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
sphere = default_sphere
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=6,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=8,
dec_alg='CSD',
sphere=data.sphere)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
# stronger regularization is required for high order SH
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=10,
dec_alg='CSD',
sphere=sphere.vertices)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fm = ForecastModel(data.gtab,
sh_order=12,
dec_alg='CSD',
sphere=sphere.vertices)
f_fit = fm.fit(data.S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = f_fit.odf(sphere)
directions, _, _ = peak_directions(fodf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, data.sticks), 2, 1)