def test_exponential_iso():
fdata, fbvals, fbvecs = dpd.get_fnames()
data_dti = nib.load(fdata).get_data()
gtab_dti = grad.gradient_table(fbvals, fbvecs)
data_multi, gtab_multi = dpd.dsi_deconv_voxels()
for data, gtab in zip([data_dti, data_multi], [gtab_dti, gtab_multi]):
sfmodel = sfm.SparseFascicleModel(
gtab, isotropic=sfm.ExponentialIsotropicModel)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere()
sffit1.odf(sphere)
sffit1.predict(gtab)
SNR = 1000
S0 = 100
mevals = np.array(([0.0015, 0.0005, 0.0005],
[0.0015, 0.0005, 0.0005]))
angles = [(0, 0), (60, 0)]
S, sticks = sims.multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sffit = sfmodel.fit(S)
pred = sffit.predict()
npt.assert_(xval.coeff_of_determination(pred, S) > 96)
def test_exponential_iso():
fdata, fbvals, fbvecs = dpd.get_data()
data_dti = nib.load(fdata).get_data()
gtab_dti = grad.gradient_table(fbvals, fbvecs)
data_multi, gtab_multi = dpd.dsi_deconv_voxels()
for data, gtab in zip([data_dti, data_multi], [gtab_dti, gtab_multi]):
sfmodel = sfm.SparseFascicleModel(
gtab, isotropic=sfm.ExponentialIsotropicModel)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere()
odf1 = sffit1.odf(sphere)
pred1 = sffit1.predict(gtab)
SNR = 1000
S0 = 100
mevals = np.array(([0.0015, 0.0005, 0.0005],
[0.0015, 0.0005, 0.0005]))
angles = [(0, 0), (60, 0)]
S, sticks = sims.multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sffit = sfmodel.fit(S)
pred = sffit.predict()
npt.assert_(xval.coeff_of_determination(pred, S) > 96)
def test_multivox_dsi():
data, gtab = dsi_deconv_voxels()
DS = DiffusionSpectrumDeconvModel(gtab)
DSfit = DS.fit(data)
PDF = DSfit.pdf()
assert_equal(data.shape[:-1] + (35, 35, 35), PDF.shape)
assert_equal(np.alltrue(np.isreal(PDF)), True)
def test_multivox_dsi():
data, gtab = dsi_deconv_voxels()
DS = DiffusionSpectrumDeconvModel(gtab)
sphere = get_sphere('symmetric724')
DSfit = DS.fit(data)
PDF = DSfit.pdf()
assert_equal(data.shape[:-1] + (35, 35, 35), PDF.shape)
assert_equal(np.alltrue(np.isreal(PDF)), True)
def test_exponential_iso():
fdata, fbvals, fbvecs = dpd.get_fnames()
data_dti = load_nifti_data(fdata)
gtab_dti = grad.gradient_table(fbvals, fbvecs)
data_multi, gtab_multi = dpd.dsi_deconv_voxels()
for data, gtab in zip([data_dti, data_multi], [gtab_dti, gtab_multi]):
sfmodel = sfm.SparseFascicleModel(
gtab, isotropic=sfm.ExponentialIsotropicModel)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere()
odf = sffit1.odf(sphere)
pred = sffit1.predict(gtab)
npt.assert_equal(pred.shape, data[0, 0, 0].shape)
npt.assert_equal(odf.shape,
data[0, 0, 0].shape[:-1] + (sphere.x.shape[0], ))
sffit2 = sfmodel.fit(data)
sphere = dpd.get_sphere()
odf = sffit2.odf(sphere)
pred = sffit2.predict(gtab)
npt.assert_equal(pred.shape, data.shape)
npt.assert_equal(odf.shape, data.shape[:-1] + (sphere.x.shape[0], ))
mask = np.zeros(data.shape[:3])
mask[2:5, 2:5, :] = 1
sffit3 = sfmodel.fit(data, mask=mask)
sphere = dpd.get_sphere()
odf = sffit3.odf(sphere)
pred = sffit3.predict(gtab)
npt.assert_equal(pred.shape, data.shape)
npt.assert_equal(odf.shape, data.shape[:-1] + (sphere.x.shape[0], ))
SNR = 1000
S0 = 100
mevals = np.array(([0.0015, 0.0005, 0.0005], [0.0015, 0.0005, 0.0005]))
angles = [(0, 0), (60, 0)]
S, sticks = sims.multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sffit = sfmodel.fit(S)
pred = sffit.predict()
npt.assert_(xval.coeff_of_determination(pred, S) > 96)
