def test_dki_predict():
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
pred = dkiM.predict(crossing_ref, S0=100)
assert_array_almost_equal(pred, signal_cross)
# just to check that it works with more than one voxel:
pred_multi = dkiM.predict(multi_params, S0=100)
assert_array_almost_equal(pred_multi, DWI)
# Check that it works with more than one voxel, and with a different S0
# in each voxel:
pred_multi = dkiM.predict(multi_params,
S0=100*np.ones(pred_multi.shape[:3]))
assert_array_almost_equal(pred_multi, DWI)
# check the function predict of the DiffusionKurtosisFit object
dkiF = dkiM.fit(DWI)
pred_multi = dkiF.predict(gtab_2s, S0=100)
assert_array_almost_equal(pred_multi, DWI)
dkiF = dkiM.fit(pred_multi)
pred_from_fit = dkiF.predict(dkiM.gtab, S0=100)
assert_array_almost_equal(pred_from_fit, DWI)
# Test the module function:
pred = dki.dki_prediction(crossing_ref, gtab_2s, S0=100)
assert_array_almost_equal(pred, signal_cross)
# Test the module function with S0 volume:
pred = dki.dki_prediction(multi_params, gtab_2s,
S0=100 * np.ones(multi_params.shape[:3]))
assert_array_almost_equal(pred, DWI)
def test_dki_predict():
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
pred = dkiM.predict(crossing_ref, S0=100)
assert_array_almost_equal(pred, signal_cross)
# just to check that it works with more than one voxel:
pred_multi = dkiM.predict(multi_params, S0=100)
assert_array_almost_equal(pred_multi, DWI)
# Check that it works with more than one voxel, and with a different S0
# in each voxel:
pred_multi = dkiM.predict(multi_params,
S0=100*np.ones(pred_multi.shape[:3]))
assert_array_almost_equal(pred_multi, DWI)
# check the function predict of the DiffusionKurtosisFit object
dkiF = dkiM.fit(DWI)
pred_multi = dkiF.predict(gtab_2s, S0=100)
assert_array_almost_equal(pred_multi, DWI)
dkiF = dkiM.fit(pred_multi)
pred_from_fit = dkiF.predict(dkiM.gtab, S0=100)
assert_array_almost_equal(pred_from_fit, DWI)
# Test the module function:
pred = dki.dki_prediction(crossing_ref, gtab_2s, S0=100)
assert_array_almost_equal(pred, signal_cross)
# Test the module function with S0 volume:
pred = dki.dki_prediction(multi_params, gtab_2s,
S0=100 * np.ones(multi_params.shape[:3]))
assert_array_almost_equal(pred, DWI)
def predict(params_file, gtab, S0_file=None, out_dir=None):
"""
Create a signal prediction from DKI params
params_file : str
Full path to a file with parameters saved from a DKI fit
gtab : GradientTable object
The gradient table to predict for
S0_file : str
Full path to a nifti file that contains S0 measurements to incorporate
into the prediction. If the file contains 4D data, the volumes that
contain the S0 data must be the same as the gtab.b0s_mask.
"""
if out_dir is None:
out_dir = op.join(op.split(params_file)[0])
if S0_file is None:
S0 = 100
else:
S0 = nib.load(S0_file).get_fdata()
# If the S0 data is 4D, we assume it comes from an acquisition that had
# B0 measurements in the same volumes described in the gtab:
if len(S0.shape) == 4:
S0 = np.mean(S0[..., gtab.b0s_mask], -1)
# Otherwise, we assume that it's already a 3D volume, and do nothing
img = nib.load(params_file)
params = img.get_fdata()
pred = dki.dki_prediction(params, gtab, S0=S0)
fname = op.join(out_dir, 'dki_prediction.nii.gz')
nib.save(nib.Nifti1Image(pred, img.affine), fname)
return fname
def predict(params_file, gtab, S0_file=None, out_dir=None):
"""
Create a signal prediction from DKI params
params_file : str
Full path to a file with parameters saved from a DKI fit
gtab : GradientTable object
The gradient table to predict for
S0_file : str
Full path to a nifti file that contains S0 measurements to incorporate
into the prediction. If the file contains 4D data, the volumes that
contain the S0 data must be the same as the gtab.b0s_mask.
"""
if out_dir is None:
out_dir = op.join(op.split(params_file)[0])
if S0_file is None:
S0 = 100
else:
S0 = nib.load(S0_file).get_data()
# If the S0 data is 4D, we assume it comes from an acquisition that had
# B0 measurements in the same volumes described in the gtab:
if len(S0.shape) == 4:
S0 = np.mean(S0[..., gtab.b0s_mask], -1)
# Otherwise, we assume that it's already a 3D volume, and do nothing
img = nib.load(params_file)
params = img.get_data()
pred = dki.dki_prediction(params, gtab, S0=S0)
fname = op.join(out_dir, 'dki_prediction.nii.gz')
nib.save(nib.Nifti1Image(pred, img.affine), fname)
return fname