def test_predict():
"""
Test the CanonicalTensorModel predict method
"""
# 1000 'voxels' with constant data in each one in all directions (+b0):
data = (np.random.rand(10 * 10 * 10).reshape(10 * 10 * 10, 1) + np.zeros(
(10 * 10 * 10, 160))).reshape(10, 10, 10, 160)
CTM = CanonicalTensorModel(data,
data_path + 'dwi.bvecs',
data_path + 'dwi.bvals',
params_file=tempfile.NamedTemporaryFile().name)
bvecs = CTM.bvecs[:, CTM.b_idx]
new_bvecs = bvecs[:, :4]
prediction = CTM.predict(new_bvecs)
npt.assert_array_equal(prediction, CTM.fit[..., :4])
def test_CanonicalTensorModel():
"""
Test the simple canonical + sphere model.
"""
# 1000 'voxels' with constant data in each one in all directions (+b0):
data = (np.random.rand(10 * 10 * 10).reshape(10 * 10 * 10, 1) + np.zeros(
(10 * 10 * 10, 160))).reshape(10, 10, 10, 160)
CTM = CanonicalTensorModel(data,
data_path + 'dwi.bvecs',
data_path + 'dwi.bvals',
params_file=tempfile.NamedTemporaryFile().name)
# XXX Smoke testing only
npt.assert_equal(CTM.fit.shape, CTM.signal.shape)
mask_array = np.zeros(ni.load(data_path + 'small_dwi.nii.gz').shape[:3])
# Only two voxels:
mask_array[1:3, 1:3, 1:3] = 1
# Fit this on some real dwi data
for mode in ['signal_attenuation', 'relative_signal', 'normalize', 'log']:
for params_file in [None, tempfile.NamedTemporaryFile().name, 'temp']:
CTM = CanonicalTensorModel(data_path + 'small_dwi.nii.gz',
data_path + 'dwi.bvecs',
data_path + 'dwi.bvals',
mask=mask_array,
params_file=params_file,
mode=mode)
# XXX Smoke testing only:
npt.assert_equal(CTM.fit.shape, CTM.signal.shape)
npt.assert_equal(CTM.principal_diffusion_direction.shape,
CTM.signal.shape[:3] + (3, ))
npt.assert_equal(CTM.fractional_anisotropy.shape,
CTM.signal.shape[:3])
# Test over-sampling:
for over_sample in [362, 246]: # Over-sample from dipy and from
# camino-points
CTM = CanonicalTensorModel(
data_path + 'small_dwi.nii.gz',
data_path + 'dwi.bvecs',
data_path + 'dwi.bvals',
mask=mask_array,
params_file=tempfile.NamedTemporaryFile().name,
over_sample=over_sample,
mode=mode)
# XXX Smoke testing only:
npt.assert_equal(CTM.fit.shape, CTM.signal.shape)
# This shouldn't be possible, because we don't have a sphere with 151
# samples handy:
npt.assert_raises(
ValueError, CanonicalTensorModel, data_path + 'small_dwi.nii.gz',
data_path + 'dwi.bvecs', data_path + 'dwi.bvals',
**dict(mask=mask_array,
params_file=tempfile.NamedTemporaryFile().name,
over_sample=151))
# If you provide an unrecognized mode, you get an error:
npt.assert_raises(
ValueError, CanonicalTensorModel, data_path + 'small_dwi.nii.gz',
data_path + 'dwi.bvecs', data_path + 'dwi.bvals',
**dict(mask=mask_array,
mode='crazy_mode',
params_file=tempfile.NamedTemporaryFile().name))
