def __img_to_tseries(self, filename_img, mask_img, confounds_filename=None, smoothing_fwhm=None,
strategy=["minimal"], chunksize=3000):
img = nb.load(filename_img)
img = resample_to_img(img, mask_img)
if confounds_filename is not None:
confounds = load_confounds(confounds_filename, strategy=strategy)
masker = NiftiMasker(mask_img, smoothing_fwhm=smoothing_fwhm)
masker.fit(img)
masker.mask_img_ = mask_img
tseries = masker.transform(img)
if confounds_filename is not None:
confounds=confounds.values
tseries = clean(tseries, confounds=confounds)
else:
tseries = clean(tseries)
tseries = self.__normalize_data(tseries)
darr_tseries = da.from_array(tseries, chunks=(chunksize, tseries.shape[1]))
return darr_tseries
def test_high_level_glm_one_session():
shapes, rk = [(7, 8, 9, 15)], 3
mask, fmri_data, design_matrices = generate_fake_fmri_data_and_design(shapes, rk)
# Give an unfitted NiftiMasker as mask_img and check that we get an error
masker = NiftiMasker(mask)
with pytest.raises(ValueError,
match="It seems that NiftiMasker has not been fitted."):
single_session_model = FirstLevelModel(mask_img=masker).fit(
fmri_data[0], design_matrices=design_matrices[0])
# Give a fitted NiftiMasker with a None mask_img_ attribute
# and check that the masker parameters are overriden by the
# FirstLevelModel parameters
masker.fit()
masker.mask_img_ = None
with pytest.warns(UserWarning,
match="Parameter memory of the masker overriden"):
single_session_model = FirstLevelModel(mask_img=masker).fit(
fmri_data[0], design_matrices=design_matrices[0])
# Give a fitted NiftiMasker
masker = NiftiMasker(mask)
masker.fit()
single_session_model = FirstLevelModel(mask_img=masker).fit(
fmri_data[0], design_matrices=design_matrices[0])
assert single_session_model.masker_ == masker
# Call with verbose (improve coverage)
single_session_model = FirstLevelModel(mask_img=None,
verbose=1).fit(
fmri_data[0], design_matrices=design_matrices[0])
single_session_model = FirstLevelModel(mask_img=None).fit(
fmri_data[0], design_matrices=design_matrices[0])
assert isinstance(single_session_model.masker_.mask_img_,
Nifti1Image)
single_session_model = FirstLevelModel(mask_img=mask).fit(
fmri_data[0], design_matrices=design_matrices[0])
z1 = single_session_model.compute_contrast(np.eye(rk)[:1])
assert isinstance(z1, Nifti1Image)
