def test_first_level_residuals():
shapes, rk = [(10, 10, 10, 100)], 3
mask, fmri_data, design_matrices =\
generate_fake_fmri_data_and_design(shapes, rk)
for i in range(len(design_matrices)):
design_matrices[i].iloc[:, 0] = 1
# Check that voxelwise model attributes cannot be
# accessed if minimize_memory is set to True
model = FirstLevelModel(mask_img=mask,
minimize_memory=True,
noise_model='ols')
model.fit(fmri_data, design_matrices=design_matrices)
with pytest.raises(ValueError, match="To access voxelwise attributes"):
residuals = model.residuals[0]
model = FirstLevelModel(mask_img=mask,
minimize_memory=False,
noise_model='ols')
# Check that trying to access residuals without fitting
# raises an error
with pytest.raises(ValueError, match="The model has not been fit yet"):
residuals = model.residuals[0]
model.fit(fmri_data, design_matrices=design_matrices)
# For coverage
with pytest.raises(ValueError, match="attribute must be one of"):
model._get_voxelwise_model_attribute("foo", True)
residuals = model.residuals[0]
mean_residuals = model.masker_.transform(residuals).mean(0)
assert_array_almost_equal(mean_residuals, 0)
def test_get_voxelwise_attributes_should_return_as_many_as_design_matrices(
shapes):
mask, fmri_data, design_matrices =\
generate_fake_fmri_data_and_design(shapes)
for i in range(len(design_matrices)):
design_matrices[i].iloc[:, 0] = 1
model = FirstLevelModel(mask_img=mask,
minimize_memory=False,
noise_model='ols')
model.fit(fmri_data, design_matrices=design_matrices)
# Check that length of outputs is the same as the number of design matrices
assert len(model._get_voxelwise_model_attribute("resid", True)) == \
len(shapes)
