def test_filter_and_mask_error():
data = np.zeros([20, 30, 40, 5])
mask = np.zeros([20, 30, 40, 2])
mask[10, 15, 20, :] = 1
data_img = nibabel.Nifti1Image(data, np.eye(4))
mask_img = nibabel.Nifti1Image(mask, np.eye(4))
masker = NiftiMasker()
params = get_params(NiftiMasker, masker)
with pytest.raises(DimensionError,
match="Input data has incompatible dimensionality: "
"Expected dimension is 3D and you provided "
"a 4D image."):
filter_and_mask(data_img, mask_img, params)
def test_filter_and_mask():
data = np.zeros([20, 30, 40, 5])
mask = np.ones([20, 30, 40])
data_img = nibabel.Nifti1Image(data, np.eye(4))
mask_img = nibabel.Nifti1Image(mask, np.eye(4))
masker = NiftiMasker()
params = get_params(NiftiMasker, masker)
# Test return_affine = False
data = filter_and_mask(data_img, mask_img, params)
assert_equal(data.shape, (5, 24000))
def test_filter_and_mask():
data = np.zeros([20, 30, 40, 5])
mask = np.ones([20, 30, 40])
data_img = nibabel.Nifti1Image(data, np.eye(4))
mask_img = nibabel.Nifti1Image(mask, np.eye(4))
masker = NiftiMasker()
params = get_params(NiftiMasker, masker)
# Test return_affine = False
data = filter_and_mask(data_img, mask_img, params)
assert_equal(data.shape, (5, 24000))
def test_cropping_code_paths():
# Will mask data with an identically sampled mask and
# with a smaller mask. The results must be identical
rng = np.random.RandomState(42)
data = np.zeros([20, 30, 40, 5])
data[10:15, 5:20, 10:30, :] = 1. + rng.rand(5, 15, 20, 5)
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine=affine)
mask = (data[..., 0] > 0).astype(int)
mask_img = nibabel.Nifti1Image(mask, affine=affine)
# the mask in mask_img has the same shape and affine as the
# data and should thus avoid resampling
# we now crop the mask to its non-zero part. Masking with this
# mask must yield the same result
cropped_mask_img = image.crop_img(mask_img)
parameters = {"smoothing_fwhm": None,
"high_pass": None,
"low_pass": None,
"t_r": None,
"detrend": None,
"standardize": None
}
# Now do the two maskings
out_data_uncropped, affine_uncropped = filter_and_mask(img,
mask_img,
parameters)
out_data_cropped, affine_cropped = filter_and_mask(img,
cropped_mask_img,
parameters)
assert_array_almost_equal(out_data_cropped, out_data_uncropped)
def squeezed_filter_and_mask(imgs, mask_img_, parameters,
memory_level=0, memory=Memory(cachedir=None),
verbose=0,
confounds=None,
copy=True,
squeeze=True):
res = filter_and_mask(imgs, mask_img_, parameters,
memory_level=memory_level, memory=memory,
verbose=verbose,
confounds=confounds,
copy=copy)
if squeeze:
res = res[0]
return res
def test_cropping_code_paths():
# Will mask data with an identically sampled mask and
# with a smaller mask. The results must be identical
rng = np.random.RandomState(42)
data = np.zeros([20, 30, 40, 5])
data[10:15, 5:20, 10:30, :] = 1. + rng.uniform(size=(5, 15, 20, 5))
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine=affine)
mask = (data[..., 0] > 0).astype(int)
mask_img = nibabel.Nifti1Image(mask, affine=affine)
# the mask in mask_img has the same shape and affine as the
# data and should thus avoid resampling
# we now crop the mask to its non-zero part. Masking with this
# mask must yield the same result
cropped_mask_img = image.crop_img(mask_img)
parameters = {
"smoothing_fwhm": None,
"high_pass": None,
"low_pass": None,
"t_r": None,
"detrend": None,
"standardize": 'zscore',
"standardize_confounds": True,
}
# Now do the two maskings
out_data_uncropped = filter_and_mask(img, mask_img, parameters)
out_data_cropped = filter_and_mask(img, cropped_mask_img, parameters)
assert_array_almost_equal(out_data_cropped, out_data_uncropped)
