def test_nifti_maps_masker():
# Check working of shape/affine checks
shape1 = (13, 11, 12)
affine1 = np.eye(4)
shape2 = (12, 10, 14)
affine2 = np.diag((1, 2, 3, 1))
n_regions = 9
length = 3
fmri11_img, mask11_img = generate_random_img(shape1,
affine=affine1,
length=length)
fmri12_img, mask12_img = generate_random_img(shape1,
affine=affine2,
length=length)
fmri21_img, mask21_img = generate_random_img(shape2,
affine=affine1,
length=length)
labels11_img, labels_mask_img = \
testing.generate_maps(shape1, n_regions, affine=affine1)
# No exception raised here
for create_files in (True, False):
with testing.write_tmp_imgs(labels11_img, create_files=create_files) \
as labels11:
masker11 = NiftiMapsMasker(labels11, resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
# enables to delete "labels11" on windows
del masker11
masker11 = NiftiMapsMasker(labels11_img,
mask_img=mask11_img,
resampling_target=None)
testing.assert_raises_regex(ValueError, 'has not been fitted. ',
masker11.transform, fmri11_img)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
NiftiMapsMasker(labels11_img).fit_transform(fmri11_img)
# Test all kinds of mismatches between shapes and between affines
for create_files in (True, False):
with testing.write_tmp_imgs(labels11_img,
mask12_img,
create_files=create_files) as images:
labels11, mask12 = images
masker11 = NiftiMapsMasker(labels11, resampling_target=None)
masker11.fit()
assert_raises(ValueError, masker11.transform, fmri12_img)
assert_raises(ValueError, masker11.transform, fmri21_img)
masker11 = NiftiMapsMasker(labels11,
mask_img=mask12,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
masker11 = NiftiMapsMasker(labels11_img,
mask_img=mask21_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
# Transform, with smoothing (smoke test)
masker11 = NiftiMapsMasker(labels11_img,
smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiMapsMasker(labels11_img,
smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit_transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
testing.assert_raises_regex(
ValueError, 'has not been fitted. ',
NiftiMapsMasker(labels11_img).inverse_transform, signals11)
# Call inverse transform (smoke test)
fmri11_img_r = masker11.inverse_transform(signals11)
assert_equal(fmri11_img_r.shape, fmri11_img.shape)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(fmri11_img))
# Test with data and atlas of different shape: the atlas should be
# resampled to the data
shape22 = (5, 5, 6)
affine2 = 2 * np.eye(4)
affine2[-1, -1] = 1
fmri22_img, _ = generate_random_img(shape22, affine=affine2, length=length)
masker = NiftiMapsMasker(labels11_img, mask_img=mask21_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(get_affine(masker._resampled_maps_img_),
affine2)
def test_nifti_maps_masker():
# Check working of shape/affine checks
shape1 = (13, 11, 12)
affine1 = np.eye(4)
shape2 = (12, 10, 14)
affine2 = np.diag((1, 2, 3, 1))
n_regions = 9
length = 3
fmri11_img, mask11_img = generate_random_img(shape1, affine=affine1,
length=length)
fmri12_img, mask12_img = generate_random_img(shape1, affine=affine2,
length=length)
fmri21_img, mask21_img = generate_random_img(shape2, affine=affine1,
length=length)
labels11_img, labels_mask_img = \
testing.generate_maps(shape1, n_regions, affine=affine1)
# No exception raised here
for create_files in (True, False):
with testing.write_tmp_imgs(labels11_img, create_files=create_files) \
as labels11:
masker11 = NiftiMapsMasker(labels11, resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
# enables to delete "labels11" on windows
del masker11
masker11 = NiftiMapsMasker(labels11_img, mask_img=mask11_img,
resampling_target=None)
testing.assert_raises_regex(
ValueError, 'has not been fitted. ',
masker11.transform, fmri11_img)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
NiftiMapsMasker(labels11_img).fit_transform(fmri11_img)
# Test all kinds of mismatches between shapes and between affines
for create_files in (True, False):
with testing.write_tmp_imgs(labels11_img, mask12_img,
create_files=create_files) as images:
labels11, mask12 = images
masker11 = NiftiMapsMasker(labels11, resampling_target=None)
masker11.fit()
assert_raises(ValueError, masker11.transform, fmri12_img)
assert_raises(ValueError, masker11.transform, fmri21_img)
masker11 = NiftiMapsMasker(labels11, mask_img=mask12,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
del masker11
masker11 = NiftiMapsMasker(labels11_img, mask_img=mask21_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
# Transform, with smoothing (smoke test)
masker11 = NiftiMapsMasker(labels11_img, smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiMapsMasker(labels11_img, smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit_transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
testing.assert_raises_regex(
ValueError, 'has not been fitted. ',
NiftiMapsMasker(labels11_img).inverse_transform, signals11)
# Call inverse transform (smoke test)
fmri11_img_r = masker11.inverse_transform(signals11)
assert_equal(fmri11_img_r.shape, fmri11_img.shape)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(fmri11_img))
# Test with data and atlas of different shape: the atlas should be
# resampled to the data
shape22 = (5, 5, 6)
affine2 = 2 * np.eye(4)
affine2[-1, -1] = 1
fmri22_img, _ = generate_random_img(shape22, affine=affine2,
length=length)
masker = NiftiMapsMasker(labels11_img, mask_img=mask21_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(
get_affine(masker._resampled_maps_img_),
affine2)
def test_nifti_maps_masker_2():
# Test resampling in NiftiMapsMasker
affine = np.eye(4)
shape1 = (10, 11, 12) # fmri
shape2 = (13, 14, 15) # mask
shape3 = (16, 17, 18) # maps
n_regions = 9
length = 3
fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length)
_, mask22_img = generate_random_img(shape2, affine=affine, length=length)
maps33_img, _ = \
testing.generate_maps(shape3, n_regions, affine=affine)
mask_img_4d = nibabel.Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
# verify that 4D mask arguments are refused
masker = NiftiMapsMasker(maps33_img, mask_img=mask_img_4d)
testing.assert_raises_regex(
DimensionError, "Input data has incompatible dimensionality: "
"Expected dimension is 3D and you provided "
"a 4D image.", masker.fit)
# Test error checking
assert_raises(ValueError,
NiftiMapsMasker,
maps33_img,
resampling_target="mask")
assert_raises(ValueError,
NiftiMapsMasker,
maps33_img,
resampling_target="invalid")
# Target: mask
masker = NiftiMapsMasker(maps33_img,
mask_img=mask22_img,
resampling_target="mask")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(mask22_img))
assert_equal(masker.mask_img_.shape, mask22_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape, (masker.maps_img_.shape[:3] + (length, )))
# Target: maps
masker = NiftiMapsMasker(maps33_img,
mask_img=mask22_img,
resampling_target="maps")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.maps_img_),
get_affine(maps33_img))
assert_equal(masker.maps_img_.shape, maps33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape, (masker.maps_img_.shape[:3] + (length, )))
# Test with clipped maps: mask does not contain all maps.
# Shapes do matter in that case
affine1 = np.eye(4)
shape1 = (10, 11, 12)
shape2 = (8, 9, 10) # mask
affine2 = np.diag((2, 2, 2, 1)) # just for mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine1, length=length)
_, mask22_img = testing.generate_fake_fmri(shape2,
length=1,
affine=affine2)
# Target: maps
maps33_img, _ = \
testing.generate_maps(shape3, n_regions, affine=affine1)
masker = NiftiMapsMasker(maps33_img,
mask_img=mask22_img,
resampling_target="maps")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.maps_img_),
get_affine(maps33_img))
assert_equal(masker.maps_img_.shape, maps33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
# Some regions have been clipped. Resulting signal must be zero
assert_less((transformed.var(axis=0) == 0).sum(), n_regions)
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape, (masker.maps_img_.shape[:3] + (length, )))
def test_nifti_maps_masker_2():
# Test resampling in NiftiMapsMasker
affine = np.eye(4)
shape1 = (10, 11, 12) # fmri
shape2 = (13, 14, 15) # mask
shape3 = (16, 17, 18) # maps
n_regions = 9
length = 3
fmri11_img, _ = generate_random_img(shape1, affine=affine,
length=length)
_, mask22_img = generate_random_img(shape2, affine=affine,
length=length)
maps33_img, _ = \
testing.generate_maps(shape3, n_regions, affine=affine)
mask_img_4d = nibabel.Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
# verify that 4D mask arguments are refused
masker = NiftiMapsMasker(maps33_img, mask_img=mask_img_4d)
testing.assert_raises_regex(DimensionError,
"Input data has incompatible dimensionality: "
"Expected dimension is 3D and you provided "
"a 4D image.",
masker.fit)
# Test error checking
assert_raises(ValueError, NiftiMapsMasker, maps33_img,
resampling_target="mask")
assert_raises(ValueError, NiftiMapsMasker, maps33_img,
resampling_target="invalid")
# Target: mask
masker = NiftiMapsMasker(maps33_img, mask_img=mask22_img,
resampling_target="mask")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(mask22_img))
assert_equal(masker.mask_img_.shape, mask22_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape, (masker.maps_img_.shape[:3] + (length,)))
# Target: maps
masker = NiftiMapsMasker(maps33_img, mask_img=mask22_img,
resampling_target="maps")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.maps_img_),
get_affine(maps33_img))
assert_equal(masker.maps_img_.shape, maps33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape, (masker.maps_img_.shape[:3] + (length,)))
# Test with clipped maps: mask does not contain all maps.
# Shapes do matter in that case
affine1 = np.eye(4)
shape1 = (10, 11, 12)
shape2 = (8, 9, 10) # mask
affine2 = np.diag((2, 2, 2, 1)) # just for mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine1, length=length)
_, mask22_img = testing.generate_fake_fmri(shape2, length=1,
affine=affine2)
# Target: maps
maps33_img, _ = \
testing.generate_maps(shape3, n_regions, affine=affine1)
masker = NiftiMapsMasker(maps33_img, mask_img=mask22_img,
resampling_target="maps")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.maps_img_),
get_affine(maps33_img))
assert_equal(masker.maps_img_.shape, maps33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.maps_img_))
assert_equal(masker.mask_img_.shape, masker.maps_img_.shape[:3])
transformed = masker.transform(fmri11_img)
assert_equal(transformed.shape, (length, n_regions))
# Some regions have been clipped. Resulting signal must be zero
assert_less((transformed.var(axis=0) == 0).sum(), n_regions)
fmri11_img_r = masker.inverse_transform(transformed)
np.testing.assert_almost_equal(get_affine(fmri11_img_r),
get_affine(masker.maps_img_))
assert_equal(fmri11_img_r.shape,
(masker.maps_img_.shape[:3] + (length,)))