def test_nifti_labels_masker_resampling():
# Test resampling in NiftiLabelsMasker
shape1 = (10, 11, 12)
affine = np.eye(4)
# mask
shape2 = (16, 17, 18)
# labels
shape3 = (13, 14, 15)
n_regions = 9
length = 3
# With data of the same affine
fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length)
_, mask22_img = generate_random_img(shape2, affine=affine, length=length)
labels33_img = testing.generate_labeled_regions(shape3,
n_regions,
affine=affine)
# Test error checking
assert_raises(ValueError,
NiftiLabelsMasker,
labels33_img,
resampling_target="mask")
assert_raises(ValueError,
NiftiLabelsMasker,
labels33_img,
resampling_target="invalid")
# Target: labels
masker = NiftiLabelsMasker(labels33_img,
mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.labels_img_),
get_affine(labels33_img))
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.labels_img_))
assert_equal(masker.mask_img_.shape, masker.labels_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.labels_img_))
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length, )))
# Test with clipped labels: mask does not contain all labels.
# Shapes do matter in that case, because there is some resampling
# taking place.
shape1 = (10, 11, 12) # fmri
shape2 = (8, 9, 10) # mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length)
_, mask22_img = generate_random_img(shape2, affine=affine, length=length)
# Target: labels
labels33_img = testing.generate_labeled_regions(shape3,
n_regions,
affine=affine)
masker = NiftiLabelsMasker(labels33_img,
mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(get_affine(masker.labels_img_),
get_affine(labels33_img))
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(get_affine(masker.mask_img_),
get_affine(masker.labels_img_))
assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3])
uniq_labels = np.unique(masker.labels_img_.get_data())
assert_equal(uniq_labels[0], 0)
assert_equal(len(uniq_labels) - 1, n_regions)
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.labels_img_))
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length, )))
# 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 = NiftiLabelsMasker(labels33_img, mask_img=mask22_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(get_affine(masker._resampled_labels_img_),
affine2)
# Test with filenames
with testing.write_tmp_imgs(fmri22_img) as filename:
masker = NiftiLabelsMasker(labels33_img, resampling_target='data')
masker.fit_transform(filename)
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,)))
def test_nifti_labels_masker_resampling():
# Test resampling in NiftiLabelsMasker
shape1 = (10, 11, 12)
affine = np.eye(4)
# mask
shape2 = (16, 17, 18)
# labels
shape3 = (13, 14, 15)
n_regions = 9
length = 3
# With data of the same affine
fmri11_img, _ = generate_random_img(shape1, affine=affine,
length=length)
_, mask22_img = generate_random_img(shape2, affine=affine,
length=length)
labels33_img = testing.generate_labeled_regions(shape3, n_regions,
affine=affine)
# Test error checking
assert_raises(ValueError, NiftiLabelsMasker, labels33_img,
resampling_target="mask")
assert_raises(ValueError, NiftiLabelsMasker, labels33_img,
resampling_target="invalid")
# Target: labels
masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.get_affine(),
labels33_img.get_affine())
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.get_affine(),
masker.labels_img_.get_affine())
assert_equal(masker.mask_img_.shape, masker.labels_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(fmri11_img_r.get_affine(),
masker.labels_img_.get_affine())
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length,)))
# Test with clipped labels: mask does not contain all labels.
# Shapes do matter in that case, because there is some resampling
# taking place.
shape1 = (10, 11, 12) # fmri
shape2 = (8, 9, 10) # mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine,
length=length)
_, mask22_img = generate_random_img(shape2, affine=affine,
length=length)
# Target: labels
labels33_img = testing.generate_labeled_regions(shape3, n_regions,
affine=affine)
masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.get_affine(),
labels33_img.get_affine())
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.get_affine(),
masker.labels_img_.get_affine())
assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3])
uniq_labels = np.unique(masker.labels_img_.get_data())
assert_equal(uniq_labels[0], 0)
assert_equal(len(uniq_labels) - 1, n_regions)
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(fmri11_img_r.get_affine(),
masker.labels_img_.get_affine())
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length,)))
# 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 = NiftiLabelsMasker(labels33_img, mask_img=mask22_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(
masker._resampled_labels_img_.get_affine(),
affine2)
# Test with filenames
with testing.write_tmp_imgs(fmri22_img) as filename:
masker = NiftiLabelsMasker(labels33_img, resampling_target='data')
masker.fit_transform(filename)
def test_nifti_labels_masker_resampling():
# Test resampling in NiftiLabelsMasker
shape1 = (10, 11, 12)
affine = np.eye(4)
# mask
shape2 = (16, 17, 18)
# labels
shape3 = (13, 14, 15)
n_regions = 9
length = 3
# With data of the same affine
fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length)
_, mask22_img = generate_random_img(shape2, affine=affine, length=length)
labels33_img = data_gen.generate_labeled_regions(shape3,
n_regions,
affine=affine)
# Test error checking
assert_raises(ValueError,
NiftiLabelsMasker,
labels33_img,
resampling_target="mask")
assert_raises(ValueError,
NiftiLabelsMasker,
labels33_img,
resampling_target="invalid")
# Target: labels
masker = NiftiLabelsMasker(labels33_img,
mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.affine,
labels33_img.affine)
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.affine,
masker.labels_img_.affine)
assert_equal(masker.mask_img_.shape, masker.labels_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(fmri11_img_r.affine,
masker.labels_img_.affine)
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length, )))
# Test with clipped labels: mask does not contain all labels.
# Shapes do matter in that case, because there is some resampling
# taking place.
shape1 = (10, 11, 12) # fmri
shape2 = (8, 9, 10) # mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length)
_, mask22_img = generate_random_img(shape2, affine=affine, length=length)
# Target: labels
labels33_img = data_gen.generate_labeled_regions(shape3,
n_regions,
affine=affine)
masker = NiftiLabelsMasker(labels33_img,
mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.affine,
labels33_img.affine)
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.affine,
masker.labels_img_.affine)
assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3])
uniq_labels = np.unique(masker.labels_img_.get_data())
assert_equal(uniq_labels[0], 0)
assert_equal(len(uniq_labels) - 1, n_regions)
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(fmri11_img_r.affine,
masker.labels_img_.affine)
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length, )))
# 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 = NiftiLabelsMasker(labels33_img, mask_img=mask22_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(masker._resampled_labels_img_.affine,
affine2)
# Test with filenames
with testing.write_tmp_imgs(fmri22_img) as filename:
masker = NiftiLabelsMasker(labels33_img, resampling_target='data')
masker.fit_transform(filename)
# test labels masker with resampling target in 'data', 'labels' to return
# resampled labels having number of labels equal with transformed shape of
# 2nd dimension. This tests are added based on issue #1673 in Nilearn
shape = (13, 11, 12)
affine = np.eye(4) * 2
fmri_img, _ = generate_random_img(shape, affine=affine, length=21)
labels_img = data_gen.generate_labeled_regions((9, 8, 6),
affine=np.eye(4),
n_regions=10)
for resampling_target in ['data', 'labels']:
masker = NiftiLabelsMasker(labels_img=labels_img,
resampling_target=resampling_target)
transformed = masker.fit_transform(fmri_img)
resampled_labels_img = masker._resampled_labels_img_
n_resampled_labels = len(np.unique(resampled_labels_img.get_data()))
assert_equal(n_resampled_labels - 1, transformed.shape[1])
# inverse transform
compressed_img = masker.inverse_transform(transformed)
# Test that compressing the image a second time should yield an image
# with the same data as compressed_img.
transformed2 = masker.fit_transform(fmri_img)
# inverse transform again
compressed_img2 = masker.inverse_transform(transformed2)
np.testing.assert_array_equal(compressed_img.get_data(),
compressed_img2.get_data())
def test_nifti_labels_masker_resampling():
# Test resampling in NiftiLabelsMasker
shape1 = (10, 11, 12)
affine = np.eye(4)
# mask
shape2 = (16, 17, 18)
# labels
shape3 = (13, 14, 15)
n_regions = 9
length = 3
# With data of the same affine
fmri11_img, _ = generate_random_img(shape1, affine=affine,
length=length)
_, mask22_img = generate_random_img(shape2, affine=affine,
length=length)
labels33_img = data_gen.generate_labeled_regions(shape3, n_regions,
affine=affine)
# Test error checking
assert_raises(ValueError, NiftiLabelsMasker, labels33_img,
resampling_target="mask")
assert_raises(ValueError, NiftiLabelsMasker, labels33_img,
resampling_target="invalid")
# Target: labels
masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.affine,
labels33_img.affine)
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.affine,
masker.labels_img_.affine)
assert_equal(masker.mask_img_.shape, masker.labels_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(fmri11_img_r.affine,
masker.labels_img_.affine)
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length,)))
# Test with clipped labels: mask does not contain all labels.
# Shapes do matter in that case, because there is some resampling
# taking place.
shape1 = (10, 11, 12) # fmri
shape2 = (8, 9, 10) # mask
shape3 = (16, 18, 20) # maps
n_regions = 9
length = 21
fmri11_img, _ = generate_random_img(shape1, affine=affine,
length=length)
_, mask22_img = generate_random_img(shape2, affine=affine,
length=length)
# Target: labels
labels33_img = data_gen.generate_labeled_regions(shape3, n_regions,
affine=affine)
masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img,
resampling_target="labels")
masker.fit()
np.testing.assert_almost_equal(masker.labels_img_.affine,
labels33_img.affine)
assert_equal(masker.labels_img_.shape, labels33_img.shape)
np.testing.assert_almost_equal(masker.mask_img_.affine,
masker.labels_img_.affine)
assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3])
uniq_labels = np.unique(masker.labels_img_.get_data())
assert_equal(uniq_labels[0], 0)
assert_equal(len(uniq_labels) - 1, n_regions)
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(fmri11_img_r.affine,
masker.labels_img_.affine)
assert_equal(fmri11_img_r.shape,
(masker.labels_img_.shape[:3] + (length,)))
# 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 = NiftiLabelsMasker(labels33_img, mask_img=mask22_img)
masker.fit_transform(fmri22_img)
np.testing.assert_array_equal(masker._resampled_labels_img_.affine,
affine2)
# Test with filenames
with testing.write_tmp_imgs(fmri22_img) as filename:
masker = NiftiLabelsMasker(labels33_img, resampling_target='data')
masker.fit_transform(filename)
# test labels masker with resampling target in 'data', 'labels' to return
# resampled labels having number of labels equal with transformed shape of
# 2nd dimension. This tests are added based on issue #1673 in Nilearn
shape = (13, 11, 12)
affine = np.eye(4) * 2
fmri_img, _ = generate_random_img(shape, affine=affine, length=21)
labels_img = data_gen.generate_labeled_regions((9, 8, 6), affine=np.eye(4),
n_regions=10)
for resampling_target in ['data', 'labels']:
masker = NiftiLabelsMasker(labels_img=labels_img,
resampling_target=resampling_target)
transformed = masker.fit_transform(fmri_img)
resampled_labels_img = masker._resampled_labels_img_
n_resampled_labels = len(np.unique(resampled_labels_img.get_data()))
assert_equal(n_resampled_labels - 1, transformed.shape[1])
# inverse transform
compressed_img = masker.inverse_transform(transformed)
# Test that compressing the image a second time should yield an image
# with the same data as compressed_img.
transformed2 = masker.fit_transform(fmri_img)
# inverse transform again
compressed_img2 = masker.inverse_transform(transformed2)
np.testing.assert_array_equal(compressed_img.get_data(),
compressed_img2.get_data())