def test_nifti_labels_masker_with_nans_and_infs_in_data():
"""Apply a NiftiLabelsMasker to 4D data containing NaNs and infs.
The masker should replace those NaNs and infs with zeros,
while raising a warning.
"""
length = 3
n_regions = 9
fmri_img, mask_img = generate_random_img((13, 11, 12),
affine=np.eye(4), length=length)
labels_img = data_gen.generate_labeled_regions((13, 11, 12),
affine=np.eye(4),
n_regions=n_regions)
# Introduce nans with data type float
# See issues:
# - https://github.com/nilearn/nilearn/issues/2580 (why floats)
# - https://github.com/nilearn/nilearn/issues/2711 (why test)
fmri_data = get_data(fmri_img).astype(np.float32)
fmri_data[:, :, 7, :] = np.nan
fmri_data[:, :, 4, 0] = np.inf
fmri_img = nibabel.Nifti1Image(fmri_data, np.eye(4))
masker = NiftiLabelsMasker(labels_img, mask_img=mask_img)
with pytest.warns(UserWarning, match="Non-finite values detected."):
sig = masker.fit_transform(fmri_img)
assert sig.shape == (length, n_regions)
assert np.all(np.isfinite(sig))
def test_standardization():
data_shape = (9, 9, 5)
n_samples = 500
signals = np.random.randn(np.prod(data_shape), n_samples)
means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
signals += means
img = nibabel.Nifti1Image(signals.reshape(data_shape + (n_samples, )),
np.eye(4))
labels = data_gen.generate_labeled_regions((9, 9, 5), 10)
# Unstandarized
masker = NiftiLabelsMasker(labels, standardize=False)
unstandarized_label_signals = masker.fit_transform(img)
# z-score
masker = NiftiLabelsMasker(labels, standardize='zscore')
trans_signals = masker.fit_transform(img)
np.testing.assert_almost_equal(trans_signals.mean(0), 0)
np.testing.assert_almost_equal(trans_signals.std(0), 1)
# psc
masker = NiftiLabelsMasker(labels, standardize='psc')
trans_signals = masker.fit_transform(img)
np.testing.assert_almost_equal(trans_signals.mean(0), 0)
np.testing.assert_almost_equal(
trans_signals, (unstandarized_label_signals /
unstandarized_label_signals.mean(0) * 100 - 100))
def test_3d_images():
# Test that the NiftiLabelsMasker works with 3D images
affine = np.eye(4)
n_regions = 3
shape3 = (2, 2, 2)
labels33_img = data_gen.generate_labeled_regions(shape3, n_regions)
mask_img = nibabel.Nifti1Image(np.ones(shape3, dtype=np.int8),
affine=affine)
epi_img1 = nibabel.Nifti1Image(np.ones(shape3), affine=affine)
epi_img2 = nibabel.Nifti1Image(np.ones(shape3), affine=affine)
masker = NiftiLabelsMasker(labels33_img, mask_img=mask_img)
epis = masker.fit_transform(epi_img1)
assert (epis.shape == (1, 3))
epis = masker.fit_transform([epi_img1, epi_img2])
assert (epis.shape == (2, 3))
def test_nifti_labels_masker_with_nans_and_infs():
length = 3
n_regions = 9
fmri_img, mask_img = generate_random_img((13, 11, 12),
affine=np.eye(4), length=length)
labels_img = data_gen.generate_labeled_regions((13, 11, 12),
affine=np.eye(4),
n_regions=n_regions)
# nans
mask_data = get_data(mask_img)
mask_data[:, :, 7] = np.nan
mask_data[:, :, 4] = np.inf
mask_img = nibabel.Nifti1Image(mask_data, np.eye(4))
masker = NiftiLabelsMasker(labels_img, mask_img=mask_img)
sig = masker.fit_transform(fmri_img)
assert sig.shape == (length, n_regions)
assert np.all(np.isfinite(sig))
def test_nifti_labels_masker_with_nans_and_infs():
length = 3
n_regions = 9
fmri_img, mask_img = generate_random_img((13, 11, 12),
affine=np.eye(4), length=length)
labels_img = data_gen.generate_labeled_regions((13, 11, 12),
affine=np.eye(4),
n_regions=n_regions)
# nans
mask_data = mask_img.get_data()
mask_data[:, :, 7] = np.nan
mask_data[:, :, 4] = np.inf
mask_img = nibabel.Nifti1Image(mask_data, np.eye(4))
masker = NiftiLabelsMasker(labels_img, mask_img=mask_img)
sig = masker.fit_transform(fmri_img)
assert_equal(sig.shape, (length, n_regions))
assert_true(np.all(np.isfinite(sig)))
def test_nifti_labels_masker_with_nans_and_infs():
length = 3
n_regions = 9
fmri_img, mask_img = generate_random_img((13, 11, 12),
affine=np.eye(4),
length=length)
labels_img = data_gen.generate_labeled_regions((13, 11, 12),
affine=np.eye(4),
n_regions=n_regions)
# Introduce nans with data type float
# See issue: https://github.com/nilearn/nilearn/issues/2580
mask_data = get_data(mask_img).astype(np.float32)
mask_data[:, :, 7] = np.nan
mask_data[:, :, 4] = np.inf
mask_img = nibabel.Nifti1Image(mask_data, np.eye(4))
masker = NiftiLabelsMasker(labels_img, mask_img=mask_img)
sig = masker.fit_transform(fmri_img)
assert sig.shape == (length, n_regions)
assert np.all(np.isfinite(sig))
def test_nifti_labels_masker_reduction_strategies():
"""Tests:
1. whether the usage of different reduction strategies work.
2. whether unrecognised strategies raise a ValueError
3. whether the default option is backwards compatible (calls "mean")
"""
test_values = [-2., -1., 0., 1., 2]
img_data = np.array([[test_values,
test_values]])
labels_data = np.array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1]]], dtype=np.int8)
affine = np.eye(4)
img = nibabel.Nifti1Image(img_data, affine)
labels = nibabel.Nifti1Image(labels_data, affine)
# What NiftiLabelsMasker should return for each reduction strategy?
expected_results = {"mean": np.mean(test_values),
"median": np.median(test_values),
"sum": np.sum(test_values),
"minimum": np.min(test_values),
"maximum": np.max(test_values),
"standard_deviation": np.std(test_values),
"variance": np.var(test_values)}
for strategy, expected_result in expected_results.items():
masker = NiftiLabelsMasker(labels, strategy=strategy)
# Here passing [img] within a list because it's a 3D object.
result = masker.fit_transform([img]).squeeze()
assert result == expected_result
with pytest.raises(ValueError, match="Invalid strategy 'TESTRAISE'"):
NiftiLabelsMasker(
labels,
strategy="TESTRAISE"
)
default_masker = NiftiLabelsMasker(labels)
assert default_masker.strategy == "mean"
def test_nifti_labels_masker_with_mask():
shape = (13, 11, 12)
affine = np.eye(4)
fmri_img, mask_img = generate_random_img(shape, affine=affine, length=3)
labels_img = data_gen.generate_labeled_regions(shape, affine=affine,
n_regions=7)
masker = NiftiLabelsMasker(
labels_img, resampling_target=None, mask_img=mask_img)
signals = masker.fit().transform(fmri_img)
bg_masker = NiftiMasker(mask_img).fit()
masked_labels = bg_masker.inverse_transform(bg_masker.transform(labels_img))
masked_masker = NiftiLabelsMasker(
masked_labels, resampling_target=None, mask_img=mask_img)
masked_signals = masked_masker.fit().transform(fmri_img)
assert np.allclose(signals, masked_signals)
def test_nifti_labels_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 = testing.generate_labeled_regions(shape1,
affine=affine1,
n_regions=n_regions)
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 = NiftiLabelsMasker(labels11_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)
# check exception when transform() called without prior fit()
masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None)
testing.assert_raises_regex(ValueError, 'has not been fitted. ',
masker11.transform, fmri11_img)
# No exception raised here
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiLabelsMasker(labels11_img,
mask_img=mask11_img,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
# Test all kinds of mismatch between shapes and between affines
masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None)
masker11.fit()
assert_raises(ValueError, masker11.transform, fmri12_img)
assert_raises(ValueError, masker11.transform, fmri21_img)
masker11 = NiftiLabelsMasker(labels11_img,
mask_img=mask12_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
masker11 = NiftiLabelsMasker(labels11_img,
mask_img=mask21_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
# Transform, with smoothing (smoke test)
masker11 = NiftiLabelsMasker(labels11_img,
smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiLabelsMasker(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. ',
NiftiLabelsMasker(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))
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_labels_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 = testing.generate_labeled_regions(shape1, affine=affine1,
n_regions=n_regions)
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 = NiftiLabelsMasker(labels11_img, mask_img=mask_img_4d)
testing.assert_raises_regex(DimensionError, "Data must be a 3D",
masker.fit)
# check exception when transform() called without prior fit()
masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None)
testing.assert_raises_regex(
ValueError, 'has not been fitted. ', masker11.transform, fmri11_img)
# No exception raised here
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask11_img,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
# Test all kinds of mismatch between shapes and between affines
masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None)
masker11.fit()
assert_raises(ValueError, masker11.transform, fmri12_img)
assert_raises(ValueError, masker11.transform, fmri21_img)
masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask12_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask21_img,
resampling_target=None)
assert_raises(ValueError, masker11.fit)
# Transform, with smoothing (smoke test)
masker11 = NiftiLabelsMasker(labels11_img, smoothing_fwhm=3,
resampling_target=None)
signals11 = masker11.fit().transform(fmri11_img)
assert_equal(signals11.shape, (length, n_regions))
masker11 = NiftiLabelsMasker(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. ',
NiftiLabelsMasker(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(fmri11_img_r.get_affine(),
fmri11_img.get_affine())
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())