def test_auto_mask():
# This mostly a smoke test
data = np.zeros((9, 9, 9))
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(mask_args=dict(opening=0))
# Check that if we have not fit the masker we get a intelligible
# error
assert_raises(ValueError, masker.transform, [[img, ]])
# Check error return due to bad data format
assert_raises(ValueError, masker.fit, img)
# Smoke test the fit
masker.fit([[img]])
# Test mask intersection
data2 = np.zeros((9, 9, 9))
data2[1:-3, 1:-3, 1:-3] = 10
img2 = Nifti1Image(data2, np.eye(4))
masker.fit([[img, img2]])
assert_array_equal(masker.mask_img_.get_data(),
np.logical_or(data, data2))
# Smoke test the transform
masker.transform([[img, ]])
# It should also work with a 3D image
masker.transform(img)
def test_different_affines():
# Mask and EIP files with different affines
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((4, 4, 4, 3)), affine=np.diag((2, 2, 2, 1)))
epi_img2 = Nifti1Image(np.ones((3, 3, 3, 3)), affine=np.diag((3, 3, 3, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
for this_epi in epis:
masker.inverse_transform(this_epi)
def test_dtype():
data = np.zeros((9, 9, 9), dtype=np.float64)
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(dtype='auto')
masker.fit([[img]])
masked_img = masker.transform([[img]])
assert(masked_img[0].dtype == np.float32)
def test_dtype():
data = np.zeros((9, 9, 9), dtype=np.float64)
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(dtype='auto')
masker.fit([[img]])
masked_img = masker.transform([[img]])
assert(masked_img[0].dtype == np.float32)
def test_auto_mask():
# This mostly a smoke test
data = np.zeros((9, 9, 9))
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(mask_args=dict(opening=0))
# Check that if we have not fit the masker we get a intelligible
# error
assert_raises(ValueError, masker.transform, [[img, ]])
# Check error return due to bad data format
assert_raises(ValueError, masker.fit, img)
# Smoke test the fit
masker.fit([[img]])
# Test mask intersection
data2 = np.zeros((9, 9, 9))
data2[1:-3, 1:-3, 1:-3] = 10
img2 = Nifti1Image(data2, np.eye(4))
masker.fit([[img, img2]])
assert_array_equal(masker.mask_img_.get_data(),
np.logical_or(data, data2))
# Smoke test the transform
masker.transform([[img, ]])
# It should also work with a 3D image
masker.transform(img)
# check exception when transform() called without prior fit()
masker2 = MultiNiftiMasker(mask_img=img)
assert_raises_regex(
ValueError,
'has not been fitted. ', masker2.transform, img2)
def test_shelving():
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((2, 2, 2, 1)))
cachedir = mkdtemp()
try:
masker_shelved = MultiNiftiMasker(mask_img=mask_img,
memory=Memory(location=cachedir,
mmap_mode='r',
verbose=0))
masker_shelved._shelving = True
masker = MultiNiftiMasker(mask_img=mask_img)
epis_shelved = masker_shelved.fit_transform([epi_img1, epi_img2])
epis = masker.fit_transform([epi_img1, epi_img2])
for epi_shelved, epi in zip(epis_shelved, epis):
epi_shelved = epi_shelved.get()
assert_array_equal(epi_shelved, epi)
epi = masker.fit_transform(epi_img1)
epi_shelved = masker_shelved.fit_transform(epi_img1)
epi_shelved = epi_shelved.get()
assert_array_equal(epi_shelved, epi)
finally:
# enables to delete "filename" on windows
del masker
shutil.rmtree(cachedir, ignore_errors=True)
def test_different_affines():
# Mask and EIP files with different affines
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((4, 4, 4, 3)),
affine=np.diag((2, 2, 2, 1)))
epi_img2 = Nifti1Image(np.ones((3, 3, 3, 3)),
affine=np.diag((3, 3, 3, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
for this_epi in epis:
masker.inverse_transform(this_epi)
def test_joblib_cache():
from joblib import hash
# Dummy mask
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
mask_img = Nifti1Image(mask, np.eye(4))
with write_tmp_imgs(mask_img, create_files=True) as filename:
masker = MultiNiftiMasker(mask_img=filename)
masker.fit()
mask_hash = hash(masker.mask_img_)
get_data(masker.mask_img_)
assert mask_hash == hash(masker.mask_img_)
# enables to delete "filename" on windows
del masker
def test_joblib_cache():
from sklearn.externals.joblib import hash
# Dummy mask
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
mask_img = Nifti1Image(mask, np.eye(4))
with write_tmp_imgs(mask_img, create_files=True) as filename:
masker = MultiNiftiMasker(mask_img=filename)
masker.fit()
mask_hash = hash(masker.mask_img_)
masker.mask_img_.get_data()
assert_true(mask_hash == hash(masker.mask_img_))
# enables to delete "filename" on windows
del masker
def test_3d_images():
# Test that the MultiNiftiMasker works with 3D images
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)), affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)), affine=np.diag((2, 2, 2, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
# This is mostly a smoke test
assert_equal(len(epis), 2)
# verify that 4D mask arguments are refused
mask_img_4d = Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
masker2 = MultiNiftiMasker(mask_img=mask_img_4d)
assert_raises_regex(DimensionError, "Data must be a 3D", masker2.fit)
def test_joblib_cache():
if not LooseVersion(nibabel.__version__) > LooseVersion('1.1.0'):
# Old nibabel do not pickle
raise SkipTest
from sklearn.externals.joblib import hash
# Dummy mask
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
mask_img = Nifti1Image(mask, np.eye(4))
with write_tmp_imgs(mask_img, create_files=True)\
as filename:
masker = MultiNiftiMasker(mask_img=filename)
masker.fit()
mask_hash = hash(masker.mask_img_)
masker.mask_img_.get_data()
assert_true(mask_hash == hash(masker.mask_img_))
def test_joblib_cache():
if not LooseVersion(nibabel.__version__) > LooseVersion('1.1.0'):
# Old nibabel do not pickle
raise SkipTest
from sklearn.externals.joblib import hash
# Dummy mask
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
mask_img = Nifti1Image(mask, np.eye(4))
with write_tmp_imgs(mask_img, create_files=True)\
as filename:
masker = MultiNiftiMasker(mask_img=filename)
masker.fit()
mask_hash = hash(masker.mask_img_)
masker.mask_img_.get_data()
assert_true(mask_hash == hash(masker.mask_img_))
def test_shelving():
if LooseVersion(sklearn.__version__) >= LooseVersion('0.15'):
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((2, 2, 2, 1)))
cachedir = mkdtemp()
try:
masker_shelved = MultiNiftiMasker(mask_img=mask_img,
memory=Memory(cachedir=cachedir,
mmap_mode='r',
verbose=0))
masker_shelved._shelving = True
masker = MultiNiftiMasker(mask_img=mask_img)
epis_shelved = masker_shelved.fit_transform([epi_img1, epi_img2])
epis = masker.fit_transform([epi_img1, epi_img2])
for epi_shelved, epi in zip(epis_shelved, epis):
epi_shelved = epi_shelved.get()
assert_array_equal(epi_shelved, epi)
epi = masker.fit_transform(epi_img1)
epi_shelved = masker_shelved.fit_transform(epi_img1)
epi_shelved = epi_shelved.get()
assert_array_equal(epi_shelved, epi)
finally:
# enables to delete "filename" on windows
del masker
shutil.rmtree(cachedir, ignore_errors=True)
else:
pass
def test_compute_multi_gray_matter_mask(strategy):
imgs = _get_random_imgs((9, 9, 5), 2)
masker = MultiNiftiMasker(mask_strategy=strategy, mask_args={'opening': 1})
masker.fit(imgs)
# Check that the order of the images does not change the output
masker2 = MultiNiftiMasker(mask_strategy=strategy,
mask_args={'opening': 1})
masker2.fit(imgs[::-1])
mask_ref = np.zeros((9, 9, 5), dtype='int8')
np.testing.assert_array_equal(get_data(masker.mask_img_), mask_ref)
np.testing.assert_array_equal(get_data(masker2.mask_img_), mask_ref)
def test_mask_strategy_errors():
# Error with unknown mask_strategy
imgs = _get_random_imgs((9, 9, 5), 2)
mask = MultiNiftiMasker(mask_strategy='foo')
with pytest.raises(ValueError,
match="Unknown value of mask_strategy 'foo'"):
mask.fit(imgs)
# Warning with deprecated 'template' strategy
mask = MultiNiftiMasker(mask_strategy='template')
with pytest.warns(UserWarning,
match="Masking strategy 'template' is deprecated."):
mask.fit(imgs)
def test_3d_images():
# Test that the MultiNiftiMasker works with 3D images
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)), affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)), affine=np.diag((2, 2, 2, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
# This is mostly a smoke test
assert_equal(len(epis), 2)
# verify that 4D mask arguments are refused
mask_img_4d = Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
masker2 = MultiNiftiMasker(mask_img=mask_img_4d)
assert_raises_regex(
DimensionError, "Input data has incompatible dimensionality: "
"Expected dimension is 3D and you provided "
"a 4D image.", masker2.fit)
def test_3d_images():
# Test that the MultiNiftiMasker works with 3D images
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((2, 2, 2, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
# This is mostly a smoke test
assert_equal(len(epis), 2)
# verify that 4D mask arguments are refused
mask_img_4d = Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
masker2 = MultiNiftiMasker(mask_img=mask_img_4d)
assert_raises_regex(DimensionError, "Data must be a 3D",
masker2.fit)
def test_nan():
data = np.ones((9, 9, 9))
data[0] = np.nan
data[:, 0] = np.nan
data[:, :, 0] = np.nan
data[-1] = np.nan
data[:, -1] = np.nan
data[:, :, -1] = np.nan
data[3:-3, 3:-3, 3:-3] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(mask_args=dict(opening=0))
masker.fit([img])
mask = masker.mask_img_.get_data()
assert_true(mask[1:-1, 1:-1, 1:-1].all())
assert_false(mask[0].any())
assert_false(mask[:, 0].any())
assert_false(mask[:, :, 0].any())
assert_false(mask[-1].any())
assert_false(mask[:, -1].any())
assert_false(mask[:, :, -1].any())
def test_nan():
data = np.ones((9, 9, 9))
data[0] = np.nan
data[:, 0] = np.nan
data[:, :, 0] = np.nan
data[-1] = np.nan
data[:, -1] = np.nan
data[:, :, -1] = np.nan
data[3:-3, 3:-3, 3:-3] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(mask_args=dict(opening=0))
masker.fit([img])
mask = get_data(masker.mask_img_)
assert mask[1:-1, 1:-1, 1:-1].all()
assert not mask[0].any()
assert not mask[:, 0].any()
assert not mask[:, :, 0].any()
assert not mask[-1].any()
assert not mask[:, -1].any()
assert not mask[:, :, -1].any()
def test_standardization():
rng = np.random.RandomState(42)
data_shape = (9, 9, 5)
n_samples = 500
signals = rng.standard_normal(size=(2, np.prod(data_shape), n_samples))
means = rng.standard_normal(size=(2, np.prod(data_shape), 1)) * 50 + 1000
signals += means
img1 = Nifti1Image(signals[0].reshape(data_shape + (n_samples,)),
np.eye(4))
img2 = Nifti1Image(signals[1].reshape(data_shape + (n_samples,)),
np.eye(4))
mask = Nifti1Image(np.ones(data_shape), np.eye(4))
# z-score
masker = MultiNiftiMasker(mask, standardize='zscore')
trans_signals = masker.fit_transform([img1, img2])
for ts in trans_signals:
np.testing.assert_almost_equal(ts.mean(0), 0)
np.testing.assert_almost_equal(ts.std(0), 1)
# psc
masker = MultiNiftiMasker(mask, standardize='psc')
trans_signals = masker.fit_transform([img1, img2])
for ts, s in zip(trans_signals, signals):
np.testing.assert_almost_equal(ts.mean(0), 0)
np.testing.assert_almost_equal(ts,
(s / s.mean(1)[:, np.newaxis] *
100 - 100).T)
def test_3d_images():
# Test that the MultiNiftiMasker works with 3D images
mask_img = Nifti1Image(np.ones((2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
epi_img1 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((4, 4, 4, 1)))
epi_img2 = Nifti1Image(np.ones((2, 2, 2)),
affine=np.diag((2, 2, 2, 1)))
masker = MultiNiftiMasker(mask_img=mask_img)
epis = masker.fit_transform([epi_img1, epi_img2])
# This is mostly a smoke test
assert_equal(len(epis), 2)
# verify that 4D mask arguments are refused
mask_img_4d = Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8),
affine=np.diag((4, 4, 4, 1)))
masker2 = MultiNiftiMasker(mask_img=mask_img_4d)
assert_raises_regex(DimensionError,
"Input data has incompatible dimensionality: "
"Expected dimension is 3D and you provided "
"a 4D image.",
masker2.fit)
def test_compute_multi_gray_matter_mask():
# Check mask is correctly is correctly calculated
imgs = [Nifti1Image(np.random.rand(9, 9, 5), np.eye(4)),
Nifti1Image(np.random.rand(9, 9, 5), np.eye(4))]
masker = MultiNiftiMasker(mask_strategy='template')
masker.fit(imgs)
# Check that the order of the images does not change the output
masker2 = MultiNiftiMasker(mask_strategy='template')
masker2.fit(imgs[::-1])
mask = masker.mask_img_
mask2 = masker2.mask_img_
mask_ref = np.zeros((9, 9, 5))
mask_ref[2:7, 2:7, 2] = 1
np.testing.assert_array_equal(get_data(mask), mask_ref)
np.testing.assert_array_equal(get_data(mask2), mask_ref)
def test_compute_multi_gray_matter_mask():
rng = np.random.RandomState(42)
# Check mask is correctly is correctly calculated
imgs = [Nifti1Image(rng.uniform(size=(9, 9, 5)), np.eye(4)),
Nifti1Image(rng.uniform(size=(9, 9, 5)), np.eye(4))]
masker = MultiNiftiMasker(mask_strategy='template',
mask_args={'opening': 1})
masker.fit(imgs)
# Check that the order of the images does not change the output
masker2 = MultiNiftiMasker(mask_strategy='template',
mask_args={'opening': 1})
masker2.fit(imgs[::-1])
mask = masker.mask_img_
mask2 = masker2.mask_img_
mask_ref = np.zeros((9, 9, 5), dtype='int8')
np.testing.assert_array_equal(get_data(mask), mask_ref)
np.testing.assert_array_equal(get_data(mask2), mask_ref)
def test_compute_multi_gray_matter_mask():
# Check mask is correctly is correctly calculated
imgs = [Nifti1Image(np.random.rand(9, 9, 5), np.eye(4)),
Nifti1Image(np.random.rand(9, 9, 5), np.eye(4))]
masker = MultiNiftiMasker(mask_strategy='template')
masker.fit(imgs)
# Check that the order of the images does not change the output
masker2 = MultiNiftiMasker(mask_strategy='template')
masker2.fit(imgs[::-1])
mask = masker.mask_img_
mask2 = masker2.mask_img_
mask_ref = np.zeros((9, 9, 5))
mask_ref[2:7, 2:7, 2] = 1
np.testing.assert_array_equal(mask.get_data(), mask_ref)
np.testing.assert_array_equal(mask2.get_data(), mask_ref)
