def test_mask_reducer():
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
imgs = []
for i in range(8):
this_img = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_img[2:4, 2:4, 2:4, :] += 10
imgs.append(nibabel.Nifti1Image(this_img, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
masker = MultiNiftiMasker(mask_img=mask_img).fit()
# Test fit on multiple image
data = mask_and_reduce(masker, imgs)
assert data.shape == (8 * 5, 6 * 8 * 10)
data = mask_and_reduce(masker, imgs, n_components=3)
assert data.shape == (8 * 3, 6 * 8 * 10)
data = mask_and_reduce(masker, imgs, reduction_ratio=0.4)
assert data.shape == (8 * 2, 6 * 8 * 10)
# Test on single image
data_single = mask_and_reduce(masker, imgs[0], n_components=3)
assert data_single.shape == (3, 6 * 8 * 10)
# Test n_jobs > 1
data = mask_and_reduce(masker,
imgs[0],
n_components=3,
n_jobs=2,
random_state=0)
assert data.shape == (3, 6 * 8 * 10)
assert_array_almost_equal(data_single, data)
# Test that reduced data is orthogonal
data = mask_and_reduce(masker, imgs[0], n_components=3, random_state=0)
assert data.shape == (3, 6 * 8 * 10)
cov = data.dot(data.T)
cov_diag = np.zeros((3, 3))
for i in range(3):
cov_diag[i, i] = cov[i, i]
assert_array_almost_equal(cov, cov_diag)
# Test reproducibility
data1 = mask_and_reduce(masker, imgs[0], n_components=3, random_state=0)
data2 = mask_and_reduce(masker, [imgs[0]] * 2,
n_components=3,
random_state=0)
assert_array_almost_equal(np.tile(data1, (2, 1)), data2)
def test_mask_reducer():
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
imgs = []
for i in range(8):
this_img = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_img[2:4, 2:4, 2:4, :] += 10
imgs.append(nibabel.Nifti1Image(this_img, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
masker = MultiNiftiMasker(mask_img=mask_img).fit()
# Test fit on multiple image
data = mask_and_reduce(masker, imgs)
assert_equal(data.shape, (8 * 5, 6 * 8 * 10))
data = mask_and_reduce(masker, imgs, n_components=3)
assert_equal(data.shape, (8 * 3, 6 * 8 * 10))
data = mask_and_reduce(masker, imgs, reduction_ratio=0.4)
assert_equal(data.shape, (8 * 2, 6 * 8 * 10))
# Test on single image
data_single = mask_and_reduce(masker, imgs[0], n_components=3)
assert_true(data_single.shape == (3, 6 * 8 * 10))
# Test n_jobs > 1
data = mask_and_reduce(masker, imgs[0], n_components=3,
n_jobs=2, random_state=0)
assert_equal(data.shape, (3, 6 * 8 * 10))
assert_array_almost_equal(data_single, data)
# Test that reduced data is orthogonal
data = mask_and_reduce(masker, imgs[0], n_components=3,
random_state=0)
assert_true(data.shape == (3, 6 * 8 * 10))
cov = data.dot(data.T)
cov_diag = np.zeros((3, 3))
for i in range(3):
cov_diag[i, i] = cov[i, i]
assert_array_almost_equal(cov, cov_diag)
# Test reproducibility
data1 = mask_and_reduce(masker, imgs[0], n_components=3,
random_state=0)
data2 = mask_and_reduce(masker, [imgs[0]] * 2, n_components=3,
random_state=0)
assert_array_almost_equal(np.tile(data1, (2, 1)), data2)