def test_anisotropic_sphere_extraction():
data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
affine = np.eye(4)
affine[0, 0] = 2
affine[2, 2] = 2
img = nibabel.Nifti1Image(data, affine)
masker = NiftiSpheresMasker([(2, 1, 2)], radius=1)
# Test the fit
masker.fit()
# Test the transform
s = masker.transform(img)
mask = np.zeros((3, 3, 3), dtype=bool)
mask[1, :, 1] = True
assert_array_equal(s[:, 0], np.mean(data[mask], axis=0))
# Now with a mask
mask_img = np.zeros((3, 2, 3))
mask_img[1, 0, 1] = 1
affine_2 = affine.copy()
affine_2[0, 0] = 4
mask_img = nibabel.Nifti1Image(mask_img, affine=affine_2)
masker = NiftiSpheresMasker([(2, 1, 2)], radius=1, mask_img=mask_img)
masker.fit()
s = masker.transform(img)
assert_array_equal(s[:, 0], data[1, 0, 1])
def test_small_radius_inverse():
affine = np.eye(4)
shape = (3, 3, 3)
data = np.random.RandomState(42).random_sample(shape)
mask = np.zeros(shape)
mask[1, 1, 1] = 1
mask[2, 2, 2] = 1
affine = np.eye(4) * 1.2
seed = (1.4, 1.4, 1.4)
masker = NiftiSpheresMasker([seed],
radius=0.1,
mask_img=nibabel.Nifti1Image(mask, affine))
spheres_data = masker.fit_transform(nibabel.Nifti1Image(data, affine))
masker.inverse_transform(spheres_data)
# Test if masking is taken into account
mask[1, 1, 1] = 0
mask[1, 1, 0] = 1
masker = NiftiSpheresMasker([seed],
radius=0.1,
mask_img=nibabel.Nifti1Image(mask, affine))
masker.fit(nibabel.Nifti1Image(data, affine))
with pytest.raises(ValueError, match='These spheres are empty'):
masker.inverse_transform(spheres_data)
masker = NiftiSpheresMasker([seed],
radius=1.6,
mask_img=nibabel.Nifti1Image(mask, affine))
masker.fit(nibabel.Nifti1Image(data, affine))
masker.inverse_transform(spheres_data)
def test_seed_extraction():
data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
img = nibabel.Nifti1Image(data, np.eye(4))
masker = NiftiSpheresMasker([(1, 1, 1)])
# Test the fit
masker.fit()
# Test the transform
s = masker.transform(img)
assert_array_equal(s[:, 0], data[1, 1, 1])
def test_sphere_extraction():
data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
img = nibabel.Nifti1Image(data, np.eye(4))
masker = NiftiSpheresMasker([(1, 1, 1)], radius=1)
# Test the fit
masker.fit()
# Test the transform
s = masker.transform(img)
mask = np.zeros((3, 3, 3), dtype=bool)
mask[:, 1, 1] = True
mask[1, :, 1] = True
mask[1, 1, :] = True
assert_array_equal(s[:, 0], np.mean(data[mask], axis=0))
# Now with a mask
mask_img = np.zeros((3, 3, 3))
mask_img[1, :, :] = 1
mask_img = nibabel.Nifti1Image(mask_img, np.eye(4))
masker = NiftiSpheresMasker([(1, 1, 1)], radius=1, mask_img=mask_img)
masker.fit()
s = masker.transform(img)
assert_array_equal(
s[:, 0], np.mean(data[np.logical_and(mask, get_data(mask_img))],
axis=0))
def test_nifti_spheres_masker_inverse_transform():
# Applying the sphere_extraction example from above backwards
data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
img = nibabel.Nifti1Image(data, np.eye(4))
masker = NiftiSpheresMasker([(1, 1, 1)], radius=1)
# Test the fit
masker.fit()
# Transform data
with pytest.raises(ValueError, match='Please provide mask_img'):
masker.inverse_transform(data[0, 0, 0, :])
# Mask describes the extend of the masker's sphere
mask = np.zeros((3, 3, 3), dtype=bool)
mask[:, 1, 1] = True
mask[1, :, 1] = True
mask[1, 1, :] = True
# Now with a mask
mask_img = np.zeros((3, 3, 3))
mask_img[1, :, :] = 1
mask_img = nibabel.Nifti1Image(mask_img, np.eye(4))
masker = NiftiSpheresMasker([(1, 1, 1)], radius=1, mask_img=mask_img)
masker.fit()
s = masker.transform(img)
# Create an array mask
array_mask = np.logical_and(mask, get_data(mask_img))
inverse_map = masker.inverse_transform(s)
# Testing whether mask is applied to inverse transform
assert_array_equal(
np.mean(get_data(inverse_map), axis=-1) != 0, array_mask)
# Test whether values are preserved
assert_array_equal(get_data(inverse_map)[array_mask].mean(0), s[:, 0])
# Test whether the mask's shape is applied
assert_array_equal(inverse_map.shape[:3], mask_img.shape)
def test_errors():
masker = NiftiSpheresMasker(([1, 2]), radius=.2)
with pytest.raises(ValueError, match='Seeds must be a list .+'):
masker.fit()