def test_fit_transform(method, n_parcel, test_image_2):
rng = np.random.RandomState(42)
fmri_imgs = [test_image_2] * 3
confounds = rng.standard_normal(size=(10, 3))
confounds_list = [confounds] * 3
parcellator = Parcellations(method=method, n_parcels=n_parcel)
signals = parcellator.fit_transform(fmri_imgs)
assert parcellator.labels_img_ is not None
if method not in ['kmeans', 'rena', 'hierarchical_kmeans']:
assert parcellator.connectivity_ is not None
assert parcellator.masker_ is not None
# fit_transform with confounds
signals = parcellator.fit_transform(fmri_imgs, confounds=confounds_list)
assert isinstance(signals, list)
assert signals[0].shape == (10, n_parcel)
def test_transform_3d_input_images():
# test list of 3D images
data = np.ones((10, 11, 12))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
img = nibabel.Nifti1Image(data, affine=np.eye(4))
imgs = [img] * 3
parcellate = Parcellations(method='ward', n_parcels=20)
X = parcellate.fit_transform(imgs)
assert isinstance(X, list)
# (number of samples, number of features)
assert np.concatenate(X).shape == (3, 20)
# inverse transform
imgs_ = parcellate.inverse_transform(X)
assert isinstance(imgs_, list)
# test single 3D image
X = parcellate.fit_transform(imgs[0])
assert isinstance(X, np.ndarray)
assert X.shape == (1, 20)
def test_transform_3d_input_images():
# test list of 3D images
data = np.ones((10, 11, 12))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
img = nibabel.Nifti1Image(data, affine=np.eye(4))
# list of 3
imgs = [img, img, img]
parcellate = Parcellations(method='ward', n_parcels=20)
X = parcellate.fit_transform(imgs)
assert_true(isinstance(X, list))
# (number of samples, number of features)
assert_equal(np.concatenate(X).shape, (3, 20))
# inverse transform
imgs_ = parcellate.inverse_transform(X)
assert_true(isinstance(imgs_, list))
# test single 3D image
X = parcellate.fit_transform(imgs[0])
assert_true(isinstance(X, np.ndarray))
assert_equal(X.shape, (1, 20))
def test_fit_transform():
rng = np.random.RandomState(42)
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds = rng.standard_normal(size=(10, 3))
confounds_list = [confounds, confounds, confounds]
for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
parcellator = Parcellations(method=method, n_parcels=5)
signals = parcellator.fit_transform(fmri_imgs)
assert parcellator.labels_img_ is not None
if method not in ['kmeans', 'rena']:
assert parcellator.connectivity_ is not None
assert parcellator.masker_ is not None
# fit_transform with confounds
signals = parcellator.fit_transform(fmri_imgs,
confounds=confounds_list)
assert isinstance(signals, list)
assert signals[0].shape == (10, 5)
def test_fit_transform():
rng = np.random.RandomState(0)
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds = rng.randn(*(10, 3))
confounds_list = [confounds, confounds, confounds]
for method in ['kmeans', 'ward', 'complete', 'average']:
parcellator = Parcellations(method=method, n_parcels=5)
signals = parcellator.fit_transform(fmri_imgs)
assert_true(parcellator.labels_img_ is not None)
if method != 'kmeans':
assert_true(parcellator.connectivity_ is not None)
assert_true(parcellator.masker_ is not None)
# fit_transform with confounds
signals = parcellator.fit_transform(fmri_imgs,
confounds=confounds_list)
assert_true(isinstance(signals, list))
assert_equal(signals[0].shape, (10, 5))