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))