def test_nifti_labels_masker(): # Check working of shape/affine checks shape1 = (13, 11, 12) affine1 = np.eye(4) shape2 = (12, 10, 14) affine2 = np.diag((1, 2, 3, 1)) n_regions = 9 length = 3 fmri11_img, mask11_img = generate_random_img(shape1, affine=affine1, length=length) fmri12_img, mask12_img = generate_random_img(shape1, affine=affine2, length=length) fmri21_img, mask21_img = generate_random_img(shape2, affine=affine1, length=length) labels11_img = testing.generate_labeled_regions(shape1, affine=affine1, n_regions=n_regions) mask_img_4d = nibabel.Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8), affine=np.diag((4, 4, 4, 1))) # verify that 4D mask arguments are refused masker = NiftiLabelsMasker(labels11_img, mask_img=mask_img_4d) testing.assert_raises_regex( DimensionError, "Input data has incompatible dimensionality: " "Expected dimension is 3D and you provided " "a 4D image.", masker.fit) # check exception when transform() called without prior fit() masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None) testing.assert_raises_regex(ValueError, 'has not been fitted. ', masker11.transform, fmri11_img) # No exception raised here signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask11_img, resampling_target=None) signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) # Test all kinds of mismatch between shapes and between affines masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None) masker11.fit() assert_raises(ValueError, masker11.transform, fmri12_img) assert_raises(ValueError, masker11.transform, fmri21_img) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask12_img, resampling_target=None) assert_raises(ValueError, masker11.fit) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask21_img, resampling_target=None) assert_raises(ValueError, masker11.fit) # Transform, with smoothing (smoke test) masker11 = NiftiLabelsMasker(labels11_img, smoothing_fwhm=3, resampling_target=None) signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) masker11 = NiftiLabelsMasker(labels11_img, smoothing_fwhm=3, resampling_target=None) signals11 = masker11.fit_transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) testing.assert_raises_regex( ValueError, 'has not been fitted. ', NiftiLabelsMasker(labels11_img).inverse_transform, signals11) # Call inverse transform (smoke test) fmri11_img_r = masker11.inverse_transform(signals11) assert_equal(fmri11_img_r.shape, fmri11_img.shape) np.testing.assert_almost_equal(get_affine(fmri11_img_r), get_affine(fmri11_img))
def test_nifti_labels_masker_resampling(): # Test resampling in NiftiLabelsMasker shape1 = (10, 11, 12) affine = np.eye(4) # mask shape2 = (16, 17, 18) # labels shape3 = (13, 14, 15) n_regions = 9 length = 3 # With data of the same affine fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) labels33_img = testing.generate_labeled_regions(shape3, n_regions, affine=affine) # Test error checking assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="mask") assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="invalid") # Target: labels masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(get_affine(masker.labels_img_), get_affine(labels33_img)) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(get_affine(masker.mask_img_), get_affine(masker.labels_img_)) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(get_affine(fmri11_img_r), get_affine(masker.labels_img_)) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length, ))) # Test with clipped labels: mask does not contain all labels. # Shapes do matter in that case, because there is some resampling # taking place. shape1 = (10, 11, 12) # fmri shape2 = (8, 9, 10) # mask shape3 = (16, 18, 20) # maps n_regions = 9 length = 21 fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) # Target: labels labels33_img = testing.generate_labeled_regions(shape3, n_regions, affine=affine) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(get_affine(masker.labels_img_), get_affine(labels33_img)) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(get_affine(masker.mask_img_), get_affine(masker.labels_img_)) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) uniq_labels = np.unique(masker.labels_img_.get_data()) assert_equal(uniq_labels[0], 0) assert_equal(len(uniq_labels) - 1, n_regions) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) # Some regions have been clipped. Resulting signal must be zero assert_less((transformed.var(axis=0) == 0).sum(), n_regions) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(get_affine(fmri11_img_r), get_affine(masker.labels_img_)) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length, ))) # Test with data and atlas of different shape: the atlas should be # resampled to the data shape22 = (5, 5, 6) affine2 = 2 * np.eye(4) affine2[-1, -1] = 1 fmri22_img, _ = generate_random_img(shape22, affine=affine2, length=length) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img) masker.fit_transform(fmri22_img) np.testing.assert_array_equal(get_affine(masker._resampled_labels_img_), affine2) # Test with filenames with testing.write_tmp_imgs(fmri22_img) as filename: masker = NiftiLabelsMasker(labels33_img, resampling_target='data') masker.fit_transform(filename)
def test_nifti_labels_masker(): # Check working of shape/affine checks shape1 = (13, 11, 12) affine1 = np.eye(4) shape2 = (12, 10, 14) affine2 = np.diag((1, 2, 3, 1)) n_regions = 9 length = 3 fmri11_img, mask11_img = generate_random_img(shape1, affine=affine1, length=length) fmri12_img, mask12_img = generate_random_img(shape1, affine=affine2, length=length) fmri21_img, mask21_img = generate_random_img(shape2, affine=affine1, length=length) labels11_img = testing.generate_labeled_regions(shape1, affine=affine1, n_regions=n_regions) mask_img_4d = nibabel.Nifti1Image(np.ones((2, 2, 2, 2), dtype=np.int8), affine=np.diag((4, 4, 4, 1))) # verify that 4D mask arguments are refused masker = NiftiLabelsMasker(labels11_img, mask_img=mask_img_4d) testing.assert_raises_regex(DimensionError, "Data must be a 3D", masker.fit) # check exception when transform() called without prior fit() masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None) testing.assert_raises_regex( ValueError, 'has not been fitted. ', masker11.transform, fmri11_img) # No exception raised here signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask11_img, resampling_target=None) signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) # Test all kinds of mismatch between shapes and between affines masker11 = NiftiLabelsMasker(labels11_img, resampling_target=None) masker11.fit() assert_raises(ValueError, masker11.transform, fmri12_img) assert_raises(ValueError, masker11.transform, fmri21_img) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask12_img, resampling_target=None) assert_raises(ValueError, masker11.fit) masker11 = NiftiLabelsMasker(labels11_img, mask_img=mask21_img, resampling_target=None) assert_raises(ValueError, masker11.fit) # Transform, with smoothing (smoke test) masker11 = NiftiLabelsMasker(labels11_img, smoothing_fwhm=3, resampling_target=None) signals11 = masker11.fit().transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) masker11 = NiftiLabelsMasker(labels11_img, smoothing_fwhm=3, resampling_target=None) signals11 = masker11.fit_transform(fmri11_img) assert_equal(signals11.shape, (length, n_regions)) testing.assert_raises_regex( ValueError, 'has not been fitted. ', NiftiLabelsMasker(labels11_img).inverse_transform, signals11) # Call inverse transform (smoke test) fmri11_img_r = masker11.inverse_transform(signals11) assert_equal(fmri11_img_r.shape, fmri11_img.shape) np.testing.assert_almost_equal(fmri11_img_r.get_affine(), fmri11_img.get_affine())
def test_nifti_labels_masker_resampling(): # Test resampling in NiftiLabelsMasker shape1 = (10, 11, 12) affine = np.eye(4) # mask shape2 = (16, 17, 18) # labels shape3 = (13, 14, 15) n_regions = 9 length = 3 # With data of the same affine fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) labels33_img = testing.generate_labeled_regions(shape3, n_regions, affine=affine) # Test error checking assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="mask") assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="invalid") # Target: labels masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.get_affine(), labels33_img.get_affine()) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.get_affine(), masker.labels_img_.get_affine()) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.get_affine(), masker.labels_img_.get_affine()) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length,))) # Test with clipped labels: mask does not contain all labels. # Shapes do matter in that case, because there is some resampling # taking place. shape1 = (10, 11, 12) # fmri shape2 = (8, 9, 10) # mask shape3 = (16, 18, 20) # maps n_regions = 9 length = 21 fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) # Target: labels labels33_img = testing.generate_labeled_regions(shape3, n_regions, affine=affine) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.get_affine(), labels33_img.get_affine()) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.get_affine(), masker.labels_img_.get_affine()) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) uniq_labels = np.unique(masker.labels_img_.get_data()) assert_equal(uniq_labels[0], 0) assert_equal(len(uniq_labels) - 1, n_regions) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) # Some regions have been clipped. Resulting signal must be zero assert_less((transformed.var(axis=0) == 0).sum(), n_regions) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.get_affine(), masker.labels_img_.get_affine()) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length,))) # Test with data and atlas of different shape: the atlas should be # resampled to the data shape22 = (5, 5, 6) affine2 = 2 * np.eye(4) affine2[-1, -1] = 1 fmri22_img, _ = generate_random_img(shape22, affine=affine2, length=length) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img) masker.fit_transform(fmri22_img) np.testing.assert_array_equal( masker._resampled_labels_img_.get_affine(), affine2) # Test with filenames with testing.write_tmp_imgs(fmri22_img) as filename: masker = NiftiLabelsMasker(labels33_img, resampling_target='data') masker.fit_transform(filename)
def test_nifti_labels_masker_resampling(): # Test resampling in NiftiLabelsMasker shape1 = (10, 11, 12) affine = np.eye(4) # mask shape2 = (16, 17, 18) # labels shape3 = (13, 14, 15) n_regions = 9 length = 3 # With data of the same affine fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) labels33_img = data_gen.generate_labeled_regions(shape3, n_regions, affine=affine) # Test error checking assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="mask") assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="invalid") # Target: labels masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.affine, labels33_img.affine) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.affine, masker.labels_img_.affine) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.affine, masker.labels_img_.affine) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length, ))) # Test with clipped labels: mask does not contain all labels. # Shapes do matter in that case, because there is some resampling # taking place. shape1 = (10, 11, 12) # fmri shape2 = (8, 9, 10) # mask shape3 = (16, 18, 20) # maps n_regions = 9 length = 21 fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) # Target: labels labels33_img = data_gen.generate_labeled_regions(shape3, n_regions, affine=affine) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.affine, labels33_img.affine) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.affine, masker.labels_img_.affine) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) uniq_labels = np.unique(masker.labels_img_.get_data()) assert_equal(uniq_labels[0], 0) assert_equal(len(uniq_labels) - 1, n_regions) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) # Some regions have been clipped. Resulting signal must be zero assert_less((transformed.var(axis=0) == 0).sum(), n_regions) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.affine, masker.labels_img_.affine) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length, ))) # Test with data and atlas of different shape: the atlas should be # resampled to the data shape22 = (5, 5, 6) affine2 = 2 * np.eye(4) affine2[-1, -1] = 1 fmri22_img, _ = generate_random_img(shape22, affine=affine2, length=length) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img) masker.fit_transform(fmri22_img) np.testing.assert_array_equal(masker._resampled_labels_img_.affine, affine2) # Test with filenames with testing.write_tmp_imgs(fmri22_img) as filename: masker = NiftiLabelsMasker(labels33_img, resampling_target='data') masker.fit_transform(filename) # test labels masker with resampling target in 'data', 'labels' to return # resampled labels having number of labels equal with transformed shape of # 2nd dimension. This tests are added based on issue #1673 in Nilearn shape = (13, 11, 12) affine = np.eye(4) * 2 fmri_img, _ = generate_random_img(shape, affine=affine, length=21) labels_img = data_gen.generate_labeled_regions((9, 8, 6), affine=np.eye(4), n_regions=10) for resampling_target in ['data', 'labels']: masker = NiftiLabelsMasker(labels_img=labels_img, resampling_target=resampling_target) transformed = masker.fit_transform(fmri_img) resampled_labels_img = masker._resampled_labels_img_ n_resampled_labels = len(np.unique(resampled_labels_img.get_data())) assert_equal(n_resampled_labels - 1, transformed.shape[1]) # inverse transform compressed_img = masker.inverse_transform(transformed) # Test that compressing the image a second time should yield an image # with the same data as compressed_img. transformed2 = masker.fit_transform(fmri_img) # inverse transform again compressed_img2 = masker.inverse_transform(transformed2) np.testing.assert_array_equal(compressed_img.get_data(), compressed_img2.get_data())
def test_nifti_labels_masker_resampling(): # Test resampling in NiftiLabelsMasker shape1 = (10, 11, 12) affine = np.eye(4) # mask shape2 = (16, 17, 18) # labels shape3 = (13, 14, 15) n_regions = 9 length = 3 # With data of the same affine fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) labels33_img = data_gen.generate_labeled_regions(shape3, n_regions, affine=affine) # Test error checking assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="mask") assert_raises(ValueError, NiftiLabelsMasker, labels33_img, resampling_target="invalid") # Target: labels masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.affine, labels33_img.affine) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.affine, masker.labels_img_.affine) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.affine, masker.labels_img_.affine) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length,))) # Test with clipped labels: mask does not contain all labels. # Shapes do matter in that case, because there is some resampling # taking place. shape1 = (10, 11, 12) # fmri shape2 = (8, 9, 10) # mask shape3 = (16, 18, 20) # maps n_regions = 9 length = 21 fmri11_img, _ = generate_random_img(shape1, affine=affine, length=length) _, mask22_img = generate_random_img(shape2, affine=affine, length=length) # Target: labels labels33_img = data_gen.generate_labeled_regions(shape3, n_regions, affine=affine) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img, resampling_target="labels") masker.fit() np.testing.assert_almost_equal(masker.labels_img_.affine, labels33_img.affine) assert_equal(masker.labels_img_.shape, labels33_img.shape) np.testing.assert_almost_equal(masker.mask_img_.affine, masker.labels_img_.affine) assert_equal(masker.mask_img_.shape, masker.labels_img_.shape[:3]) uniq_labels = np.unique(masker.labels_img_.get_data()) assert_equal(uniq_labels[0], 0) assert_equal(len(uniq_labels) - 1, n_regions) transformed = masker.transform(fmri11_img) assert_equal(transformed.shape, (length, n_regions)) # Some regions have been clipped. Resulting signal must be zero assert_less((transformed.var(axis=0) == 0).sum(), n_regions) fmri11_img_r = masker.inverse_transform(transformed) np.testing.assert_almost_equal(fmri11_img_r.affine, masker.labels_img_.affine) assert_equal(fmri11_img_r.shape, (masker.labels_img_.shape[:3] + (length,))) # Test with data and atlas of different shape: the atlas should be # resampled to the data shape22 = (5, 5, 6) affine2 = 2 * np.eye(4) affine2[-1, -1] = 1 fmri22_img, _ = generate_random_img(shape22, affine=affine2, length=length) masker = NiftiLabelsMasker(labels33_img, mask_img=mask22_img) masker.fit_transform(fmri22_img) np.testing.assert_array_equal(masker._resampled_labels_img_.affine, affine2) # Test with filenames with testing.write_tmp_imgs(fmri22_img) as filename: masker = NiftiLabelsMasker(labels33_img, resampling_target='data') masker.fit_transform(filename) # test labels masker with resampling target in 'data', 'labels' to return # resampled labels having number of labels equal with transformed shape of # 2nd dimension. This tests are added based on issue #1673 in Nilearn shape = (13, 11, 12) affine = np.eye(4) * 2 fmri_img, _ = generate_random_img(shape, affine=affine, length=21) labels_img = data_gen.generate_labeled_regions((9, 8, 6), affine=np.eye(4), n_regions=10) for resampling_target in ['data', 'labels']: masker = NiftiLabelsMasker(labels_img=labels_img, resampling_target=resampling_target) transformed = masker.fit_transform(fmri_img) resampled_labels_img = masker._resampled_labels_img_ n_resampled_labels = len(np.unique(resampled_labels_img.get_data())) assert_equal(n_resampled_labels - 1, transformed.shape[1]) # inverse transform compressed_img = masker.inverse_transform(transformed) # Test that compressing the image a second time should yield an image # with the same data as compressed_img. transformed2 = masker.fit_transform(fmri_img) # inverse transform again compressed_img2 = masker.inverse_transform(transformed2) np.testing.assert_array_equal(compressed_img.get_data(), compressed_img2.get_data())