255c1bbfbccef1c96c1e102335752e962816e31f,nilearn/tests/test_parcellations.py,,test_parcellations_transform_single_nifti_image,#,104

Before Change

    fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))

    for method in ["kmeans", "ward", "complete", "average"]:
        parcellator = Parcellations(method=method, n_parcels=parcels)
        parcellator.fit(fmri_img)
        // transform to signals
        signals = parcellator.transform(fmri_img)
        // Test if the signals extracted are of same shape as inputs
        // Here, we take index 0 since we return list even for single subject
        assert_equal(signals[0].shape, (fmri_img.shape[3], parcels))


def test_parcellations_transform_multi_nifti_images():
    data = np.ones((10, 11, 12, 10))
    data[6, 7, 8] = 2
    data[9, 10, 11] = 3

After Change

    fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))

    if LooseVersion(sklearn.__version__) >= LooseVersion("0.15"):
        for method in ["kmeans", "ward", "complete", "average"]:
            parcellator = Parcellations(method=method, n_parcels=parcels)
            parcellator.fit(fmri_img)
            // transform to signals
            signals = parcellator.transform(fmri_img)
            // Test if the signals extracted are of same shape as inputs
            // Here, we take index 0 since we return list even for single
            // subject
            assert_equal(signals[0].shape, (fmri_img.shape[3], parcels))
    else:
        parcellator2 = Parcellations(method="ward", n_parcels=parcels)
        parcellator2.fit(fmri_img)
        signals2 = parcellator.transform(fmri_img)
        assert_equal(signals[0].shape, (fmri_img.shape[3], parcels))


def test_parcellations_transform_multi_nifti_images():
    data = np.ones((10, 11, 12, 10))
    data[6, 7, 8] = 2
    data[9, 10, 11] = 3

Instances