39e8146b3d7354d1e34cd41a4eca19e70c053a4a,dipy/stats/analysis.py,,bundle_analysis,#Any#Any#Any#Any#Any#Any#Any#Any#,169

Before Change

    for io in range(n):
        mbundles, _ = load_trk(os.path.join(model_bundle_folder, mb[io]))
        bundles, _ = load_trk(os.path.join(bundle_folder, bd[io]))
        orig_bundles, _ = load_trk(os.path.join(orig_bundle_folder,
                                   org_bd[io]))

        mbundle_streamlines = set_number_of_points(mbundles,
                                                   nb_points=no_disks)

After Change

    


    t = time()

    dt = dict()

    //mb = os.listdir(model_bundle_folder)
    mb = glob(model_bundle_folder+"/*.trk")
    
    mb.sort()
    //bd = os.listdir(bundle_folder)
    //bd.sort()
    
    bd = glob(bundle_folder+"/*.trk")
    bd.sort()
    
    //org_bd = os.listdir(orig_bundle_folder)
    org_bd = glob(orig_bundle_folder+"/*.trk")
    org_bd.sort()
    n = len(org_bd)

    for io in range(n):
        mbundles, _ = load_trk(mb[io]) //os.path.join(model_bundle_folder, mb[io]))
        bundles, _ = load_trk(bd[io]) //os.path.join(bundle_folder, bd[io]))
        orig_bundles, _ = load_trk(org_bd[io])//os.path.join(orig_bundle_folder,
                                   //org_bd[io]))

        mbundle_streamlines = set_number_of_points(mbundles,
                                                   nb_points=no_disks)

        metric = AveragePointwiseEuclideanMetric()
        qb = QuickBundles(threshold=25., metric=metric)
        clusters = qb.cluster(mbundle_streamlines)
        centroids = Streamlines(clusters.centroids)

        print("Number of centroids ", len(centroids.data))
        print("Model bundle ", mb[io])
        print("Number of streamlines in bundle in common space ",
              len(bundles))
        print("Number of streamlines in bundle in original space ",
              len(orig_bundles))

        _, indx = cKDTree(centroids.data, 1,
                          copy_data=True).query(bundles.data, k=1)

        //metric_files_names = os.listdir(metric_folder)
        metric_files_names = ["fa.nii.gz", "md.nii.gz", "csd_peaks.pam5"]
        _, affine = load_nifti(os.path.join(metric_folder, "fa.nii.gz"))

        affine_r = np.linalg.inv(affine)
        transformed_orig_bundles = transform_streamlines(orig_bundles,
                                                         affine_r)

        for mn in range(0, len(metric_files_names)):

            ind = np.array(indx)
            fm = metric_files_names[mn][:-7]
            bm = mb[io][14:-4]
            print("bm = ", bm)
            dt = dict()
            metric_name = os.path.join(metric_folder,
                                       metric_files_names[mn])

            print("metric = ", metric_files_names[mn])
            if metric_files_names[mn][-7:] == ".nii.gz":
                metric, _ = load_nifti(metric_name)

                dti_measures(transformed_orig_bundles, metric, dt, fm,
                             bm, subject, group, ind, out_dir)

            else:
                fm = metric_files_names[mn][:3]
                metric = load_peaks(metric_name)
                peak_values(bundles, metric, dt, fm, bm, subject, group,
                            ind, out_dir)


    print("total time taken in minutes = ", (-t + time())/60)

def gaussian_weights(bundle, n_points=100, return_mahalnobis=False,
                     stat=np.mean):

Instances