152314d80495bd34054b329fa169bb2dca399400,dipy/stats/analysis.py,,bundle_analysis,#Any#Any#Any#Any#Any#Any#Any#Any#,257

Before Change

            indx = assignment_map(bundles, mbundles, no_disks)
    
            //metric_files_names = os.listdir(metric_folder)
            metric_files_names = ["csd_peaks.pam5"]
            //["csa_peaks.pam5", "csd_peaks.pam5"] 
            //["fa.nii.gz", "md.nii.gz", "ad.nii.gz", "rd.nii.gz"]
            _, 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)
                if bm[0:2]=="-C":
                    print("it"s a CC, miss!")
                else:
                    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)
                        peak_values(transformed_orig_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,

After Change

            //metric_files_names = os.listdir(metric_folder)
            metric_files_names_dti = glob(os.path.join(metric_folder,"*.nii.gz"))
            
            metric_files_names_csa = glob(os.path.join(metric_folder,"*.pam5"))
            //metric_files_names = ["csd_peaks.pam5"]
            //["csa_peaks.pam5", "csd_peaks.pam5"] 
            //["fa.nii.gz", "md.nii.gz", "ad.nii.gz", "rd.nii.gz"]
            
            
            _, affine = load_nifti(metric_files_names_dti[0])
    
            affine_r = np.linalg.inv(affine)
            transformed_orig_bundles = transform_streamlines(orig_bundles,
                                                             affine_r)
    
            for mn in range(len(metric_files_names_dti)):
    
                
                fm = metric_files_names_dti[mn][:-7]
                bm = mb[io][14:-4]
                print("bm = ", bm)

                dt = dict()
                metric_name = os.path.join(metric_folder,
                                           metric_files_names_dti[mn])
    
                print("metric = ", metric_files_names_dti[mn])

                metric, _ = load_nifti(metric_name)

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


            for mn in range(len(metric_files_names_csa)):
                
                fm = metric_files_names_csa[mn][:3]
                metric = load_peaks(metric_name)
                //peak_values(bundles, metric, dt, fm, bm, subject, group,
                //            ind, out_dir)
                peak_values(transformed_orig_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,

Instances