7dc35cbcb7d12dc1dc11688f22bb0ddda393847d,tests/test_track.py,,test_track_ensemble,#Any#Any#,112

Before Change

    // Load atlas parcellation (and its wm-gm interface reduced version for
    // seeding)
    atlas_data = np.array(nib.load(labels_im_file).dataobj).astype("uint16")
    atlas_data_wm_gm_int = np.asarray(nib.load(atlas_data_wm_gm_int).dataobj
                                      ).astype("uint16")

    // Build mask vector from atlas for later roi filtering
    parcels = []
    i = 0
    for roi_val in np.unique(atlas_data)[1:]:
        parcels.append(atlas_data == roi_val)
        i = i + 1

    dwi_img = nib.load(dwi_file)
    dwi_data = dwi_img.get_fdata()

    model, _ = track.reconstruction(conn_model, gtab, dwi_data, wm_in_dwi)

After Change

    dwi_data = dwi_img.get_fdata()

    temp_dir = tempfile.TemporaryDirectory()
    recon_path = temp_dir.name + "/model_file.npy"
    model, _ = track.reconstruction(conn_model, gtab, dwi_data, wm_in_dwi)
    np.save(recon_path, model)

    streamlines = track.track_ensemble(target_samples, atlas_data_wm_gm_int,
                                       labels_im_file,
                   recon_path, sphere, directget, curv_thr_list, step_list,

Instances