How to use the histomicstk.preprocessing.color_normalization.reinhard_stats function in histomicstk

ts_metadata = ts.getMetadata()

        print json.dumps(ts_metadata, indent=2)

        is_wsi = ts_metadata['magnification'] is not None

        #
        # Compute colorspace statistics (mean, variance) for whole slide
        #
        wsi_mean = args.source_mu_lab
        wsi_stddev = args.source_std_lab

        if is_wsi:
            print('\n>> Computing mean and variance for whole slide ...\n')

            wsi_mean, wsi_stddev = htk_cnorm.reinhard_stats(
                img_path[i], args.sample_fraction, args.analysis_mag)

        #
        # Compute tissue/foreground mask at low-res for whole slide images
        #
        if is_wsi:

            print('\n>> Computing tissue/foreground mask at low-res ...\n')

            im_fgnd_mask_lres, fgnd_seg_scale = \
                cli_utils.segment_wsi_foreground_at_low_res(ts)

        #
        # Compute foreground fraction of tiles in parallel using Dask
        #
        tile_fgnd_frac_list = [1.0]

cli_utils.disp_time_hms(fgnd_frac_comp_time)))

    # =========================================================================
    # ========================= Compute reinhard stats ========================
    # =========================================================================
    src_mu_lab = None
    src_sigma_lab = None

    print('\n>> Computing reinhard color normalization stats ...\n')

    start_time = time.time()

    # src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
    #     args.inputImageFile, 0.01, magnification=args.analysis_mag,
    #     tissue_seg_mag=0.625)
    src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
        args.inputImageFile, 0.01, magnification=args.analysis_mag)

    print('Reinahrd stats')
    print(src_mu_lab, src_sigma_lab)

    rstats_time = time.time() - start_time

    print('Reinhard stats computation time = {}'.format(
        cli_utils.disp_time_hms(rstats_time)))

    # =========================================================================
    # ======================== Detect Nuclie in Parallel -  Dask ==============
    # =========================================================================
    print('\n>> Detecting cell ...\n')
    start_time = time.time()

print('Tile foreground fraction computation time = {}'.format(
            cli_utils.disp_time_hms(fgnd_frac_comp_time)))

    #
    # Compute reinhard stats for color normalization
    #
    src_mu_lab = None
    src_sigma_lab = None

    if is_wsi and process_whole_image:

        print('\n>> Computing reinhard color normalization stats ...\n')

        start_time = time.time()

        src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
            args.inputImageFile, 0.01, magnification=args.analysis_mag)

        rstats_time = time.time() - start_time

        print('Reinhard stats computation time = {}'.format(
            cli_utils.disp_time_hms(rstats_time)))

    #
    # Detect nuclei in parallel using Dask
    #
    print('\n>> Detecting nuclei ...\n')

    start_time = time.time()

    tile_nuclei_list = []

print('Tile foreground fraction computation time = {}'.format(
            cli_utils.disp_time_hms(fgnd_frac_comp_time)))

    #
    # Compute reinhard stats for color normalization
    #
    src_mu_lab = None
    src_sigma_lab = None

    if is_wsi and process_whole_image:

        print('\n>> Computing reinhard color normalization stats ...\n')

        start_time = time.time()

        src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
            args.inputImageFile, 0.01, magnification=args.analysis_mag)

        rstats_time = time.time() - start_time

        print('Reinhard stats computation time = {}'.format(
            cli_utils.disp_time_hms(rstats_time)))

    #
    # Detect and compute nuclei features in parallel using Dask
    #
    print('\n>> Detecting nuclei and computing features ...\n')

    start_time = time.time()

    tile_result_list = []

tile_magnification = ts_metadata['magnification']

        is_wsi = tile_magnification is not None

        #
        # Compute colorspace statistics (mean, variance) for whole slide
        #
        wsi_mean = args.source_mu_lab
        wsi_stddev = args.source_std_lab

        if is_wsi:

            print('\n>> Computing mean and variance for whole slide ...\n')

            wsi_mean, wsi_stddev = htk_cnorm.reinhard_stats(
                img_paths[i], args.sample_fraction, args.analysis_mag)

        #
        # Compute tissue/foreground mask at low-res for whole slide images
        #
        if is_wsi:

            print('\n>> Computing tissue/foreground mask at low-res ...\n')

            im_fgnd_mask_lres, fgnd_seg_scale = \
                cli_utils.segment_wsi_foreground_at_low_res(ts)

        #
        # Compute foreground fraction of tiles in parallel using Dask
        #
        tile_fgnd_frac_list = [1.0]

percent_fgnd_tiles = 100.0 * num_fgnd_tiles / num_tiles

            fgnd_frac_comp_time = time.time() - start_time

            print('Number of foreground tiles = {0:d} ({1:2f}%%)'.format(
                num_fgnd_tiles, percent_fgnd_tiles))

            print('Tile foreground fraction computation time = {}'.format(
                cli_utils.disp_time_hms(fgnd_frac_comp_time)))

            print('\n>> Computing reinhard color normalization stats ...\n')

            start_time = time.time()

            src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
                img_paths[i], 0.01, magnification=args.analysis_mag)

            rstats_time = time.time() - start_time

            print('Reinhard stats computation time = {}'.format(
                cli_utils.disp_time_hms(rstats_time)))

            #
            # Detect boundary and centroids in parallel using Dask
            #
            print('\n>> Detecting boundary and centroids ...\n')

            start_time = time.time()

            tile_result_list = []