How to use the histomicstk.cli.utils.get_stain_matrix function in histomicstk

tile_position=tile_position,
        format=large_image.tilesource.TILE_FORMAT_NUMPY,
        **it_kwargs)

    # get tile image
    im_tile = tile_info['tile'][:, :, :3]

    # perform color normalization
    im_nmzd = htk_cnorm.reinhard(im_tile,
                                 args.reference_mu_lab,
                                 args.reference_std_lab,
                                 src_mu=src_mu_lab,
                                 src_sigma=src_sigma_lab)

    # perform color decovolution
    w = cli_utils.get_stain_matrix(args)

    im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains

    im_nuclei_stain = im_stains[:, :, 0].astype(np.float)

    # segment nuclear foreground
    im_nuclei_fgnd_mask = im_nuclei_stain < args.foreground_threshold

    # segment nuclei
    im_nuclei_seg_mask = htk_nuclear.detect_nuclei_kofahi(
        im_nuclei_stain,
        im_nuclei_fgnd_mask,
        args.min_radius,
        args.max_radius,
        args.min_nucleus_area,
        args.local_max_search_radius

# Read Input Image
    print('>> Reading input image')

    print(args.inputImageFile)

    ts = large_image.getTileSource(args.inputImageFile)

    im_input = ts.getRegion(
        format=large_image.tilesource.TILE_FORMAT_NUMPY,
        **utils.get_region_dict(args.region, args.maxRegionSize, ts)
    )[0]

    # Create stain matrix
    print('>> Creating stain matrix')

    w = utils.get_stain_matrix(args)
    print(w)

    # Perform color deconvolution
    print('>> Performing color deconvolution')
    im_stains = htk_cd.color_deconvolution(im_input, w).Stains

    # write stain images to output
    print('>> Outputting individual stain images')

    print(args.outputStainImageFile_1)
    skimage.io.imsave(args.outputStainImageFile_1, im_stains[:, :, 0])

    print(args.outputStainImageFile_2)
    skimage.io.imsave(args.outputStainImageFile_2, im_stains[:, :, 1])

    print(args.outputStainImageFile_3)

def main(args):
    args = utils.splitArgs(args)
    args.snmf.I_0 = numpy.array(args.snmf.I_0)

    print(">> Starting Dask cluster and sampling pixels")
    utils.create_dask_client(args.dask)
    sample = utils.sample_pixels(args.sample)

    # Create stain matrix
    print('>> Creating stain matrix')

    args.snmf.w_init = utils.get_stain_matrix(args.stains, 2)

    print(args.snmf.w_init)

    # Perform color deconvolution
    print('>> Performing color deconvolution')

    w_est = htk_cdeconv.rgb_separate_stains_xu_snmf(sample.T, **vars(args.snmf))
    w_est = htk_cdeconv.complement_stain_matrix(w_est)

    with open(args.returnParameterFile, 'w') as f:
        for i, stain in enumerate(w_est.T):
            f.write('stainColor_{} = {}\n'.format(i+1, ','.join(map(str, stain))))

tile_position=tile_position,
        format=large_image.tilesource.TILE_FORMAT_NUMPY,
        **it_kwargs)

    # get tile image
    im_tile = tile_info['tile'][:, :, :3]

    # perform color normalization
    im_nmzd = htk_cnorm.reinhard(im_tile,
                                 args.reference_mu_lab,
                                 args.reference_std_lab,
                                 src_mu=src_mu_lab,
                                 src_sigma=src_sigma_lab)

    # perform color decovolution
    w = cli_utils.get_stain_matrix(args)

    im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains

    im_nuclei_stain = im_stains[:, :, 0].astype(np.float)

    # segment nuclear foreground
    im_nuclei_fgnd_mask = im_nuclei_stain < args.foreground_threshold

    # segment nuclei
    im_nuclei_seg_mask = htk_nuclear.detect_nuclei_kofahi(
        im_nuclei_stain,
        im_nuclei_fgnd_mask,
        args.min_radius,
        args.max_radius,
        args.min_nucleus_area,
        args.local_max_search_radius