How to use the pymars.sampling.calculate_error function in pymars

"""
    species_errors = np.zeros(len(species_limbo))
    with TemporaryDirectory() as temp_dir:
        for idx, species in enumerate(species_limbo):
            test_model = trim(
                starting_model.filename, [species], f'reduced_model_{species}.cti', 
                phase_name=phase_name
                )
            test_model_file = soln2cti.write(
                test_model, f'reduced_model_{species}.cti', path=temp_dir
                )
            reduced_model_metrics = sample_metrics(
                test_model_file, ignition_conditions, phase_name=phase_name, 
                num_threads=num_threads
                )
            species_errors[idx] = calculate_error(metrics, reduced_model_metrics)
    
    return species_errors

return previous_model

    species_removed = [sp for sp in solution.species_names
                       if sp not in (species_retained + species_safe)
                       ]

    # Cut the exclusion list from the model.
    reduced_model = trim(model_file, species_removed, f'reduced_{model_file}')
    reduced_model_filename = soln2cti.write(
        reduced_model, f'reduced_{reduced_model.n_species}.cti', path=path
        )

    reduced_model_metrics = sample_metrics(
        reduced_model_filename, ignition_conditions, num_threads=num_threads, path=path
        )
    error = calculate_error(sampled_metrics, reduced_model_metrics)
    
    # If desired, now identify limbo species for future sensitivity analysis
    limbo_species = []
    if threshold_upper:
        species_retained += trim_drg(
            matrix, solution.species_names, species_targets, threshold_upper
            )
        limbo_species = [
            sp for sp in solution.species_names
            if sp not in (species_retained + species_safe + species_removed)
            ]

    return ReducedModel(
        model=reduced_model, filename=reduced_model_filename, 
        error=error, limbo_species=limbo_species
        )

):
        return previous_model

    # Cut the exclusion list from the model.
    reduced_model = trim(
        model_file, species_removed, f'reduced_{model_file}', phase_name=phase_name
        )
    reduced_model_filename = soln2cti.write(
        reduced_model, f'reduced_{reduced_model.n_species}.cti', path=path
        )

    reduced_model_metrics = sample_metrics(
        reduced_model_filename, ignition_conditions, phase_name=phase_name, 
        num_threads=num_threads, path=path
        )
    error = calculate_error(sampled_metrics, reduced_model_metrics)

    return ReducedModel(
        model=reduced_model, filename=reduced_model_filename, error=error
        )

return previous_model

    species_removed = [sp for sp in solution.species_names
                       if sp not in (species_retained + species_safe)
                       ]

    # Cut the exclusion list from the model.
    reduced_model = trim(model_file, species_removed, f'reduced_{model_file}')
    reduced_model_filename = soln2cti.write(
        reduced_model, f'reduced_{reduced_model.n_species}.cti', path=path
        )

    reduced_model_metrics = sample_metrics(
        reduced_model_filename, ignition_conditions, num_threads=num_threads, path=path
        )
    error = calculate_error(sampled_metrics, reduced_model_metrics)
    
    # If desired, now identify limbo species for future sensitivity analysis
    limbo_species = []
    if threshold_upper:
        species_retained += trim_pfa(
            matrix, solution.species_names, species_targets, threshold_upper
            )
        limbo_species = [
            sp for sp in solution.species_names
            if sp not in (species_retained + species_safe + species_removed)
            ]

    return ReducedModel(
        model=reduced_model, filename=reduced_model_filename, 
        error=error, limbo_species=limbo_species
        )

species_errors = np.delete(species_errors, idx)
            species_remove = species_limbo.pop(idx)

            test_model = trim(
                current_model.filename, [species_remove], f'reduced_model_{species_remove}.cti', 
                phase_name=phase_name
                )
            test_model_file = soln2cti.write(
                test_model, output_filename=f'reduced_model_{species_remove}.cti', path=temp_dir
                )

            reduced_model_metrics = sample_metrics(
                test_model_file, ignition_conditions, phase_name=phase_name, 
                num_threads=num_threads, path=path
                )
            error = calculate_error(initial_metrics, reduced_model_metrics)

            logging.info(f'{test_model.n_species:^17} | {species_remove:^17} | {error:^.2f}')

            # Ensure new error isn't too high
            if error > error_limit:
                break
            else:
                current_model = ReducedModel(model=test_model, filename=test_model_file, error=error)

            # If using the greedy algorithm, now need to reevaluate all species errors
            if algorithm_type == 'greedy':
                species_errors = evaluate_species_errors(
                    current_model, ignition_conditions, initial_metrics, species_limbo, 
                    phase_name=phase_name, num_threads=num_threads
                    )
                if min(species_errors) > error_limit: