def METHOD_NAME(locator, internal_loads_df, indoor_comfort_df,
                          use_type_name, use_type_metadata, use_type_ratios_dict):
    """
    Takes a list of use-types and their respective ratios to aggregate and create a new use-type
    with schedules, internal loads and indoor comfort data
    :param schedules_path:
    :param internal_loads_df:
    :param indoor_comfort_df:
    :param use_type_name:
    :param use_type_metadata:
    :param use_type_ratios_dict:
    :return:
    """
    list_uses = use_type_ratios_dict.keys()
    list_var_names = [COLUMN_VAR_NAME_TEMPLATE.format(i) for i in range(len(use_type_ratios_dict))]
    list_var_values = [COLUMN_VAR_VAL_TEMPLATE.format(i) for i in range(len(use_type_ratios_dict))]
    # Creating required parameters
    properties_dict = {}
    for i, (k, v) in enumerate(use_type_ratios_dict.items()):
        properties_dict[list_var_names[i]] = k
        properties_dict[list_var_values[i]] = v
    properties_df = pd.DataFrame([properties_dict])
    occupant_densities = calculate_occupant_density(list_uses, internal_loads_df)
    print("Calculating internal loads...")
    new_internal_loads_df = calculate_mixed_loads(properties_df, internal_loads_df, occupant_densities, list_uses,
                                                  use_type_name, list_var_names, list_var_values)
    print("Calculating indoor comfort...")
    new_indoor_comfort_df = calculate_mixed_loads(properties_df, indoor_comfort_df, occupant_densities, list_uses,
                                                  use_type_name, list_var_names, list_var_values)
    prop_df_c = properties_df.copy()
    prop_df_c['Name'] = '0'  # Set a `Name` column as index for function to work
    prop_df_c.set_index('Name', inplace=True)
    schedule_data_all_uses = ScheduleData(locator)
    internal_loads = internal_loads_df.set_index('code')
    print("Calculating schedules...")
    schedule_new_data, schedule_complementary_data = calc_single_mixed_schedule(list_uses, occupant_densities,
                                                                                prop_df_c, internal_loads, '0',
                                                                                schedule_data_all_uses, list_var_names,
                                                                                list_var_values, use_type_metadata)
    print("Writing to disk...")
    use_type_properties_path = locator.get_database_use_types_properties()
    with pd.ExcelWriter(use_type_properties_path) as writer:
        new_internal_loads_df.to_excel(writer, sheet_name='INTERNAL_LOADS', index=False)
        new_indoor_comfort_df.to_excel(writer, sheet_name='INDOOR_COMFORT', index=False)
    schedule_path = os.path.join(locator.get_database_use_types_folder(), '{}.csv'.format(use_type_name))
    save_cea_schedule(schedule_new_data, schedule_complementary_data, schedule_path)