# Source code for pipelines.data_integration

    import sys
    import os

    try:
        thisFilePath = os.path.abspath(__file__)
    except NameError:
        print("Error: __file__ is not available. Ensure the script is being run from a file.")
        print("[Notice] Path to DIMA package may not be resolved properly.")
        thisFilePath = os.getcwd()  # Use current directory or specify a default

    dimaPath = os.path.normpath(os.path.join(thisFilePath, "..",'..'))  # Move up to project root

    if dimaPath not in sys.path:  # Avoid duplicate entries
        sys.path.append(dimaPath)

    import yaml
    import logging
    from datetime import datetime
    # Importing chain class from itertools
    from itertools import chain

    # Import DIMA modules
    import src.hdf5_writer as hdf5_lib
    import utils.g5505_utils as utils
    from instruments.readers import filereader_registry

    allowed_file_extensions = filereader_registry.file_extensions

    def _generate_datetime_dict(datetime_steps):
        """ Generate the datetime augment dictionary from datetime steps. """
        datetime_augment_dict = {}
        for datetime_step in datetime_steps:
            #tmp = datetime.strptime(datetime_step, '%Y-%m-%d %H-%M-%S')
            datetime_augment_dict[datetime_step] = [
                datetime_step.strftime('%Y-%m-%d'), datetime_step.strftime('%Y_%m_%d'), datetime_step.strftime('%Y.%m.%d'), datetime_step.strftime('%Y%m%d')
            ]
        return datetime_augment_dict

    def load_config_and_setup_logging(yaml_config_file_path, log_dir):
        """Load YAML configuration file, set up logging, and validate required keys and datetime_steps."""

        # Define required keys
        required_keys = [
            'experiment', 'contact', 'input_file_directory', 'output_file_directory',
            'instrument_datafolder', 'project', 'actris_level'
        ]

        # Supported integration modes
        supported_integration_modes = ['collection', 'single_experiment']

        # Set up logging
        date = utils.created_at("%Y_%m").replace(":", "-")
        utils.setup_logging(log_dir, f"integrate_data_sources_{date}.log")

        # Load YAML configuration file
        with open(yaml_config_file_path, 'r') as stream:
            try:
                config_dict = yaml.load(stream, Loader=yaml.FullLoader)
            except yaml.YAMLError as exc:
                logging.error("Error loading YAML file: %s", exc)
                raise ValueError(f"Failed to load YAML file: {exc}")

        # Check if required keys are present
        missing_keys = [key for key in required_keys if key not in config_dict]
        if missing_keys:
            raise KeyError(f"Missing required keys in YAML configuration: {missing_keys}")

        # Validate integration_mode
        integration_mode = config_dict.get('integration_mode', 'N/A')  # Default to 'collection'
        if integration_mode not in supported_integration_modes:
            raise RuntimeWarning(
                f"Unsupported integration_mode '{integration_mode}'. Supported modes are {supported_integration_modes}. Setting '{integration_mode}' to 'single_experiment'."
                )

        # Validate datetime_steps format if it exists
        if 'datetime_steps' in config_dict:
            datetime_steps = config_dict['datetime_steps']
            expected_format = '%Y-%m-%d %H-%M-%S'

            # Check if datetime_steps is a list or a falsy value
            if datetime_steps and not isinstance(datetime_steps, list):
                raise TypeError(f"datetime_steps should be a list of strings or a falsy value (None, empty), but got {type(datetime_steps)}")

            for step_idx, step in enumerate(datetime_steps):
                try:
                    # Attempt to parse the datetime to ensure correct format
                    config_dict['datetime_steps'][step_idx] = datetime.strptime(step, expected_format)
                except ValueError:
                    raise ValueError(f"Invalid datetime format for '{step}'. Expected format: {expected_format}")
            # Augment datatime_steps list as a dictionary. This to speed up single-experiment file generation
            config_dict['datetime_steps_dict'] = _generate_datetime_dict(datetime_steps)
        else:
            # If datetime_steps is not present, set the integration mode to 'collection'
            logging.info("datetime_steps missing, setting integration_mode to 'collection'.")
            config_dict['integration_mode'] = 'collection'

        # Validate filename_format if defined
        if 'filename_format' in config_dict:
            if not isinstance(config_dict['filename_format'], str):
                raise ValueError(f'"Specified filename_format needs to be of String type" ')

            # Split the string and check if each key exists in config_dict
            keys = [key.strip() for key in config_dict['filename_format'].split(',')]
            missing_keys = [key for key in keys if key not in config_dict]

            # If there are any missing keys, raise an assertion error
            # assert not missing_keys, f'Missing key(s) in config_dict: {", ".join(missing_keys)}'
            if not missing_keys:
                config_dict['filename_format'] = ','.join(keys)
            else:
                config_dict['filename_format'] = None
                print(f'"filename_format" should contain comma-separated keys that match existing keys in the YAML config file.')
                print('Setting "filename_format" as None')
        else:
            config_dict['filename_format'] = None

        # Compute complementary metadata elements

        # Create output filename prefix
        if not config_dict['filename_format']: # default behavior
            config_dict['filename_prefix'] = '_'.join([config_dict[key] for key in ['experiment', 'contact']])
        else:
            config_dict['filename_prefix'] = '_'.join([config_dict[key] for key in config_dict['filename_format'].split(sep=',')])

        # Set default dates from datetime_steps if not provided
        current_date = datetime.now().strftime('%Y-%m-%d')
        dates = config_dict.get('datetime_steps',[])
        if not config_dict.get('dataset_startdate'):
            config_dict['dataset_startdate'] = min(config_dict['datetime_steps']).strftime('%Y-%m-%d') if dates else current_date  # Earliest datetime step

        if not config_dict.get('dataset_enddate'):
            config_dict['dataset_enddate'] = max(config_dict['datetime_steps']).strftime('%Y-%m-%d') if dates else current_date # Latest datetime step

        config_dict['expected_datetime_format'] = '%Y-%m-%d %H-%M-%S'

        return config_dict

    def copy_subtree_and_create_hdf5(src, dst, select_dir_keywords, select_file_keywords, allowed_file_extensions, root_metadata_dict):

        """Helper function to copy directory with constraints and create HDF5."""
        src = src.replace(os.sep,'/')
        dst = dst.replace(os.sep,'/')

        logging.info("Creating constrained copy of the experimental campaign folder %s at: %s", src, dst)

        path_to_files_dict = utils.copy_directory_with_contraints(src, dst, select_dir_keywords, select_file_keywords, allowed_file_extensions)
        logging.info("Finished creating a copy of the experimental campaign folder tree at: %s", dst)

        logging.info("Creating HDF5 file at: %s", dst)
        hdf5_path = hdf5_lib.create_hdf5_file_from_filesystem_path(dst, path_to_files_dict, select_dir_keywords, root_metadata_dict)
        logging.info("Completed creation of HDF5 file %s at: %s", hdf5_path, dst)

        return hdf5_path

    def run_pipeline(path_to_config_yamlFile, log_dir='logs/'):

        """Integrates data sources specified by the input configuration file into HDF5 files.

        Parameters:
            yaml_config_file_path (str): Path to the YAML configuration file.
            log_dir (str): Directory to save the log file.

        Returns:
            list: List of Paths to the created HDF5 file(s).
        """

        config_dict = load_config_and_setup_logging(path_to_config_yamlFile, log_dir)

        path_to_input_dir = config_dict['input_file_directory']
        path_to_output_dir = config_dict['output_file_directory']
        select_dir_keywords = config_dict['instrument_datafolder']

        # Define root folder metadata dictionary
        root_metadata_dict = {key : config_dict[key] for key in ['project', 'experiment', 'contact', 'actris_level']}

        # Get dataset start and end dates
        dataset_startdate = config_dict['dataset_startdate']
        dataset_enddate = config_dict['dataset_enddate']

        # Determine mode and process accordingly
        output_filename_path = []
        campaign_name_template = lambda filename_prefix, suffix: '_'.join([filename_prefix, suffix])
        date_str = f'{dataset_startdate}_{dataset_enddate}'

        # Create path to new raw datafolder and standardize with forward slashes
        path_to_rawdata_folder = os.path.join(
            path_to_output_dir, 'collection_' + campaign_name_template(config_dict['filename_prefix'], date_str), "").replace(os.sep, '/')

        # Process individual datetime steps if available, regardless of mode
        if config_dict.get('datetime_steps_dict', {}):
            # Single experiment mode
            for datetime_step, file_keywords in config_dict['datetime_steps_dict'].items():
                date_str = datetime_step.strftime('%Y-%m-%d')
                single_campaign_name = campaign_name_template(config_dict['filename_prefix'], date_str)
                path_to_rawdata_subfolder = os.path.join(path_to_rawdata_folder, single_campaign_name, "")

                path_to_integrated_stepwise_hdf5_file = copy_subtree_and_create_hdf5(
                                                            path_to_input_dir, path_to_rawdata_subfolder, select_dir_keywords,
                                                            file_keywords, allowed_file_extensions, root_metadata_dict)

                output_filename_path.append(path_to_integrated_stepwise_hdf5_file)

            # Collection mode processing if specified
            if 'collection' in config_dict.get('integration_mode', 'single_experiment'):
                path_to_filenames_dict = {path_to_rawdata_folder: [os.path.basename(path) for path in output_filename_path]} if output_filename_path else {}
                hdf5_path = hdf5_lib.create_hdf5_file_from_filesystem_path(path_to_rawdata_folder, path_to_filenames_dict, [], root_metadata_dict)
                output_filename_path.append(hdf5_path)
        else:
            path_to_integrated_stepwise_hdf5_file = copy_subtree_and_create_hdf5(
                                                        path_to_input_dir, path_to_rawdata_folder, select_dir_keywords, [],
                                                        allowed_file_extensions, root_metadata_dict)
            output_filename_path.append(path_to_integrated_stepwise_hdf5_file)

        return output_filename_path

    if __name__ == "__main__":

        if len(sys.argv) < 2:
            print("Usage: python data_integration.py <function_name> <function_args>")
            sys.exit(1)

        # Extract the function name from the command line arguments
        function_name = sys.argv[1]

        # Handle function execution based on the provided function name
        if function_name == 'run':

            if len(sys.argv) != 3:
                print("Usage: python data_integration.py run <path_to_config_yamlFile>")
                sys.exit(1)
            # Extract path to configuration file, specifying the data integration task
            path_to_config_yamlFile = sys.argv[2]
            run_pipeline(path_to_config_yamlFile)