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)


