284 lines
13 KiB
Python
284 lines
13 KiB
Python
import sys
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import os
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import re
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try:
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thisFilePath = os.path.abspath(__file__)
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except NameError:
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print("Error: __file__ is not available. Ensure the script is being run from a file.")
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print("[Notice] Path to DIMA package may not be resolved properly.")
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thisFilePath = os.getcwd() # Use current directory or specify a default
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dimaPath = os.path.normpath(os.path.join(thisFilePath, "..",'..')) # Move up to project root
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if dimaPath not in sys.path: # Avoid duplicate entries
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sys.path.append(dimaPath)
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import yaml
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import logging
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from datetime import datetime
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# Importing chain class from itertools
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from itertools import chain
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# Import DIMA modules
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try:
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from dima.src import hdf5_writer as hdf5_lib
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from dima.utils import g5505_utils as utils
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from dima.instruments import filereader_registry
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except ModuleNotFoundError:
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print(':)')
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import src.hdf5_writer as hdf5_lib
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import utils.g5505_utils as utils
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from instruments import filereader_registry
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allowed_file_extensions = filereader_registry.file_extensions
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def _generate_datetime_dict(datetime_steps):
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""" Generate the datetime augment dictionary from datetime steps. """
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datetime_augment_dict = {}
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for datetime_step in datetime_steps:
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datetime_augment_dict[datetime_step] = [
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datetime_step.strftime('%Y-%m-%d'), datetime_step.strftime('%Y_%m_%d'),
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datetime_step.strftime('%Y.%m.%d'), datetime_step.strftime('%Y%m%d')
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]
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return datetime_augment_dict
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def _generate_output_path_fragment(filename_prefix, integration_mode, dataset_startdate, dataset_enddate, index=None):
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"""Generate consistent directory or file name fragment based on mode."""
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if integration_mode == 'collection':
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return f'collection_{index}_{filename_prefix}_{dataset_enddate}'
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else:
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return f'{filename_prefix}_{dataset_enddate}'
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def load_config_and_setup_logging(yaml_config_file_path, log_dir):
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"""Load YAML configuration file, set up logging, and validate required keys and datetime_steps."""
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# Define required keys
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required_keys = [
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'experiment', 'contact', 'input_file_directory', 'output_file_directory',
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'instrument_datafolder', 'project', 'actris_level'
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]
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# Supported integration modes
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supported_integration_modes = ['collection', 'single_experiment']
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# Set up logging
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date = utils.created_at("%Y_%m").replace(":", "-")
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utils.setup_logging(log_dir, f"integrate_data_sources_{date}.log")
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# Load YAML configuration file
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with open(yaml_config_file_path, 'r') as stream:
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try:
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config_dict = yaml.load(stream, Loader=yaml.FullLoader)
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except yaml.YAMLError as exc:
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logging.error("Error loading YAML file: %s", exc)
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raise ValueError(f"Failed to load YAML file: {exc}")
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# Check if required keys are present
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missing_keys = [key for key in required_keys if key not in config_dict]
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if missing_keys:
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raise KeyError(f"Missing required keys in YAML configuration: {missing_keys}")
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# Look for all placeholders like ${VAR_NAME}
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input_dir = config_dict['input_file_directory']
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placeholders = re.findall(r'\$\{([^}^{]+)\}', input_dir)
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success = utils.load_env_from_root()
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print(f'Success : {success}')
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for var in placeholders:
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env_value = os.environ.get(var)
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if env_value is None:
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raise ValueError(f"Environment variable '{var}' is not set but used in the config.")
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input_dir = input_dir.replace(f"${{{var}}}", env_value)
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config_dict['input_file_directory'] = input_dir
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# Check the instrument_datafolder required type and ensure the list is of at least length one.
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if isinstance(config_dict['instrument_datafolder'], list) and not len(config_dict['instrument_datafolder'])>=1:
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raise ValueError('Invalid value for key "instrument_datafolder". Expected a list of strings with at least one item.'
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'Each item represents a subfolder name in the input file directory, where the name'
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'must match the format "<subfolder>[/<subfolder>]".'
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'The first subfolder name is required, and the second is optional. '
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'Examples of valid values: "level1", "level1/level2".')
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# Define the pattern for valid subfolder names: `subfolder` or `subfolder/subfolder`
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#valid_pattern = re.compile(r'^[^/]+(/[^/]+)?$')
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# Validate each subfolder name
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#for folder in config_dict['instrument_folder']:
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# if not isinstance(folder, str) or not valid_pattern.match(folder):
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# raise ValueError(
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# 'Invalid value for key "instrument_folder" in YAML file.'
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# 'Each item must be a string matching the format '
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# '"<subfolder>[/<subfolder>]". The first subfolder name is required, and the second is optional. '
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# 'Examples of valid values: "level1", "level1/level2". '
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# f'Invalid item: {folder}'
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# )
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# Validate integration_mode
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integration_mode = config_dict.get('integration_mode', 'N/A') # Default to 'collection'
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if integration_mode not in supported_integration_modes:
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raise RuntimeWarning(
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f"Unsupported integration_mode '{integration_mode}'. Supported modes are {supported_integration_modes}. Setting '{integration_mode}' to 'single_experiment'."
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)
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# Validate datetime_steps format if it exists
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if 'datetime_steps' in config_dict:
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datetime_steps = config_dict['datetime_steps']
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expected_format = '%Y-%m-%d %H-%M-%S'
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# Check if datetime_steps is a list or a falsy value
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if datetime_steps and not isinstance(datetime_steps, list):
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raise TypeError(f"datetime_steps should be a list of strings or a falsy value (None, empty), but got {type(datetime_steps)}")
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for step_idx, step in enumerate(datetime_steps):
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try:
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# Attempt to parse the datetime to ensure correct format
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config_dict['datetime_steps'][step_idx] = datetime.strptime(step, expected_format)
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except ValueError:
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raise ValueError(f"Invalid datetime format for '{step}'. Expected format: {expected_format}")
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# Augment datatime_steps list as a dictionary. This to speed up single-experiment file generation
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config_dict['datetime_steps_dict'] = _generate_datetime_dict(datetime_steps)
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else:
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# If datetime_steps is not present, set the integration mode to 'collection'
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logging.info("datetime_steps missing, setting integration_mode to 'collection'.")
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config_dict['integration_mode'] = 'collection'
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# Validate filename_format if defined
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if 'filename_format' in config_dict:
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if not isinstance(config_dict['filename_format'], str):
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raise ValueError(f'"Specified filename_format needs to be of String type" ')
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# Split the string and check if each key exists in config_dict
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keys = [key.strip() for key in config_dict['filename_format'].split(',')]
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missing_keys = [key for key in keys if key not in config_dict]
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# If there are any missing keys, raise an assertion error
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# assert not missing_keys, f'Missing key(s) in config_dict: {", ".join(missing_keys)}'
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if not missing_keys:
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config_dict['filename_format'] = ','.join(keys)
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else:
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config_dict['filename_format'] = None
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print(f'"filename_format" should contain comma-separated keys that match existing keys in the YAML config file.')
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print('Setting "filename_format" as None')
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else:
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config_dict['filename_format'] = None
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# Compute complementary metadata elements
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# Create output filename prefix
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if not config_dict['filename_format']: # default behavior
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config_dict['filename_prefix'] = '_'.join([config_dict[key] for key in ['experiment', 'contact']])
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else:
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config_dict['filename_prefix'] = '_'.join([config_dict[key] for key in config_dict['filename_format'].split(sep=',')])
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# Set default dates from datetime_steps if not provided
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current_date = datetime.now().strftime('%Y-%m-%d')
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dates = config_dict.get('datetime_steps',[])
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if not config_dict.get('dataset_startdate'):
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config_dict['dataset_startdate'] = min(config_dict['datetime_steps']).strftime('%Y-%m-%d') if dates else current_date # Earliest datetime step
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if not config_dict.get('dataset_enddate'):
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config_dict['dataset_enddate'] = max(config_dict['datetime_steps']).strftime('%Y-%m-%d') if dates else current_date # Latest datetime step
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config_dict['expected_datetime_format'] = '%Y-%m-%d %H-%M-%S'
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return config_dict
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def copy_subtree_and_create_hdf5(src, dst, select_dir_keywords, select_file_keywords, allowed_file_extensions, root_metadata_dict):
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"""Helper function to copy directory with constraints and create HDF5."""
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src = src.replace(os.sep,'/')
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dst = dst.replace(os.sep,'/')
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logging.info("Creating constrained copy of the experimental campaign folder %s at: %s", src, dst)
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path_to_files_dict = utils.copy_directory_with_contraints(src, dst, select_dir_keywords, select_file_keywords, allowed_file_extensions)
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logging.info("Finished creating a copy of the experimental campaign folder tree at: %s", dst)
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logging.info("Creating HDF5 file at: %s", dst)
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#hdf5_path = hdf5_lib.create_hdf5_file_from_filesystem_path_new(dst, path_to_files_dict, select_dir_keywords, root_metadata_dict)
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hdf5_path = hdf5_lib.create_hdf5_file_from_filesystem_path(dst, path_to_files_dict, select_dir_keywords, root_metadata_dict)
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logging.info("Completed creation of HDF5 file %s at: %s", hdf5_path, dst)
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return hdf5_path
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def run_pipeline(path_to_config_yamlFile, log_dir='logs/'):
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config_dict = load_config_and_setup_logging(path_to_config_yamlFile, log_dir)
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path_to_input_dir = config_dict['input_file_directory']
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path_to_output_dir = config_dict['output_file_directory']
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select_dir_keywords = config_dict['instrument_datafolder']
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# Define root folder metadata dictionary
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root_metadata_dict = {key : config_dict[key] for key in ['project', 'experiment', 'contact', 'actris_level']}
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# Get dataset start and end dates
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dataset_startdate = config_dict['dataset_startdate']
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dataset_enddate = config_dict['dataset_enddate']
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integration_mode = config_dict.get('integration_mode', 'single_experiment')
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filename_prefix = config_dict['filename_prefix']
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output_filename_path = []
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# Determine top-level campaign folder path
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top_level_foldername = _generate_output_path_fragment(
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filename_prefix, integration_mode, dataset_startdate, dataset_enddate, index=1
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)
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path_to_rawdata_folder = os.path.join(
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path_to_output_dir, top_level_foldername, ""
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).replace(os.sep, '/')
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# Process individual datetime steps if available, regardless of mode
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if config_dict.get('datetime_steps_dict', {}):
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for datetime_step, file_keywords in config_dict['datetime_steps_dict'].items():
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single_date_str = datetime_step.strftime('%Y%m%d')
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subfolder_name = f"{filename_prefix}_{single_date_str}"
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subfolder_name = f"experimental_step_{single_date_str}"
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path_to_rawdata_subfolder = os.path.join(path_to_rawdata_folder, subfolder_name, "")
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path_to_integrated_stepwise_hdf5_file = copy_subtree_and_create_hdf5(
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path_to_input_dir, path_to_rawdata_subfolder, select_dir_keywords,
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file_keywords, allowed_file_extensions, root_metadata_dict)
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output_filename_path.append(path_to_integrated_stepwise_hdf5_file)
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# Collection mode post-processing
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if integration_mode == 'collection':
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path_to_filenames_dict = {path_to_rawdata_folder: [os.path.basename(path) for path in output_filename_path]} if output_filename_path else {}
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hdf5_path = hdf5_lib.create_hdf5_file_from_filesystem_path(
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path_to_rawdata_folder, path_to_filenames_dict, [], root_metadata_dict
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)
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output_filename_path.append(hdf5_path)
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else:
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path_to_integrated_stepwise_hdf5_file = copy_subtree_and_create_hdf5(
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path_to_input_dir, path_to_rawdata_folder, select_dir_keywords, [],
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allowed_file_extensions, root_metadata_dict)
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output_filename_path.append(path_to_integrated_stepwise_hdf5_file)
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return output_filename_path
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python data_integration.py <function_name> <function_args>")
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sys.exit(1)
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function_name = sys.argv[1]
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if function_name == 'run':
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if len(sys.argv) != 3:
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print("Usage: python data_integration.py run <path_to_config_yamlFile>")
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sys.exit(1)
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path_to_config_yamlFile = sys.argv[2]
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run_pipeline(path_to_config_yamlFile)
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