acsm-fairifier/pipelines/steps/compute_automated_flags.py

import sys, os

try:
    thisFilePath = os.path.abspath(__file__)
    print(thisFilePath)
except NameError:
    print("[Notice] The __file__ attribute is unavailable in this environment (e.g., Jupyter or IDLE).")
    print("When using a terminal, make sure the working directory is set to the script's location to prevent path issues (for the DIMA submodule)")
    #print("Otherwise, path to submodule DIMA may not be resolved properly.")
    thisFilePath = os.getcwd()  # Use current directory or specify a default

import numpy as np
import pandas as pd
import argparse

import yaml, json

projectPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..'))  # Move up to project root
#print('Project path:', projectPath)
dimaPath = os.path.normpath('/'.join([projectPath,'dima']))
#print('DIMA path:', dimaPath)


# Set up project root directory
sys.path.insert(0,projectPath)
sys.path.insert(0,dimaPath)
import dima.src.hdf5_ops as dataOps 
import pipelines.steps.utils as stepUtils
import dima.utils.g5505_utils as utils
import json

def compute_cpc_flags():
    # TODO: ask rob where to find this information.

    return 0

def compute_diagnostic_variable_flags(data_table, validity_thresholds_dict):
    """
    Create indicator variables that check whether a particular diagnostic variable is within
    pre-specified/acceptable limits, which are defined by `variable_limits`.

    Parameters:
        data_table (pd.DataFrame): The input data table with variables to calibrate.
        variable_limits (dict): Dictionary mapping diagnostic-variables to their limits, e.g.,
            {
              'ABsamp': {
                  'lower_lim': {'value': 20000, 'description': "not specified yet"},
                  'upper_lim': {'value': 500000, 'description': "not specified yet"}
              }
            }
         
    Returns:
        pd.DataFrame: A new data table with calibrated variables, containing the original columns
                      and additional indicator variables, representing flags.
    """

    # Initialize a dictionary to store indicator variables
    indicator_variables = {}

    indicator_variables['t_base'] = data_table['t_base']

    # Loop through the column names in the data table
    for diagnostic_variable in data_table.columns:
        print(diagnostic_variable)
        # Skip if the diagnostic variable is not in variable_limits
        if diagnostic_variable not in validity_thresholds_dict['validity_thresholds']['variables']:
            print(f'Diagnostic variable {diagnostic_variable} has not defined limits in {validity_thresholds_dict}.')
            continue

        # Get lower and upper limits for diagnostic_variable from variable limits dict
        variable_ranges = validity_thresholds_dict['validity_thresholds']['variables'][diagnostic_variable]
        lower_lim = variable_ranges['lower_lim']
        upper_lim = variable_ranges['upper_lim']
        
        # Create an indicator variable for the current diagnostic variable
        tmp = data_table[diagnostic_variable]
        indicator_variables['flag_'+diagnostic_variable] = ((tmp >= lower_lim) & (tmp <= upper_lim)).to_numpy()

    # Add indicator variables to the new data table
    new_data_table = pd.DataFrame(indicator_variables)

    return new_data_table


# all_dat[VaporizerTemp_C >= heater_lower_lim & VaporizerTemp_C <= heater_upper_lim ,flag_heater_auto:="V"]
# all_dat[ABsamp >= AB_lower_lim & ABsamp <= AB_upper_lim ,flag_AB_auto:="V"]
# all_dat[FlowRate_ccs >= flow_lower_lim & FlowRate_ccs <= flow_upper_lim ,flag_flow_auto:="V"]
# all_dat[FilamentEmission_mA >= filament_lower_lim & FilamentEmission_mA <= filament_upper_lim ,flag_filament_auto:="V"]

if __name__ == '__main__':

    # Set up argument parsing
    parser = argparse.ArgumentParser(description="Calibrate species data using calibration factors.")
    parser.add_argument('data_file', type=str, help="Path to the input HDF5 file containing the data table.")
    parser.add_argument('dataset_name', type=str, help ='Relative path to data_table (i.e., dataset name) in HDF5 file')
    parser.add_argument('validity_thersholds_file', type=str, help="Path to the input YAML file containing calibration factors.")
    #parser.add_argument('output_file', type=str, help="Path to save the output calibrated data as a CSV file.")

    args = parser.parse_args()

    # Load input data and calibration factors
    try:
        #data_table = pd.read_json(args.data_file)

        print(args.data_file)

        dataManager = dataOps.HDF5DataOpsManager(args.data_file)
        dataManager.load_file_obj()
        dataset_name = '/'+args.dataset_name
        data_table = dataManager.extract_dataset_as_dataframe('/'+args.dataset_name)

        dataManager.extract_and_load_dataset_metadata()
        dataset_metadata_df = dataManager.dataset_metadata_df.copy()
        print(dataset_metadata_df.head())

        dataset_name_idx = dataset_metadata_df.index[(dataset_metadata_df['dataset_name']==args.dataset_name).to_numpy()]
        data_table_metadata = dataset_metadata_df.loc[dataset_name_idx,:]
        parent_instrument = data_table_metadata.loc[dataset_name_idx,'parent_instrument'].values[0]
        parent_file = data_table_metadata.loc[dataset_name_idx,'parent_file'].values[0]
        
        dataManager.unload_file_obj()        
        print(args.validity_thersholds_file)

        with open(args.validity_thersholds_file, 'r') as stream:
            validity_thersholds_dict = yaml.load(stream, Loader=yaml.FullLoader)
    except Exception as e:
        print(f"Error loading input files: {e}")
        exit(1)

    path_to_output_dir, ext = os.path.splitext(args.data_file)

    print('Path to output directory :', path_to_output_dir)



    # Perform calibration
    try:
        # Define output directory of apply_calibration_factors() step
        suffix = 'flags'
        if len(parent_instrument.split('/')) >= 2:
            instFolder = parent_instrument.split('/')[0]
            category = parent_instrument.split('/')[1]
        else:
            instFolder = parent_instrument.split('/')[0]
            category = ''

        path_to_output_folder, ext = os.path.splitext('/'.join([path_to_output_dir,f'{instFolder}_{suffix}',category]))    
        processingScriptRelPath = os.path.relpath(thisFilePath,start=projectPath)

        if not os.path.exists(path_to_output_folder):
            os.makedirs(path_to_output_folder)

        print('Processing script %s:', processingScriptRelPath)
        print('Output directory: %s', path_to_output_folder)

        # Compute diagnostic flags based on validity thresholds defined in configuration_file_dict

        flags_table = compute_diagnostic_variable_flags(data_table, validity_thersholds_dict) 
        metadata = {'actris_level' : 1, 
                     'processing_script': processingScriptRelPath.replace(os.sep,'/'),
                     'processing_date' : utils.created_at()
                     } 
        
        # Save output tables to csv file and save/or update data lineage record
        filename, ext = os.path.splitext(parent_file)
        path_to_flags_file = '/'.join([path_to_output_folder, f'{filename}_flags.csv'])
        #path_to_calibration_factors_file = '/'.join([path_to_output_folder, f'{filename}_calibration_factors.csv'])
        
        flags_table.to_csv(path_to_flags_file, index=False)
        
        status = stepUtils.record_data_lineage(path_to_flags_file, projectPath, metadata)

        print(f"Flags saved to {path_to_flags_file}")
        print(f"Data lineage saved to {path_to_output_dir}")

    except Exception as e:
        print(f"Error during calibration: {e}")
        exit(1)