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 projectPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..')) # Move up to project root if projectPath not in sys.path: sys.path.insert(0,projectPath) import argparse import pandas as pd import json, yaml import numpy as np from utils import get_metadata from utils import metadata_dict_to_dataframe from pipelines.steps.utils import load_project_yaml_files def join_tables(csv_files: list): """ Joins multiple CSV files based on their metadata-defined datetime column. Parameters ---------- csv_files : list List of paths to CSV files. Returns ------- pd.DataFrame Merged DataFrame. """ if not all(isinstance(item, str) for item in csv_files): raise TypeError(f"Invalid parameter. csv_files contain non-str items: {[item for item in csv_files if not isinstance(item, str)]}") if not all(os.path.exists(item) and item.endswith('.csv') for item in csv_files): raise RuntimeError("Parameter csv_files contains either an unreachable/broken path or a non-CSV file.") acum_df = pd.read_csv(csv_files[0]) left_datetime_var = get_metadata(csv_files[0]).get('datetime_var', None) acum_df = acum_df.drop_duplicates(subset=[left_datetime_var]) if left_datetime_var is None: raise ValueError(f"Missing datetime_var metadata in {csv_files[0]}") for idx in range(1, len(csv_files)): append_df = pd.read_csv(csv_files[idx]) right_datetime_var = get_metadata(csv_files[idx]).get('datetime_var', None) if right_datetime_var is None: raise ValueError(f"Missing datetime_var metadata in {csv_files[idx]}") append_df = append_df.drop_duplicates(subset=[right_datetime_var]) acum_df = acum_df.merge(append_df, left_on=left_datetime_var, right_on=right_datetime_var, how='left') return acum_df #import argparse #import os #import pandas as pd from third_party.acsmProcessingSoftware.src import rawto012 #from utils import load_project_yaml_files, metadata_dict_to_dataframe, join_tables # Adjust imports based on actual file locations def main(paths_to_processed_files : list, path_to_flags : str, month : int = None): # Set up argument parsing acum_df = join_tables(paths_to_processed_files) acsm_to_ebas = load_project_yaml_files(projectPath, "acsm_to_ebas.yaml") # Select variables that are both in the acsm_to_ebas dict and acum_df reduced_set_of_vars = [key for key in acum_df.columns if key in acsm_to_ebas['renaming_map'].keys()] acum_df = acum_df.loc[:, reduced_set_of_vars].rename(columns=acsm_to_ebas['renaming_map']) flags_acum_df = join_tables([path_to_flags]) flags_acum_df = flags_acum_df.rename(columns=acsm_to_ebas['renaming_map']) # Ensure time columns are datetime acum_df['ACSM_time'] = pd.to_datetime(acum_df['ACSM_time']) flags_acum_df['ACSM_time'] = pd.to_datetime(flags_acum_df['ACSM_time']) # Apply month filter if specified if month: acum_df = acum_df[acum_df['ACSM_time'].dt.month == month] flags_acum_df = flags_acum_df[flags_acum_df['ACSM_time'].dt.month == month] # Count the number of NaT (null) values num_nats = acum_df['ACSM_time'].isna().sum() total_rows = len(acum_df) percentage_nats = (num_nats / total_rows) * 100 print(f"Total rows: {total_rows}") print(f"NaT (missing) values: {num_nats}") print(f"Percentage of data loss: {percentage_nats:.2f}%") num_nats = flags_acum_df['ACSM_time'].isna().sum() total_rows = len(flags_acum_df) percentage_nats = (num_nats / total_rows) * 100 print(f"Total rows: {total_rows}") print(f"NaT (missing) values: {num_nats}") print(f"Percentage of data loss: {percentage_nats:.2f}%") nat_acum = acum_df['ACSM_time'].isna() nat_flags = flags_acum_df['ACSM_time'].isna() valid_rows = ~(nat_acum | nat_flags) # Compute valid rows in one step # Load YAML files detection_limits = load_project_yaml_files(projectPath, "limits_of_detection.yaml") station_params = load_project_yaml_files(projectPath, "station_params.yaml") # Extract dictionaries from required keys lod_dict = detection_limits.get('LOD', {}).get('variables', {}) jfj_dict = station_params.get('stations', {}).get('JFJ', {}) # Convert dictionaries to DataFrames using the existing function lod_df = metadata_dict_to_dataframe(lod_dict, shape=(len(acum_df), len(lod_dict))) jfj_df = metadata_dict_to_dataframe(jfj_dict, shape=(len(acum_df), len(jfj_dict))) # Ensure indexes are properly aligned for merging acum_df = acum_df.reset_index() # Convert index to a column for merging # Merge with LOD DataFrame acum_df = acum_df.merge(lod_df, left_index=True, right_index=True, how='left') # Merge with JFJ DataFrame acum_df = acum_df.merge(jfj_df, left_index=True, right_index=True, how='left') acum_df = acum_df.rename(columns=acsm_to_ebas['renaming_map']) # Save results output_dir = os.path.join(projectPath,'data') output_file1 = os.path.join(output_dir, 'JFJ_ACSM-017_2024.txt') output_file2 = os.path.join(output_dir, 'JFJ_ACSM-017_FLAGS_2024.txt') #output_file1 = os.path.join(output_dir, f'JFJ_ACSM-017_2024_month{args.month}.txt' if args.month else 'JFJ_ACSM-017_2024.txt') #output_file2 = os.path.join(output_dir, f'JFJ_ACSM-017_FLAGS_2024_month{args.month}.txt' if args.month else 'JFJ_ACSM-017_FLAGS_2024.txt') acum_df.loc[:, :].to_csv(output_file1, sep='\t', index=None, date_format="%Y/%m/%d %H:%M:%S") flags_acum_df.loc[:, :].to_csv(output_file2, sep='\t', index=None, date_format="%Y/%m/%d %H:%M:%S") # Run external processing application app = rawto012.Application() infile = output_file1 acq_err_log = output_file2 outdir = output_dir app.process(infile, acq_err_log, outdir=outdir) if __name__ == '__main__': parser = argparse.ArgumentParser(description="Process and calibrate ACSM data for JFJ station.") parser.add_argument('--acsm_paths', type=str, required=True, nargs=3, help="Paths to the ACSM timeseries calibrated CSV file, the error CSV file, and the calibration factors CSV file.") parser.add_argument('--acsm_flags_path', type=str, required=True, help="Path to the ACSM flags CSV file.") parser.add_argument('--month', type=int, choices=range(1, 13), help="Filter data for a specific month (1-12).") args = parser.parse_args() # Load data csv_files = args.acsm_paths # list of filenames flags_file = args.acsm_flags_path month = args.month main(csv_files, flags_file, month)