results_fuel_correction_csvs_emissions.py

# import pandas as pd
# import os
# import re
#
# # 📌 Load final corrected fuel flow data
# final_corrected_fuel_flow_path = "results_df_corrected_fuel_emissions_v2.csv"
# final_df = pd.read_csv(final_corrected_fuel_flow_path)
#
# # 📌 Base directory for emissions CSVs
# base_dir = "main_results_figures/results/"
#
# # 🔍 Pattern to extract metadata from emissions filenames
# pattern = r"([^/]+)/\1_(\d{4}-\d{2}-\d{2})_(daytime|nighttime)/emissions/(GTF(?:2035_wi|2035)?)_SAF_(\d+)_A20N_full_WAR_\d+_\d+_\d+\.csv"
#
# # 📌 Process only emissions files
# for root, _, files in os.walk(base_dir):
#     if "emissions" not in root:  # 🚨 Skip all non-emissions folders
#         continue
#
#     for file in files:
#         if not file.endswith(".csv"):
#             continue
#
#         file_path = os.path.join(root, file)
#
#         # 🔍 Extract metadata from filename
#         match = re.search(pattern, file_path.replace("\\", "/"))  # Convert to Unix-style path for regex
#         if match:
#             trajectory, season, diurnal, engine, saf_level = match.groups()
#             saf_level = int(saf_level)  # Convert to integer
#             print(f"\n📌 Processing: {engine} | SAF: {saf_level} | {trajectory} | {season} | {diurnal}")
#
#             # 🚨 Skip GTF1990 & GTF2000
#             if engine in ["GTF1990", "GTF2000"]:
#                 print("⛔ Skipping GTF1990 and GTF2000!")
#                 continue
#
#             # 📌 Load emissions CSV
#             emissions_df = pd.read_csv(file_path)
#
#             # 📌 Filter `final_df` for matching rows
#             filtered_final_df = final_df[
#                 (final_df['trajectory'] == trajectory) &
#                 (final_df['season'] == season) &
#                 (final_df['diurnal'] == diurnal) &
#                 (final_df['engine'] == engine) &
#                 (final_df['saf_level'] == saf_level)
#             ].copy()
#
#             # 🚨 Check if `final_df` has matching rows
#             if filtered_final_df.empty:
#                 print("⚠️ No matching rows found in final_df!")
#                 continue
#
#             # ✅ Merge based on `index` (and `time` if available)
#             merge_cols = ['index']
#             if 'time' in emissions_df.columns and 'time' in filtered_final_df.columns:
#                 merge_cols.append('time')
#
#             merged_df = emissions_df.merge(
#                 filtered_final_df[['index', 'time', 'fuel_flow_corrected']],
#                 on=merge_cols,
#                 how="left"
#             )
#
#             # 🔍 Fill missing values: If `fuel_flow_corrected` is NaN, use `fuel_flow` from emissions_df
#             merged_df = merged_df.assign(fuel_flow_corrected=merged_df['fuel_flow_corrected'].fillna(merged_df['fuel_flow']))
#
#             # ✅ Overwrite the original CSV file
#             merged_df.to_csv(file_path, index=False)
#             print(f"✅ Updated {file_path} with `fuel_flow_corrected`!")
#         else:
#             print(f"🚨 Skipping non-emissions file: {file_path}")  # No more errors for climate files