lintting applied

src/bathymetric_adjustment.py

@@ -190,8 +190,8 @@ def correct_rating_for_ai_based_bathymetry(fim_dir, huc, strm_order, bathy_file_
  aib_df0 = aib_bathy_data_df[
  ['feature_id', 'missing_xs_area_m2', 'missing_wet_perimeter_m', 'Bathymetry_source']
  ]
- #test = aib_df[aib_df.duplicated(subset='feature_id', keep=False)]
- aib_df = aib_df0.drop_duplicates(subset=['feature_id'], keep = 'first')
+ # test = aib_df[aib_df.duplicated(subset='feature_id', keep=False)]
+ aib_df = aib_df0.drop_duplicates(subset=['feature_id'], keep='first')
  aib_df.index = range(len(aib_df))
 
  # Get src_full from each branch
@@ -214,7 +214,7 @@ def correct_rating_for_ai_based_bathymetry(fim_dir, huc, strm_order, bathy_file_
  # Merge in missing bathy data and fill Nans
  if "missing_xs_area_m2" not in src_df.columns:
  src_df.drop(columns=["Bathymetry_source"], inplace=True)
- src_df = src_df.merge(aib_df, on='feature_id', how='left', validate='many_to_one') 
+ src_df = src_df.merge(aib_df, on='feature_id', how='left', validate='many_to_one')
  # print([src,src_df.columns]) src_df.Bathymetry_source
  else:
  src_df = pd.read_csv(src, low_memory=False)
@@ -299,7 +299,7 @@ def apply_src_adjustment_for_bathymetry(
  log_text = ""
  try:
  if os.path.exists(bathy_file_ehydro):
- msg = f"correcting rating curve for ehydro bathy for huc : {huc}"
+ msg = f"Correcting rating curve for ehydro bathy for huc : {huc}"
  log_text += msg + '\n'
  print(msg)
  log_text += correct_rating_for_ehydro_bathymetry(fim_dir, huc, bathy_file_ehydro, verbose)
@@ -319,7 +319,7 @@ def apply_src_adjustment_for_bathymetry(
  log_text = ""
  try:
  if os.path.exists(bathy_file_aibased):
- msg = f"correcting rating curve for AI-based bathy for huc : {huc}"
+ msg = f"Correcting rating curve for AI-based bathy for huc : {huc}"
  log_text += msg + '\n'
  print(msg + '\n')