Prefix unused arguments with _

code/adamont_forcings.py

@@ -25,7 +25,7 @@ def find_nearest_altitude(array,value):
     idx = (np.abs(array-value)).argmin()
     return idx
 
-def find_glacier_idx(glacier_massif, massif_number, altitudes, glacier_altitude, aspects, glacier_aspect):
+def find_glacier_idx(glacier_massif, massif_number, altitudes, glacier_altitude, _aspects, _glacier_aspect):
     #### Aspects not used for ADAMONT
     massif_altitudes_idx = np.where(massif_number == float(glacier_massif))[0]
     if(len(massif_altitudes_idx) > 0):

code/glacier_evolution.py

@@ -425,8 +425,8 @@ def array2raster(newRasterfn, rasterOrigin, pixelWidth, pixelHeight, array):
     outband.FlushCache()
 
 # Compute the scaling factor fs for the delta h function
-def generate_fs(year_smb, year_start, DEM_sorted_current_glacier_u, DEM_sorted_CG_n_u, delta_h_DEM_current_glacier, 
-                masked_DEM_current_glacier_u, delta_h_dh_current_glacier, masked_ID_current_glacier_u, pixel_area, glacierArea):
+def generate_fs(year_smb, _year_start, DEM_sorted_current_glacier_u, DEM_sorted_CG_n_u, delta_h_DEM_current_glacier, 
+                masked_DEM_current_glacier_u, delta_h_dh_current_glacier, _masked_ID_current_glacier_u, pixel_area, glacierArea):
     # We compute the fs factor in order to scale the normalized delta h parameters
     vol_distrib = 0
 
@@ -493,7 +493,7 @@ def get_aspect_deg(aspect):
 
 
  # We crop the initial rasters to the extent of the GLIMS 2003 or 2015 database
-def crop_inital_rasters_to_GLIMS(path_glacier_ID_rasters, path_glacier_DEM_rasters, path_glacier_outline, glacier_shapefile, glacierID, midfolder, year_start):
+def crop_inital_rasters_to_GLIMS(path_glacier_ID_rasters, path_glacier_DEM_rasters, path_glacier_outline, _glacier_shapefile, glacierID, _midfolder, year_start):
     if(year_start == 2004):
         print("glacierID: " + str(glacierID))
 
@@ -587,7 +587,7 @@ def get_flowline_20_length(flowline_feature, flowline_coords, alt_20_flowline_id
     return flowline_20_length, fixed_length
 
 # Computes the slope of the lowermost 20% of a glacier    
-def get_slope20(masked_DEM_current_glacier_u, DEM_sorted_current_glacier_u, glacierName, flowline, path_raster_current_DEM, yearly_glacier_slope20):
+def get_slope20(_masked_DEM_current_glacier_u, DEM_sorted_current_glacier_u, glacierName, flowline, path_raster_current_DEM, yearly_glacier_slope20):
 
     raster_current_DEM = gdal.Open(path_raster_current_DEM) 
 
@@ -1310,13 +1310,13 @@ def store_rasters(masked_DEM_current_glacier_u, masked_ID_current_glacier_u, mid
 
 def glacier_evolution(masked_DEM_current_glacier, masked_ID_current_glacier, 
                       delta_h_dh_current_glacier, delta_h_DEM_current_glacier, 
-                      DEM_sorted_current_glacier,  
+                      _DEM_sorted_current_glacier,  
                       daily_meteo_data, meteo_anomalies,
-                      flowline, raster_current_DEM, current_glacier_DEM, store_plots, 
+                      flowline, _raster_current_DEM, current_glacier_DEM, store_plots, 
                       glacierName, glacierID, glimsID, massif, lat, lon, aspect,
                       midfolder, pixel_area, glaciers_with_errors, glims_rabatel,
                       lasso_scaler, lasso_model, ensemble_SMB_models, 
-                      year_range, ref_start, ref_end, SAFRAN_idx, overwrite):
+                      year_range, ref_start, _ref_end, SAFRAN_idx, overwrite):
 
     print("Applying glacier evolution...")
 

code/safran_forcings.py

@@ -175,7 +175,7 @@ def compute_local_anomalies(idx, glacier_CPDDs, glacier_winter_snow, glacier_sum
 # Computes monthly meteo anomalies at glacier scale
 def compute_monthly_anomalies(idx, glacier_mon_temp, glacier_mon_snow,
                               local_mon_temp_anomalies, local_mon_snow_anomalies,
-                              raw_local_mon_temp_anomalies, raw_local_mon_snow_anomalies):
+                              _raw_local_mon_temp_anomalies, _raw_local_mon_snow_anomalies):
 
     # The monthly meteo anomalies, as well as the seasonal ones, are always computed with respect to the 1984-2014 period
     mon_range = range(0, 12)

code/smb_model_training.py

@@ -245,7 +245,7 @@ def plot_ic_criterion(model, name, color):
     plt.xlabel('alpha')
     plt.ylabel('criterion')
 
-def lasso_CV_model_selection(X, y, fig_idx, year_groups, plot):
+def lasso_CV_model_selection(X, y, fig_idx, _year_groups, plot):
 
     # #############################################################################
     # LassoLarsIC: least angle regression with BIC/AIC criterion
@@ -342,7 +342,7 @@ def lasso_CV_model_selection(X, y, fig_idx, year_groups, plot):
     return model_CV
 
 
-def generate_SMB_models(SMB_raw, season_raw_meteo_anomalies_SMB, mon_temp_anomalies, mon_snow_anomalies, spatiotemporal_flag, first_year, glims_rabatel, glims_2015, glims_2003, glims_1985, glims_1967, fig_idx):
+def generate_SMB_models(SMB_raw, season_raw_meteo_anomalies_SMB, mon_temp_anomalies, mon_snow_anomalies, spatiotemporal_flag, first_year, glims_rabatel, _glims_2015, glims_2003, glims_1985, glims_1967, fig_idx):
 
 
     # We create the full matrix for the spatiotemporal multiple linear regression

code/smb_validation.py

@@ -130,7 +130,7 @@ def interpolate_glims_variable(variable_name, glims_glacier, glims_2003, glims_1
 
     return interp_1984_2014
 
-def interpolate_extended_glims_variable(variable_name, glims_glacier, glims_2015, glims_1985, glims_1967):
+def interpolate_extended_glims_variable(variable_name, glims_glacier, glims_2015, glims_1985, _glims_1967):
     # In case there are multiple results, we choose the one with the most similar area
 
     # 2015 glacier inventory