Positional argument changes for logger

metcalcpy/calc_difficulty_index.py

@@ -203,9 +203,9 @@ def forecast_difficulty(logger, sigmaij, muij, threshold, fieldijn,
         A6_1_xlist = [3.0, 9.0, 12.0, 21.0]                                                         
         A6_1_ylist = [0.0, 1.5, 1.5, 0.0]                                                   
         Aplin =\
-                plin(A6_1_xlist, A6_1_ylist, xunits=xunits,
+                plin(None, A6_1_xlist, A6_1_ylist, xunits=xunits,
                         right=A6_1_right, left=A6_1_left,                                       
-                        name=A6_1_name, logger=logger)
+                        name=A6_1_name)
         safe_log(logger, "debug", "Default Aplin object created.")
     safe_log(logger, "debug", "Calling _difficulty_index function.")                                                                  
     dij = _difficulty_index(sigmaij, muij, threshold, fieldijn,

metcalcpy/event_equalize.py

@@ -37,8 +37,8 @@ def safe_log(logger, log_level, message):
         if callable(log_method):
             log_method(message)
 
-def event_equalize(series_data, indy_var, series_var_vals, fix_vars,
-                   fix_vals_permuted, equalize_by_indep, multi, logger):
+def event_equalize(logger, series_data, indy_var, series_var_vals, fix_vars,
+                   fix_vals_permuted, equalize_by_indep, multi):
     """Performs event equalisation.
 
     event_equalize assumes that the input series_data contains data indexed by fcst_valid_beg,

metcalcpy/event_equalize_against_values.py

@@ -31,7 +31,7 @@ def safe_log(logger, log_level, message):
         if callable(log_method):
             log_method(message)
 
-def event_equalize_against_values(series_data, input_unique_cases, logger):
+def event_equalize_against_values(logger, series_data, input_unique_cases):
     """Performs event equalisation.
 
     event_equalize_against_values assumes that the input series_data contains data

metcalcpy/util/utils.py

@@ -811,7 +811,7 @@ def perform_event_equalization(params, input_data):
 
     # perform EE for each forecast variable on the axis 1
     output_ee_data = \
-        equalize_axis_data(None, fix_vals_keys, fix_vals_permuted_list, params, input_data, axis='1')
+        equalize_axis_data(fix_vals_keys, fix_vals_permuted_list, params, input_data, axis='1')
 
     # if the second Y axis is present - run event equalizer on Y1
     # and then run event equalizer on Y1 and Y2 equalized data
@@ -829,7 +829,7 @@ def perform_event_equalization(params, input_data):
             all_series[key] = list(set(all_series[key]))
 
         # run EE on run event equalizer on Y1 and Y2
-        output_ee_data = event_equalize(all_ee_records, params['indy_var'],
+        output_ee_data = event_equalize(None, all_ee_records, params['indy_var'],
                                         all_series,
                                         fix_vals_keys,
                                         fix_vals_permuted_list, True,