Updates to avoid future depreciation warnings

workflow/scripts/osemosys_global/powerplant/activity.py

@@ -58,7 +58,8 @@ def activity_output_pwr(df_ratios, thermal_fuel_list_oar):
     df_oar = df_ratios.copy()
     mask = df_oar['TECHNOLOGY'].apply(lambda x: x[3:6] in thermal_fuel_list_oar)
     df_oar['FUEL'] = 0
-    df_oar['FUEL'][mask] = 1
+    df_oar.loc[mask, "FUEL"] = 1
+
     df_oar = df_oar.loc[~((df_oar['MODE_OF_OPERATION'] > 1) &
                           (df_oar['FUEL'] == 0))]
     df_oar['FUEL'] = ('ELC' + 
@@ -88,6 +89,7 @@ def activity_input_pwr(df_oar, thermal_fuel_list_iar, renewables_list,
     df_iar = df_oar.copy()
 
     df_iar['FUEL'] = 0
+    df_iar['FUEL'] = df_iar['FUEL'].astype(str)
 
     # Deal with GAS techs first...  OCG and CCG
     # OCG Mode 1: Domestic GAS

workflow/scripts/osemosys_global/powerplant/costs.py

@@ -63,7 +63,7 @@ def costs_end(df_weo_regions, df_costs, df_oar_final, df_trans_capex,
 
     # Create formatted CSVs
     for each_cost in ['Capital', 'O&M']:
-        df_costs_temp = df_costs.loc[df_costs['parameter'].str.contains(each_cost)]
+        df_costs_temp = df_costs.copy().loc[df_costs.copy()['parameter'].str.contains(each_cost)]
 
         df_costs_temp.drop(['technology', 'parameter'], 
                            axis = 1, 
@@ -72,7 +72,8 @@ def costs_end(df_weo_regions, df_costs, df_oar_final, df_trans_capex,
                                        'TECHNOLOGY',
                                        'YEAR'
                                       ]]
-        df_costs_final['YEAR'] = df_costs_final['YEAR'].astype(int)
+        df_costs_final.loc[:,'YEAR'] = df_costs_final['YEAR'].astype(int)
+
         df_costs_final = df_costs_final.drop_duplicates()
         df_costs_final = (df_costs_final
                           .loc[(df_costs_final['TECHNOLOGY']
@@ -96,17 +97,21 @@ def costs_end(df_weo_regions, df_costs, df_oar_final, df_trans_capex,
         df_costs_final.drop(['technology_code', 'weo_region'], 
                             axis = 1, 
                             inplace = True)
-        df_costs_final = df_costs_final.fillna(-9)
+        df_costs_final = df_costs_final.infer_objects().fillna(-9)
         df_costs_final = pd.pivot_table(df_costs_final, 
                                         index = ['REGION', 'YEAR'], 
                                         columns = 'TECHNOLOGY', 
                                         values = 'value').reset_index()
         df_costs_final = df_costs_final.replace([-9],[np.nan])
 
-        df_costs_final = df_costs_final.interpolate(method = 'linear', 
-                                                    limit_direction='forward').round(2)
-        df_costs_final = df_costs_final.interpolate(method = 'linear', 
-                                                    limit_direction='backward').round(2)
+        for col in df_costs_final.columns:
+            if df_costs_final[col].dtype != 'object':
+                df_costs_final[col] = df_costs_final[col].interpolate(method = 'linear', 
+                                                            limit_direction='forward').round(2)
+
+                df_costs_final[col] = df_costs_final[col].interpolate(method = 'linear', 
+                                                            limit_direction='backward').round(2)
+
         df_costs_final = pd.melt(df_costs_final, 
                                  id_vars = ['REGION', 'YEAR'], 
                                  value_vars = [x for x in df_costs_final.columns

workflow/scripts/osemosys_global/powerplant/data.py

@@ -1,7 +1,6 @@
 """Functions to extract relevent data"""
 
 import pandas as pd
-import numpy as np
 from datetime import datetime
 import itertools
 import logging
@@ -30,7 +29,7 @@ def set_generator_table(plexos_prop: pd.DataFrame, plexos_memb: pd.DataFrame,
                             index="powerplant",
                             columns="property",
                             values="value",
-                            aggfunc=np.sum,
+                            aggfunc='sum',
                             fill_value=0,
                            )
     df_gen["total_capacity"] = (df_gen["Max Capacity"].astype(float)) * (
@@ -59,9 +58,10 @@ def set_generator_table(plexos_prop: pd.DataFrame, plexos_memb: pd.DataFrame,
     )
 
     ## Extract start year from Commission Date
-    df_gen_base["Commission Date"] = (pd.TimedeltaIndex(df_gen_base["Commission Date"].astype(int),
+    df_gen_base["Commission Date"] = (pd.to_timedelta(df_gen_base["Commission Date"].astype(int),
                                                 unit='d') + 
                                datetime(1900, 1, 1))
+
     df_gen_base["start_year"] = df_gen_base["Commission Date"].dt.year
     df_gen_base.drop("Commission Date", axis=1, inplace=True)
 
@@ -329,5 +329,6 @@ def newIar(df_in, tech, new_iar_ccg,
     else: 
         logging.warning(f'Default IAR used for new {tech} power plants')
         iar = new_iar_default
-    df_out['VALUE'] = round(1/iar, 3)
+    df_out.loc[:,'VALUE'] = round(1/iar, 3)
+
     return df_out

workflow/scripts/osemosys_global/powerplant/main.py

@@ -104,15 +104,15 @@ def main(
     # return generator_table
     gen_table = set_generator_table(plexos_prop, plexos_memb, 
                                 op_life_dict, tech_code_dict)
-    
+
     # Calculate average technology efficiencies.
     df_eff_node, df_eff_tech = average_efficiency(gen_table, avg_csp_eff, 
                                                   avg_urn_eff)
 
     # Calculate residual capacity.
     df_res_cap, custom_techs = res_capacity(gen_table, tech_list, tech_code, 
                                                  custom_res_cap, duplicate_techs)
-    
+
     df_ratios = activity_master_start(gen_table, nodes_extra_list, 
                                       duplicate_techs, mode_list)
 
@@ -123,7 +123,7 @@ def main(
 
     df_oar_upstream, df_oar_int = activity_upstream(df_iar_base, renewables_list, 
                                                     thermal_fuel_list_mining)
-    
+
     df_iar_trn, df_oar_trn, df_int_trn_oar, df_int_trn_iar = activity_transmission(df_oar_base, 
                                                                                    plexos_prop, 
                                                                                    interface_data)
@@ -142,7 +142,7 @@ def main(
     df_capact_final = capact(df_oar_final)
 
     df_crossborder_final = activity_transmission_limit(cross_border_trade, df_oar_final)
-    
+
     df_op_life_trn, df_op_life_out = set_op_life(tech_code_dict, df_iar_final, 
                                        df_oar_final, op_life_dict)
 
@@ -191,7 +191,7 @@ def main(
              )
 
     df_af_final = availability_factor(availability, tech_set)
-    
+
     # OUTPUT CSV's
 
     df_res_cap.to_csv(os.path.join(output_data_dir, "ResidualCapacity.csv"), index=None)

workflow/scripts/osemosys_global/powerplant/residual_capacity.py

@@ -27,9 +27,8 @@ def res_capacity(df_gen_base, tech_list, df_tech_code, df_custom_res_cap, duplic
     ]
 
     df_res_cap = df_gen_base[res_cap_cols]
-
-    for each_year in range(start_year, end_year+1):
-        df_res_cap[str(each_year)] = 0
+
+    df_res_cap = df_res_cap.reindex(columns=[*df_res_cap.columns.tolist(), *list(range(start_year, end_year+1))], fill_value=0)
 
     df_res_cap = pd.melt(
         df_res_cap,
@@ -38,7 +37,10 @@ def res_capacity(df_gen_base, tech_list, df_tech_code, df_custom_res_cap, duplic
         var_name="model_year",
         value_name="value",
     )
+
     df_res_cap["model_year"] = df_res_cap["model_year"].astype(int)
+    df_res_cap["value"] = df_res_cap["value"].astype(float)
+
     df_res_cap.loc[
         (df_res_cap["model_year"] >= df_res_cap["start_year"])
         & (df_res_cap["model_year"] <= df_res_cap["retirement_year_model"]),

workflow/scripts/osemosys_global/powerplant/user_defined_capacity.py

@@ -72,7 +72,7 @@ def set_user_defined_capacity(tech_capacity, op_life_dict, df_tech_set,
         df.loc[(df['YEAR']>=df['FIRST_YEAR']) &
                (df['YEAR']>df['BUILD_YEAR']),
                'VALUE'] = df['MAX_BUILD']
-        df.fillna(0,
+        df.infer_objects().fillna(0,
                   inplace=True)
         max_cap_techs_df = df[['REGION',
                                'TECHNOLOGY',
@@ -93,7 +93,7 @@ def set_user_defined_capacity(tech_capacity, op_life_dict, df_tech_set,
                                        inplace=True)
 
         # For technologies with start year before model start year, add to ResidualCapacity
-        df_res_cap_ud = df_min_cap_inv.loc[df_min_cap_inv['YEAR'] < min(years)]
+        df_res_cap_ud = df_min_cap_inv.copy().loc[df_min_cap_inv.copy()['YEAR'] < min(years)]
         df_res_cap_ud.rename(columns={'YEAR':'START_YEAR'},
                              inplace=True)
         df_res_cap_ud_final = pd.DataFrame(list(itertools.product(df_res_cap_ud['TECHNOLOGY'].unique(),
@@ -122,7 +122,9 @@ def set_user_defined_capacity(tech_capacity, op_life_dict, df_tech_set,
                                                    'YEAR',
                                                    'VALUE']]
 
-        df_res_cap = pd.concat([df_res_cap, df_res_cap_ud_final])
+
+        df_res_cap = pd.concat([df_res_cap, df_res_cap_ud_final 
+                                if not df_res_cap_ud_final.empty else None])
 
         # For technologies with start year at or after model start year, add to 
         # TotalAnnualMinCapacityInvestment