Replace unspecified GHGs & energy inputs with proportional avoided el…

…ectricity generation exchanges; addresses #3

warmer/controls.py

@@ -3,34 +3,80 @@
 """
 Functions to enable modifications to WARM data
 """
+import pandas as pd
 
 controls_dict = {'electricity': 'control_displaced_electricity_emissions',
                  'forest': 'remove_forest_carbon_storage',
                  'fertilizer': 'control_avoided_fertilizer_emissions',
                  }
 
-
 def control_displaced_electricity_emissions(df_a, df_b):
-    # Remove GHGs Unspecified from select processes
-    b = df_b.query('from_process_name.str.contains("combustion") and '
-                   'to_flow_name == "GHGs, unspecified"', engine='python')
-    b = b.query('Amount < 0')
-    df_b = df_b.drop(b.index)
-
-    # TODO Add negative electricity output (avoided product) 1.376E+03 kWh/MTCO2E
-    elec = df_a.query('from_process_name == "Electricity generation, at grid, National"')
-
+
+    e_gen = 'Electricity generation, at grid, National'
+    ghg_u = 'GHGs, unspecified'
+
+    # TODO: derive 1.376E+03 kWh/MTCO2E factor from e_gen elementary flows
+        # plus factors from new olca_get_results.get_impact_factors fxn
+    # df_ghgs_to_e = df_b.query('from_process_name == @e_gen and '
+    #                           'Amount != 0')
+
+    # For combustion processes w/ a "GHGs, unspecified" output flow and no
+    # elec. output (avoided product), substitute the former for the latter
+    # NOTE: all elec. gen. flows are given as inputs w/i the olca mtx exports
+    df_b_e = df_b.query('from_process_name.str.contains("combustion") and '
+                        'to_flow_name == @ghg_u and '
+                        # 'to_flow_name == "GHGs, unspecified"'
+                        # 'Amount < 0'
+                        'Amount != 0')
+    prcs_ghg_u = set(df_b_e['from_process_ID'])
+
+    df_a_e = df_a.query('from_process_name == @e_gen and '
+                        'Amount != 0')
+    prcs_e_gen = set(df_a_e['to_process_ID'])
+
+    df_target = df_a.query('to_process_ID in @prcs_ghg_u and '
+                           'to_process_ID not in @prcs_e_gen and '
+                           'Amount != 0')
+    prcs_target = set(df_target['to_process_ID'])
+
+    # Construct new e_gen exchanges for target processes & concat into df_a
+    # Must invert sign when converting output flow to avoided input
+    # TODO: clarify sign handling here and in olca_warm_matrix_io
+    flows_e = dict(zip(df_b_e['from_process_ID'],
+                       df_b_e['Amount']*1376*-1))
+
+    df_elec_to = (df_a.query('to_process_ID in @prcs_target and '
+                             'Amount != 0')
+                      .filter(regex='^to_')
+                      .drop_duplicates()
+                      .reset_index(drop=True))
+    df_elec_from = (df_a.query('from_process_name == @e_gen and '
+                                'Amount != 0')
+                        .filter(regex='^from_')
+                        .drop_duplicates())
+    df_elec_from = (pd.concat([df_elec_from]*len(df_elec_to))
+                      .reset_index(drop=True))
+
+    df_elec = pd.concat([df_elec_from, df_elec_to], axis='columns')
+    df_elec['Amount'] = df_elec['to_process_ID'].map(flows_e)
+    df_a = pd.concat([df_a, df_elec])
+
+    # Remove "GHGs, unspecified" and "Energy, unspecified" (avoided consumption)
+    # from target processes
+    unspec = set([ghg_u, 'Energy, unspecified'])
+    df_b = df_b.query('not (from_process_ID in @prcs_ghg_u and '
+                      'to_flow_name in @unspec)')
     return df_a, df_b
-    
+
 def remove_forest_carbon_storage(df_a, df_b):
     # Remove Carbon/resource/biotic
     b = df_b.query('to_flow_name == "Carbon" and '
                    'to_flow_category.str.contains("resource/biotic")', engine='python')
     b = b.query('Amount < 0')
     df_b = df_b.drop(b.index)
-    
+
     return df_a, df_b
-    
+
 def control_avoided_fertilizer_emissions(df_a, df_b):
     # Remove carbon dioxide from Anaerobic digestion
     b = df_b.query('to_flow_name == "Carbon dioxide" and '
@@ -39,7 +85,7 @@ def control_avoided_fertilizer_emissions(df_a, df_b):
                    engine='python')
     b = b.query('Amount < 0')
     df_b = df_b.drop(b.index)
-    
+
     # TODO Add negative fertilizer output as avoided product
 
     return df_a, df_b

warmer/olca_warm_matrix_io.py

@@ -362,7 +362,7 @@ def format_tables(df, opt, opt_map):
                     }
         # Drop all 0 exchanges prior to setting diagonal to 0
         df = df.query('Amount != 0').reset_index(drop=True)
-        
+
         # Find and set mtx_a diagonal exchanges to 0, then invert all signs
         df['Amount'] = -1 * np.where(
             ((df['to_process_ID'] == df['from_process_ID'].str.rstrip('/US')) &
@@ -399,7 +399,7 @@ def get_exchanges(opt_fmt='tables', opt_mixer='pop', opt_map='all',
     :param opt_map: str, {'all','fedefl','useeio'}
     :param query_fg: bool, True calls query_fg_processes
     :param df_subset: pd.DataFrame, see query_fg_processes
-    :param controls: list
+    :param controls: list, subset of warmer.controls.controls_dict
     """
     if opt_fmt not in {'tables', 'matrices'}:
         print(f'"{opt_fmt}" not a valid format option')
@@ -419,7 +419,8 @@ def get_exchanges(opt_fmt='tables', opt_mixer='pop', opt_map='all',
 
     df_a, df_b = map(melt_mtx, [mtx_a, mtx_b], ['a', 'b'])
     df_a, df_b = label_exch_dfs(df_a, df_b, idx_a, idx_b)
-    df_a, df_b = query_fg_processes(df_a, df_b)
+
+    controls = ['electricity']
 
     # Insert controls before mapping
     if not controls:
@@ -431,6 +432,9 @@ def get_exchanges(opt_fmt='tables', opt_mixer='pop', opt_map='all',
         else:
             print(f'control {c} does not exist.')
 
+
+    # df_a, df_b = map(lambda x: x.query('Amount != 0'), (df_a, df_b))
+    # return df_a, df_b
     if opt_map in {'all', 'useeio'}:
         df_a = map_useeio_processes(df_a)
     if opt_map in {'all', 'fedefl'}: