Repaired comparison SimStadt vs Urbi

python_scripts/simstadt_urbi/compare_heat_demands.py

@@ -5,15 +5,15 @@ import matplotlib.pyplot as plt
 PROJ_DIR = Path(__file__).parent
 CITYGML = 'Grombühl_v2'
 URBI_HEAT_DEMAND = 'totalPrimaryEnergyDemandHeating [kWh/a]'
-S_DEMAND = 'Simstadt heat demand [MWh / a]' # Inkl Warmwasser?
+S_DEMAND = 'Simstadt heat demand [MWh / a]'  # Inkl Warmwasser?
 U_DEMAND = 'Urbi+ heat demand [MWh / a]'
 AREA = 'Area [m²]'
 TYPE = 'Usage Type'
-SUFFIX = '_Bensfeld'
+SUFFIX = ''  # Could be 'Bensheim', for example
 
 # Feldberg : 2.493 (Average : 3.0°C, Min : -18°C)
 # Original : 2.487 (Average : 7.9°C, Min : -12°C), Würzburg
-# Bensfeld : 2.475 (Average : 10.2°C, Min : -10°C)
+# Bensheim : 2.475 (Average : 10.2°C, Min : -10°C)
 
 
 def get_simstadt_heat_demand():
@@ -26,17 +26,19 @@ def get_simstadt_heat_demand():
                               decimal='.')
 
     simstadt_df = simstadt_df.rename({
-                                      'Total Yearly Heat+DHW demand': S_DEMAND,
-                                      'PrimaryUsageZoneArea': AREA,
-                                      'PrimaryUsageZoneType': TYPE,
-                                      }, axis='columns')
+        'Total Yearly Heat+DHW demand': S_DEMAND,
+        'PrimaryUsageZoneArea': AREA,
+        'PrimaryUsageZoneType': TYPE,
+    }, axis='columns')
 
     simstadt_df[S_DEMAND] = simstadt_df[S_DEMAND] / 1000  # kWh -> MWh
     simstadt_df = simstadt_df.set_index('GMLId')
 
-    by_type_df = simstadt_df.groupby([TYPE]).sum(numeric_only=True)[[AREA, S_DEMAND]]
+    by_type_df = simstadt_df.groupby([TYPE]).sum(
+        numeric_only=True)[[AREA, S_DEMAND]]
 
-    by_type_df['Specific heat demand [kWh / (m².a)]'] = by_type_df[S_DEMAND] / by_type_df[AREA] * 1000
+    by_type_df['Specific heat demand [kWh / (m².a)]'] = by_type_df[S_DEMAND] / \
+        by_type_df[AREA] * 1000
 
     print(by_type_df.round(1).to_string())
 
@@ -67,14 +69,17 @@ def compare_results(simstadt_df, urbi_df):
     simstadt_urbi = simstadt_df.join(urbi_df)[[S_DEMAND, U_DEMAND]]
     simstadt_urbi = simstadt_urbi.dropna()
 
-    simstadt_urbi.round(1).to_csv(f'simstadt_vs_urbi_heat_demands_{CITYGML}{SUFFIX}.csv', sep=';', decimal='.')
+    simstadt_urbi.round(1).to_csv(
+        f'simstadt_vs_urbi_heat_demands_{CITYGML}{SUFFIX}.csv', sep=';', decimal='.')
 
     plt.rcParams["figure.figsize"] = (10, 10)
-    ax = simstadt_urbi.plot.scatter(x=S_DEMAND, y=U_DEMAND, title=f'Urbi vs SimStadt Heat-demand\n{CITYGML}{SUFFIX}')
+    fig = simstadt_urbi.plot.scatter(
+        x=S_DEMAND, y=U_DEMAND, title=f'Urbi vs SimStadt Heat-demand\n{CITYGML}{SUFFIX}')
     max_heat_demand = simstadt_urbi.max().max()
-    ax.plot([0, max_heat_demand], [0, max_heat_demand])
-    ax.axis('equal')
-    plt.savefig(f'simstadt_vs_urbi_heat_demands_{CITYGML}{SUFFIX}.png', bbox_inches='tight')
+    fig.plot([0, max_heat_demand], [0, max_heat_demand])
+    fig.axis('equal')
+    plt.savefig(
+        f'simstadt_vs_urbi_heat_demands_{CITYGML}{SUFFIX}.png', bbox_inches='tight')
 
     print("Correlation:")
     print(simstadt_urbi.corr())