Upload engine.py

demo2/pages/engine.py

@@ -215,6 +215,13 @@ def run_engine(hazard_type, scenario, gdf_intensity, policies=[]):
         # Increase thresholds from “slight to moderate” as fewer people will be displaced.
         if 1 in policies and thresholds['metric7'] == DS_NO:
             thresholds['metric7'] = DS_SLIGHT
+            
+    if hazard_type == HAZARD_FLOOD:
+        # Policy-1: Loans for reconstruction for minor to moderate damages
+        # Changes: Damage state thresholds for “displacement”
+        # Increase thresholds from “slight to moderate” as fewer people will be displaced.
+        if 1 in policies:
+            thresholds['metric7'] = 1
 
         # Policy-3: Cat-bond agreement for education and health facilities
         # Changes: Damage state thresholds for “loss of access to hospitals” and “loss of access to schools”
@@ -238,12 +245,19 @@ def run_engine(hazard_type, scenario, gdf_intensity, policies=[]):
         if 4 in policies:
             thresholds['metric6'] = 1
     
-    if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+    if hazard_type == HAZARD_EARTHQUAKE:
+        # Policy-5: Technical assistance for debris removal in education facilities
+        # loss of education is solved via this policy. For both flood and debris
+        # loss of education metric is fixed.
+        if 5 in policies:
+            thresholds['metric7'] = DS_SLIGHT
+            
+    if hazard_type == HAZARD_FLOOD:
         # Policy-5: Technical assistance for debris removal in education facilities
         # loss of education is solved via this policy. For both flood and debris
         # loss of education metric is fixed.
         if 5 in policies:
-            thresholds['metric2'] = 1
+            thresholds['metric7'] = 1           
 
     #%% metric 1 number of unemployed workers in each building
     df_workers_per_building = df_workers[df_workers['ds'] > thresholds['metric1']].groupby('bldid',as_index=False).agg({'individ':'count'})