pulling out lighting averages

backend/app/db/functions/property_functions.py

@@ -82,3 +82,18 @@ def create_property_targets(property_id: int, portfolio_id: int, epc_target=None
         session.commit()
 
     return True
+
+
+def update_property_data(property_data):
+    Session = sessionmaker(bind=db_engine)
+    now = datetime.datetime.now()
+    with Session() as session:
+        new_target = PropertyTargetsModel(
+            property_id=property_id,
+            portfolio_id=portfolio_id,
+            created_at=now,
+            epc=epc_target,
+            heat_demand=heat_demand_target
+        )
+        session.add(new_target)
+        session.commit()

backend/app/plan/router.py

@@ -70,6 +70,12 @@ walls_decile_data = {
                       'Decile 9', 'Decile 10'], 'decile_boundaries': [6., 49., 51., 55., 64., 71., 76., 83., 96.,
                                                                       120., 2279.]}
 
+lighting_averages = [
+    {'lighting-description': 'good lighting efficiency', 'low-energy-lighting': 99.26666666666667},
+    {'lighting-description': 'excellent lighting efficiency', 'low-energy-lighting': 100.0},
+    {'lighting-description': 'below average lighting efficiency', 'low-energy-lighting': 0.0}
+]
+
 
 @router.post("/trigger")
 async def trigger_plan(body: PlanTriggerRequest):
@@ -128,6 +134,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         )
         p.set_is_in_conservation_area(in_conservation_area)
 
+    # TODO: This won't work perfectly as we need the table of lighting averages by constituency
     cleaner = EpcClean(data=[x.data for x in input_properties])
     cleaner.clean()
 

model_data/EpcClean.py

@@ -31,7 +31,8 @@ class EpcClean:
         "lighting-description"
     ]
 
-    def __init__(self, data: List[Dict[str, Any]]) -> None:
+    def __init__(self, data: List[Dict[str, Any]],
+                 lighting_averages: List[Dict[str, str | float]] | None = None) -> None:
         """
         EpcClean constructor.
 
@@ -41,7 +42,10 @@ class EpcClean:
         self.unique_vals: Dict[str, Any] = {}
         self.cleaned: Dict[str, List[Any]] = {}
 
-        self.lighting_averages = self._calculate_lighting_averages()
+        if not lighting_averages:
+            self.lighting_averages = self._calculate_lighting_averages()
+        else:
+            self.lighting_averages = lighting_averages
 
     def _calculate_lighting_averages(self):
 

model_data/app.py

@@ -74,6 +74,8 @@ def app():
 
     # Incorporate input data into cleaning
     cleaner = EpcClean(data)
+    lighting_averages = cleaner.lighting_averages
+    # TODO: WE need to store lighting_averages to a db
     cleaner.clean()
     # TODO: cleaner.cleaned datasets to a db
 

model_data/epc_attributes/LightingAttributes.py

@@ -22,10 +22,12 @@ class LightingAttributes:
 
         if ('good lighting efficiency' in description) or ('excellent lighting efficiency' in description) or \
             ('below average lighting efficiency' in description):
+            average = [
+                x for x in self.averages if x["lighting-description"] == description
+            ][0]["low-energy-lighting"]
+
             return {
-                "low_energy_proportion": self.averages[
-                    self.averages["lighting-description"] == description
-                    ]["low-energy-lighting"].values[0]
+                "low_energy_proportion": average
             }
 
         match = re.search(r'\d+', description)