set up the rest of the solar recommendation

backend/Property.py

@@ -221,11 +221,15 @@ class Property(Definitions):
 
             setattr(self, attribute, value)
 
-    def get_components(self, cleaned):
+    def get_components(self, cleaned, photo_supply_lookup, floor_area_decile_thresholds):
         """
         Given the cleaning that has been performed, we'll use this to identify the property
         components, from roof to walls to windows, heating and hot water
         :param cleaned: This is the dictionary of components found in cleaner.cleaned
+        :param photo_supply_lookup: This is the lookup table for the photo supply, used to estimate the percentage
+                                    of the roof that is suitable for solar panels
+        :param floor_area_decile_thresholds: This is the decile thresholds for the floor area, used in estimating the
+                                             solar pv roof area
         :return:
         """
 
@@ -295,6 +299,9 @@ class Property(Definitions):
         self.set_floor_type()
         self.set_floor_level()
         self.set_windows_count()
+        self.set_solar_panel_area(
+            photo_supply_lookup=photo_supply_lookup, floor_area_decile_thresholds=floor_area_decile_thresholds
+        )
 
     def set_age_band(self):
         """
@@ -849,7 +856,9 @@ class Property(Definitions):
                     return i  # Returns the decile index (0 to 9)
             return len(thresholds)
 
-        floor_area_decile = classify_floor_area(self.floor_area, floor_area_decile_thresholds)
+        floor_area_decile = classify_floor_area(
+            self.floor_area, floor_area_decile_thresholds["floor_area_decile_thresholds"].values
+        )
 
         # Given the photo_supply_lookup, we esimate the percentage of the roof that is suitable for solar panels
 

backend/app/plan/router.py

@@ -67,6 +67,12 @@ async def trigger_plan(body: PlanTriggerRequest):
         cleaning_data = read_parquet_from_s3(
             bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
         )
+        photo_supply_lookup = read_parquet_from_s3(
+            bucket_name=get_settings().DATA_BUCKET, file_key="solar_pv_supply/photo_supply_lookup.parquet",
+        )
+        floor_area_decile_thresholds = read_parquet_from_s3(
+            bucket_name=get_settings().DATA_BUCKET, file_key="solar_pv_supply/floor_area_decile_thresholds.parquet",
+        )
 
         input_properties = []
         for config in plan_input:
@@ -129,7 +135,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         for p in input_properties:
 
             # Property recommendations
-            p.get_components(cleaned)
+            p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
 
             # This is temp - this should happen after scoring
             cleaned_property_data = DataProcessor.apply_averages_cleaning(

etl/testing_data/solar_research.py

@@ -101,7 +101,7 @@ def app():
     save_dataframe_to_s3_parquet(
         df=aggregated,
         bucket_name="retrofit-data-dev",
-        file_key=f"solar_pv_supply/photo_supply_lookup.parquet",
+        file_key="solar_pv_supply/photo_supply_lookup.parquet",
     )
 
     floor_area_decile_thresholds = pd.DataFrame(decile_thresholds, columns=["floor_area_decile_thresholds"])

recommendations/Costs.py

@@ -831,5 +831,35 @@ class Costs:
             "labour_days": labour_days
         }
 
-    def solar_pv(self, wattage):
-        pass
+    def solar_pv(self, wattage: float):
+
+        """
+        Calculates the total cost for solar PV based data provided by the MCS dashboard, which contains
+        costing data for installations of renewable and clean energy measures.
+
+        The data in the dashboard is filtered on domestic building installations and then the data across the
+        various regions is manually collected. There is currently no automated way to get the data from the MCS
+        dashboard
+        :param wattage:
+        :return:
+        """
+
+        # Get the cost data relevant to the region
+        regional_cost = MCS_SOLAR_PV_COST_DATA["-".join(["average_cost_per_kwh", self.region])]
+
+        kw = wattage / 1000
+        total_cost = kw * regional_cost
+
+        subtotal_before_vat = total_cost / (1 + self.VAT_RATE)
+        vat = total_cost - subtotal_before_vat
+
+        # Labour hours are based on estimates from online research but an average team seems to consist of 3 people
+        # and most jobs take around 2 days. Assuming an 8 hour day for 3 people across 2 days, gives us 72 hours of
+        # labour
+        return {
+            "total": total_cost,
+            "subtotal": subtotal_before_vat,
+            "vat": vat,
+            "labour_hours": 72,
+            "labour_days": 2,
+        }

recommendations/SolarPvRecommendations.py

@@ -16,7 +16,7 @@ class SolarPvRecommendations:
         self.property = property_instance
         self.costs = Costs(self.property)
 
-        self.recommendations = []
+        self.recommendation = []
 
     def recommend(self):
         """
@@ -35,12 +35,27 @@ class SolarPvRecommendations:
             None, 0, self.property.DATA_ANOMALY_MATCHES
         ]
 
-        if not is_valid_property_type or not is_valid_roof_type or has_no_existing_solar_pv:
+        if not is_valid_property_type or not is_valid_roof_type or not has_no_existing_solar_pv:
             return
 
         # We now have a property which is potentially suitable for solar PV
         number_solar_panels = np.floor(self.property.solar_pv_roof_area / self.SOLAR_PANEL_AREA)
-        solar_panel_capacity = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+        solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
 
         # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
         # of solar PV installations
+        cost_result = self.costs.solar_pv(wattage=solar_panel_wattage)
+
+        kw = int(np.round(solar_panel_wattage / 1000))
+
+        self.recommendation = [
+            {
+                "parts": [],
+                "type": "solar_pv",
+                "description": f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on the roof",
+                "starting_u_value": None,
+                "new_u_value": None,
+                "sap_points": None,
+                **cost_result,
+            }
+        ]