revising scenarios, fixing setting of energy sources

backend/Property.py

@@ -1171,7 +1171,9 @@ class Property:
             'has_exhaust_source_heat_pump': 'Electricity',
             'has_community_heat_pump': 'Electricity',
             'has_wood_pellets': 'Wood Pellets',
-            'has_community_scheme': 'Varied (Community Scheme)'
+            'has_community_scheme': 'Varied (Community Scheme)',
+            "has_dual_fuel_mineral_and_wood": 'Wood Logs',
+            "has_electricaire": 'Electricity',
         }
 
         # Hot water
@@ -1197,9 +1199,9 @@ class Property:
             'community scheme': 'Community Scheme'
         }
 
-        self.heating_energy_source = [
+        self.heating_energy_source = list({
             fuel for key, fuel in heating_fuel_mapping.items() if self.main_heating.get(key, False)
-        ]
+        })
         if len(self.heating_energy_source) == 0 or len(self.heating_energy_source) > 1:
             raise Exception("Investigate me")
 

backend/app/plan/router.py

@@ -320,11 +320,11 @@ def extract_propert_on_site_recommendations(config, already_installed, non_invas
 
     # Because we have some non-invasive recommendations that match on address and postcode, but not UPRN
     # we need to check existence of uprn
-    has_uprn = "non_invasive_recommendations" in non_invasive_recommendations[0]
+    has_uprn = "uprn" in non_invasive_recommendations[0]
     if has_uprn:
         property_non_invasive_recommendations = next((
             x for x in non_invasive_recommendations if
-            (x["uprn"] == uprn)
+            (str(x["uprn"]) == str(uprn))
         ), {})
 
         # We patch the non-invasive recs that are ['cavity_extract_and_refill']
@@ -334,7 +334,7 @@ def extract_propert_on_site_recommendations(config, already_installed, non_invas
             (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
         ), {})
 
-    if isinstance(property_non_invasive_recommendations["recommendations"], str):
+    if isinstance(property_non_invasive_recommendations.get("recommendations"), str):
         import ast
         property_non_invasive_recommendations["recommendations"] = ast.literal_eval(
             property_non_invasive_recommendations["recommendations"]
@@ -498,8 +498,8 @@ async def trigger_plan(body: PlanTriggerRequest):
 
         logger.info("Setting property features")
         [p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_preds) for p in input_properties]
-
         logger.info("Performing solar analysis")
+
         # TODO: Tidy this up
         # TODO: If a property is semi-detached, we might get roof surfaces for the main building + the neighbour
         # TODO: If we can't get high image quality, should we use the solar API? Maybe just for semi-detached units with

backend/app/plan/schemas.py

@@ -34,7 +34,7 @@ class PlanTriggerRequest(BaseModel):
         # Specific measures
         "air_source_heat_pump",
         "internal_wall_insulation",
-        "external_wall_insulation"
+        "external_wall_insulation",
     }
 
     _allowed_goals = {"Increasing EPC"}

etl/bill_savings/KwhData.py

@@ -25,7 +25,8 @@ class KwhData:
         "county",
         "windows-description", "windows-energy-eff", "flat-top-storey",
         "flat-storey-count", "unheated-corridor-length", "solar-water-heating-flag", "mechanical-ventilation",
-        "low-energy-lighting", "environment-impact-current", "energy-tariff", "current-energy-rating"
+        "low-energy-lighting", "environment-impact-current", "energy-tariff", "current-energy-rating",
+        "floor-level"
     ]
 
     NUMERICAL_COLUMNS = [

etl/customers/newhaven/newhaven_study.py

@@ -11,7 +11,7 @@ EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certif
 CUSTOMER_DATA_DIRECTORY = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/Data"
 
 USER_ID = 8
-PORTFOLIO_ID = 88
+PORTFOLIO_ID = 89
 
 
 def make_asset_list():
@@ -88,20 +88,20 @@ def make_asset_list():
         columns={"Wall Area [m^2]": "insulation_wall_area", "Building Area [m^2]": "floor_area"}
     )
 
-    had_an_epc = asset_list[~pd.isnull(asset_list["current-energy-efficiency"])]
+    # had_an_epc = asset_list[~pd.isnull(asset_list["current-energy-efficiency"])]
-    below_b = asset_list[asset_list["current-energy-efficiency"].astype(float) <= 80].shape
+    # below_b = asset_list[asset_list["current-energy-efficiency"].astype(float) <= 80].shape
-    below_c = asset_list[asset_list["current-energy-efficiency"].astype(float) <= 69].shape
+    # below_c = asset_list[asset_list["current-energy-efficiency"].astype(float) <= 69].shape
-    had_an_epc["energy-efficiency-rating"].value_counts()
+    # had_an_epc["energy-efficiency-rating"].value_counts()
-    asset_list["current-energy-rating"].value_counts()
+    # asset_list["current-energy-rating"].value_counts()
-    asset_list["co2-emissions-current"].mean()
+    # asset_list["co2-emissions-current"].mean()
-    # Get the underlying data of a histograme
+    # # Get the underlying data of a histograme
-    import matplotlib.pyplot as plt
+    # import matplotlib.pyplot as plt
-    n, bins, patches = plt.hist(asset_list["co2-emissions-current"], bins=100, color="blue", alpha=0.7)
+    # n, bins, patches = plt.hist(asset_list["co2-emissions-current"], bins=100, color="blue", alpha=0.7)
-
+    #
-    bins = np.arange(0, asset_list["co2-emissions-current"].max(), 1)  # Bins from 50 to 150 with a step of 10
+    # bins = np.arange(0, asset_list["co2-emissions-current"].max(), 1)  # Bins from 50 to 150 with a step of 10
-
+    #
-    # Step 3: Calculate the frequency of data in each bin
+    # # Step 3: Calculate the frequency of data in each bin
-    hist, bin_edges = np.histogram(asset_list["co2-emissions-current"], bins=bins)
+    # hist, bin_edges = np.histogram(asset_list["co2-emissions-current"], bins=bins)
 
     # Take properties below a B - there are 2844 units
     asset_list = asset_list[asset_list["current-energy-efficiency"].astype(float) <= 80]
@@ -110,7 +110,7 @@ def make_asset_list():
     asset_list = asset_list[~pd.isnull(asset_list["current-energy-efficiency"])]
 
     # Take a 10% sample, for properties that have an EPC, with a seed
-    asset_list = asset_list.sample(frac=0.1, random_state=42)
+    asset_list = asset_list.sample(frac=0.25, random_state=42)
 
     AVG_FLOOR_HEIGHT = asset_list["floor-height"].median()
 
@@ -119,11 +119,11 @@ def make_asset_list():
     ):
 
         if address_base_property_description == "Self Contained Flat (Includes Maisonette / Apartment)":
-            if epc_property_type == "Flat":
+            if epc_property_type in ["Flat"]:
                 return 1
             if epc_property_type == "Maisonette":
                 return 2
-            raise NotImplementedError("Implement me")
+            return None
 
         if pd.isnull(floor_height):
             return np.round(building_height / AVG_FLOOR_HEIGHT)
@@ -140,6 +140,8 @@ def make_asset_list():
         ),
         axis=1
     )
+    # Drop any entires with null floors because that means the ordnance survey data doesn't align with the epc data
+    asset_list = asset_list[~pd.isnull(asset_list["number_of_floors"])]
 
     # D    0.419929
     # C    0.391459
@@ -262,16 +264,16 @@ def make_asset_list():
         "already_installed_file_path": "",
         "patches_file_path": "",
         "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
-        "scenario_name": "Demand Reduction - no solid wall",
+        "scenario_name": "Demand Reduction - no solid wall, windows, LEDs",
         "multi_plan": True,
         "exclusions": [
-            "internal_wall_insulation", "external_wall_insulation", "floor_insulation", "heating", "solar_pv"
+            "internal_wall_insulation", "external_wall_insulation", "floor_insulation", "heating", "solar_pv",
+            "lighting", "windows"
         ],
         "budget": None,
     }
     print(body1)
 
-    # Scenario B
     body2 = {
         "portfolio_id": str(PORTFOLIO_ID),
         "housing_type": "Private",
@@ -281,15 +283,34 @@ def make_asset_list():
         "already_installed_file_path": "",
         "patches_file_path": "",
         "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
-        "scenario_name": "Demand Reduction, Heating Systems, Solar PV - no solid wall",
+        "scenario_name": "Demand Reduction - no solid wall, floors or heating",
         "multi_plan": True,
-        "exclusions": ["internal_wall_insulation", "external_wall_insulation", "floor_insulation"],
+        "exclusions": [
+            "internal_wall_insulation", "external_wall_insulation", "floor_insulation", "heating", "solar_pv",
+        ],
         "budget": None,
     }
     print(body2)
 
-    # Scenario C - deep fabric, no exclusions
+    # Scenario B
     body3 = {
+        "portfolio_id": str(PORTFOLIO_ID),
+        "housing_type": "Private",
+        "goal": "Increasing EPC",
+        "goal_value": "A",
+        "trigger_file_path": filename,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+        "scenario_name": "Demand Reduction, Heating Systems, Solar PV - no solid wall or floors",
+        "multi_plan": True,
+        "exclusions": ["internal_wall_insulation", "external_wall_insulation", "floor_insulation"],
+        "budget": None,
+    }
+    print(body3)
+
+    # Scenario 4 - deep fabric, no IWI, floor
+    body4 = {
         "portfolio_id": str(PORTFOLIO_ID),
         "housing_type": "Private",
         "goal": "Increasing EPC",
@@ -302,4 +323,4 @@ def make_asset_list():
         "multi_plan": True,
         "budget": None,
     }
-    print(body3)
+    print(body4)