fixed generate_scenarios_data

backend/Property.py

@@ -566,8 +566,6 @@ class Property:
         if not self.data:
             raise ValueError("Property does not contain data")
 
-        self.set_basic_property_dimensions()
-
         for description, attribute in cleaned.items():
 
             if self.data[description] in self.DATA_ANOMALY_MATCHES:
@@ -615,6 +613,7 @@ class Property:
 
             setattr(self, self.ATTRIBUTE_MAP[description], attributes[0])
 
+        self.set_basic_property_dimensions()
         self.set_wall_type()
         self.set_floor_type()
         self.set_floor_level()
@@ -629,15 +628,6 @@ class Property:
         """
         self.solar_panel_configuration = solar_panel_configuration
 
-        if not self.roof["is_flat"]:
-            default_roof_area = estimate_pitched_roof_area(
-                floor_area=self.insulation_floor_area,
-            )
-        else:
-            default_roof_area = self.insulation_floor_area
-
-        self.roof_area = default_roof_area
-
     def set_current_energy(self, kwh_client, kwh_predictions):
         """
         Given what we know about the property now, estimates the current energy consumption using the UCL paper
@@ -972,6 +962,13 @@ class Property:
                 self.floor_area / self.number_of_floors
             )
 
+        if not self.roof["is_flat"]:
+            self.roof_area = estimate_pitched_roof_area(
+                floor_area=self.insulation_floor_area,
+            )
+        else:
+            self.roof_area = self.insulation_floor_area
+
     def set_floor_level(self):
         self.floor_level = (
             FLOOR_LEVEL_MAP[self.data["floor-level"]]

etl/epc/generate_scenarios_data.py

@@ -3,25 +3,24 @@ import itertools
 
 import pandas as pd
 from etl.epc.Record import EPCRecord
+from etl.bill_savings.KwhData import KwhData
 from backend.SearchEpc import SearchEpc
 
 from sqlalchemy.orm import sessionmaker
 
-from backend.app.config import get_settings
+from backend.app.config import get_settings, get_prediction_buckets
 from backend.app.db.connection import db_engine
 from backend.app.db.functions.materials_functions import get_materials
+from backend.ml_models.api import ModelApi
 
 from backend.app.plan.utils import get_cleaned
 
 from backend.Property import Property
-from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 
 from recommendations.Recommendations import Recommendations
 from utils.logger import setup_logger
 from utils.s3 import read_dataframe_from_s3_parquet, save_dataframe_to_s3_parquet
 
-from datetime import datetime
-
 now = datetime.now().strftime("%d-%m-%Y-%H-%M-%S")
 
 logger = setup_logger()
@@ -41,21 +40,16 @@ cleaning_data = read_dataframe_from_s3_parquet(
 materials = get_materials(session)
 cleaned = get_cleaned()
 
-# TODO: THIS IS A TEMPORARY FIX
-new_walls_description_mapping = pd.DataFrame(cleaned["walls-description"])
-new_walls_description_mapping.loc[
-    ~new_walls_description_mapping["thermal_transmittance_unit"].isnull(),
-    "thermal_transmittance_unit",
-] = "w/m-¦k"
-
-cleaned["walls-description"] = new_walls_description_mapping.to_dict(orient="records")
-
 uprn_filenames = read_dataframe_from_s3_parquet(
     bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
 )
-photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(
-    bucket=get_settings().DATA_BUCKET
+
+kwh_client = KwhData(bucket="retrofit-data-dev", read_consumption_data=False)
+kwh_client.retail_price_comparison = pd.DataFrame(
+    [{"Date": datetime.today().strftime("%Y-%m-%d"),
+      'Average standard variable tariff (Large legacy suppliers)': 1}]
 )
+kwh_client.retail_price_comparison["Date"] = pd.to_datetime(kwh_client.retail_price_comparison["Date"])
 
 scenario_properties = [
     {
@@ -132,7 +126,6 @@ scenario_properties = [
     },
 ]
 
-
 recommendations_scoring_data = []
 
 for scenario_property in scenario_properties:
@@ -173,7 +166,17 @@ for scenario_property in scenario_properties:
     )
 
     p.get_spatial_data(uprn_filenames)
-    p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
+
+    kwh_predictions = {
+        "heating_kwh_predictions": pd.DataFrame([{"id": p.uprn, "predictions": 12000}]),
+        "hotwater_kwh_predictions": pd.DataFrame([{"id": p.uprn, "predictions": 3000}]),
+    }
+    p.set_features(cleaned, kwh_client, kwh_predictions)
+    p.solar_panel_configuration = {
+        "panel_performance": pd.DataFrame(
+            [{"panneled_roof_area": 34, "n_panels": 10, "array_wattage": 4000, "initial_ac_kwh_per_year": 3800}]
+        )
+    }
 
     recommender = Recommendations(property_instance=p, materials=materials)
     property_recommendations = recommender.recommend()
@@ -277,20 +280,12 @@ recommendations_scoring_data.insert(0, "impact", impact_col)
 id_col = recommendations_scoring_data.pop("id")
 recommendations_scoring_data.insert(0, "id", id_col)
 
-from backend.ml_models.api import ModelApi
-
-model_api = ModelApi(portfolio_id="generate-scenarios-data", timestamp=created_at)
-
-all_predictions = model_api.predict_all(
-    df=recommendations_scoring_data,
-    bucket=get_settings().DATA_BUCKET,
-    prediction_buckets={
-        "sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
-        "heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
-        "carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET,
-    },
+model_api = ModelApi(
+    portfolio_id="generate-scenarios-data", timestamp=created_at, prediction_buckets=get_prediction_buckets()
 )
 
+all_predictions = model_api.predict_all(df=recommendations_scoring_data, bucket=get_settings().DATA_BUCKET)
+
 save_dataframe_to_s3_parquet(
     recommendations_scoring_data,
     "retrofit-data-dev",

recommendations/SolarPvRecommendations.py

@@ -175,9 +175,7 @@ class SolarPvRecommendations:
             if self.property.roof["is_flat"]:
                 roof_area = self.property.insulation_floor_area
             else:
-                roof_area = estimate_pitched_roof_area(
-                    floor_area=self.property.insulation_floor_area, floor_height=self.property.data["floor-height"]
-                )
+                roof_area = estimate_pitched_roof_area(floor_area=self.property.insulation_floor_area, )
             solar_configurations = pd.DataFrame(
                 [
                     {