updating how we simulate the impact of floor insultion

.idea/Model.iml

@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">

.idea/misc.xml

@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
   <component name="PythonCompatibilityInspectionAdvertiser">
     <option name="version" value="3" />
   </component>

backend/Property.py

@@ -197,10 +197,11 @@ class Property:
             if len(recommendation["parts"]) > 1:
                 raise NotImplementedError("Have more than 1 floor insulation part - handle this case")
 
-            recommendation_record["floor_thermal_transmittance_ending"] = recommendation["new_u_value"]
+            # recommendation_record["floor_thermal_transmittance_ending"] = recommendation["new_u_value"]
             # We don't really see above average for this in the training data
             recommendation_record["floor_insulation_thickness_ending"] = "average"
-            recommendation_record["floor_energy_eff_ending"] = "Good"
+            # This is rarely ever populated in the training data
+            # recommendation_record["floor_energy_eff_ending"] = "Good"
         else:
             if recommendation_record["floor_thermal_transmittance_ending"] is None:
                 raise ValueError("We should not have a None value for the u value")

backend/app/utils.py

@@ -75,8 +75,8 @@ def sap_to_epc(sap_points: int | float):
     :return:
     """
 
-    if sap_points <= 0 or sap_points > 100:
+    if sap_points <= 0:
-        raise ValueError("SAP points should be between 1 and 100.")
+        raise ValueError("SAP points should be above 0.")
 
     if sap_points >= 92:
         return "A"

etl/testing_data/retrofitted_properties.py

@@ -0,0 +1,55 @@
+"""
+This script will create an input csv for the recommendation engine and upload it to S3, which can be used for
+testing
+"""
+import pandas as pd
+from utils.s3 import save_csv_to_s3
+
+USER_ID = 8
+PORTFOLIO_ID = 62
+
+
+def app():
+    """
+    This portfolio contains propertyies that we have demo'd in pilots, or properties that were provided to us
+    as proprties that are being treated under funding scehemes and we have pre/post EPRs for
+    :return:
+    """
+
+    test_file = pd.DataFrame(
+        [
+            # Pilot properties
+            {'address': '113 Tenby Road', 'postcode': 'B13 9LT', 'Notes': ''},
+            {'address': '139 School Road', 'postcode': 'B28 8JF', 'Notes': ''},
+            {'address': '77 Simmons Drive', 'postcode': 'B32 1SL', 'Notes': ''},
+            {'address': 'Flat 2, 54 Wedgewood Road', 'postcode': 'B32 1LS', 'Notes': ''},
+            # Warmfront ECO4 Properties
+            {'address': '73 Long Chaulden', 'postcode': 'HP1 2HX', 'Notes': ''},
+            {'address': '8 Lindlings', 'postcode': 'HP1 2HA', 'Notes': ''},
+            {'address': '44 Lindlings', 'postcode': 'HP1 2HE', 'Notes': ''},
+            {'address': '46 Chaulden Terrace', 'postcode': 'HP1 2AN', 'Notes': ''},
+            # Osmosis SHDF Properties
+            {'address': '4, Heather Shaw', 'postcode': 'BA14 7JS', 'Notes': ''},
+            {'address': '16 Glastonbury Road', 'postcode': 'M32 9PE', 'Notes': ''},
+            {'address': '31 Loddon Way', 'postcode': 'BA15 1HG', 'Notes': ''},
+            {'address': '62 Pearmain Drive', 'postcode': 'NG3 3DJ', 'Notes': ''},
+        ]
+
+    )
+
+    # Store the data in s3
+    filename = f"{USER_ID}/{PORTFOLIO_ID}/eco4_shdf_retrofits.csv"
+    save_csv_to_s3(
+        dataframe=test_file,
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename
+    )
+
+    body = {
+        "portfolio_id": str(PORTFOLIO_ID),
+        "housing_type": "Social",
+        "goal": "Increase EPC",
+        "goal_value": "A",
+        "trigger_file_path": filename
+    }
+    print(body)

recommendations/SolarPvRecommendations.py

@@ -5,8 +5,8 @@ from recommendations.Costs import Costs
 class SolarPvRecommendations:
     # Approximate area of the solar panels
     SOLAR_PANEL_AREA = 1.6
-    # Wattage per panel
+    # Wattage per panel - this is based on the average wattage of a solar panel being between 250w and 420w
-    SOLAR_PANEL_WATTAGE = 360
+    SOLAR_PANEL_WATTAGE = 250
 
     def __init__(self, property_instance):
         """
@@ -47,7 +47,7 @@ class SolarPvRecommendations:
         # of solar PV installations
         cost_result = self.costs.solar_pv(wattage=solar_panel_wattage)
 
-        kw = int(np.round(solar_panel_wattage / 1000))
+        kw = np.floor(solar_panel_wattage / 100) / 10
 
         self.recommendation = [
             {