added additional secondary heating recommendation

.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="Stonewater-wave-3" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Fastapi-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="Stonewater-wave-3" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Fastapi-backend" project-jdk-type="Python SDK" />
   <component name="PyCharmProfessionalAdvertiser">
     <option name="shown" value="true" />
   </component>

backend/apis/GoogleSolarApi.py

@@ -792,9 +792,14 @@ class GoogleSolarApi:
             property_instance = [p for p in input_properties if p.id == unit["property_id"]][0]
             # At this level, we check if the property is suitable for solar and if now, skip
             # Or if we have a solar non-invasive recommendation
+
+            non_invasive_rec = next(
+                (r for r in property_instance.non_invasive_recommendations if r["type"] == "solar_pv"), {}
+            ).get("array_wattage")
+
             if (
                 (not property_instance.is_solar_pv_valid()) or
-                [r for r in property_instance.non_invasive_recommendations if r["type"] == "solar_pv"]
+                non_invasive_rec is not None
             ):
                 continue
 

backend/app/plan/router.py

@@ -394,7 +394,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         logger.info("Getting the inputs")
         plan_input = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path)
         # Check for duplicate UPRNS
-        input_uprns = [x.get("uprn") for x in plan_input if "uprn" in x]
+        input_uprns = [x.get("uprn") for x in plan_input if "uprn" in x and x.get("uprn")]
         if input_uprns:
             # Check for dupes
             if len(input_uprns) != len(set(input_uprns)):

etl/customers/ksquared/Wave3 Modelling.py

@@ -2,6 +2,7 @@ import os
 import time
 import re
 
+from etl.epc.settings import EARLIEST_EPC_DATE
 from dotenv import load_dotenv
 from tqdm import tqdm
 import pandas as pd
@@ -236,7 +237,7 @@ def caha():
             address = remap_address(address)
 
         find_epc_searcher = RetrieveFindMyEpc(address=address, postcode=postcode)
-        find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
+        find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data(sap_2012_date=EARLIEST_EPC_DATE)
         time.sleep(0.5)
         # We need uprn
         searcher = SearchEpc(
@@ -249,18 +250,102 @@ def caha():
         searcher.find_property(skip_os=True)
         newest_epc = searcher.newest_epc
 
+        uprn = newest_epc["uprn"]
+        if address in ["Flat D, 11 Victoria Avenue", "Flat B, 11 Victoria Avenue"]:
+            uprn = None
+
         extracted_data.append(
             {
-                "uprn": newest_epc["uprn"],
+                "uprn": uprn,
                 **find_epc_data,
             }
         )
 
         asset_list.append(
             {
-                "uprn": newest_epc["uprn"],
+                "uprn": uprn,
-                "address": home["Address letter or number"],
+                "address": address,
                 "postcode": home["Postcode"],
                 "property_type": newest_epc["property-type"],
             }
         )
+
+    non_invasive_recommendations = [
+        {
+            "uprn": r["uprn"],
+            "recommendations": r["recommendations"]
+        } for r in extracted_data
+    ]
+    # for r in non_invasive_recommendations:
+    #     new_recommendations = []
+    #     extracted = [r for r in extracted_data if r["uprn"] == r["uprn"]][0]
+    #     for rec in r["recommendations"]:
+    #         if extracted["hotwater-description"] == "Gas boiler/circulator, no cylinder thermostat":
+    #             if rec["type"] in ["hot_water_tank_insulation", "cylinder_thermostat"]:
+    #                 continue
+    #         rec["survey"] = False
+    #         new_recommendations.append(rec)
+    #     r["recommendations"] = new_recommendations
+
+    # We model the two properties separately
+    asset_list = pd.DataFrame(asset_list)
+    # Drop Flat D, 11 Victoria Avenue
+    asset_list1 = asset_list[asset_list["address"] != "Flat D, 11 Victoria Avenue"]
+    asset_list2 = asset_list[asset_list["address"] == "Flat D, 11 Victoria Avenue"]
+
+    # Store the asset list in s3
+    filename = f"{USER_ID}/{CAHA_PORTFOLIO_ID}/asset_list1.csv"
+    save_csv_to_s3(
+        dataframe=asset_list1,
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename
+    )
+
+    filename2 = f"{USER_ID}/{CAHA_PORTFOLIO_ID}/asset_list2.csv"
+    save_csv_to_s3(
+        dataframe=asset_list2,
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename2
+    )
+
+    # Store the non-invasive recommendations in s3
+    non_invasive_recommendations_filename = f"{USER_ID}/{CAHA_PORTFOLIO_ID}/non_invasive_recommendations.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(non_invasive_recommendations),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=non_invasive_recommendations_filename
+    )
+
+    body = {
+        "portfolio_id": str(CAHA_PORTFOLIO_ID),
+        "housing_type": "Social",
+        "goal": "Increasing EPC",
+        "goal_value": "C",
+        "trigger_file_path": filename,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+        "valuation_file_path": "",
+        "scenario_name": "Wave 3 Packages",
+        "multi_plan": True,
+        "budget": None,
+        "exclusions": ["boiler_upgrade"]
+    }
+    print(body)
+
+    body2 = {
+        "portfolio_id": str(CAHA_PORTFOLIO_ID),
+        "housing_type": "Social",
+        "goal": "Increasing EPC",
+        "goal_value": "C",
+        "trigger_file_path": filename2,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+        "valuation_file_path": "",
+        "scenario_name": "Wave 3 Packages",
+        "multi_plan": True,
+        "budget": None,
+        "exclusions": ["boiler_upgrade"]
+    }
+    print(body2)

etl/customers/stonewater/Wave 3 Preparation.py

@@ -1411,5 +1411,45 @@ def find_remaining_surveys():
 
     assert needed.shape[0] + costed.shape[0] == surveyed.shape[0]
 
+
+def append_stonewater_id():
+    """
+    This completes an adhoc request from Stonewater to add in their organisation Reference onto the model
+    :return:
+    """
+
+    model_proposed_sample = pd.read_excel(
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Stonewater - Bid Packages WIP 13.11.24.xlsx",
+        sheet_name="Modelled Packages",
+        header=13
+    )
+    model_proposed_sample = model_proposed_sample[~pd.isnull(model_proposed_sample["Address ID"])]
+    model_proposed_sample["Address ID"] = model_proposed_sample["Address ID"].astype(int)
+
+    original_archetypes = pd.read_excel(
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Stonewater SHDF_3_0_Board Triage 22.05.24 "
+        "- Archetyped V3.1.xlsx",
+        header=4
+    )
+    original_archetypes = original_archetypes[~pd.isnull(original_archetypes["Address ID"])]
+    original_archetypes = original_archetypes[original_archetypes["Address ID"] != "Address ID"]
+    original_archetypes["Address ID"] = original_archetypes["Address ID"].astype(int)
+
+    matched = model_proposed_sample.merge(
+        original_archetypes[["Address ID", 'Org. ref.']],
+        on="Address ID",
+        how="left"
+    )
+
+    if pd.isnull(matched["Org. ref."]).sum():
+        raise ValueError("Something went wrong")
+
+    # Save as CSV
+    matched.to_excel(
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Stonewater IDs.xlsx",
+        sheet_name="Proposed Wave 3 Sample",
+        index=False
+    )
+
 # if __name__ == "__main__":
 #     main()

etl/find_my_epc/RetrieveFindMyEpc.py

@@ -1,3 +1,4 @@
+import pandas as pd
 import requests
 from bs4 import BeautifulSoup
 from datetime import datetime
@@ -25,7 +26,7 @@ class RetrieveFindMyEpc:
 
         self.address_cleaned = self.address.replace(",", "").replace(" ", "").lower()
 
-    def retrieve_newest_find_my_epc_data(self):
+    def retrieve_newest_find_my_epc_data(self, sap_2012_date=None):
         """
         For a post code and address, we pull out all the required data from the find my epc website
         """
@@ -188,7 +189,7 @@ class RetrieveFindMyEpc:
                 raise ValueError(f"Missing key: {key}")
 
         # Finally, we format the recommendations
-        recommendations = self.format_recommendations(recommendations)
+        recommendations = self.format_recommendations(recommendations, assessment_data, sap_2012_date)
 
         resulting_data = {
             'epc_certificate': epc_certificate,
@@ -205,11 +206,12 @@ class RetrieveFindMyEpc:
         return resulting_data
 
     @staticmethod
-    def format_recommendations(recommendations):
+    def format_recommendations(recommendations, assessment_data, sap_2012_date=None):
         """
         This function converts the recommendations to a format that we can use in the engine as a non-intrusive survey
-        :param recommendations:
+        :param recommendations: The recommendations from the EPC
-        :return:
+        :param assessment_data: The assessment data from the EPC
+        :param sap_2012_date: The date of the SAP 2012 update
         """
 
         measure_map = {
@@ -246,17 +248,23 @@ class RetrieveFindMyEpc:
             "Double glazing": ["double_glazing"],
         }
 
+        survey = True
+        if sap_2012_date is not None:
+            certificate_date = datetime.strptime(assessment_data["Date of certificate"], "%d %B %Y")
+            if certificate_date < pd.to_datetime(sap_2012_date):
+                survey = False
+
         formatted_recommendations = []
         for rec in recommendations:
-
             mapped = measure_map[rec["measure"]]
             for measure in mapped:
-                formatted_recommendations.append(
+                to_append = {
-                    {
+                    "type": measure,
-                        "type": measure,
+                    "sap_points": rec["sap_points"],
-                        "sap_points": rec["sap_points"],
+                    "survey": survey,
-                        "survey": True
+                }
-                    }
+                if measure == "solar_pv":
-                )
+                    to_append["suitable"] = True
+                formatted_recommendations.append(to_append)
 
         return formatted_recommendations

recommendations/HotwaterRecommendations.py

@@ -60,15 +60,21 @@ class HotwaterRecommendations:
 
             # If there is no system present, but access to the mains, we
 
+        has_tank_recommendation = [r for r in self.recommendations if r["type"] == "hot_water_tank_insulation"]
+
         if (
             (self.property.hotwater["heater_type"] in ["electric immersion"]) &
             (self.property.data["hot-water-energy-eff"] == "Very Poor") &
-            (self.property.hotwater["no_system_present"] is None)
+            (self.property.hotwater["no_system_present"] is None) &
+            len(has_tank_recommendation) == 0
         ):
             self.recommend_tank_insulation(phase=phase)
             return
 
-        if self.property.hotwater["clean_description"] == "From main system, no cylinder thermostat":
+        has_cylinder_recommendation = [r for r in self.recommendations if r["type"] == "cylinder_thermostat"]
+
+        if ((self.property.hotwater["clean_description"] == "From main system, no cylinder thermostat") &
+            (len(has_cylinder_recommendation) == 0)):
             self.recommend_cylinder_thermostat(phase=phase)
             return
 

recommendations/SecondaryHeating.py

@@ -13,7 +13,7 @@ class SecondaryHeating:
     ACCEPTED_MAINHEAT_DESCRIPTIONS = ["Boiler and radiators, mains gas"]
     ACCEPTED_SECONDHEAT_DESCRIPTIONS = ["Room heaters, electric"]
     # These are the heaters where works are required to remove them
-    FIXED_HEATER_DESCRIPTIONS = ["Room heaters, electric"]
+    FIXED_HEATER_DESCRIPTIONS = ["Room heaters, electric", 'Portable electric heaters (assumed)']
 
     def __init__(self, property_instance: Property):
         self.property = property_instance
@@ -34,7 +34,7 @@ class SecondaryHeating:
 
         if self.property.data['secondheat-description'] in self.FIXED_HEATER_DESCRIPTIONS:
             # We have an associated cost otherwise, there is no cost
-            n_rooms = self.property.data['number-heated-rooms']
+            n_rooms = self.property.data['number-habitable-rooms'] - self.property.data['number-heated-rooms']
         else:
             n_rooms = 0