From 4452cd4347e3b455ac85279165bc7f6872ac53d5 Mon Sep 17 00:00:00 2001
From: Jun-te Kim <junte@domna.homes>
Date: Wed, 9 Apr 2025 14:24:19 +0000
Subject: [PATCH] deem score worked out

---
 etl/hubspot_to_deemed_calculator.py | 63 ++++++++++++++++++++++++++---
 1 file changed, 58 insertions(+), 5 deletions(-)

diff --git a/etl/hubspot_to_deemed_calculator.py b/etl/hubspot_to_deemed_calculator.py
index 443615f..1c5c93e 100644
--- a/etl/hubspot_to_deemed_calculator.py
+++ b/etl/hubspot_to_deemed_calculator.py
@@ -23,7 +23,7 @@ for deal in deals:
         "Address": deal.deal_name,
         "Trickle Vent": 1 if deal.needs_trickle_ventilation else 0,
         "wetrooms": deal.no_of_wet_rooms,
-        "hubspot_wall_insulation_info": deal.existing_wall_insulation,
+        "hubspot_wall_insulation": deal.existing_wall_insulation,
         "POST INSTALL SAP SCORE": deal.post_sap_score,
     })
 
@@ -56,16 +56,69 @@ for survey in list_of_surveys:
             data = {
                 "Address": survey.pre_site_note.survey_information.address,
                 "HubSpot Address": filtered_df["Address"].values[0],
+                "Pre SAP from sharepoint": number,
+                "Post SAP from surveyor": filtered_df["POST INSTALL SAP SCORE"].values[0],
                 "Surveyor's Name": survey.pre_site_note.assessor_information.name,
                 "floor_area_group" : floor_banding,
                 "wetrooms" : filtered_df["wetrooms"].values[0],
-                "Trickle Vent" : filtered_df["Ventilation Requirements"].values[0],
+                "Trickle Vent" : filtered_df["Trickle Vent"].values[0],
+                "survey_stated_work_type": filtered_df["hubspot_work_type"].values[0],
             }
 
-            insulation = None
+            csr_insulation = None
             merged_df = pd.DataFrame()
             if survey.csr:
                 if survey.csr.insulation_info:
-                    insultation = survey.csr.insulation_info.type.upper()
+                    csr_insulation = survey.csr.insulation_info.type.upper()
+
+            hubspot_wall_insulation = None
+            hubspot_wall_insulation = filtered_df["hubspot_wall_insulation"].values[0]
+            data.update({"csr_insulation": csr_insulation}) 
+            data.update({"hubspot_wall_insulation": hubspot_wall_insulation}) 
+
+            if funding_type == "GBIS":
+                if csr_insulation is None and hubspot_wall_insulation.upper() == "EMPTY":
+                    data.update({"funding": funding_type.upper()}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_empty, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
+                elif "FOAM" in csr_insulation.upper() and "FOAM" in hubspot_wall_insulation.upper():
+                    data.update({"funding": funding_type.upper() + " Remedial"}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_foam, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
+                else:
+                    data.update({"funding": funding_type.upper() + " Remedial"}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_general, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
+            elif funding_type == "ECO4":
+                if csr_insulation is None and hubspot_wall_insulation.upper() == "EMPTY":
+                    formatted_funding_type = f"{funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
+                    data.update({"funding": formatted_funding_type}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_empty, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
+                elif "FOAM" in csr_insulation.upper() and "FOAM" in hubspot_wall_insulation.upper():
+                    formatted_funding_type = f"REMEDIAL - {funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
+                    data.update({"funding": formatted_funding_type}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_foam, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
+                else:
+                    formatted_funding_type = f"REMEDIAL - {funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
+                    data.update({"funding": formatted_funding_type}) 
+                    df = pd.DataFrame([data])
+                    merged_df = pd.merge(df, price_general, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')               
+            else:
+                raise RuntimeError(f"UNKNOWN FUNDING TYPE {funding_type}")
+            
+            if not merged_df.empty:
+                total_price.append(merged_df)
+        
+final_df = pd.concat(total_price, ignore_index=True)
+
+final_df.to_csv("survery_data.csv", index=False)
+
+
+print(f"WEEK COMMENCING {WEEK_COMMENCING}")
+print("Excel file 'survey_data.xlsx' created successfully!")
+
+
+
 
-                    
\ No newline at end of file