From 854c784bd9c4341546ea57d2a0549b40552fbd92 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Fri, 25 Oct 2024 19:32:15 +0100
Subject: [PATCH] working on the costing methodology

---
 etl/customers/aiha/xml_extraction.py | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/etl/customers/aiha/xml_extraction.py b/etl/customers/aiha/xml_extraction.py
index 29ac44c6..4d4705c9 100644
--- a/etl/customers/aiha/xml_extraction.py
+++ b/etl/customers/aiha/xml_extraction.py
@@ -3,6 +3,7 @@ from io import BytesIO
 
 import pandas as pd
 
+from etl.ownership.config import EXCLUDED_UPRNS
 from etl.xml_survey_extraction.XmlParser import XmlParser
 
 SURVEY_FOLDER_PATH = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/RESIDENT SURVEYS"
@@ -91,6 +92,7 @@ def main():
     #   - AIH001-11 - The layout of this unit is confusing, is there roof access?
     #   - AIH001-12 - Why was there not access to the cylinder?
     #   - AIH001-12 - Is the need to draught proofing due to the windows?
+    #   - AIH001-04 - is the flat roof area correct?
 
     recommended_measures = [
         {
@@ -132,6 +134,7 @@ def main():
                 {
                     "measure": "Loft Insulation",
                     "description": "300mm loft insulation",
+                    "floor_area": 80,  # Based on area of 1st floor
                     "sap_points": 8,
                     "ending_sap": 61
                 },
@@ -152,6 +155,7 @@ def main():
                 {
                     "measure": "Flat Roof Insulation",
                     "description": "100mm flat roof insulation",
+                    "floor_area": 39.1482,  # based on area of top floor
                     "sap_points": 4,
                     "ending_sap": 52
                 },
@@ -185,6 +189,7 @@ def main():
                 {
                     "measure": "Flat Roof Insulation",
                     "description": "100mm flat roof insulation",
+                    "floor_area": 49.48,  # based on area of top floor
                     "sap_points": 5,
                     "ending_sap": 59,
                 },
@@ -256,6 +261,7 @@ def main():
                 {
                     "measure": "Loft Insulation",
                     "description": "300mm loft insulation",
+                    "floor_area": 54.2864,  # Based on area of top floor
                     "sap_points": 2,
                     "ending_sap": 58,
                 },
@@ -390,6 +396,7 @@ def main():
                 {
                     "measure": "Flat Roof Insulation",
                     "description": "100mm flat roof insulation",
+                    "floor_area": 33.06,  # Based on area of the extension
                     "sap_points": 2,
                     "ending_sap": 61,
                 },
@@ -445,7 +452,8 @@ def main():
                 },
                 {
                     "measure": "Loft Insulation",
-                    "description": "300mm loft insulation",
+                    "description": "300mm loft insulation",  # Based on area of main building
+                    "floor_area": 59.20,
                     "sap_points": 1,
                     "ending_sap": 69,
                 },
@@ -511,10 +519,18 @@ def main():
                 n_floors = property_data["number_of_floors"]
                 cost_of_scalfolding = [x for x in scaffolding_data if x["number_of_floors"] == n_floors][0]["price"]
                 measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) + cost_of_scalfolding
+                continue
 
-            blah
+            if measure_unit == "floor_m2":
+                floor_area = measure["floor_area"]
+                measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) * floor_area
+                continue
 
-            measure["total"] = pricing_data[pricing_data["item"] == measure["measure"]]["unit_price"].values[0]
+            if measure_unit == "hlp_m2":
+                hlp = measure["hlp"]
+                measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) * hlp
+
+            raise Exception("Unknown unit type")
 
     # Step 1: Normalize the recommended_measures data into a DataFrame.
     normalized_measures = []