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Finished costings WIP
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Khalim Conn-Kowlessar
parent
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commit
8325f1bf7a
1 changed files with 44 additions and 32 deletions
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@ -3,10 +3,10 @@ from io import BytesIO
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import pandas as pd
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from etl.ownership.config import EXCLUDED_UPRNS
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from etl.xml_survey_extraction.XmlParser import XmlParser
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SURVEY_FOLDER_PATH = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/RESIDENT SURVEYS"
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CONTINGENCY_RATE = 0.26
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def main():
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@ -274,6 +274,7 @@ def main():
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{
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"measure": "Internal Wall Insulation",
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"description": "100mm internal wall insulation",
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"hlp": 24.13 * 2.63,
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"sap_points": 5,
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"ending_sap": 69,
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},
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@ -292,12 +293,14 @@ def main():
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{
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"measure": "Internal Wall Insulation",
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"description": "100mm internal wall insulation",
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"hlp": (22.35 * 3.24) + (22.13 * 2.53),
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"sap_points": 8,
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"ending_sap": 52,
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},
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{
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"measure": "Cavity Wall Insulation",
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"description": "CWI to rdSAP default standard",
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"hlp": (2.68 * 2.39) + (5.93 * 2.63) + (6.13 * 2.39), # 1st & 2nd extension
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"sap_points": 1,
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"ending_sap": 53,
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},
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@ -328,6 +331,7 @@ def main():
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{
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"measure": "Internal Wall Insulation",
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"description": "100mm internal wall insulation",
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"hlp": (18.50 * 3.12) + (19.00 * 2.75),
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"sap_points": 5,
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"ending_sap": 68,
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},
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@ -346,12 +350,15 @@ def main():
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{
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"measure": "Double Glazing",
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"description": "Installation of double glazing",
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"n_windows": 20, # Counted the bay windows each as 3
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"windows_area": 10.66,
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"sap_points": 2,
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"ending_sap": 48,
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},
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{
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"measure": "Draught Proofing",
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"description": "Window draught proofing improvements",
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"n_windows": 20, # Counted the bay windows each as 3
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"sap_points": 1,
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"ending_sap": 49,
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},
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@ -390,6 +397,7 @@ def main():
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{
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"measure": "Roof Insulation",
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"description": "100mm+ RIR insulation on all surfaces (ceiling u=0.16, walls u=0.3)",
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"floor_area": 39.75, # based on the floor area of the RIR
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"sap_points": 6,
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"ending_sap": 59,
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},
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@ -403,6 +411,7 @@ def main():
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{
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"measure": "Cavity Wall Insulation",
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"description": "CWI to rdSAP default standard",
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"hlp": (35.40 * 2.65) + (26.70 * 2.73) + (16.30 * 2.71), # 1st & 2nd extension
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"sap_points": 6,
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"ending_sap": 67,
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},
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@ -441,6 +450,7 @@ def main():
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{
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"measure": "Cavity Wall Insulation",
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"description": "CWI to rdSAP default standard",
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"hlp": (11.00 * 2.6) + (11.00 * 2.65) + (4.60 * 2.7),
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"sap_points": 5,
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"ending_sap": 68,
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},
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@ -483,11 +493,11 @@ def main():
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{'item': '80mm cylinder insulation', 'unit_price': 50, 'unit': 'unit'},
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{'item': '100mm internal wall insulation', 'unit_price': 244.8, 'unit': 'hlp_m2'},
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{'item': 'CWI to rdSAP default standard', 'unit_price': 14.21, 'unit': 'hlp_m2'},
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{'item': 'Window draught proofing improvements', 'unit_price': None, 'unit': 'unit'},
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{'item': '100mm flat roof insulation', 'unit_price': None, 'unit': 'floor_m2'},
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{'item': 'Window draught proofing improvements', 'unit_price': 63, 'unit': 'window'},
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{'item': '100mm flat roof insulation', 'unit_price': 195, 'unit': 'floor_m2'},
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{'item': 'Switch to 24-hour tariff', 'unit_price': 0, 'unit': None},
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{'item': '3.2kWp Solar PV system', 'unit_price': 3686, 'unit': 'unit_needs_scaffolding'},
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{'item': 'Installation of double glazing', 'unit_price': None, 'unit': 'window'},
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{'item': 'Installation of double glazing', 'unit_price': 1074, 'unit': 'window'},
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{'item': 'Ecoforest ecoAIR EVI 4-20 20kW air source heat pump', 'unit_price': 21189, 'unit': 'unit'},
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{'item': '2kWp Solar PV system', 'unit_price': 3201, 'unit': 'unit_needs_scaffolding'},
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{'item': '100mm+ RIR insulation on all surfaces (ceiling u=0.16, walls u=0.3)', 'unit_price': 244.80,
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@ -500,51 +510,49 @@ def main():
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pricing_data = pd.DataFrame(pricing_data)
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for recommendation in recommended_measures:
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property_data = measures_data[measures_data["survey_key"] == recommendation["survey_key"]].squeeze()
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total_cost = 0
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for measure in recommendation["recommended_measures"]:
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measure_pricing = pricing_data[pricing_data["item"] == measure["description"]]
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measure_unit = measure_pricing["unit"].values[0]
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if measure_unit is None:
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blah
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continue
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if measure_unit == "unit":
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measure["Total Cost"] = float(measure_pricing["unit_price"].values[0])
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continue
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if measure_unit == "unit_needs_scaffolding":
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# We need the number of floors
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if measure_unit in ["unit", None]:
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measure_cost = float(measure_pricing["unit_price"].values[0])
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elif measure_unit == "unit_needs_scaffolding":
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n_floors = property_data["number_of_floors"]
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cost_of_scalfolding = [x for x in scaffolding_data if x["number_of_floors"] == n_floors][0]["price"]
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measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) + cost_of_scalfolding
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continue
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scaffolding_cost = [x for x in scaffolding_data if x["number_of_floors"] == n_floors][0]["price"]
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measure_cost = float(measure_pricing["unit_price"].values[0]) + scaffolding_cost
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elif measure_unit == "floor_m2":
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measure_cost = float(measure_pricing["unit_price"].values[0]) * measure["floor_area"]
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elif measure_unit == "hlp_m2":
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measure_cost = float(measure_pricing["unit_price"].values[0]) * measure["hlp"]
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elif measure_unit == "window":
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measure_cost = float(measure_pricing["unit_price"].values[0]) * measure["n_windows"]
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else:
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raise Exception("Unknown unit type")
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if measure_unit == "floor_m2":
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floor_area = measure["floor_area"]
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measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) * floor_area
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continue
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measure["Total Cost"] = measure_cost
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total_cost += measure_cost
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if measure_unit == "hlp_m2":
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hlp = measure["hlp"]
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measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) * hlp
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raise Exception("Unknown unit type")
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recommendation["total_cost"] = total_cost
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# Step 1: Normalize the recommended_measures data into a DataFrame.
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normalized_measures = []
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for survey in recommended_measures:
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survey_key = survey["survey_key"]
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starting_sap = survey["starting_sap"]
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total_cost = survey.get("total_cost", 0)
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for measure in survey.get("recommended_measures", []):
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normalized_measures.append({
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"survey_key": survey_key,
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"starting_sap": starting_sap,
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"measure": measure["measure"],
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"description": measure.get("description", ""),
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"sap_points": measure.get("sap_points", 0)
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"sap_points": measure.get("sap_points", 0),
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"measure_cost": measure.get("Total Cost", 0),
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"total_cost": total_cost
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})
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# Convert the normalized list into a DataFrame.
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@ -559,12 +567,16 @@ def main():
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fill_value=None
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).reset_index()
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# Step 3: Calculate the total sap points for each survey.
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total_sap_points = measures_df.groupby("survey_key")["sap_points"].sum().reset_index()
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total_sap_points.columns = ["survey_key", "total_sap_points"]
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# Step 3: Calculate the total sap points and total cost for each survey.
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sap_cost_totals = measures_df.groupby("survey_key").agg(
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total_sap_points=("sap_points", "sum"),
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total_cost_of_measures=("measure_cost", "sum")
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).reset_index()
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# Merge total sap points into the pivoted measures.
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pivoted_measures = pd.merge(pivoted_measures, total_sap_points, on="survey_key", how="left")
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pivoted_measures = pd.merge(pivoted_measures, sap_cost_totals, on="survey_key", how="left")
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pivoted_measures["Cost Contingency"] = pivoted_measures["total_cost_of_measures"] * CONTINGENCY_RATE
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pivoted_measures["Total Cost"] = pivoted_measures["total_cost_of_measures"] + pivoted_measures["Cost Contingency"]
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# Step 4: Extract starting SAP for each survey key.
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starting_sap_df = measures_df.drop_duplicates(subset=["survey_key"])[["survey_key", "starting_sap"]]
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