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working on proposed sample for stonewater
This commit is contained in:
Khalim Conn-Kowlessar
parent
bccf3c621b
commit
7e26fb4b86
1 changed files with 201 additions and 2 deletions
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@ -486,7 +486,7 @@ def extract_epr(pdf_path):
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data["Postcode"] = data["Address"].split(",")[-1].strip()
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# Extract Current and Potential SAP ratings
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sap_match = re.search(r"GG \(1-20\)(\d{1,2})(\d{1,2})", text)
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sap_match = re.search(r"GG \(1-20\)\s*(\d{1,2})\s*(\d{1,2})", text)
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current_sap, _ = int(sap_match.group(1)), int(sap_match.group(2))
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data["Current SAP Rating"] = current_sap
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@ -896,7 +896,6 @@ def main():
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# Find Osmosis IDs that are in the packages board but not in the matching looking
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missing_ids = set(retrofit_packages_board["Address ID"]) - set(matching_lookup["Address ID"])
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missing_ids = list(missing_ids)
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print(len(missing_ids))
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if missing_ids:
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# We check that the missing ids have no data yet
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if len(missing_ids) != 8:
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@ -937,6 +936,7 @@ def main():
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"Actual SAP Rating",
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"Modelled SAP Band",
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"Modelled SAP Rating",
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"Package Ref",
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] + measure_columns
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],
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on=["Address ID", "Name"],
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@ -995,7 +995,206 @@ def main():
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if stonewater_data["Address ID"].duplicated().sum():
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raise Exception("Duplicate Address IDs")
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# Save this data to excel
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stonewater_data.to_excel(CUSTOMER_FOLDER_PATH + "/Stonewater - costed retrofit packages.xlsx", index=False)
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cost_sheet = [
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{
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"measure": "EWI 0.30 w.m2.K", "cost": 298.35, "unit": "m2"
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},
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{
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"measure": "CWI RdSAP Default", "cost": 14.21, "unit": "m2"
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},
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{
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"measure": "Poss Extract CWI & Refill (issues identified)", "cost": 14.21 + 25, "unit": "m2"
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},
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{
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"measure": "IWI 0.30 w.m2.K", "cost": 244.80, "unit": "m2"
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},
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{
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"measure": "EWI/IWI 0.3", "cost": (298.35 + 244.8) / 2, "unit": "m2"
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},
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{
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"measure": "Loft Insulation 0.11 w.m2.K", "cost": 16.07, "unit": "m2"
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},
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{
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"measure": "Flat Roof 0.11 w.m2.K", "cost": 195, "unit": "m2"
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},
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{
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"measure": "DG Window 1.30 w.m2.K", "cost": 1140, "unit": "each"
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},
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{
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"measure": "Secondary 2.40", "cost": 974, "unit": "each"
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},
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{
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"measure": "Ins. Door 1.30 w.m2.K", "cost": None, "unit": "each"
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},
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{
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"measure": "Ins. Door 1.40 w.m2.K", "cost": None, "unit": "each"
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},
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{
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"measure": "DMEV", "cost": 900, "unit": "each"
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},
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{
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"measure": "ASHP Vaillant 102607 5kw", "cost": None, "unit": "each"
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},
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{
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"measure": "HHRSH Quantum 150", "cost": None, "unit": "each"
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},
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{
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"measure": "Dual Stat Tank 210lt 50mm Foam", "cost": None, "unit": "each"
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},
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{
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"measure": "Dual Stat Tank 160lt 50mm Foam", "cost": None, "unit": "each"
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},
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{
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"measure": "Dual Stat Tank 110lt 50mm Foam", "cost": None, "unit": "each"
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},
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{
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"measure": "Smart Thermostat", "cost": 1200, "unit": "each"
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},
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{
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"measure": "TRV's", "cost": 350, "unit": "each"
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},
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{
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"measure": "Solar PV - 3.0kwp", "cost": 4365.0, "unit": "each"
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},
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{
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"measure": "Solar PV - 1.5kwp", "cost": 3881, "unit": "each"
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},
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{
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"measure": "LEL", "cost": 35, "unit": "per bulb"
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},
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{
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"measure": "Roof 0.16 - Walls 0.30", "cost": 180, "unit": "floor area m2"
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},
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{
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"measure": "Roof 0.16 - Walls 0.16", "cost": 180, "unit": "floor area m2"
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},
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]
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cost_sheet = pd.DataFrame(cost_sheet)
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# Save cost sheet - ideally this will be used as a secondary sheet for Stonewater
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cost_sheet.to_excel(CUSTOMER_FOLDER_PATH + "/Stonewater - cost sheet.xlsx", index=False)
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stonewater_data["Room in Roof"].value_counts()
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# stonewater_data[~pd.isnull(stonewater_data["Room in Roof"])]["survey_folder"].values
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create_proposed_wave_3_bid(
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costed_packages_filepath=os.path.join(
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CUSTOMER_FOLDER_PATH, "Stonewater - Costed Retrofit Packages 20241030 (WIP).xlsx"
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),
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archetypes_sheet_filepath=os.path.join(
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CUSTOMER_FOLDER_PATH, "Stonewater SHDF_3_0_Board Triage 22.05.24 - Archetyped V3.1.xlsx"
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)
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)
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def create_proposed_wave_3_bid(costed_packages_filepath, archetypes_sheet_filepath):
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# We read in the costed packages
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costed_packages = pd.read_excel(costed_packages_filepath)
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archetypes_to_cost = costed_packages[
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[
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"Name", "Address ID", "Archetype ID", "Current SAP Rating", "Current EPC Band", "Modelled SAP Band",
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"Modelled SAP Rating", 'Total Cost of Measures', 'Contingency Cost',
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'Total Cost of Measures inc Contingency'
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]
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].copy()
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# We take properties that are EPC D and below (61% of units)
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archetypes_to_cost = archetypes_to_cost[archetypes_to_cost["Current EPC Band"].isin(["D", "E", "F", "G"])]
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archetypes_to_cost["Has been modelled"] = ~pd.isnull(archetypes_to_cost["Modelled SAP Band"])
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average_cost = archetypes_to_cost[
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archetypes_to_cost["Has been modelled"]
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]['Total Cost of Measures inc Contingency'].mean()
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print(average_cost)
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# These are the Arhetypes that will likely be suitable for Wave 3
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archetypes_sheet = pd.read_excel(archetypes_sheet_filepath, header=4)
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archetypes_sheet = archetypes_sheet[~pd.isnull(archetypes_sheet["Address ID"])]
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archetypes_sheet = archetypes_sheet[archetypes_sheet["Address ID"] != "Address ID"]
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archetypes_sheet["Address ID"] = archetypes_sheet["Address ID"].astype(int)
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# We merge the property details onto the costed archetypes
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archetypes_to_cost = archetypes_to_cost.merge(
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archetypes_sheet[["Address ID", "Property Type", "Wall Type", "Roof Type", "Heating"]],
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on="Address ID",
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how="left"
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)
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proposed_sample = archetypes_sheet[archetypes_sheet["Archetype ID"].isin(archetypes_to_cost["Archetype ID"])]
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proposed_sample = proposed_sample[
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[
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"Name", "Postcode", "UPRN", "UDPRN", "Address ID", "Osm. ID", "Archetype ID",
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"Property Type", "Wall Type", "Roof Type", "Heating"
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]
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]
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# We classify into high and low confidence
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match_classification = []
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for _, home in tqdm(proposed_sample.iterrows(), total=len(proposed_sample)):
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surveyed = archetypes_to_cost[archetypes_to_cost["Archetype ID"] == home["Archetype ID"]]
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# We now check if we have a perfect match
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surveyed = surveyed[
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(surveyed["Property Type"] == home["Property Type"]) &
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(surveyed["Wall Type"] == home["Wall Type"]) &
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(surveyed["Roof Type"] == home["Roof Type"]) &
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(surveyed["Heating"] == home["Heating"])
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]
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if surveyed.empty:
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match_classification.append(
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{
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"Address ID": home["Address ID"],
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"Match to Surveyed": "Approximate"
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}
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)
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continue
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match_classification.append(
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{
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"Address ID": home["Address ID"],
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"Match to Surveyed": "Exact"
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}
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)
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match_classification = pd.DataFrame(match_classification)
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proposed_sample = proposed_sample.merge(
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match_classification,
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on="Address ID",
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how="left",
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)
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# Merge on the cost per archetype
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cost_per_archetype = (
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archetypes_to_cost.groupby("Archetype ID")[['Total Cost of Measures inc Contingency']].mean().reset_index()
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)
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proposed_sample = proposed_sample.merge(
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cost_per_archetype,
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on="Archetype ID",
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how="left"
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)
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# We add on a boolean to indicate if a property from that archetype has been modelled
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proposed_sample = proposed_sample.merge(
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archetypes_to_cost.groupby("Archetype ID")[["Has been modelled"]].any().reset_index(),
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on="Archetype ID",
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how="left"
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)
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proposed_sample["Total Cost of Measures inc Contingency"] = np.where(
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~proposed_sample["Has been modelled"],
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None, proposed_sample["Total Cost of Measures inc Contingency"]
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)
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# Save excel
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proposed_sample.to_excel(CUSTOMER_FOLDER_PATH + "/Stonewater - Proposed Wave 3 Bid (WIP).xlsx", index=False)
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# if __name__ == "__main__":
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# main()
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