mirror of
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implementing decent homes wf
This commit is contained in:
Khalim Conn-Kowlessar
parent
335164eaf1
commit
d3f941349a
3 changed files with 444 additions and 2 deletions
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@ -900,7 +900,7 @@ async def model_engine(body: PlanTriggerRequest):
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r["uplift_project_score"]
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) = funding.get_innovation_uplift(
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measure=r,
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starting_sap=p.data["current-energy-efficiency"],
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starting_sap=int(p.data["current-energy-efficiency"]),
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floor_area=p.floor_area,
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is_cavity=p.walls["is_cavity_wall"],
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current_wall_uvalue=current_wall_u_value,
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@ -310,7 +310,7 @@ class KwhData:
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False: "N",
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None: "N",
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"Y": "Y",
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"N": "N"
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"N": "N",
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}
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for v in bools_to_remap:
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epc[v] = bool_map[epc[v]]
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442
etl/customers/waltham_forest/decent_homes_pilot.py
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442
etl/customers/waltham_forest/decent_homes_pilot.py
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@ -0,0 +1,442 @@
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import json
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import os
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import pandas as pd
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from datetime import datetime
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def years_between(d1, d2):
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# precise year difference (accounts for months/days)
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return (d1.year - d2.year) - ((d1.month, d1.day) < (d2.month, d2.day))
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def get_element(elements, label):
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"""Safely get an element dict by display label (your JSON keys)."""
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return elements.get(label)
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def adequacy_result_by_text(attr_desc: str):
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"""
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Generic adequacy parser.
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Pass if description clearly says 'Adequate' and not 'Inadequate'.
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Fail if it says 'Inadequate' (or equivalent).
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Unknown -> 'no_data'
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"""
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if not attr_desc or not isinstance(attr_desc, str):
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return "no_data"
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text = attr_desc.strip().lower()
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# Common patterns
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if "inadequate" in text or "unsatisfactory" in text or "problems" in text:
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return "fail"
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if "adequate" in text or "standard" in text or "appropriate" in text:
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return "pass"
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return "no_data"
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def append_result(decent_homes, variable, result, install_date=None):
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decent_homes.append({
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"variable": variable,
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"result": result,
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"hhsrs_rank": None,
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"hhsrs_score": None,
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"install_date": install_date
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})
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# Read in static json, which is transformed by Jun-te's script
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folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Waltham Forest/Decent Homes Pilot"
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filenames = ["flat 1.json", "house 1.json"]
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houses_waltham_forest_data = pd.read_excel(
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os.path.join(folder, "LBWF - Example Asset Data September 2025.xlsx"),
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sheet_name="Houses Asset Data"
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)
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flats_waltham_forest_data = pd.read_excel(
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os.path.join(folder, "LBWF - Example Asset Data September 2025.xlsx"),
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sheet_name="CHINGFORD ROAD 236-254 Asset Bl"
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)
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# Standardised variables which will form the enums in the db
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HHSRS_VARIABLES = [
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"damp_and_mould_growth",
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"excess_cold",
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"excess_heat",
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"asbestos_and_mm_fibres",
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"biocides",
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"carbon_monoxide_and_fuel_combustion_products",
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"lead",
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"radiation",
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"uncombusted_fuel_gas",
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"volatile_organic_compounds",
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"crowding_and_space",
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"entry_by_intruders",
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"lighting",
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"noise",
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"domestic_hygiene_pests_and_refuse",
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"food_safety",
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"personal_hygiene_sanitation_and_drainage",
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"water_supply",
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"falls_associated_with_baths",
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"falls_on_level_surfaces",
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"falls_on_stairs_and_steps",
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"falls_between_levels",
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"electrical_hazards",
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"fire",
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"flames_hot_surfaces_and_materials",
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"collision_and_entrapment",
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"explosions",
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"ergonomics",
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"structural_collapse_and_falling_elements"
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]
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CRITERION_B_VARIABLES = [
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"external_walls_structure", "lintels", "brickwork_spalling", "wall_finish", "roof_structure", "roof_finish",
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"chimneys", "windows", "external_doors", "kitchens", "bathrooms", "central_heating_boiler",
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"central_heating_distribution_system", "heating_other", "electrical_systems",
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]
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CRITERION_C_VARIABLES = [
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"kitchen_facilities",
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]
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# Criterion C explicit age limits (different from component lifespans used elsewhere)
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CRITERION_C_AGE_LIMITS = {
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"kitchen_years_max": 20,
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"bathroom_years_max": 30,
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}
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# Field labels as they appear in your JSON (based on your code)
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LABEL_KITCHEN = "Adequacy of Kitchen and Type in Property"
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LABEL_BATHROOM = "Adequacy of Bathroom Location in Property"
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LABEL_NOISE = "Adequacy of Noise Insulation in Property"
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LABEL_COMMON_CIRC = "Circulation Space in Common Area" # flats only
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STANDARD_HHSRS_MAPPING = {"pass": "TYPRISK", "fail": "MODRISK", "no_data": "TOBEASSESS"}
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# Criterion A - mapping of HHSRS variables to Waltham forest element codes
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HHSRS_MAPPING = {
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"damp_and_mould_growth": {"HHSRSDAMP": STANDARD_HHSRS_MAPPING},
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"excess_cold": {"HHSRSCOLD": STANDARD_HHSRS_MAPPING},
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"excess_heat": {"HHSRSHEAT": STANDARD_HHSRS_MAPPING},
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"asbestos_and_mm_fibres": {"HHSRSASB": STANDARD_HHSRS_MAPPING},
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"biocides": {"HHSRSBIOC": STANDARD_HHSRS_MAPPING},
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"carbon_monoxide_and_fuel_combustion_products": {
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"HHSRSCO": STANDARD_HHSRS_MAPPING,
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"HHSRSSO2": STANDARD_HHSRS_MAPPING,
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"HHSRSNO2": STANDARD_HHSRS_MAPPING
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},
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"lead": {"HHSRSLEAD": STANDARD_HHSRS_MAPPING},
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"radiation": {"HHSRSRADIA": STANDARD_HHSRS_MAPPING},
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"uncombusted_fuel_gas": {"HHSRSFUEL": STANDARD_HHSRS_MAPPING},
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"volatile_organic_compounds": {"HHSRSORGAN": STANDARD_HHSRS_MAPPING},
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"crowding_and_space": {"HHSRSCROWD": STANDARD_HHSRS_MAPPING},
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"entry_by_intruders": {"HHSRSENTRY": STANDARD_HHSRS_MAPPING},
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"lighting": {"HHSRSLIGHT": STANDARD_HHSRS_MAPPING},
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"noise": {"HHSRSNOISE": STANDARD_HHSRS_MAPPING},
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"domestic_hygiene_pests_and_refuse": {"HHSRSDOMES": STANDARD_HHSRS_MAPPING},
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"food_safety": {"HHSRSFOOD": STANDARD_HHSRS_MAPPING},
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"personal_hygiene_sanitation_and_drainage": {"HHSRSPERS": STANDARD_HHSRS_MAPPING},
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"water_supply": {"HHSRSWATER": STANDARD_HHSRS_MAPPING},
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"falls_associated_with_baths": {"HHSRSFBATH": STANDARD_HHSRS_MAPPING},
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"falls_on_level_surfaces": {"HHSRSFLEVE": STANDARD_HHSRS_MAPPING},
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"falls_on_stairs_and_steps": {"HHSRSFSTAI": STANDARD_HHSRS_MAPPING},
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"falls_between_levels": {"HHSRSFBETW": STANDARD_HHSRS_MAPPING},
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"electrical_hazards": {"HHSRSELEC": STANDARD_HHSRS_MAPPING},
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"fire": {"HHSRSFIRE": STANDARD_HHSRS_MAPPING},
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"flames_hot_surfaces_and_materials": {"HHSRSFLAME": STANDARD_HHSRS_MAPPING},
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"collision_and_entrapment": {"HHSRSENTRP": STANDARD_HHSRS_MAPPING, "HHSRSCLOW": STANDARD_HHSRS_MAPPING},
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"explosions": {"HHSRSEXPLO": STANDARD_HHSRS_MAPPING},
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"ergonomics": {"HHSRSPOSI": STANDARD_HHSRS_MAPPING},
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"structural_collapse_and_falling_elements": {"HHSRSSTRUC": STANDARD_HHSRS_MAPPING}
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}
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print(houses_waltham_forest_data[
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houses_waltham_forest_data["ELEMENT CODE"] == "INTHTIMP"
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][["ATTRIBUTE CODE", "ATTRIBUTE CODE DESCRIPTION"]].drop_duplicates())
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print(flats_waltham_forest_data[
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flats_waltham_forest_data["ELEMENT CODE"] == "INTBTHADEQ"
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][["ATTRIBUTE CODE", "ATTRIBUTE CODE DESCRIPTION"]].drop_duplicates())
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# Criterion B
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CRITERION_B_MAPPING = {
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# TODO: Needs to be sorted!!!
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# "external_walls_structure": {
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# "EXTWALLSTR": {"pass": "GOOD", "fail": "POOR", "no_data": "Unknown if Structural Defects in External Area"}
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# }
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"lintels": {
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"EXTLINTELS": {"pass": "GOOD", "fail": "POOR", "no_data": "Unknown Condition of Lintels"}
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}
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}
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# Criterion C
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CRITERION_C_MAPPING = {
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# "kitchen_less_than_20_years_old":
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}
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COMPONENT_LIFESPANS = {
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"kitchen": {"house": 30, "flat_below_6_storeys": 30, "flat_above_6_storeys": 30},
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"bathroom": {"house": 50, "flat_below_6_storeys": 50, "flat_above_6_storeys": 50}
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}
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# Database design
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# creation_date, uprn, variable, result, hhsrs_score (optional, numeric), hhsrs_rank (A-J), install_date (for
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# components which expire, e.g. kitchen)
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decent_homes = []
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# Use to capture criterion A, B, C and D. Should be:
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# {"uprn": int, "creation_date": datetime, "criterion_a": bool, "criterion_b": bool, "criterion_c": bool,
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# "criterion_d": bool, "decent_homes": bool"}
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property_decent_homes = []
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for fn in filenames:
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with open(os.path.join(folder, fn), "rb") as f:
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data = json.load(f)
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from pprint import pprint
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pprint(data["elements"])
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property_info = data["property_info"]
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if property_info["PROP TYPE"] in ["HOU"]:
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property_type = "house"
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elif property_info["PROP TYPE"] == "FLA":
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raise Exception("Implement distrinction between below and above 6 storeys")
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property_type = "flat"
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else:
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raise NotImplementedError("Unknown property type")
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# Criterion A
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for hhsrs_variable, mapping in HHSRS_MAPPING.items():
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element_code = list(mapping.keys())[0]
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# Find the data in the JSON within data["elements"]
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check_pass = []
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for k, v in data["elements"].items():
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if v["ELEMENT CODE"] == element_code:
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# We check the attribute code
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# Check if pass
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if v["ATTRIBUTE CODE"] == mapping[element_code]["pass"]:
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result = "pass"
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elif v["ATTRIBUTE CODE"] == mapping[element_code]["fail"]:
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result = "fail"
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elif v["ATTRIBUTE CODE"] == mapping[element_code]["no_data"]:
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result = "no_data"
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else:
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raise ValueError("Unknown attribute code")
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check_pass.append(result)
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# We check if we have a pass, fail or no_data
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if all([x == "pass" for x in check_pass]):
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hhsrs_result = "pass"
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elif any([x == "fail" for x in check_pass]):
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hhsrs_result = "fail"
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elif any([x == "no_data" for x in check_pass]):
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hhsrs_result = "no_data"
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else:
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raise NotImplementedError("Mixed results not implemented")
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decent_homes.append(
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{"variable": hhsrs_variable, 'result': hhsrs_result, "hhsrs_rank": None, "hhsrs_score": None,
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"install_date": None}
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)
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# Criterion B
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# --- Criterion C ---
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today = pd.Timestamp.today().normalize()
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# Guard: property type string already set earlier
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is_flat = (property_info["PROP TYPE"] == "FLA")
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# 1) Kitchen age ≤ 20 years
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kitchen = get_element(data["elements"], LABEL_KITCHEN)
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if kitchen:
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kit_install_raw = kitchen.get("INSTALL DATE")
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try:
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kit_install = pd.to_datetime(kit_install_raw)
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kit_age_years = years_between(today.to_pydatetime(), kit_install.to_pydatetime())
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kitchen_age_result = "pass" if kit_age_years <= CRITERION_C_AGE_LIMITS["kitchen_years_max"] else "fail"
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# For transparency, store next renewal as install + 20 years (criterion C perspective)
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kit_next_due = kit_install + pd.DateOffset(years=CRITERION_C_AGE_LIMITS["kitchen_years_max"])
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except Exception:
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kitchen_age_result = "no_data"
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kit_next_due = None
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else:
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kitchen_age_result = "no_data"
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kit_next_due = None
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append_result(decent_homes, "kitchen_less_than_20_years_old", kitchen_age_result, kit_next_due)
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# 2) Kitchen adequate space/layout
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# Prefer explicit codes if you have them, fall back to text in ATTRIBUTE CODE DESCRIPTION
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if kitchen:
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kit_attr_desc = kitchen.get("ATTRIBUTE CODE DESCRIPTION", "")
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# If you prefer codes, you can also branch here on kitchen.get("ATTRIBUTE CODE") == "STDKITADQ"
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kitchen_adequacy_result = adequacy_result_by_text(kit_attr_desc)
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else:
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kitchen_adequacy_result = "no_data"
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append_result(decent_homes, "kitchen_adequate_space_and_layout", kitchen_adequacy_result)
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# 3) Bathroom age ≤ 30 years
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bath = get_element(data["elements"], LABEL_BATHROOM)
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if bath:
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bth_install_raw = bath.get("INSTALL DATE")
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try:
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bth_install = pd.to_datetime(bth_install_raw)
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bth_age_years = years_between(today.to_pydatetime(), bth_install.to_pydatetime())
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bathroom_age_result = "pass" if bth_age_years <= CRITERION_C_AGE_LIMITS["bathroom_years_max"] else "fail"
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bth_next_due = bth_install + pd.DateOffset(years=CRITERION_C_AGE_LIMITS["bathroom_years_max"])
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except Exception:
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bathroom_age_result = "no_data"
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bth_next_due = None
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else:
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bathroom_age_result = "no_data"
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bth_next_due = None
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append_result(decent_homes, "bathroom_less_than_30_years_old", bathroom_age_result, bth_next_due)
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# 4) Bathroom/WC appropriately located
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if bath:
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# You already observed codes like STDBTHADQ / ADPBTHADQ as 'pass'
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bth_attr_code = bath.get("ATTRIBUTE CODE", "")
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bth_attr_desc = bath.get("ATTRIBUTE CODE DESCRIPTION", "")
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known_pass_codes = {"STDBTHADQ", "ADPBTHADQ"}
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if bth_attr_code in known_pass_codes:
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bathroom_location_result = "pass"
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else:
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# Fallback to text adequacy check
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bathroom_location_result = adequacy_result_by_text(bth_attr_desc)
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else:
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bathroom_location_result = "no_data"
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append_result(decent_homes, "bathroom_wc_appropriately_located", bathroom_location_result)
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# 5) Adequate external noise insulation
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noise = get_element(data["elements"], LABEL_NOISE)
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if noise:
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noise_desc = noise.get("ATTRIBUTE CODE DESCRIPTION", "")
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noise_result = adequacy_result_by_text(noise_desc)
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else:
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noise_result = "no_data"
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append_result(decent_homes, "adequate_external_noise_insulation", noise_result)
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# 6) Adequate common entrance areas (flats only)
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if is_flat:
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raise Exception("Pls check this")
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common = get_element(data["elements"], LABEL_COMMON_CIRC)
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if common:
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circ_desc = common.get("ATTRIBUTE CODE DESCRIPTION", "")
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common_areas_result = adequacy_result_by_text(circ_desc)
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else:
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common_areas_result = "no_data"
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append_result(decent_homes, "adequate_common_entrance_areas", common_areas_result)
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# ---------------- Criterion D ----------------
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# heating system type
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heating = get_element(data["elements"], "Heating Improvement Required in Property")
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if heating:
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# Example: ATTRIBUTE CODE == "GOOD" means pass, "POOR" means fail
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heat_type_code = heating.get("ATTRIBUTE CODE", "")
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if heat_type_code in {"NOTAPPLIC"}:
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heating_type_result = "pass"
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elif heat_type_code in {"WETINSFULL"}:
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heating_type_result = "fail"
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else:
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raise NotImplementedError("No other observed codes yet")
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else:
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raise NotImplementedError("Heating element missing in dataset")
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append_result(decent_homes, "efficient_heating_system_type", heating_type_result)
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# heating distribution
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heating_dist = get_element(data["elements"], "Heating Distribution System in Property")
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if heating_dist:
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dist_desc = heating_dist.get("ATTRIBUTE CODE DESCRIPTION", "")
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heating_dist_result = adequacy_result_by_text(dist_desc)
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else:
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raise NotImplementedError("Heating distribution element missing in dataset")
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append_result(decent_homes, "efficient_heating_distribution", heating_dist_result)
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# insulation
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loft = get_element(data["elements"], "Size in mm of Loft Insulation Thickness in Property")
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wall = get_element(data["elements"], "Wall Insulation Improvement in External Area")
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heating = get_element(data["elements"], "Heating Improvement Required in Property")
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# To determine how much loft insulation is required
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# Loft insulation check (example threshold: ≥ 270mm = pass)
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if loft:
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# We have a specific code, where further loft insulation is needed
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loft_code = loft.get("ATTRIBUTE CODE", "")
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if loft_code == "LOFTINSRQD":
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loft_result = "fail"
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elif loft_code.isnumeric():
|
||||
loft_result = "pass"
|
||||
else:
|
||||
raise NotImplementedError("Unknown loft insulation code - pls check")
|
||||
else:
|
||||
raise NotImplementedError("Loft insulation data missing - pls check")
|
||||
append_result(decent_homes, "loft_insulation_sufficient", loft_result)
|
||||
|
||||
# Wall insulation check (simple adequacy parser)
|
||||
if wall:
|
||||
wall_desc = wall.get("ATTRIBUTE CODE DESCRIPTION", "")
|
||||
wall_result = adequacy_result_by_text(wall_desc)
|
||||
else:
|
||||
raise NotImplementedError("Wall insulation data missing - pls check")
|
||||
append_result(decent_homes, "wall_insulation_sufficient", wall_result)
|
||||
|
||||
# ---------------- Criterion A overall ----------------
|
||||
a_vars = set(HHSRS_MAPPING.keys())
|
||||
latest_a_results = {r["variable"]: r["result"] for r in decent_homes if r["variable"] in a_vars}
|
||||
|
||||
if any(v == "fail" for v in latest_a_results.values()):
|
||||
criterion_a_result = "fail"
|
||||
elif all(v == "pass" for v in latest_a_results.values()):
|
||||
criterion_a_result = "pass"
|
||||
else:
|
||||
criterion_a_result = "no_data"
|
||||
|
||||
# ---------------- Criterion C overall ----------------
|
||||
criterion_c_vars = [
|
||||
"kitchen_less_than_20_years_old",
|
||||
"kitchen_adequate_space_and_layout",
|
||||
"bathroom_less_than_30_years_old",
|
||||
"bathroom_wc_appropriately_located",
|
||||
"adequate_external_noise_insulation",
|
||||
]
|
||||
if is_flat:
|
||||
criterion_c_vars.append("adequate_common_entrance_areas")
|
||||
|
||||
latest_c_results = {r["variable"]: r["result"] for r in decent_homes if r["variable"] in criterion_c_vars}
|
||||
|
||||
count_fails = sum(1 for v in latest_c_results.values() if v == "fail")
|
||||
# optionally count no_data too if you want strict interpretation
|
||||
criterion_c_result = "fail" if count_fails >= 3 else "pass"
|
||||
|
||||
# ---------------- Criterion D overall ----------------
|
||||
criterion_d_vars = [
|
||||
"efficient_heating_system_type",
|
||||
"efficient_heating_distribution",
|
||||
"loft_insulation_sufficient",
|
||||
"wall_insulation_sufficient",
|
||||
]
|
||||
latest_d_results = {r["variable"]: r["result"] for r in decent_homes if r["variable"] in criterion_d_vars}
|
||||
|
||||
if any(v == "fail" for v in latest_d_results.values()):
|
||||
criterion_d_result = "fail"
|
||||
elif all(v == "pass" for v in latest_d_results.values()):
|
||||
criterion_d_result = "pass"
|
||||
else:
|
||||
criterion_d_result = "no_data"
|
||||
|
||||
# ---------------- Append to property_decent_homes ----------------
|
||||
property_decent_homes.append({
|
||||
"uprn": property_info.get("UPRN"), # update field name if needed
|
||||
"creation_date": datetime.now().date().isoformat(),
|
||||
"criterion_a": criterion_a_result,
|
||||
"criterion_b": None, # not yet implemented
|
||||
"criterion_c": criterion_c_result,
|
||||
"criterion_d": criterion_d_result,
|
||||
"decent_homes": (
|
||||
criterion_a_result == "pass"
|
||||
and criterion_c_result == "pass"
|
||||
)
|
||||
})
|
||||
Loading…
Add table
Reference in a new issue