diff --git a/.idea/Model.iml b/.idea/Model.iml
index 81384fa9..ac61a988 100644
--- a/.idea/Model.iml
+++ b/.idea/Model.iml
@@ -6,7 +6,7 @@
-
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 3b05c6ac..242c02bb 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,6 +1,6 @@
-
+
diff --git a/model_data/app.py b/model_data/app.py
index 74fa5c15..fe065f26 100644
--- a/model_data/app.py
+++ b/model_data/app.py
@@ -72,6 +72,7 @@ def handler():
# TODO: Do this at a constituency level
constituencies = {p.data["constituency"] for p in input_properties}
property_types = ["bungalow", "flat", "house", "maisonette", "park home"]
+ floor_areas = ["unknown", "s", "m", "l", "xl", "xxl", "xxxl"]
# We pull properties from local authorities, by property type. This will allow us to build
# a dataset of up to 10k properties per local authority/property type combination
@@ -82,24 +83,28 @@ def handler():
data = []
for c in tqdm(constituencies):
for pt in property_types:
- data.extend(
- pagenated_epc_download(
- client=epc_client,
- params={
- "constituency": c,
- "property-type": pt,
- "from-month": 8,
- "from-year": 2014,
- },
- page_size=5000,
- n_pages=10,
+ for fa in floor_areas:
+ data.extend(
+ pagenated_epc_download(
+ client=epc_client,
+ params={
+ "constituency": c,
+ "property-type": pt,
+ "from-month": 8,
+ "from-year": 2014,
+ "floor-area": fa,
+ },
+ page_size=5000,
+ n_pages=10,
+ )
)
- )
# Incorporate input data into cleaning
cleaner = EpcClean(data + [p.data for p in input_properties])
cleaner.clean()
+ z = [x for x in data if x["floor-description"] == "(anheddiad arall islaw)"]
+
address_meta = [
{
"postcode": x["postcode"].upper(),
@@ -137,6 +142,60 @@ def handler():
uvalue_estimates = UvalueEstimations(data=data)
uvalue_estimates.get_estimates(cleaner=cleaner)
+ x = {'low-energy-fixed-light-count': '', 'address': 'Flat 28, 22, Adelina Grove', 'uprn-source': 'Address Matched',
+ 'floor-height': '', 'heating-cost-potential': '668', 'unheated-corridor-length': '7.73',
+ 'hot-water-cost-potential': '190', 'construction-age-band': 'England and Wales: 1991-1995',
+ 'potential-energy-rating': 'D', 'mainheat-energy-eff': 'Very Poor', 'windows-env-eff': 'Average',
+ 'lighting-energy-eff': 'Average', 'environment-impact-potential': '46',
+ 'glazed-type': 'double glazing, unknown install date', 'heating-cost-current': '1081', 'address3': '',
+ 'mainheatcont-description': 'No time or thermostatic control of room temperature',
+ 'sheating-energy-eff': 'N/A', 'property-type': 'Flat', 'local-authority-label': 'Tower Hamlets',
+ 'fixed-lighting-outlets-count': '', 'energy-tariff': 'dual', 'mechanical-ventilation': 'natural',
+ 'hot-water-cost-current': '190', 'county': 'Greater London Authority', 'postcode': 'E1 3BX',
+ 'solar-water-heating-flag': 'N', 'constituency': 'E14000555', 'co2-emissions-potential': '5.2',
+ 'number-heated-rooms': '2', 'floor-description': '(another dwelling below)',
+ 'energy-consumption-potential': '301', 'local-authority': 'E09000030', 'built-form': 'Semi-Detached',
+ 'number-open-fireplaces': '0', 'windows-description': 'Fully double glazed', 'glazed-area': 'Normal',
+ 'inspection-date': '2018-09-05', 'mains-gas-flag': 'N', 'co2-emiss-curr-per-floor-area': '53',
+ 'address1': 'Flat 28', 'heat-loss-corridor': 'unheated corridor', 'flat-storey-count': '',
+ 'constituency-label': 'Bethnal Green and Bow', 'roof-energy-eff': 'Average', 'total-floor-area': '103.0',
+ 'building-reference-number': '4441803568', 'environment-impact-current': '44', 'co2-emissions-current': '5.5',
+ 'roof-description': 'Pitched, insulated (assumed)', 'floor-energy-eff': 'NO DATA!',
+ 'number-habitable-rooms': '2', 'address2': '22, Adelina Grove', 'hot-water-env-eff': 'Poor',
+ 'posttown': 'LONDON', 'mainheatc-energy-eff': 'Very Poor', 'main-fuel': 'electricity (not community)',
+ 'lighting-env-eff': 'Average', 'windows-energy-eff': 'Average', 'floor-env-eff': 'N/A',
+ 'sheating-env-eff': 'N/A', 'lighting-description': 'Low energy lighting in 25% of fixed outlets',
+ 'roof-env-eff': 'Average', 'walls-energy-eff': 'Good', 'photo-supply': '', 'lighting-cost-potential': '84',
+ 'mainheat-env-eff': 'Very Poor', 'multi-glaze-proportion': '100', 'main-heating-controls': '2701',
+ 'lodgement-datetime': '2018-09-06 17:25:59', 'flat-top-storey': 'Y', 'current-energy-rating': 'E',
+ 'secondheat-description': 'None', 'walls-env-eff': 'Good', 'transaction-type': 'rental (private)',
+ 'uprn': '6032920', 'current-energy-efficiency': '48', 'energy-consumption-current': '316',
+ 'mainheat-description': 'Electric ceiling heating', 'lighting-cost-current': '147',
+ 'lodgement-date': '2018-09-06', 'extension-count': '1', 'mainheatc-env-eff': 'Very Poor',
+ 'lmk-key': '175926409402018090617255958380158', 'wind-turbine-count': '0', 'tenure': 'rental (private)',
+ 'floor-level': '4th', 'potential-energy-efficiency': '67', 'hot-water-energy-eff': 'Average',
+ 'low-energy-lighting': '25', 'walls-description': 'Solid brick, as built, insulated (assumed)',
+ 'hotwater-description': 'Electric immersion, off-peak'}
+ from utils.uvalue_estimates import classify_decile_newvalues
+ total_floor_area_group_decile = UvalueEstimations.classify_decile_newvalues(
+ decile_boundaries=uvalue_estimates.walls_decile_data["decile_boundaries"],
+ decile_labels=uvalue_estimates.walls_decile_data["decile_labels"],
+ new_values=[float(x["total-floor-area"])],
+ )[0]
+
+ u_value_estimate = uvalue_estimates.walls[
+ (uvalue_estimates.walls["local-authority"] == x["local-authority"]) &
+ (uvalue_estimates.walls["property-type"] == x["property-type"]) &
+ (uvalue_estimates.walls["built-form"] == x["built-form"]) &
+ (uvalue_estimates.walls["walls-energy-eff"] == x["walls-energy-eff"]) &
+ (uvalue_estimates.walls["walls-env-eff"] == x["walls-env-eff"]) &
+ (uvalue_estimates.walls["total-floor-area_group"] == total_floor_area_group_decile)
+ ]
+
+ uvalue_estimates.walls[
+ uvalue_estimates.walls
+ ]
+
# all_data = {
# "input_properties": input_properties,
# "cleaner": cleaner,
diff --git a/model_data/epc_attributes/FloorAttributes.py b/model_data/epc_attributes/FloorAttributes.py
index b649cdd2..0d8ea493 100644
--- a/model_data/epc_attributes/FloorAttributes.py
+++ b/model_data/epc_attributes/FloorAttributes.py
@@ -12,12 +12,23 @@ class FloorAttributes(BaseUtility):
OBSERVED_ERRORS = ["Conservatory"]
+ WELSH_TEXT = {
+ "(anheddiad arall islaw)": "(another dwelling below)",
+ }
+
def __init__(self, description: str):
self.description: str = description.lower()
self.nodata = (not description) or (description in self.DATA_ANOMALY_MATCHES) or (
description in self.OBSERVED_ERRORS)
+ # Try and perform a translation, incase it's in welsh
+ translation = self.WELSH_TEXT.get(self.description)
+
+ if translation:
+ self.nodata = False
+ self.description = translation
+
if not self.nodata and not any(
rt in self.description for rt in
self.FLOOR_TYPES + self.DWELLING_BELOW + ["average thermal transmittance"]