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271 lines
9.8 KiB
Python
271 lines
9.8 KiB
Python
import pandas as pd
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from tqdm import tqdm
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import os
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from model_data.BoreholeClient import BoreholeClient
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from model_data.LandRegistryClient import LandRegistryClient
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from model_data.ConservationAreaClient import ConservationAreaClient
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from model_data.temp_inputs import input_data
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from model_data.Property import Property
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from model_data.config import EPC_AUTH_TOKEN
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from epc_api.client import EpcClient
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from model_data.downloader import pagenated_epc_download
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from model_data.EpcClean import EpcClean
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from model_data.OpenUprnClient import OpenUprnClient
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from model_data.analysis.UvalueEstimations import UvalueEstimations
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LAND_REGISTRY_PATHS = [
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2022 (1).csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2021.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2020.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2019.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2018.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part1.csv",
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part2.csv",
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]
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def handler():
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# To begin with, the input data is a list of dictionaries, however we would read this file in
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epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
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input_properties = [
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Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client)
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for config in input_data
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]
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for p in input_properties:
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p.search_address_epc()
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p.set_year_built()
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uprns = [p.data['uprn'] for p in input_properties]
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open_uprn_client = OpenUprnClient(
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path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/osopenuprn_202306_csv/osopenuprn_202305.csv",
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uprns=uprns
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)
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open_uprn_client.read()
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# We're using Ordinance Survey Open Uprn data
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# to find the coordinates of each address, which we will then be able to use at a later stage
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for p in input_properties:
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p.get_coordinates(open_uprn_client)
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conservation_area_client = ConservationAreaClient(
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historic_england_path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/Historic_Eng_Conservation_Areas/Conservation_Areas.shp",
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gov_path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/gov-conservation-area.geojson"
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)
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conservation_area_client.read()
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# Check if the property is in a conversation area
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for p in input_properties:
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p.set_is_in_conservation_area(conservation_area_client)
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local_authorities = {p.data['local-authority'] for p in input_properties}
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# TODO: Do this at a constituency level
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constituencies = {p.data["constituency"] for p in input_properties}
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property_types = ["bungalow", "flat", "house", "maisonette", "park home"]
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# We pull properties from local authorities, by property type. This will allow us to build
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# a dataset of up to 10k properties per local authority/property type combination
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data = []
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for la in tqdm(local_authorities):
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for pt in property_types:
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data.extend(
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pagenated_epc_download(
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client=epc_client,
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params={"local-authority": la, "property-type": pt},
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page_size=5000,
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n_pages=10,
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)
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)
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# Incorporate input data into cleaning
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cleaner = EpcClean(data + [p.data for p in input_properties])
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cleaner.clean()
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address_meta = [
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{
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"postcode": x["postcode"].upper(),
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"address1": x["address1"].upper(),
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"address2": x["address2"].upper(),
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"address3": x["address3"].upper(),
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"address": x["address"],
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"uprn": x["uprn"]
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} for x in data
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]
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# Land registry
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land_registry_client = LandRegistryClient(
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paths=LAND_REGISTRY_PATHS,
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addresses=address_meta
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)
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lr_data = land_registry_client.read()
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# Borehole
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borehole_client = BoreholeClient(
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path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/borehole/borehole.dbf"
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)
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borehole_client.read()
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# Now, for our input properties, we need to identify the components of the building, based
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# on the cleaning we've done
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for p in input_properties:
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p.get_components(cleaner)
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# TODO: Add property age band into this
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uvalue_estimates = UvalueEstimations(data=data)
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uvalue_estimates.get_estimates(cleaner=cleaner)
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# all_data = {
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# "input_properties": input_properties,
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# "cleaner": cleaner,
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# "uvalue_estimates": uvalue_estimates,
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# "land_registry_client": land_registry_client,
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# "borehole_client": borehole_client,
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# "conservation_area_client": conservation_area_client,
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# "open_uprn_client": open_uprn_client,
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# "data": data
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# }
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# import pickle
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# with open("all_data.pkl", "wb") as f:
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# pickle.dump(all_data, f)
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# input_properties[4].data["address1"]
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# input_properties[4].data["postcode"]
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# floors_df["address1"].values[4]
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# floors_df["original_description"].values[4]
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#
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# df = pd.DataFrame(
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# [
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# x.data for x in input_properties
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# ]
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# )
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# df["property-type"].unique()
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#
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# from model_data.recommendations.WallRecommendations import WallRecommendations
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# all_res = []
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# for p in input_properties:
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# inst = WallRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates)
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# inst.recommend()
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# n_recs = len(inst.recommendations)
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# all_res.append(n_recs)
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#
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# self = WallRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
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# input_properties[6].walls
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# self.recommend()
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# df = pd.DataFrame(self.recommendations[0]["parts"])
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# recommendations = pd.DataFrame(self.recommendations)
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#
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# from model_data.recommendations.FloorRecommendations import FloorRecommendations
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# self = FloorRecommendations(property_instance=input_properties[4], uvalue_estimates=uvalue_estimates)
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# self.recommendations
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# self.recommend()
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# self.recommendations
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#
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# # We need to deduce a U-value for "Good" energy effieciency
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#
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# mainheating = pd.DataFrame(
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# [{"address1": p.address1, "postcode": p.postcode, **p.main_heating} for p in input_properties])
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# hotwater = pd.DataFrame([{"address1": p.address1, **p.hotwater} for p in input_properties])
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#
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# mainheating[["address1", "postcode"]]
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#
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# # TODO: I want to knwo what "Good" efficiency means for the description
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# # 'Flat 28, 22 Adelina Grove' 'Solid brick, as built, insulated (assumed)'
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# # so to do this, filter on the local authority code and property type, where we have U
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# # values for the wall and take a median!
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#
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# p = input_properties[6]
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# df = pd.DataFrame(data)
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#
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# res = []
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# for p in input_properties:
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# distances = []
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# for borehole in tqdm(borehole_client.data, total=len(borehole_client.data)):
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# dist_meeters, _ = borehole_client.distance_between_bng_coords(
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# x1_bng=p.coordinates['x_coordinate'],
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# y1_bng=p.coordinates['y_coordinate'],
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# x2_bng=float(borehole['EASTING']),
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# y2_bng=float(borehole['NORTHING'])
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# )
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# distances.append(dist_meeters)
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#
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# res.append(
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# {
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# "uprn": int(p.data["uprn"]),
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# "meters_to_nearest_borehole": min(distances)
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# }
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#
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# )
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# res = pd.DataFrame(res)
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#
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# properties_dataset = [
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# {
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# **p.data,
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# "in_conservation_area": p.in_conservation_area,
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# **p.coordinates,
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#
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# } for p in input_properties
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# ]
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#
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# properties_dataset = pd.DataFrame(properties_dataset)
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# properties_dataset = properties_dataset.merge(res, on="uprn", how="left")
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#
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# properties_dataset.to_csv("properties_dataset.csv")
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# We test estimating gain
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import pandas as pd
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pd.set_option('display.max_rows', 500)
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pd.set_option('display.max_columns', 500)
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pd.set_option('display.width', 1000)
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df = pd.DataFrame(data)
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# We want to estimate for making improvements on different property components
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response = "environment-impact-current"
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base_features = [
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"property-type",
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"built-form",
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# "construction-age-band",
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"number-habitable-rooms",
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]
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component_features = [
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"walls-description",
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"floor-description",
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]
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model_data = df[[response] + component_features + base_features]
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model_data = model_data.reset_index()
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model_data["idx"] = model_data.index.copy()
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summary = (
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model_data
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.groupby(component_features + base_features)
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.agg({response: 'median', "idx": 'size'})
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.reset_index()
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)
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summary = summary.sort_values("walls-description")
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example = summary[
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(summary["walls-description"].isin(
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[
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"Solid brick, as built, no insulation (assumed)",
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"Solid brick, as built, partial insulation (assumed)",
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"Solid brick, as built, insulated (assumed)",
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]
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)) &
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(summary["property-type"] == "House") &
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(summary["built-form"] == "Detached") &
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# (summary["construction-age-band"] == "England and Wales: 1976-1982")
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(summary["number-habitable-rooms"] == "4")
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]
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