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123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
import pandas as pd
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from pprint import pprint
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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.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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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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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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# What's going on here?
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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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pprint(input_properties[0].coordinates)
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local_authorities = {p.data['local-authority'] for p in input_properties}
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data = []
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for la in tqdm(local_authorities):
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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},
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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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cleaner = EpcClean(data)
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cleaner.clean()
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# example cleaned data
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# Why do we need this stuff?
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# https://docs.google.com/spreadsheets/d/1ek9ItDv7xHwFm_FK6B0PyOBwvi6U4qRPuncBsVlCHUA/edit#gid=0
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cleaner.cleaned.keys()
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floors = pd.DataFrame(cleaner.cleaned['floor-description'])
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hotwater = pd.DataFrame(cleaner.cleaned['hotwater-description'])
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mainheat = pd.DataFrame(cleaner.cleaned["mainheat-description"])
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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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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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# There are ~1.4 million entries in this dataset and so we firstly want to reduce the number of
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# entries in here if possible before we produce any form of comparison between our properties, to infer
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# the distance from the property to the nearest borehole
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# Let's take a sample
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borehold_compare_to = borehole_client.data[0]
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property = input_properties[0]
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# for each property, find the nearest borehole
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# This is just an example, looking at the distance from a property to a borehole
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dist_m, dist_km = borehole_client.distance_between_bng_coords(
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x1_bng=property.coordinates["x_coordinate"],
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y1_bng=property.coordinates["y_coordinate"],
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x2_bng=borehold_compare_to["X"],
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y2_bng=borehold_compare_to["Y"],
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)
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