mirror of
https://github.com/Hestia-Homes/Model.git
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113 lines
4.9 KiB
Python
113 lines
4.9 KiB
Python
from tqdm import tqdm
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import os
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import pandas as pd
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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.analysis.UvalueEstimations import UvalueEstimations
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from model_data.simulation_system.core.Settings import EARLIEST_EPC_DATE
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from pathlib import Path
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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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EPC_DIRECTORY = Path(__file__).parent / "model_data" / "simulation_system" / "data" / "all-domestic-certificates"
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def app():
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"""
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For a pre-defined list of constituencies and property data_types, we'll download EPC data from the API
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and produce a dataset of cleaned fields so that when we get new properties, we can quickly
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sanitise any description data
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:return:
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"""
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# epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
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#
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# constituencies = {'E14000555', 'E14000726', 'E14000720', 'E14000721', 'E14000553', 'E14000752'}
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# property_types = ["bungalow", "flat", "house", "maisonette", "park home"]
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# floor_areas = ["unknown", "s", "m", "l", "xl", "xxl", "xxxl"]
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#
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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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# # For particularly old EPC data, we have inconsistent records so we'll only include EPCS that were
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# # conducted after 2010, since SAP09 was introduced in 2009 an later SAP12 was introduced in England
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# # and Wales from 31 July 2014
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# # Download data from August 2014 onwards
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# data = []
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# for c in tqdm(constituencies):
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# for pt in property_types:
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# for fa in floor_areas:
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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={
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# "constituency": c,
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# "property-type": pt,
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# "from-month": 8,
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# "from-year": 2014,
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# "floor-area": fa,
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# },
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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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# Production of sample data for land registry
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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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#
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# import pickle
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# with open("sample_addresses.pkl", "wb") as f:
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# pickle.dump(address_meta, f)
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epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
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for directory in tqdm(epc_directories):
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data = pd.read_csv(directory / "certificates.csv", low_memory=False)
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# Rename the columns to the same format as the api returns
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data.columns = [c.replace("_", "-").lower() for c in data.columns]
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# Take just date before the date threshold
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data = data[data["lodgement-date"] >= EARLIEST_EPC_DATE]
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# Convert to list of dictioaries as returned by the api
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data = data.to_dict("records")
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# Incorporate input data into cleaning
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cleaner = EpcClean(data)
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lighting_averages = cleaner.lighting_averages
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#
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# TODO: All of these outputs can be stored by constituency so we can reduce the amount
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# of data we fetch
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#
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# TODO: WE need to store lighting_averages to a s3
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# We should also extend these averages so they're by more variables (property type, age band,
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# constituency,
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# etc)
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cleaner.clean()
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# TODO: cleaner.cleaned datasets to s3
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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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# TODO: Store these to a s3
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uvalue_estimates.walls
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uvalue_estimates.floors
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uvalue_estimates.roofs
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