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94 lines
4 KiB
Python
94 lines
4 KiB
Python
from tqdm import tqdm
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import os
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import pandas as pd
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import msgpack
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from model_data.EpcClean import EpcClean
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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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from utils.s3 import save_data_to_s3
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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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ENVIRONMENT = os.getenv("ENVIRONMENT", "dev")
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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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Currently, this application is just run on a local machine
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"""
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cleaned_data = {}
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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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directory_destructured = str(directory).split("/")[-1].split("-")
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gss_code = directory_destructured[1]
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local_authority = directory_destructured[2]
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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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cleaner.clean()
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# Extended cleaned_data
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for k, data in cleaner.cleaned.items():
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if k not in cleaned_data:
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cleaned_data[k] = data
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else:
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existing_descriptions = [x["original_description"] for x in cleaned_data[k]]
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new_data = [x for x in data if x["original_description"] not in existing_descriptions]
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cleaned_data[k].extend(new_data)
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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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# Basic check to make sure all descriptions are unique
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for _, cleaned in cleaned_data.items():
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descriptions = [x["original_description"] for x in cleaned]
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if len(descriptions) != len(set(descriptions)):
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raise ValueError("Duplicated descriptions found, check me")
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# We store a singular file however we could store the data under the following file path:
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# cleaned_epc_data/{component}/{original_description}/cleaned.bson
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# where component is one of the keys of cleaned_data. If we store it against the original data, this
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# data being read in will be extremely small, meaning quicker load times. We'll begin by storing as a single
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# file and monitor usage patterns to see if it makes sense to split the data up
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save_data_to_s3(
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data=msgpack.packb(cleaned_data, use_bin_type=True),
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s3_file_name="cleaned_epc_data/cleaned.bson",
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bucket_name=f"retrofit-data-{ENVIRONMENT}"
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)
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if __name__ == "__main__":
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print("Initialising cleaner app run")
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app()
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