mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
from tqdm import tqdm
|
|
import os
|
|
from model_data.BoreholeClient import BoreholeClient
|
|
from model_data.LandRegistryClient import LandRegistryClient
|
|
|
|
from model_data.temp_inputs import input_data
|
|
from model_data.Property import Property
|
|
from model_data.config import EPC_AUTH_TOKEN
|
|
from epc_api.client import EpcClient
|
|
from model_data.downloader import pagenated_epc_download
|
|
from model_data.EpcClean import EpcClean
|
|
from model_data.OpenUprnClient import OpenUprnClient
|
|
|
|
LAND_REGISTRY_PATHS = [
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2022 (1).csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2021.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2020.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2019.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2018.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part1.csv",
|
|
os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part2.csv",
|
|
]
|
|
|
|
|
|
def handler():
|
|
# To begin with, the input data is a list of dictionaries, however we would read this file in
|
|
|
|
epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
|
|
|
|
input_properties = [
|
|
Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client)
|
|
for config in input_data
|
|
]
|
|
|
|
for p in input_properties:
|
|
p.search_address_epc()
|
|
|
|
uprns = [p.data['uprn'] for p in input_properties]
|
|
|
|
open_uprn_client = OpenUprnClient(
|
|
path=os.path.abspath(
|
|
os.path.dirname(__file__)
|
|
) + "/model_data/local_data/osopenuprn_202306_csv/osopenuprn_202305.csv",
|
|
uprns=uprns
|
|
)
|
|
open_uprn_client.read()
|
|
|
|
# What's going on here?
|
|
# We're using Ordinance Survey Open Uprn data
|
|
# to find the coordinates of each address, which we will then be able to use at a later stage
|
|
for p in input_properties:
|
|
p.get_coordinates(open_uprn_client)
|
|
|
|
local_authorities = {p.data['local-authority'] for p in input_properties}
|
|
|
|
data = []
|
|
for la in tqdm(local_authorities):
|
|
data.extend(
|
|
pagenated_epc_download(
|
|
client=epc_client,
|
|
params={"local-authority": la},
|
|
page_size=5000,
|
|
n_pages=10,
|
|
)
|
|
)
|
|
|
|
# Incorporate input data into cleaning
|
|
cleaner = EpcClean(data + [p.data for p in input_properties])
|
|
cleaner.clean()
|
|
|
|
address_meta = [
|
|
{
|
|
"postcode": x["postcode"].upper(),
|
|
"address1": x["address1"].upper(),
|
|
"address2": x["address2"].upper(),
|
|
"address3": x["address3"].upper(),
|
|
"address": x["address"],
|
|
"uprn": x["uprn"]
|
|
} for x in data
|
|
]
|
|
|
|
# Land registry
|
|
land_registry_client = LandRegistryClient(
|
|
paths=LAND_REGISTRY_PATHS,
|
|
addresses=address_meta
|
|
)
|
|
land_registry_client.read()
|
|
|
|
# Borehole
|
|
borehole_client = BoreholeClient(
|
|
path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/borehole/borehole.dbf"
|
|
)
|
|
borehole_client.read()
|
|
|
|
# Now, for our input properties, we need to identify the components of the building, based
|
|
# on the cleaning we've done
|
|
for p in input_properties:
|
|
p.get_components(cleaner)
|
|
|
|
# Now, given the components, we want to idenfity upgrade options
|
|
import pandas as pd
|
|
walls_df = pd.DataFrame(
|
|
[{"address1": p.address1, **p.walls} for p in input_properties]
|
|
)
|
|
|
|
# Key values
|
|
# This is based on the standards set out by part L of the building regulations
|
|
WALLS_MAX_U_VALUE = 0.18
|
|
|
|
input_properties[1].data["address1"]
|
|
walls_df["address1"].values[1]
|
|
walls_df["original_description"].values[1]
|
|
# Walls
|
|
# Property 0
|
|
# '28 Distillery Wharf', 'Average thermal transmittance 0.16 W/m-¦K'
|
|
# Because the insulation is already within the max threshold for new builds
|
|
# Also, I know that the property was built after 1990 so was built with insulation (let's get land registry data)
|
|
# It's likely not worth doing an insulation upgrade, however if anything, we would do internal wall insulation
|
|
# logic:
|
|
# if building built after 1990 + we're able to identify U-value + U-value less than 0.18
|
|
# and if in or close to a conversation area, recommend internal wall insulation
|
|
# Property 1
|
|
|
|
from model_data.recommendations.WallRecommendations import WallRecommendations
|
|
self = WallRecommendations(property_instance=input_properties[0])
|