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124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
import os
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import time
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from tqdm import tqdm
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import pandas as pd
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from dotenv import load_dotenv
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from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
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from backend.SearchEpc import SearchEpc
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from utils.s3 import save_csv_to_s3
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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USER_ID = 8
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PORTFOLIO_ID = 121
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def app():
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"""
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Prepares the inputs to produce the remote assessments for Cottons
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:return:
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"""
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# Read in the asset list
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cottons_asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Cottons/Cottons Asset List EPC Data Pull with "
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"valuations.xlsx"
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)
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# A number are missing EPCs due to the space in the postcode
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# Breakdowns:
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# C 119
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# D 106
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# E 26
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# B 5
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#
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# Take the EPC D/E properties
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asset_list = cottons_asset_list[
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cottons_asset_list["EPC rating on register"].isin(["D", "E"])
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]
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asset_list = asset_list.reset_index(drop=True)
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asset_list["row_id"] = asset_list.index
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asset_list["uprn"] = asset_list["uprn"].astype(int)
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extracted_data = []
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model_asset_list = []
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for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
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add1 = home["address1"]
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pc = home["postcode"]
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# Retrieve the EPC data
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epc_searcher = SearchEpc(
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address1=add1,
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postcode=pc, uprn=home["uprn"], auth_token=EPC_AUTH_TOKEN, os_api_key=""
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)
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epc_searcher.find_property(skip_os=True)
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find_epc_searcher = RetrieveFindMyEpc(address=epc_searcher.newest_epc["address1"],
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postcode=epc_searcher.newest_epc["postcode"])
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find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
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time.sleep(0.5)
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# We need uprn
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extracted_data.append(
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{
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"uprn": home["uprn"],
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**find_epc_data,
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}
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)
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model_asset_list.append(
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{
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"uprn": home["uprn"],
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"address": epc_searcher.newest_epc["address1"],
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"postcode": epc_searcher.newest_epc["postcode"],
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}
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)
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non_invasive_recommendations = [
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{
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"uprn": r["uprn"],
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"recommendations": r["recommendations"]
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} for r in extracted_data
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]
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valuations_data = asset_list[["uprn", "Zoopla Valuation"]].copy().rename(columns={"Zoopla Valuation": "valuation"})
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valuations_data = valuations_data[~pd.isnull(valuations_data["valuation"])]
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filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
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save_csv_to_s3(
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dataframe=pd.DataFrame(model_asset_list),
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bucket_name="retrofit-plan-inputs-dev",
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file_name=filename
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)
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# Store the non-invasive recommendations in s3
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non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.csv"
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save_csv_to_s3(
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dataframe=pd.DataFrame(non_invasive_recommendations),
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bucket_name="retrofit-plan-inputs-dev",
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file_name=non_invasive_recommendations_filename
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)
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# Store the valuations data in s3
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valuations_filename = f"{USER_ID}/{PORTFOLIO_ID}/valuations.csv"
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save_csv_to_s3(
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dataframe=valuations_data,
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bucket_name="retrofit-plan-inputs-dev",
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file_name=valuations_filename
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)
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body = {
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"portfolio_id": str(PORTFOLIO_ID),
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"housing_type": "Social",
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"goal": "Increasing EPC",
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"goal_value": "C",
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"trigger_file_path": filename,
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"already_installed_file_path": "",
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"patches_file_path": "",
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"non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
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"valuation_file_path": valuations_filename,
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"scenario_name": "Wave 3 Packages",
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"multi_plan": True,
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"budget": None,
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"exclusions": ['air_source_heat_pump', 'boiler_upgrade', 'floor_insulation']
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}
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print(body)
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