mirror of
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75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
"""
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This script will create an input csv for the recommendation engine and upload it to S3, which can be used for
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testing
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"""
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import os
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import numpy as np
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import pandas as pd
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from epc_api.client import EpcClient
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from utils.s3 import save_csv_to_s3
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FILE_SIZE = 100
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN", None)
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USER_ID = 2
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PORTFOLIO_ID = 47
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def app():
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starting_csv = pd.read_csv("input_property_list.csv")
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remaining_files_to_sample = FILE_SIZE - len(starting_csv)
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# For the remaining addresses, 80% of them will be EPC D and below and the remaining 20% will be EPC A and above
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n_epc_d_below = int(remaining_files_to_sample * 0.8)
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n_epc_c_above = remaining_files_to_sample - n_epc_d_below
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n_g = int(np.ceil(n_epc_d_below / 4))
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n_f = int(np.ceil(n_epc_d_below / 4))
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n_e = int(np.ceil(n_epc_d_below / 4))
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n_d = int(np.ceil(n_epc_d_below / 4))
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n_c = int(np.ceil(n_epc_c_above / 3))
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n_b = int(np.ceil(n_epc_c_above / 3))
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n_a = int(np.ceil(n_epc_c_above / 3))
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epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
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g_data = epc_client.domestic.search(params={"energy-band": "g"}, size=n_g)
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f_data = epc_client.domestic.search(params={"energy-band": "f"}, size=n_f)
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e_data = epc_client.domestic.search(params={"energy-band": "e"}, size=n_e)
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d_data = epc_client.domestic.search(params={"energy-band": "d"}, size=n_d)
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c_data = epc_client.domestic.search(params={"energy-band": "c"}, size=n_c)
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b_data = epc_client.domestic.search(params={"energy-band": "b"}, size=n_b)
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a_data = epc_client.domestic.search(params={"energy-band": "a"}, size=n_a)
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# Combine the final data
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final_data = (
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g_data["rows"] + f_data["rows"] + e_data["rows"] + d_data["rows"] + c_data["rows"] + b_data["rows"]
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+ a_data["rows"]
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)
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# TODO: For the moment, don't use park homes
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final_csv_data = pd.DataFrame(
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[{"address": x["address"], "postcode": x["postcode"], "Notes": None} for x
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in final_data if
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x["property-type"] not in ["Park home"]]
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)
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final_csv_data = pd.concat([starting_csv, final_csv_data]).reset_index(drop=True)
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# Store the data in s3
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filename = f"{USER_ID}/{PORTFOLIO_ID}/test_inputs.csv"
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save_csv_to_s3(
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dataframe=final_csv_data,
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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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body = {
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"portfolio_id": str(PORTFOLIO_ID),
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"housing_type": "Social",
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"goal": "Increase EPC",
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"goal_value": "B",
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"trigger_file_path": filename
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}
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print(body)
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