import inspect
import pandas as pd
from pathlib import Path
from tqdm import tqdm
from etl.epc.settings import EARLIEST_EPC_DATE
from etl.spatial.OpenUprnClient import OpenUprnClient

src_file_path = inspect.getfile(lambda: None)

EPC_DIRECTORY = Path("/Users/khalimconn-kowlessar/Downloads/all-domestic-certificates")
epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]

aggregation = []
for directory in tqdm(epc_directories):
    data = pd.read_csv(directory / "certificates.csv", low_memory=False)
    # Rename the columns to the same format as the api returns
    data.columns = [c.replace("_", "-").lower() for c in data.columns]

    data = data[data["posttown"].str.contains("London", case=False, na=False)]
    if data.empty:
        continue
    # Take just date before the date threshold
    data = data[data["lodgement-date"] >= EARLIEST_EPC_DATE]

    data = data[~pd.isnull(data["uprn"])]
    data["uprn"] = data["uprn"].astype(int)
    # Take just the newest EPC per uprn, based on lodgement-date
    data = data.sort_values("lodgement-date", ascending=False).drop_duplicates("uprn")
    # Take EPC D and below
    data = data[data["current-energy-rating"].isin(["D", "E", "F", "G"])]
    data["postal_region"] = data["postcode"].str.split(" ").str[0]

    # Take homes that don't have a gas boiler
    off_gas = data[~data["main-fuel"].str.contains("mains gas", case=False, na=False)]

    if off_gas.empty:
        continue

    # Remote properties with conservation area issues
    uprns = off_gas["uprn"].unique()
    # Get data
    ca_data = OpenUprnClient.get_spatial_data(uprns, "retrofit-data-dev")
    off_gas = off_gas.merge(
        ca_data[["UPRN", "conservation_status", "is_listed_building", "is_heritage_building"]].rename(
            columns={"UPRN": "uprn"}
        ),
        how="left",
        on="uprn",
    )
    # Remove any restricted units
    off_gas = off_gas[
        (off_gas["conservation_status"] != True)
        & (off_gas["is_listed_building"] != True)
        & (off_gas["is_heritage_building"] != True)
        ]

    off_gas = off_gas[
        off_gas["tenure"].isin(["rental (private)", "Rented (private)", "owner-occupied", "Owner-occupied"])
    ]

    region_summary = off_gas.groupby("postal_region").size().reset_index(name="count")

    aggregation.append(region_summary)

postal_region_aggregation = pd.concat(aggregation)
# Re-aggregate
postal_region_aggregation = postal_region_aggregation.groupby("postal_region")["count"].sum().reset_index()

postal_region_aggregation = postal_region_aggregation.sort_values("count", ascending=False)
postal_region_aggregation = postal_region_aggregation.rename(
    columns={"postal_region": "Postcode Region", "count": "Number of Homes"}
)
postal_region_aggregation.to_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/GLA/Off Gas EPC D-G Postal Regions - without conservation "
    "area.xlsx",
    index=False
)