"""
On the standardised asset list, this script will flag the pilot assets.
"""
import pandas as pd
import os
import numpy as np
from tqdm import tqdm

PILOT_PROJECT_CODE = "MHS-000-PILOT"
MHS_PHASE_1_PROJECT_CODE = "MHS-001"

asset_list = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/MHS HOMES (Full Asset List) - for programme build - "
    "Standardised.xlsx",
    sheet_name="Standardised Asset List",
)
flat_data = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/MHS HOMES (Full Asset List) - for programme build - "
    "Standardised.xlsx",
    sheet_name="Flat Data",
)

pilot = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/MHS 334 x Pilot reviewed - KB notes end column.xlsx"
)
ciga_checks = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/MHS CIGA Check 03042025_201.xlsx"
)
ciga_checks["row_id"] = ciga_checks.index

asset_list["project_code"] = None

asset_list["project_code"] = np.where(
    asset_list["landlord_property_id"].isin(pilot["Place Reference"]),
    PILOT_PROJECT_CODE,
    asset_list["project_code"],
)
# We now flag the next phase of the programme
asset_list["project_code"] = np.where(
    (~pd.isnull(asset_list["cavity_reason"]) | ~pd.isnull(asset_list["solar_reason"])) & pd.isnull(
        asset_list["project_code"]),
    MHS_PHASE_1_PROJECT_CODE,
    asset_list["project_code"],
)

# We now flag the CIGA checks
manual_fixes = {
    "123 Columbine Close, Rochester": "2213861230"
}
ciga_lookup = []
for _, row in tqdm(ciga_checks.iterrows(), total=len(ciga_checks)):

    if manual_fixes.get(row["Matched Address"]):
        ll_pid = manual_fixes[row["Matched Address"]]
        df = asset_list[
            (asset_list["landlord_property_id"].astype(str) == ll_pid)
        ]
        ciga_lookup.append(
            {
                "domna_property_id": df["domna_property_id"].values[0],
                "row_id": row["row_id"],
            }
        )
        continue

    df = asset_list[
        (asset_list["domna_postcode"] == row["Postcode"])
    ]

    df = df[
        (df["domna_address_1"].astype(str) == str(row["Address1"]))
    ]

    if df.empty:
        df = asset_list[
            (asset_list["domna_postcode"] == row["Matched Postcode"])
        ]
        df = df[(df["domna_address_1"].astype(str) == str(row["Address1"]))]

    if df.shape[0] > 1:
        df = asset_list[
            (asset_list["domna_full_address"].str.lower().str.replace(",", "").str.contains(
                row["Matched Address"].lower().replace(",", ""), na=False))
        ]
        if df.empty:
            df = asset_list[
                (asset_list["domna_full_address"].str.lower().str.replace(",", "").str.contains(
                    row["Address2"].lower().replace(",", ""), na=False))
            ]

        df = df[(df["domna_address_1"].astype(str) == str(row["Address1"]))]

    if df.shape[0] != 1:
        raise Exception("More than one match found for {row['Address1']} in the asset list")

    ciga_lookup.append(
        {
            "domna_property_id": df["domna_property_id"].values[0],
            "row_id": row["row_id"],
        }
    )

ciga_lookup = pd.DataFrame(ciga_lookup)

ciga_lookup = ciga_lookup.merge(
    ciga_checks[["row_id", "Guarantee"]].rename(
        columns={"Guarantee": "ciga_guarantee"}
    ), how="left", on="row_id"
)
ciga_lookup["ciga_check_complete"] = True

asset_list = asset_list.merge(
    ciga_lookup[["domna_property_id", "ciga_guarantee"]],
    how="left",
    on="domna_property_id"
)

# Check we matched addresses correctly
# match_check = ciga_lookup.merge(
#     ciga_checks, how="left", on="row_id"
# ).merge(
#     asset_list[["domna_property_id", "domna_full_address"]], how="left", on="domna_property_id"
# )
# match_check = match_check[["Matched Address", "domna_full_address"]]

# Save

filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/12052025 MHS Standardised Asset List - "
            "programme.xlsx")
# Store the data in two tabs. One for the asset list with the EPC data and the second with the flat data

with pd.ExcelWriter(filename) as writer:
    asset_list.to_excel(writer, sheet_name="Standardised Asset List", index=False)
    flat_data.to_excel(writer, sheet_name="Flat Data", index=False)