"""
This script marks which properties have been reviewed by the Medway.
"""
import pandas as pd
import numpy as np
from tqdm import tqdm
import os

asset_list = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Medway/MEDWAY Asset List - Standardised.xlsx",
    sheet_name="Standardised Asset List",
)
flat_data = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Medway/MEDWAY Asset List - Standardised.xlsx",
    sheet_name="Flat Data",
)

reviewed_assets = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Medway/Programme Final Check.xlsx",
)
exclude_from_programme = reviewed_assets.copy()  # [reviewed_assets["Khalim - include in programme"] == "No"].copy()
exclude_from_programme["exclusion_reason"] = None
exclude_from_programme["exclusion_reason"] = np.where(
    (exclude_from_programme["UPRN"] == "SOLD"),
    "Sold",
    exclude_from_programme["exclusion_reason"],
)
exclude_from_programme["exclusion_reason"] = np.where(
    (exclude_from_programme["Include in SHDF Bid?"] == "Definite"),
    "Included in SHDF Bid",
    exclude_from_programme["exclusion_reason"],
)

exclude_from_programme["exclusion_reason"] = np.where(
    (exclude_from_programme['Move Forward'] == "EPC C"),
    "Excluded from Programme",
    exclude_from_programme["exclusion_reason"],
)

# exclude_from_programme = exclude_from_programme[~pd.isnull(exclude_from_programme["exclusion_reason"])]
exclude_from_programme = exclude_from_programme.reset_index(drop=True)
exclude_from_programme["row_id"] = exclude_from_programme.index

# Match to asset list
matched = []
for _, x in tqdm(exclude_from_programme.iterrows(), total=len(exclude_from_programme)):

    if x["No."] == 218 and x["Postcode"] == "ME8 6QB":
        pc = "ME8 6QP"
    elif x["No."] == 198 and x["Postcode"] == "ME8 6HL":
        pc = "ME8 6LU"
    elif x["No."] == "39a" and x["Postcode"] == "ME7 2BU":
        pc = "ME7 2BU"
    else:
        pc = x["Postcode"]

    hn = x["No."]

    m = asset_list[
        (asset_list["domna_address_1"] == str(hn)) &
        (asset_list["domna_postcode"] == str(pc))
        ]

    if m.empty:
        m = asset_list[
            (asset_list["domna_full_address"].str.replace(",", "").str.lower().str.contains(
                x["Address"].lower().strip()))
        ]

    if m.shape[0] == 1:
        matched.append(
            {
                "full_address": m["domna_full_address"].values[0],
                "postcode": m["domna_postcode"].values[0],
                "review_no": x["No."],
                "review_address": x["Address"],
                "review_postcode": x["Postcode"],
                "exclusion_reason": x["exclusion_reason"],
                "landlord_property_id": m["landlord_property_id"].values[0],
                "ciga_guarantee": x["Unnamed: 21"]
            }
        )
        continue

    raise NotImplementedError("FIX ME")

matched = pd.DataFrame(matched)

matched = matched.rename(
    columns={"review_address": "ciga_check_address"}
)

asset_list = asset_list.merge(
    matched[["landlord_property_id", "exclusion_reason", "ciga_guarantee", "ciga_check_address"]],
    how="left", on="landlord_property_id"
)

# Store as an excel
filename = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Medway/Reviewed Standardised 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)