import pandas as pd
from pprint import pprint
from tqdm import tqdm
import os
from model_data.BoreholeClient import BoreholeClient
from model_data.LandRegistryClient import LandRegistryClient

from model_data.temp_inputs import input_data
from model_data.Property import Property
from model_data.config import EPC_AUTH_TOKEN
from epc_api.client import EpcClient
from model_data.downloader import pagenated_epc_download
from model_data.EpcClean import EpcClean
from model_data.OpenUprnClient import OpenUprnClient

LAND_REGISTRY_PATHS = [
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2022 (1).csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2021.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2020.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2019.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2018.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part1.csv",
    os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part2.csv",
]


def handler():
    # To begin with, the input data is a list of dictionaries, however we would read this file in

    epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)

    input_properties = [
        Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client)
        for config in input_data
    ]

    for p in input_properties:
        p.search_address_epc()

    uprns = [p.data['uprn'] for p in input_properties]

    open_uprn_client = OpenUprnClient(
        path=os.path.abspath(
            os.path.dirname(__file__)
        ) + "/model_data/local_data/osopenuprn_202306_csv/osopenuprn_202305.csv",
        uprns=uprns
    )
    open_uprn_client.read()

    # What's going on here?
    # We're using Ordinance Survey Open Uprn data
    # to find the coordinates of each address, which we will then be able to use at a later stage
    for p in input_properties:
        p.get_coordinates(open_uprn_client)

    pprint(input_properties[0].coordinates)

    local_authorities = {p.data['local-authority'] for p in input_properties}

    data = []
    for la in tqdm(local_authorities):
        data.extend(
            pagenated_epc_download(
                client=epc_client,
                params={"local-authority": la},
                page_size=5000,
                n_pages=10,
            )
        )

    cleaner = EpcClean(data)

    cleaner.clean()

    # example cleaned data
    # Why do we need this stuff?
    # https://docs.google.com/spreadsheets/d/1ek9ItDv7xHwFm_FK6B0PyOBwvi6U4qRPuncBsVlCHUA/edit#gid=0
    cleaner.cleaned.keys()
    floors = pd.DataFrame(cleaner.cleaned['floor-description'])
    hotwater = pd.DataFrame(cleaner.cleaned['hotwater-description'])
    mainheat = pd.DataFrame(cleaner.cleaned["mainheat-description"])

    address_meta = [
        {
            "postcode": x["postcode"].upper(),
            "address1": x["address1"].upper(),
            "address2": x["address2"].upper(),
            "address3": x["address3"].upper(),
            "address": x["address"],
            "uprn": x["uprn"]
        } for x in data
    ]

    # Land registry
    land_registry_client = LandRegistryClient(
        paths=LAND_REGISTRY_PATHS,
        addresses=address_meta
    )
    land_registry_client.read()

    # Borehole
    borehole_client = BoreholeClient(
        path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/borehole/borehole.dbf"
    )
    borehole_client.read()

    # There are ~1.4 million entries in this dataset and so we firstly want to reduce the number of
    # entries in here if possible before we produce any form of comparison between our properties, to infer
    # the distance from the property to the nearest borehole

    # Let's take a sample
    borehold_compare_to = borehole_client.data[0]
    property = input_properties[0]

    # for each property, find the nearest borehole
    # This is just an example, looking at the distance from a property to a borehole
    dist_m, dist_km = borehole_client.distance_between_bng_coords(
        x1_bng=property.coordinates["x_coordinate"],
        y1_bng=property.coordinates["y_coordinate"],
        x2_bng=borehold_compare_to["X"],
        y2_bng=borehold_compare_to["Y"],
    )