Model/backend/SearchEpc.py

import os
import time
import re

import usaddress
import pandas as pd
import numpy as np
from epc_api.client import EpcClient
from backend.OrdnanceSurvey import OrdnanceSuveyClient
from BaseUtility import Definitions
from utils.logger import setup_logger
from typing import List
from fuzzywuzzy import process
from backend.app.utils import sap_to_epc

logger = setup_logger()

vartypes = {
    'low-energy-fixed-light-count': "Int64",
    # 'address': 'str',
    # 'uprn-source': 'str',
    'floor-height': 'float',
    'heating-cost-potential': 'float',
    'unheated-corridor-length': 'float',
    'hot-water-cost-potential': 'float',
    'construction-age-band': 'str',
    'potential-energy-rating': 'str',
    'mainheat-energy-eff': 'str',
    'windows-env-eff': 'str',
    'lighting-energy-eff': 'str',
    'environment-impact-potential': "Int64",
    'glazed-type': 'str',
    'heating-cost-current': 'float',
    # 'address3': 'str',
    'mainheatcont-description': 'str',
    'sheating-energy-eff': 'str',
    'property-type': 'str',
    'local-authority-label': 'str',
    'fixed-lighting-outlets-count': "Int64",
    'energy-tariff': 'str',
    'mechanical-ventilation': 'str',
    'hot-water-cost-current': 'str',
    'county': 'str',
    # 'postcode': 'str',
    'solar-water-heating-flag': 'str',
    'constituency': 'str',
    'co2-emissions-potential': 'float',
    'number-heated-rooms': 'float',
    'floor-description': 'str',
    'energy-consumption-potential': 'float',
    'local-authority': 'str',
    'built-form': 'str',
    'number-open-fireplaces': "Int64",
    'windows-description': 'str',
    'glazed-area': 'str',
    # 'inspection-date': str,
    'mains-gas-flag': 'str',
    'co2-emiss-curr-per-floor-area': 'float',
    # 'address1': 'str',
    'heat-loss-corridor': 'str',
    'flat-storey-count': "Int64",
    'constituency-label': 'str',
    'roof-energy-eff': 'str',
    'total-floor-area': 'float',
    'building-reference-number': 'str',
    'environment-impact-current': 'float',
    'co2-emissions-current': 'float',
    'roof-description': 'str',
    'floor-energy-eff': 'str',
    'number-habitable-rooms': 'float',
    # 'address2': 'str',
    'hot-water-env-eff': 'str',
    'posttown': 'str',
    'mainheatc-energy-eff': 'str',
    'main-fuel': 'str',
    'lighting-env-eff': 'str',
    'windows-energy-eff': 'str',
    'floor-env-eff': 'str',
    'sheating-env-eff': 'str',
    'lighting-description': 'str',
    'roof-env-eff': 'str',
    'walls-energy-eff': 'str',
    'photo-supply': 'float',
    'lighting-cost-potential': 'float',
    'mainheat-env-eff': 'str',
    'multi-glaze-proportion': 'float',
    'main-heating-controls': 'str',
    # 'lodgement-datetime',
    'flat-top-storey': 'str',
    'current-energy-rating': 'str',
    'secondheat-description': 'str',
    'walls-env-eff': 'str',
    'transaction-type': 'str',
    # 'uprn': "Int64",
    'current-energy-efficiency': 'float',
    'energy-consumption-current': 'float',
    'mainheat-description': 'str',
    'lighting-cost-current': 'float',
    # 'lodgement-date',
    'extension-count': "Int64",
    'mainheatc-env-eff': 'str',
    # 'lmk-key': 'str',
    'wind-turbine-count': "Int64",
    'tenure': 'str',
    'floor-level': 'str',
    'potential-energy-efficiency': "Int64",
    'hot-water-energy-eff': 'str',
    'low-energy-lighting': 'float',
    'walls-description': 'str',
    'hotwater-description': 'str'
}


class SearchEpc:
    """
    Given address information about a home, this class is responsible for retrieving the EPC data associated
    to the property.

    For a home, we might have address lines 1, 2, 3 and 4, as well as a postcode.

    Often, simply searching the EPC database with address line 1 and postcode will be enough to find
    the property, but there are some cases where this is not true and we might need to utilise other
    combinations about the home to find the property
    """

    MAX_RETRIES = 5

    SUCCESS = {
        "status": 200,
        "message": "success",
        "error": None
    }

    NODATA = {
        "status": 201,
        "message": "No data",
        "error": None
    }

    def __init__(
        self,
        address1: str,
        postcode: str,
        auth_token: str,
        os_api_key: str,
        full_address: str | None = None,
        max_retries: int = None,
        uprn: [int, None] = None,
        size=None,
        property_type=None,
        fast=False
    ):
        """
        Address lines 1 and postcode are mandatory fields. The other address lines are optional
        but can be used to find the epc for the home, if address1 and postcode are insufficient
        :param address1: string, propery's address line 1
        :param postcode: string, propery's postcode
        :param full_address: string, optional parameter, the full address of the property
        :param max_retries: int, optional, number of retries to make when searching the api
        :param uprn: int, optional, the uprn of the property
        :param size: int, optional, the number of results to return. If not provided, defaults to 25 which is the api's
                        default
        :param property_type: str, optional, the property type of the property, if known before hand
        """

        self.address1 = address1
        self.postcode = postcode
        self.full_address = full_address
        self.uprn = uprn
        self.house_number = self.get_house_number(self.address1)
        self.numeric_house_number = self.extract_numeric_housenumber_part(self.house_number)

        self.max_retries = max_retries if max_retries is not None else self.MAX_RETRIES

        self.client = EpcClient(auth_token=auth_token)
        self.ordnance_survey_client = OrdnanceSuveyClient(
            address=self.address1, postcode=self.postcode, api_key=os_api_key
        )

        self.data = None
        self.newest_epc = None
        self.older_epcs = None
        self.full_sap_epc = None

        # These are the address and postcode values, which we store in the database
        self.address_clean = None
        self.postcode_clean = None

        self.size = size if size is not None else 25

        self.property_type = property_type
        self.fast = fast

    @staticmethod
    def get_house_number(address: str, postcode=None) -> str | None:
        """
        This method uses the usaddress library to parse an address and extract the primary house or flat number.
        """

        try:
            # Updated regex to catch house numbers including alphanumeric ones
            pattern = r'(?i)(?:flat|apartment)\s*(\d+\w*)|^\s*(\d+\w*)'
            match = re.search(pattern, address)
            if match:
                return next(g for g in match.groups() if g is not None)

            parsed = usaddress.parse(address)
            # First, try to get the 'OccupancyIdentifier' if 'OccupancyType' is detected
            for part, type_ in parsed:
                if type_ == 'OccupancyIdentifier':
                    if postcode is not None:
                        if part == postcode.split(" ")[0]:
                            continue
                        if part == postcode.split(" ")[1]:
                            continue
                    return part  # This assumes the first 'OccupancyIdentifier' after 'OccupancyType' is the primary
                    # number

            # Fallback to 'AddressNumber' if no 'OccupancyIdentifier' is found
            address_number = next((part for part, type_ in parsed if type_ == 'AddressNumber'), None)
            if address_number:
                return address_number.replace(",", "")  # Remove any trailing commas

        except Exception as e:
            raise Exception(f"Error parsing address: {e}")

        return None

    @staticmethod
    def extract_numeric_housenumber_part(house_number: str | None) -> int | None:
        # Regular expression to find the first occurrence of one or more digits

        if house_number is None:
            return None

        match = re.search(r'\d+', house_number)

        if match:
            return int(match.group())
        else:
            return None

    def get_epc(self, params=None, size=None):
        # Get the EPC data with retries
        size = size if size is not None else self.size
        if params is None:
            if self.uprn:
                params = {"uprn": self.uprn}
            else:
                params = {"address": self.address1, "postcode": self.postcode}

        for retry in range(self.max_retries):
            try:

                if "uprn" in params:
                    # We use the direct call method inside, since we need to implement uprn as a valid
                    # parameter for the search function
                    url = os.path.join(self.client.domestic.host, "search")
                    response = self.client.domestic.call(method="get", url=url, params=params)
                else:
                    response = self.client.domestic.search(params=params, size=size)

                if response:
                    self.data = response
                    return self.SUCCESS

                if retry > 0:
                    logger.info("Failed previous attempt but retry successful")
                # If we got nothing, final try
                if not response:
                    return {
                        "status": 204,
                        "message": "no data",
                        "error": None
                    }

                return {
                    "status": 200,
                    "message": "success",
                    "error": None
                }

            except Exception as e:
                if retry < self.max_retries - 1:
                    # If not the last retry, wait for 3 seconds before retrying
                    time.sleep(3)
                else:
                    # If it's the last retry, we continue
                    return {
                        "status": 500,
                        "message": "Could not retrieve EPC data",
                        "error": str(e)
                    }

    def filter_rows(self, rows, property_type=None, address=None):
        """
        This method should not be used when property_type and address are both not None
        :param rows:
        :param property_type:
        :param address:
        :return:
        """
        # Given the results from the EPC api, attempts to reduce the number of rows
        uprns = {r["uprn"] for r in rows}

        if (property_type is None) and (address is None):
            return rows

        if len(uprns) == 1:
            return rows

        if property_type is not None:
            # We can do a filter on the property type
            rows_filtered = [r for r in rows if r["property-type"] == property_type]

            if rows_filtered:
                return rows_filtered

            return rows

        if address is not None:
            # We can do a filter on the property type
            # We check if the full address contains the postcode and if it does, remove
            if self.postcode in address:
                address = address.replace(self.postcode, "").strip().rstrip(",")

            # We check if post town is included in the address
            if any([r["posttown"].lower() in address.lower() for r in rows]):
                best_match = process.extractOne(
                    address, [", ".join([r["address"], r["posttown"]]) for r in rows], score_cutoff=0
                )
            else:
                best_match = process.extractOne(address, [r["address"] for r in rows], score_cutoff=0)
            # Get all of the scores
            rows_filtered = [r for r in rows if r["address"] == best_match[0]]

            if rows_filtered:
                return rows_filtered

            return rows

    @staticmethod
    def format_address(newest_epc):
        """
        Format address and postcode for storage in the database
        """
        postcode = newest_epc["postcode"]
        address = newest_epc["address"]

        # Format them
        address = address.replace(postcode, "").strip()
        address = address.rstrip(",").strip()
        address = address.title()

        postcode = postcode.upper()

        return address, postcode

    def extract_epc_data(self, address=None):

        """
        Given a successful search, this method will format the data and return it
        :return:
        """

        if self.data is None:
            raise ValueError("data is missing, run search first")

        rows = self.data["rows"]

        # We perform some checks on the rows
        # Firstly, we should only have 1 urpn so if we have multiple, we'll need to filter down the
        # property further

        rows = self.filter_rows(rows, property_type=self.property_type, address=None)
        rows = self.filter_rows(rows, property_type=None, address=address)

        # We now check for a full sap epc:
        full_sap_epc = [r for r in rows if r["transaction-type"] == "new dwelling"]
        full_sap_epc = full_sap_epc[0] if full_sap_epc else {}

        # Finally, we identify the newest epc and the rest, and then return
        newest_epc, older_epcs = self.filter_newest_epc(list_of_epcs=rows)

        # Ge the uprn from the newest record for this home
        uprns = {r["uprn"] for r in rows if r["uprn"]}
        # We can sometimes have no uprn for a property
        if (len(uprns) == 0) and len(rows) > 0:
            logger.warning("Found data but missing uprn")
        elif len(uprns) != 1:
            # There is a possibility that we have multiple UPRNs for a single property, which is an error
            addresses = {r["address"] for r in rows}
            if len(addresses) == 1:
                # Take the uprn from the most recent
                uprns = {newest_epc["uprn"]}
            else:
                raise ValueError("Multiple UPRNs found - investigate me")

        uprn = uprns.pop() if uprns else None

        if self.fast:
            return newest_epc, [], {}, "", "", None

        # Retrieve postcode and address
        address_epc, postcode_epc = self.format_address(newest_epc=newest_epc)

        return newest_epc, older_epcs, full_sap_epc, address_epc, postcode_epc, uprn

    @staticmethod
    def filter_newest_epc(list_of_epcs: List):
        newest_response = [
            r for r in list_of_epcs if
            r["lodgement-datetime"] == max([x["lodgement-datetime"] for x in list_of_epcs])
        ]

        if not newest_response:
            return {}, []

        if len(newest_response) != 1:
            # It is possible (but rare, and likely an error on EPC lodgement) that we have multiple EPCs that
            # were lodged at the exact same time. In this case, we will take the first one
            newest_response = [newest_response[0]]

        older_epcs = [epc for epc in list_of_epcs if epc["lmk-key"] != newest_response[0]["lmk-key"]]

        return newest_response[0], older_epcs

    @staticmethod
    def _get_epc_mode(col: str, epc_data: pd.DataFrame):
        """
        Simple method to extract the mode value from the EPC data
        :param col: name of the column to take the mode of
        :param epc_data: pandas dataframe of epc data
        """

        mode_value = epc_data[[col]].mode(dropna=True)
        if len(mode_value) != 1:
            raise NotImplementedError("TODO: Handle multiple modes")
        mode_value = mode_value.iloc[0][col]

        return mode_value

    def fetch_nearby_epcs(
        self, initial_postcode: str,
        lmks_to_drop: list[str] | None = None,
        built_form: str = "",
        property_type: str = "",
        exclude_old: bool = False
    ):
        """
        Fetches and processes EPC data for a given initial postcode, applying successive trimming
        to the postcode and filtering the data until a non-empty result set is found.

        The function queries the EPC API with the provided postcode, and if no data is found or
        if the data doesn't meet certain criteria, it progressively shortens the postcode by
        removing the last character and retries the query. This process continues until a valid
        set of EPC data is obtained or the postcode is exhausted.

        Additional filtering is applied to the obtained EPC data based on 'lmk-key', 'built-form',
        and 'property-type'. The data is also processed to extract and numerically interpret house
        numbers, calculate house number distances, and apply weights based on these distances.

        :param initial_postcode: The initial full postcode for the EPC data query.
        :param lmks_to_drop: List of 'lmk-key' values to be excluded from the EPC data.
        :param built_form: The 'built-form' value to be used for filtering the EPC data.
        :param property_type: The 'property-type' value to be used for filtering the EPC data.
        :param exclude_old: Flag to exclude EPC data older than 10 years.
        :return:
        """

        property_type_api_map = {
            "Bungalow": "bungalow",
            "Flat": "flat",
            "House": "house",
            "Maisonette": "maisonette",
            "Park home": "park home",
        }

        postcode = initial_postcode
        while postcode:
            # Fetch data from EPC API
            params = {"postcode": postcode}
            if property_type:
                params["property-type"] = property_type_api_map[property_type]

            # We take the 20 nearest homes of the relevant type, so not to pull in too many irrelevant homes
            epc_response = self.get_epc(params=params, size=100)

            if epc_response["status"] == 200:
                epc_data = pd.DataFrame(self.data["rows"])

                if lmks_to_drop is not None:
                    epc_data = epc_data[~epc_data["lmk-key"].isin(lmks_to_drop)]

                try:
                    epc_data['lodgement-datetime'] = pd.to_datetime(
                        epc_data['lodgement-datetime'], format='%Y-%m-%d %H:%M:%S', errors='coerce'
                    )
                except Exception as e:
                    logger.error("Problem formatting lodgement-datime, appling fallback: " + str(e))
                    epc_data['lodgement-datetime'] = pd.to_datetime(epc_data['lodgement-datetime'], errors='coerce')

                if exclude_old:
                    # Exclude EPC data older than 10 years
                    epc_data = epc_data[
                        epc_data["lodgement-datetime"] > (pd.Timestamp.now() - pd.DateOffset(years=10))
                        ]

                if not epc_data.empty:
                    # Further processing of the EPC data

                    epc_data = epc_data.sort_values("lodgement-datetime", ascending=False).groupby("uprn").head(1)
                    epc_data["house_number"] = epc_data["address"].apply(lambda add1: self.get_house_number(add1))
                    epc_data["numeric_house_number"] = epc_data["house_number"].apply(
                        lambda house_num: self.extract_numeric_housenumber_part(house_num)
                    )

                    if self.numeric_house_number is None:
                        # If we don't have a house number, we treat all weights as equal
                        epc_data["weight"] = 1
                    else:
                        epc_data["house_number_distance"] = abs(
                            epc_data["numeric_house_number"] - self.numeric_house_number
                        )
                        # # We add 1, just in case we have a 0 weight (e.g. comparing house number 7a to 7b, or 9A to 9)
                        # epc_data["weight"] = 1 / (epc_data["house_number_distance"] + 1)
                        # # If we have a home without a house number, fill that weight with average
                        # epc_data["weight"] = epc_data["weight"].fillna(epc_data["weight"].mean())
                        # # Finally, we might not have any house numbers whatsoever so everything could be
                        # # missing, so we fill with 1
                        # epc_data["weight"] = epc_data["weight"].fillna(1)
                        # TODO: Testing
                        # If the postcode is different from the initial postcode, it doesn't make sense to have
                        # any weightings
                        if all(pd.isnull(epc_data["house_number_distance"])) or (postcode != initial_postcode):
                            epc_data["weight"] = 1
                        else:
                            epc_data["weight"] = 1 / np.sqrt(epc_data["house_number_distance"] + 1)
                            epc_data["weight"] = epc_data["weight"].fillna(epc_data["weight"].mean())

                    estimation_property_type = self._estimate_str(
                        key="property-type", estimation_data=epc_data
                    ) if property_type == "" else property_type

                    epc_built_form = self._estimate_str(
                        key="built-form",
                        estimation_data=epc_data[epc_data["property-type"] == estimation_property_type]
                    )

                    if built_form == "Semi-Detached" and epc_built_form in ["End-Terraced", "Mid-Terraced"]:
                        estimation_built_form = "End-Terraced"
                    elif (built_form == "") or (pd.isnull(built_form)):
                        estimation_built_form = epc_built_form
                    else:
                        estimation_built_form = built_form

                    # We handle some edge cases experiences with maisonettes - if built form is detatched, just filter
                    # on maisonette
                    # We also add some additional logic for Park homes, because they are far less common than other
                    # property types

                    is_maisonette_with_bad_built_form = (estimation_property_type == "Maisonette") & (
                        estimation_built_form in ["Detached", "Semi-Detached"]
                    )

                    is_park_home_without_built_form = (estimation_property_type == "Park home") & (
                        sum(epc_data["built-form"] == estimation_built_form) == 0
                    )

                    has_missing_built_form = not estimation_built_form

                    if is_maisonette_with_bad_built_form or is_park_home_without_built_form or has_missing_built_form:
                        epc_data = epc_data[epc_data["property-type"] == estimation_property_type]
                    else:
                        epc_data = epc_data[
                            (epc_data["built-form"] == estimation_built_form) & (
                                epc_data["property-type"] == estimation_property_type)
                            ]

                    if not epc_data.empty:
                        return epc_data  # Return the filtered data if it's not empty

            # Shorten the postcode by one character for the next iteration
            postcode = postcode[:-1].rstrip()

        # If loop finishes without a valid response, raise an exception
        raise Exception("Unable to find postcode data after trimming - investigate me")

    def estimate_epc(self, property_type, built_form, lmks_to_drop=None, exclude_old=False):
        """
        For a property that does not have an EPC, we retrieve the EPC data for the closest properties
        and estimate the EPC for the property in question.

        Note - do we have postcodes with just a single address? We would need to use a different approach
        to find the closest homes
        :param property_type:   This is the property type of the property we are estimating, that can be retrieved from
                                the ordnance survey api
        :param built_form:      This is the built form of the property we are estimating, that can be retrieved from
                                the ordnance survey api
        :param lmks_to_drop:    This is a list of LMK keys that should be dropped from the estimation process. This
                                is used as an override for testing, to drop EPCs for the property we are testing
        :param exclude_old:     Used to drop any expired EPCs (more than 10 years old)
        :return:
        """

        # From the ordnance survey data, we want to determine the property type and then use only similar property
        # types for the estimation process
        epc_data = self.fetch_nearby_epcs(
            initial_postcode=self.postcode,
            lmks_to_drop=lmks_to_drop,
            built_form=built_form,
            property_type=property_type,
            exclude_old=exclude_old
        )

        # If we have missing lodgment date, we fill it with inspection-date
        epc_data["lodgement-datetime"] = epc_data["lodgement-datetime"].fillna(epc_data["inspection-date"])
        # If we still have missing dates, we set it to the mean of the non NA dates
        epc_data["lodgement-datetime"] = epc_data["lodgement-datetime"].fillna(epc_data["lodgement-datetime"].mean())

        # For each attribute, we need to determine the datatype and use an appropriate method
        # to estimate.
        estimated_epc = {}
        for key, vartype in vartypes.items():
            epc_data[key] = np.where(pd.isnull(epc_data[key]), None, epc_data[key])
            epc_data[key] = np.where(epc_data[key] == "", None, epc_data[key])
            estimation_data = epc_data[[key, "weight", "lodgement-datetime"]].copy()
            estimation_data = estimation_data[~pd.isnull(estimation_data[key])]
            estimation_data = estimation_data[~estimation_data[key].isin(Definitions.DATA_ANOMALY_MATCHES)]
            if vartype == "Int64":
                # We have some edge cases where we get the error "invalid literal for int() with base 10: '1.0'"
                # so this handles this
                estimation_data[key] = estimation_data[key].astype(float).astype(vartype)
            else:
                estimation_data[key] = estimation_data[key].astype(vartype)

            if estimation_data.shape[0] == 0:
                estimated_epc[key] = None
                continue

            if vartype == "Int64":
                estimated_value = self._estimate_int(estimation_data, key)
            elif vartype == "float":
                estimated_value = self._estimate_float(estimation_data, key)
            elif vartype == "str":
                estimated_value = self._estimate_str(estimation_data, key)
            else:
                raise NotImplementedError("estimation method not implemented for type")

            estimated_epc[key] = estimated_value

        # Insert an estimated lodgement datetime, with a weighted average
        estimated_epc["lodgement-datetime"] = self.calculate_weighted_lodgement_datetime(epc_data=epc_data)
        # Extract logement date
        # It is possible that there is still no lodgement date, so we need to handle this
        if pd.isnull(estimated_epc["lodgement-datetime"]):
            estimated_epc["lodgement-date"] = None
        else:
            estimated_epc["lodgement-date"] = estimated_epc["lodgement-datetime"].strftime("%Y-%m-%d")

        estimated_epc["current-energy-rating"] = sap_to_epc(estimated_epc["current-energy-efficiency"])

        estimated_epc["postcode"] = self.postcode
        estimated_epc["uprn"] = self.uprn
        estimated_epc["address"] = self.full_address
        # Indicate that this epc was estimated
        estimated_epc["estimated"] = True

        return estimated_epc

    @staticmethod
    def calculate_weighted_lodgement_datetime(epc_data):
        numeric_dates = pd.to_datetime(epc_data['lodgement-datetime']).view('int64')

        # Calculate the weighted sum of dates
        weighted_sum = (numeric_dates * epc_data['weight']).sum()

        # Calculate the sum of weights
        total_weights = epc_data['weight'].sum()

        # Calculate the weighted mean in numeric format
        weighted_mean_numeric = weighted_sum / total_weights

        # Convert the numeric weighted mean back to datetime
        weighted_mean_datetime = pd.to_datetime(weighted_mean_numeric)

        return weighted_mean_datetime

    @staticmethod
    def _estimate_int(estimation_data, key):
        return round(np.average(a=estimation_data[key], weights=estimation_data["weight"]))

    @staticmethod
    def _estimate_float(estimation_data, key):
        return round(np.average(a=estimation_data[key], weights=estimation_data["weight"]), 2)

    @staticmethod
    def _estimate_str(estimation_data, key):
        agg = estimation_data.groupby(key)["weight"].sum().reset_index()
        agg = agg[agg["weight"] == agg["weight"].max()]
        if agg.shape[0] != 1:
            # If we have multiple modes, we take the more recent data on average
            recent_grouped = estimation_data[
                estimation_data[key].isin(agg[key].values)
            ].groupby(key)["lodgement-datetime"].mean()

            newest_group = recent_grouped.idxmax()
            return newest_group

        return agg[key].values[0]

    def find_property(self, skip_os=False):
        """
        This method will attempt to identify a property. It will, at first, use the EPC api to try and
        find the EPC for the property and the associated UPRN. If this fails, it will use the Ordnance Survey API to
        find the UPRN of the address.

        Because no result may have been provided by the EPC api because of formatting issues with the address,
        if the ordnance survey api is used and the uprn retrieved, the EPC api is queried again with the UPRN, just
        as a final check to see if there is any EPC data.

        If there is no EPC data, the epc data will be estimated based on the surrounding properties
        """

        # Step 1: use the epc api to find the property and uprn
        response = self.get_epc()

        if response["status"] == 200:
            (
                self.newest_epc, self.older_epcs, self.full_sap_epc, self.address_clean, self.postcode_clean, self.uprn
            ) = self.extract_epc_data(address=self.full_address)
            return

        # Step 2: If we don't have an EPC, we use the ordnance survey api to find the uprn
        if skip_os:
            if self.ordnance_survey_client.property_type is not None:
                # We can try and estimate
                estimated_epc = self.estimate_epc(
                    property_type=self.ordnance_survey_client.property_type,
                    built_form=self.ordnance_survey_client.built_form
                )
                self.newest_epc = estimated_epc
                self.older_epcs = []
                self.full_sap_epc = {}

                # Finally, set a standardised address 1 and postcode
                self.address_clean = (
                    self.ordnance_survey_client.address_os if self.ordnance_survey_client.address_os else self.address1
                )
                self.postcode_clean = (
                    self.ordnance_survey_client.postcode_os if self.ordnance_survey_client.postcode_os else
                    self.postcode
                )
            return

        os_response = self.ordnance_survey_client.get_places_api()

        if os_response["status"] != 200:
            # Investigate this if it happens
            raise Exception("Unable to find property - investigate me")

        # Step 3: Now that we have a urpn, do another check against the epc api, this time searching with the uprn
        self.uprn = self.ordnance_survey_client.most_relevant_result["UPRN"]
        response = self.get_epc()
        if response["status"] == 200:
            (
                self.newest_epc, self.older_epcs, self.full_sap_epc, self.address_clean, self.postcode_clean, self.uprn
            ) = self.extract_epc_data()
            return

        # Step 4: If we still don't have an EPC, we estimate the EPC data
        self.full_address = self.ordnance_survey_client.most_relevant_result["ADDRESS"]
        estimated_epc = self.estimate_epc(
            property_type=self.ordnance_survey_client.property_type,
            built_form=self.ordnance_survey_client.built_form
        )
        self.newest_epc = estimated_epc
        self.older_epcs = []
        self.full_sap_epc = {}

        # Finally, set a standardised address 1 and postcode
        self.address_clean = self.ordnance_survey_client.address_os
        self.postcode_clean = self.ordnance_survey_client.postcode_os
        return