Model/backend/Property.py

import os
import ast
from itertools import groupby
import pandas as pd

from etl.epc.Dataset import TrainingDataset
from etl.epc.settings import LATEST_FIELD, MANDATORY_FIXED_FEATURES
from etl.epc_clean.epc_attributes.all_cleaners import all_cleaner_map
from etl.solar.SolarPhotoSupply import SolarPhotoSupply
from utils.logger import setup_logger
from utils.s3 import read_dataframe_from_s3_parquet
from etl.epc.settings import DATA_ANOMALY_MATCHES
from recommendations.rdsap_tables import FLOOR_LEVEL_MAP
from recommendations.recommendation_utils import (
    estimate_perimeter,
    get_wall_type,
    estimate_external_wall_area,
    esimtate_pitched_roof_area,
    estimate_windows,
)

ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
DATA_BUCKET = os.environ.get(
    "DATA_BUCKET", "retrofit-data-dev" if ENVIRONMENT == "dev" else None
)

logger = setup_logger()


class Property:
    ATTRIBUTE_MAP = {
        "floor-description": "floor",
        "hotwater-description": "hotwater",
        "main-fuel": "main_fuel",
        "mainheat-description": "main_heating",
        "mainheatcont-description": "main_heating_controls",
        "roof-description": "roof",
        "walls-description": "walls",
        "windows-description": "windows",
        "lighting-description": "lighting",
    }

    floor = None
    hotwater = None
    main_fuel = None
    main_heating = None
    main_heating_controls = None
    roof = None
    walls = None
    windows = None
    lighting = None
    energy_source = None

    spatial = None
    base_difference_record = None

    DATA_ANOMALY_MATCHES = DATA_ANOMALY_MATCHES

    # Surplus information, that can be provided as optional inputs, by a customer
    n_bathrooms = None
    n_bedrooms = None

    def __init__(
        self,
        id,
        postcode,
        address,
        epc_record,
        already_installed=None,
        non_invasive_recommendations=None,
        measures=None,
        **kwargs
    ):

        self.epc_record = epc_record

        self.id = id

        self.address = address
        self.postcode = postcode
        self.data = {
            k.replace("_", "-"): v for k, v in epc_record.get("prepared_epc").items()
        }
        self.old_data = epc_record.get("old_data")
        self.property_dimensions = None
        # This is a list of measures that have already been installed in the property, typically found as a result
        # of the non-invasive surveys. We reflect that this has been installed in the recommendations, but remove the
        # cost and instead, provide a message that the measure has already been installed

        self.already_installed = ast.literal_eval(already_installed['already_installed']) if already_installed else []
        self.non_invasive_recommendations = (
            ast.literal_eval(non_invasive_recommendations['recommendations']) if
            non_invasive_recommendations else []
        )
        # This is a list of measures that have been recommended for the property
        self.measures = ast.literal_eval(measures) if measures else None

        self.uprn = epc_record.get("uprn")
        self.full_sap_epc = epc_record.get("full_sap_epc")
        self.in_conservation_area, self.is_listed, self.is_heritage = None, None, None
        self.restricted_measures = False
        self.year_built = epc_record.get("year_built")
        self.number_of_rooms = epc_record.prepared_epc.get("number_habitable_rooms")
        self.age_band = epc_record.get("age_band")
        self.construction_age_band = epc_record.get("construction_age_band")
        self.number_of_floors = epc_record.get("number_of_floors")
        self.perimeter = None
        self.wall_type = None
        self.floor_type = None

        self.energy = {
            "primary_energy_consumption": epc_record.get("energy_consumption_current"),
            "co2_emissions": epc_record.get("co2_emissions_current"),
        }
        self.ventilation = {
            "ventilation": epc_record.get("mechanical_ventilation"),
        }
        self.solar_pv = {
            "solar_pv": epc_record.get("photo_supply"),
        }
        self.solar_hot_water = {
            "solar_hot_water": epc_record.get("solar_water_heating_flag"),
            "solar_hot_water_boolean": epc_record.get("solar_water_heating_flag_bool"),
        }
        self.wind_turbine = {
            "wind_turbine": epc_record.prepared_epc.get("wind_turbine_count"),
        }
        self.number_of_open_fireplaces = {
            "number_of_open_fireplaces": epc_record.prepared_epc.get(
                "number_open_fireplaces"
            ),
        }
        self.number_of_extensions = {
            "number_of_extensions": epc_record.prepared_epc.get("extension_count"),
        }
        self.number_of_storeys = {
            "number_of_storeys": epc_record.prepared_epc.get("flat_storey_count"),
        }
        self.heat_loss_corridor = {
            "heat_loss_corridor": epc_record.prepared_epc.get("heat_loss_corridor"),
            "length": epc_record.prepared_epc.get("unheated_corridor_length"),
            "heat_loss_corridor_boolean": epc_record.get("heat_loss_corridor_bool"),
        }
        self.mains_gas = epc_record.prepared_epc.get("mains_gas_flag")
        self.floor_height = epc_record.prepared_epc.get("floor_height")
        self.insulation_wall_area = None
        self.floor_area = epc_record.prepared_epc.get("total_floor_area")
        self.pitched_roof_area = None
        self.insulation_floor_area = None
        self.number_lighting_outlets = epc_record.prepared_epc.get(
            "fixed_lighting_outlets_count"
        )
        self.floor_level = None
        self.number_of_windows = None
        self.solar_pv_percentage = None

        self.current_adjusted_energy = None
        self.expected_adjusted_energy = None
        self.current_energy_bill = None
        self.expected_energy_bill = None

        self.recommendations_scoring_data = []

        self.parse_kwargs(kwargs)

    @classmethod
    def extract_kwargs(cls, kwargs):
        """
        This method is to be used in the router, to extract the kwargs from the request and prevent any errors such as
        non-integer values, or inputs that clash with the __init__ method of this class
        :param kwargs:
        :return:
        """
        n_bathrooms = kwargs.get("n_bathrooms", None)
        if n_bathrooms not in [None, ""]:
            # We add on a small value to ensure that the number of bathrooms is rounded up, in case the value is 0.5
            n_bathrooms = int(round(float(n_bathrooms) + 1e-5))

        n_bedrooms = kwargs.get("n_bedrooms", None)
        if n_bedrooms not in [None, ""]:
            n_bedrooms = int(round(float(n_bedrooms) + 1e-5))

        return {
            "n_bathrooms": n_bathrooms,
            "n_bedrooms": n_bedrooms,
        }

    def parse_kwargs(self, kwargs):
        # We extract the elements from kwargs that we recognise. Anything additional is ignored
        self.n_bathrooms = kwargs.get("n_bathrooms", None)
        self.n_bedrooms = kwargs.get("n_bedrooms", None)

    def create_base_difference_epc_record(self, cleaned_lookup: dict):
        """
        Creates a EPCDifferenceRecord object, which is used to store the difference between the current and
        expected EPC
        It will be the same starting and ending EPC, as we don't have the expected EPC yet
        """

        # difference_record = self.epc_record - self.epc_record

        # TODO: change these lower and replace in the settings file
        print(
            "CHANGE THE LATEST FIELD TO REMOVE NUMBER HABITABLE ROOMS IF WE WANT TO USE STARTING/ENDING"
        )
        fixed_data_col_names = MANDATORY_FIXED_FEATURES + LATEST_FIELD
        print("NEED TO CHANGE THE DASH TO LOWER CASE")
        fixed_data_col_names = [
            x.lower().replace("_", "-") for x in fixed_data_col_names
        ]

        fixed_data = {
            k.replace("-", "_"): v
            for k, v in self.data.items()
            if k in fixed_data_col_names
        }

        # difference_record.append_fixed_data(fixed_data)

        difference_record = self.epc_record.create_EPCDifferenceRecord(
            self.epc_record, fixed_data
        )

        self.base_difference_record = TrainingDataset(
            datasets=[difference_record], cleaned_lookup=cleaned_lookup
        )

        # TODO: adjust the base difference record with the previously calculated u values + features
        # estimated_perimeter is different to the perimeter in the epc record

        # self.base_difference_record.df

    def simulate_all_representative_recommendations(
        self, property_representative_recommendations,
    ):
        """
        This method was put together to simulate the impact of the representative recommendations on the property
        all at once, for usage within the mds report
        :return:
        """

        recommendation_record = self.base_difference_record.df.to_dict("records")[
            0
        ].copy()

        scoring_dict = self.create_recommendation_scoring_data(
            property_id=self.id,
            recommendation_record=recommendation_record,
            recommendations=property_representative_recommendations,
            primary_recommendation_id=self.id,
            non_invasive_recommendations=self.non_invasive_recommendations,
        )

        return scoring_dict

    def adjust_difference_record_with_recommendations(
        self, property_recommendations, property_representative_recommendations
    ):
        """
        This method will adjust the difference record, based on the recommendations made for the property

        In order to score the measures, we need to consider the phase of the retrofit.

        :param property_recommendations: dictionary of recommendations for the property
        :param property_representative_recommendations: dictionary of representative recommendations for the property
        """

        self.recommendations_scoring_data = []
        phases = sorted(
            [
                r[0]["phase"]
                for r in property_recommendations
                if r[0]["phase"] is not None
            ]
        )

        for phase in phases:
            property_recommendations_by_phase = [
                r for r in property_recommendations if r[0]["phase"] == phase
            ][0]
            previous_phases = [p for p in phases if p < phase]
            previous_phase_representatives = [
                r
                for r in property_representative_recommendations
                if r["phase"] in previous_phases
            ]
            # For solid wall insulation, we will actually have 2 representative recommendations, since we consider
            # both internal and external wall insulation as possible measures. We will use the representative that
            # has the lowest efficiency.
            # Take the representative with the lowest efficiency, by phase

            # To be safe, we sort by phase
            previous_phase_representatives = sorted(
                previous_phase_representatives, key=lambda x: x["phase"]
            )

            previous_phase_representatives = [
                min(group, key=lambda x: x["efficiency"])
                for _, group in groupby(
                    previous_phase_representatives, key=lambda x: x["phase"]
                )
            ]

            recommendation_record = self.base_difference_record.df.to_dict("records")[
                0
            ].copy()

            for rec in property_recommendations_by_phase:
                # We simulate the impact of the recommendation at this current phase, and all of the prior phases

                if rec["type"] == "mechanical_ventilation":
                    continue

                scoring_dict = self.create_recommendation_scoring_data(
                    property_id=self.id,
                    recommendation_record=recommendation_record,
                    recommendations=previous_phase_representatives + [rec],
                    primary_recommendation_id=rec["recommendation_id"],
                    non_invasive_recommendations=self.non_invasive_recommendations,
                )
                self.recommendations_scoring_data.append(scoring_dict)

    @staticmethod
    def create_recommendation_scoring_data(
        property_id,
        recommendation_record,
        recommendations: list,
        primary_recommendation_id: int,
        non_invasive_recommendations: list = None,
    ):
        """
        This function will iterate through a list of recommendations and apply a simulation for each recommendation
        This allows us to later multiple measures and see the impact of the measures on the property
        :param property_id: The id of the property
        :param recommendation_record: The record of the property, which will be updated
        :param recommendations: The list of recommendations to apply
        :param primary_recommendation_id: The id of the primary recommendation, which is used to identify the record
        :param non_invasive_recommendations: The list of non-invasive recommendations
        :return: The updated recommendation record
        """

        output = recommendation_record.copy()
        non_invasive_recommendations = [] if non_invasive_recommendations is None else non_invasive_recommendations

        for col in [
            "walls_insulation_thickness",
            "floor_insulation_thickness",
            "roof_insulation_thickness",
        ]:
            if output[col] is None:
                output[col] = "none"

        for recommendation in recommendations:
            # For the list of recommendations we have, we iteratively update the output

            # We update the description to indicate it's insulated
            if recommendation["type"] in [
                "internal_wall_insulation",
                "external_wall_insulation",
                "cavity_wall_insulation",
            ]:

                # # If we have a non-incasive recommendation that the cavity wall is partially filled, we skip the
                # # cavity wall insulation recommendation (since on the EPC, the property will look like how it did
                # # before any works)
                # if "cavity_surveyed_as_filled_is_partial" in non_invasive_recommendations:
                #     continue

                # The upgrade made here is to the u-value of the walls and the description of the
                # insulation thickness
                output["walls_thermal_transmittance_ending"] = recommendation[
                    "new_u_value"
                ]
                # Setting the insulation thickness here to above average should be tested further because we
                # don't see a high volume of instances for this
                output["walls_insulation_thickness_ending"] = "average"
                # In some edge cases, or when running the mds report we might see the energy efficiency already
                # in Good or Very Good
                if output["walls_energy_eff_ending"] not in ["Good", "Very Good"]:
                    output["walls_energy_eff_ending"] = "Good"

                # Note: often when the wall is insulatied, the internal/external insulation is not noted so we should
                #       test the impact of using these booleans
                if recommendation["type"] == "external_wall_insulation":
                    output["external_insulation_ending"] = True
                    output["internal_insulation_ending"] = False

                if recommendation["type"] == "internal_wall_insulation":
                    output["external_insulation_ending"] = False
                    output["internal_insulation_ending"] = True

                if recommendation["type"] == "cavity_wall_insulation":
                    output["is_filled_cavity_ending"] = True

            else:
                if output["walls_thermal_transmittance_ending"] is None:
                    raise ValueError("We should not have a None value for the u value")

                if output["walls_insulation_thickness_ending"] is None:
                    output["walls_insulation_thickness_ending"] = "none"

            # Update description to indicate it's insulate
            if recommendation["type"] in [
                "solid_floor_insulation",
                "suspended_floor_insulation",
                "exposed_floor_insulation",
            ]:
                if len(recommendation["parts"]) > 1:
                    raise NotImplementedError(
                        "Have more than 1 floor insulation part - handle this case"
                    )

                # We don't really see above average for this in the training data
                output["floor_insulation_thickness_ending"] = "average"
            else:
                if output["floor_thermal_transmittance_ending"] is None:
                    raise ValueError("We should not have a None value for the u value")

                if output["floor_insulation_thickness_ending"] is None:
                    output["floor_insulation_thickness_ending"] = "none"

            if recommendation["type"] in [
                "loft_insulation",
                "room_roof_insulation",
                "flat_roof_insulation",
            ]:
                output["roof_thermal_transmittance_ending"] = recommendation[
                    "new_u_value"
                ]

                parts = recommendation["parts"]
                if len(parts) != 1:
                    raise ValueError(
                        "More than one part for roof insulation - investiage me"
                    )

                # This is based on the values we have in the training data
                valid_numeric_values = [
                    12,
                    25,
                    50,
                    75,
                    100,
                    150,
                    200,
                    250,
                    270,
                    300,
                    350,
                    400,
                ]

                proposed_depth = int(parts[0]["depth"])
                if proposed_depth not in valid_numeric_values:
                    # Take the nearest value for scoring
                    proposed_depth = min(
                        valid_numeric_values, key=lambda x: abs(x - proposed_depth)
                    )

                output["roof_insulation_thickness_ending"] = str(proposed_depth)
                if recommendation["type"] == "loft_insulation":
                    if proposed_depth >= 270:
                        output["roof_energy_eff_ending"] = "Very Good"
                    else:
                        if output["roof_energy_eff_ending"] not in ["Good", "Very Good"]:
                            output["roof_energy_eff_ending"] = "Good"
                else:
                    output["roof_energy_eff_ending"] = "Very Good"
            else:
                # Fill missing roof u-values - this fill is not based on recommended upgrades
                if output["roof_thermal_transmittance_ending"] is None:
                    raise ValueError("We should not have a None value for the u value")

                if output["roof_insulation_thickness_ending"] is None:
                    output["roof_insulation_thickness_ending"] = "none"

            if recommendation["type"] == "sealing_open_fireplace":
                output["number_open_fireplaces_ending"] = 0

            if recommendation["type"] == "low_energy_lighting":
                output["low_energy_lighting_ending"] = 100
                output["lighting_energy_eff_ending"] = "Very Good"

            if recommendation["type"] == "windows_glazing":
                output["multi_glaze_proportion_ending"] = 100
                if output["windows_energy_eff_ending"] not in ["Average", "Good", "Very Good"]:
                    output["windows_energy_eff_ending"] = "Average"

                is_secondary_glazing = recommendation["is_secondary_glazing"]

                if output["glazing_type_ending"] == "multiple":
                    pass
                elif output["glazing_type_ending"] == "single":
                    output["glazing_type_ending"] = (
                        "secondary" if is_secondary_glazing else "double"
                    )
                elif output["glazing_type_ending"] == "double":
                    output["glazing_type_ending"] = (
                        "multiple" if is_secondary_glazing else "double"
                    )
                elif output["glazing_type_ending"] == "secondary":
                    output["glazing_type_ending"] = (
                        "secondary" if is_secondary_glazing else "multiple"
                    )
                elif output["glazing_type_ending"] in ["triple", "high performance"]:
                    output["glazing_type_ending"] = "multiple"
                else:
                    raise ValueError("Invalid glazing type - implement me")

                if is_secondary_glazing:
                    output["glazed_type_ending"] = "secondary glazing"
                else:
                    output["glazed_type_ending"] = (
                        "double glazing installed during or after 2002"
                    )

            if recommendation["type"] in [
                "heating", "hot_water_tank_insulation", "heating_control", "secondary_heating"
            ]:
                # We update the data, as defined in the recommendaton

                simulation_config = recommendation["simulation_config"]
                # If any entries in simulation_config are None, we will set them to "Unknown" which is the cleaning
                # value
                for key, value in simulation_config.items():
                    if value is None:
                        simulation_config[key] = "Unknown"

                output.update(simulation_config)

            if recommendation["type"] == "solar_pv":
                output["photo_supply_ending"] = recommendation["photo_supply"]

            if recommendation["type"] not in [
                "sealing_open_fireplace", "low_energy_lighting",
                "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",
                "loft_insulation", "room_roof_insulation", "flat_roof_insulation",
                "solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation",
                "windows_glazing", "solar_pv", "heating", "hot_water_tank_insulation",
                "heating_control", "secondary_heating"
            ]:
                raise NotImplementedError(
                    "Implement me, given type %s" % recommendation["type"]
                )

        output["id"] = "+".join([str(property_id), str(primary_recommendation_id)])

        return output

    def get_components(
        self, cleaned, photo_supply_lookup, floor_area_decile_thresholds
    ):
        """
        Given the cleaning that has been performed, we'll use this to identify the property
        components, from roof to walls to windows, heating and hot water
        :param cleaned: This is the dictionary of components found in cleaner.cleaned
        :param photo_supply_lookup: This is the lookup table for the photo supply, used to estimate the percentage
                                    of the roof that is suitable for solar panels
        :param floor_area_decile_thresholds: This is the decile thresholds for the floor area, used in estimating the
                                             solar pv roof area
        :return:
        """

        if not cleaned:
            raise ValueError("Cleaner does not contain cleaned data")

        if not self.data:
            raise ValueError("Property does not contain data")

        self.set_basic_property_dimensions()

        for description, attribute in cleaned.items():

            if self.data[description] in self.DATA_ANOMALY_MATCHES:
                template = cleaned[description][0]
                fill_dict = dict(zip(template.keys(), [None] * len(template)))
                fill_dict.update(
                    {
                        "original_description": self.data[description],
                        "clean_description": self.data[description],
                    }
                )
                setattr(
                    self,
                    self.ATTRIBUTE_MAP[description],
                    fill_dict,
                )
                continue

            attributes = [
                x
                for x in cleaned[description]
                if x["original_description"] == self.data[description]
            ]

            if len(attributes) > 1:
                raise ValueError(
                    "Either No attributes or multiple found for %s" % description
                )

            if len(attributes) == 0:
                # We attempt to perform the clean on the fly
                cleaner_cls = all_cleaner_map[description]
                cleaner_cls = cleaner_cls(self.data[description])
                processed = {
                    "original_description": self.data[description],
                    "clean_description": cleaner_cls.description.replace(
                        "(assumed)", ""
                    )
                    .rstrip()
                    .capitalize(),
                    **cleaner_cls.process(),
                }

                attributes = [processed]

            setattr(self, self.ATTRIBUTE_MAP[description], attributes[0])

        self.set_wall_type()
        self.set_floor_type()
        self.set_floor_level()
        self.set_windows_count()
        self.set_solar_panel_area(
            photo_supply_lookup=photo_supply_lookup,
            floor_area_decile_thresholds=floor_area_decile_thresholds,
        )
        self.set_energy_source()

    def set_spatial(self, spatial: pd.DataFrame):
        """
        Sets whether the property is in a conservation area given the output of the ConservationAreaClient

        Will store a dictionary, spatial, which is used to populate the property spatial table in the database

        :param spatial:  Dataframe, containing the spatial data for the property
        """
        self.in_conservation_area = spatial["conservation_status"].values[0]
        self.is_listed = spatial["is_listed_building"].values[0]
        self.is_heritage = spatial["is_heritage_building"].values[0]

        # We do an equals True, in the case of one of these variables being True
        if (
            (self.in_conservation_area == True)
            | (self.is_listed == True)
            | (self.is_heritage == True)
        ):
            self.restricted_measures = True

        spatial_dict = spatial.to_dict("records")[0]
        self.spatial = {
            "x_coordinate": spatial_dict["X_COORDINATE"],
            "y_coordinate": spatial_dict["Y_COORDINATE"],
            "latitude": spatial_dict["LATITUDE"],
            "longitude": spatial_dict["LONGITUDE"],
            "conservation_status": spatial_dict["conservation_status"],
            "is_listed_building": spatial_dict["is_listed_building"],
            "is_heritage_building": spatial_dict["is_heritage_building"],
        }

    def _clean_upload_data(self, to_update):
        for k, v in to_update.items():
            if v in self.DATA_ANOMALY_MATCHES:
                to_update[k] = None
        return to_update

    def get_full_property_data(self, current_valuation=None):
        """
        This method extracts the data which is pushed to the database, containing core information, from the EPC
        about a property
        :return:
        """

        property_data = {
            "creation_status": "READY",
            "uprn": int(self.data["uprn"]),
            "building_reference_number": int(self.data["building-reference-number"]),
            "has_pre_condition_report": True,
            "has_recommendations": True,
            "property_type": self.data["property-type"],
            "built_form": self.data["built-form"],
            "local_authority": self.data["local-authority-label"],
            "constituency": self.data["constituency-label"],
            "number_of_rooms": self.number_of_rooms,
            "year_built": self.year_built,
            "tenure": self.data["tenure"],
            "current_epc_rating": self.data["current-energy-rating"],
            "current_sap_points": self.data["current-energy-efficiency"],
            "current_valuation": current_valuation,
        }

        property_data = self._clean_upload_data(property_data)

        return property_data

    @classmethod
    def _prepare_rating_field(cls, field, rating_lookup):
        """
        Utility function for usage in the lambda, for preparing the _rating fields
        """
        return (
            rating_lookup[field].value
            if (field not in cls.DATA_ANOMALY_MATCHES) and (field is not None)
            else None
        )

    def get_property_details_epc(self, portfolio_id: int, rating_lookup):

        property_details_epc = {
            "property_id": self.id,
            "portfolio_id": portfolio_id,
            "full_address": self.data["address"],
            "total_floor_area": float(self.data["total-floor-area"]),
            "walls": self.walls["clean_description"],
            "walls_rating": self._prepare_rating_field(
                self.data["walls-energy-eff"], rating_lookup
            ),
            "roof": self.roof["clean_description"],
            "roof_rating": self._prepare_rating_field(
                self.data["roof-energy-eff"], rating_lookup
            ),
            "floor": self.floor["clean_description"],
            "floor_rating": self._prepare_rating_field(
                self.data["floor-energy-eff"], rating_lookup
            ),
            "windows": self.windows["clean_description"],
            "windows_rating": self._prepare_rating_field(
                self.data["windows-energy-eff"], rating_lookup
            ),
            "heating": self.main_heating["clean_description"],
            "heating_rating": self._prepare_rating_field(
                self.data["mainheat-energy-eff"], rating_lookup
            ),
            "heating_controls": self.main_heating_controls["clean_description"],
            "heating_controls_rating": self._prepare_rating_field(
                self.data["mainheatc-energy-eff"], rating_lookup
            ),
            "hot_water": self.hotwater["clean_description"],
            "hot_water_rating": self._prepare_rating_field(
                self.data["hot-water-energy-eff"], rating_lookup
            ),
            "lighting": self.lighting["clean_description"],
            "lighting_rating": self._prepare_rating_field(
                self.data["lighting-energy-eff"], rating_lookup
            ),
            "mainfuel": self.main_fuel["clean_description"],
            "ventilation": self.ventilation["ventilation"],
            "solar_pv": self.solar_pv["solar_pv"],
            "solar_hot_water": self.solar_hot_water["solar_hot_water_boolean"],
            "wind_turbine": self.wind_turbine["wind_turbine"],
            "floor_height": self.floor_height,
            "heat_loss_corridor": self.heat_loss_corridor["heat_loss_corridor_boolean"],
            "unheated_corridor_length": self.heat_loss_corridor["length"],
            "number_of_open_fireplaces": self.number_of_open_fireplaces[
                "number_of_open_fireplaces"
            ],
            "number_of_extensions": self.number_of_extensions["number_of_extensions"],
            "number_of_storeys": self.number_of_storeys["number_of_storeys"],
            "mains_gas": self.mains_gas,
            "energy_tariff": self.data["energy-tariff"],
            "primary_energy_consumption": self.energy["primary_energy_consumption"],
            "co2_emissions": self.energy["co2_emissions"],
            "adjusted_energy_consumption": self.current_adjusted_energy,
            "estimated": self.data.get("estimated", False),
        }

        return property_details_epc

    def get_spatial_data(self, uprn_filenames):
        """
        Given a property's UPRN, this method will pull the associated spatial data from s3
        :return:
        """

        if self.uprn is None:
            logger.warning(
                "We do not have a UPRN for this property - this needs to be implemented"
            )
            self.in_conservation_area = False
            self.is_listed = False
            self.is_heritage = False
            self.restricted_measures = True
            return

        # We get the file name for the uprn
        filtered_df = uprn_filenames[
            (uprn_filenames["lower"] <= self.uprn)
            & (uprn_filenames["upper"] >= self.uprn)
            ]
        if filtered_df.empty:
            logger.warning("Could not find file containing UPRNS")
            return None

        filename = filtered_df.iloc[0]["filenames"]

        spatial_data = read_dataframe_from_s3_parquet(
            bucket_name=DATA_BUCKET, file_key=f"spatial/{filename}"
        )

        spatial = spatial_data[spatial_data["UPRN"] == self.uprn]

        # Pull out spatial features
        self.set_spatial(spatial)

    def _filter_property_dimensions(self, property_dimensions):
        """
        Will filter the property dimensions dataframe to only include the relevant rows for the property
        :param property_dimensions:
        :return: filtered property dimensions dataframe
        """

        result = property_dimensions[
            (property_dimensions["PROPERTY_TYPE"] == self.data["property-type"])
        ]

        if (
            self.construction_age_band is not None
            and self.construction_age_band not in self.DATA_ANOMALY_MATCHES
        ):
            result = result[
                (result["CONSTRUCTION_AGE_BAND"] == self.construction_age_band)
            ]

        if (
            self.data["built-form"] not in self.DATA_ANOMALY_MATCHES
            and self.data["built-form"] in result["BUILT_FORM"]
        ):
            result = result[(result["BUILT_FORM"] == self.data["built-form"])]

        return result[
            ["NUMBER_HABITABLE_ROOMS", "TOTAL_FLOOR_AREA", "FLOOR_HEIGHT"]
        ].mean()

    def set_basic_property_dimensions(self):
        """
        This method sets the number of floors of the property, using a simple approach based on an estimate for
        average room size, number of rooms and total floor area

        It sets the perimeter of the property, using a simple approach based on an estimate for average room size,
        number of rooms and total floor area

        Also sets floor area, number of rooms, using backup cleaned values if this data is not present, based on
        medians across the EPC data
        :return:
        """

        # TODO: These functions should work on an EPCRecord object, so that the format is more standardised.
        #       They could also be added as attributes to the EPC Record

        self.perimeter = estimate_perimeter(
            self.floor_area / self.number_of_floors,
            self.number_of_rooms / self.number_of_floors,
        )

        self.insulation_wall_area = estimate_external_wall_area(
            num_floors=self.number_of_floors,
            floor_height=self.floor_height,
            perimeter=self.perimeter,
            built_form=self.data["built-form"],
        )

        self.insulation_floor_area = self.floor_area / self.number_of_floors

        self.pitched_roof_area = esimtate_pitched_roof_area(
            floor_area=self.insulation_floor_area, floor_height=self.floor_height
        )

    def set_floor_level(self):
        self.floor_level = (
            FLOOR_LEVEL_MAP[self.data["floor-level"]]
            if self.data["floor-level"] not in self.DATA_ANOMALY_MATCHES
               and self.data["floor-level"] is not None
            else None
        )

        if self.floor_level is None:

            if self.data["property-type"] != "Flat":
                return

            if self.floor["another_property_below"]:
                self.floor_level = 1
            else:
                self.floor_level = 0
            return

        # We perform some extra checks, if the property is not on the ground floor, as we have found cases
        # where a property is marked as being on the first floor
        if self.floor_level > 0:

            # We check if there is another property below
            if not self.floor["another_property_below"]:
                self.floor_level = 0
            return

        if self.floor_level == 0:
            # Check if another property below
            if self.floor["another_property_below"]:
                self.floor_level = 1
            return

    def set_wall_type(self):
        """
        This method sets the wall type of the property, using a simple approach based on the wall description
        :return:
        """
        self.wall_type = get_wall_type(**self.walls)

    def set_floor_type(self):
        """
        This method sets the floor type of the property, which is used for calculating u-values

        Section 5.6 of the BRE indicates that
        "to simplify data collection no distinction is made in terms of U-value between an exposed floor (to
        outside air below) and a semi-exposed floor (to an enclosed but unheated space below)
        and the U-values in Table S12 are used.

        Therefore, we treat the exposed floor and suspended floor as the same type of floor, which is used for
        calculating u-values
        """

        if self.floor["is_suspended"] | self.floor["another_property_below"]:
            self.floor_type = "suspended"
        elif self.floor["is_solid"]:
            self.floor_type = "solid"
        elif self.floor["is_to_unheated_space"] | self.floor["is_to_external_air"]:
            self.floor_type = "exposed_floor"
        elif self.floor["thermal_transmittance"] is not None:
            self.floor_type = "solid"
        else:
            raise NotImplementedError("Implement this floor type")

    @staticmethod
    def _extract_component(
        component_data, component_rename_cols, component_drop_cols, rename_prefix=None
    ):
        for k in component_rename_cols:
            component_data[f"{rename_prefix}_{k}"] = component_data.get(k)

        component_data = {
            k: v
            for k, v in component_data.items()
            if k not in component_drop_cols + component_rename_cols
        }

        return component_data

    def set_adjusted_energy(
        self, current_adjusted_energy, expected_adjusted_energy, current_energy_bill, expected_energy_bill
    ):
        """
        Stores these values for usage later
        """
        self.current_adjusted_energy = current_adjusted_energy
        self.expected_adjusted_energy = expected_adjusted_energy
        self.current_energy_bill = current_energy_bill
        self.expected_energy_bill = expected_energy_bill

    def set_windows_count(self):
        """
        Using the estimate_windows function, this method will set the number of windows in the property
        :return:
        """

        self.number_of_windows = estimate_windows(
            property_type=self.data["property-type"],
            built_form=self.data["built-form"],
            construction_age_band=self.construction_age_band,
            floor_area=self.floor_area,
            number_habitable_rooms=self.number_of_rooms,
            extension_count=float(self.data["extension-count"]),
        )

    def set_solar_panel_area(self, photo_supply_lookup, floor_area_decile_thresholds):
        """
        Sets the approximate area of the solar panels
        :return:
        """

        if (self.insulation_floor_area is None) and (self.pitched_roof_area is None):
            raise ValueError(
                "Need to set insulation floor area and pitched roof area before setting solar pv roof area"
            )

        photo_supply_matched = SolarPhotoSupply.filter_photo_supply_lookup(
            photo_supply_lookup=photo_supply_lookup,
            floor_area_decile_thresholds=floor_area_decile_thresholds,
            tenure=self.data["tenure"],
            built_form=self.data["built-form"],
            property_type=self.data["property-type"],
            construction_age_band=self.construction_age_band,
            is_flat=self.roof["is_flat"],
            is_pitched=self.roof["is_pitched"],
            is_roof_room=self.roof["is_roof_room"],
            floor_area=self.floor_area,
        )

        percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
        percentage_of_roof = percentage_of_roof / 100

        self.solar_pv_percentage = percentage_of_roof

    def get_solar_pv_roof_area(self, percentage_of_roof):
        """
        Given a percentage of the roof, this method will return the estimated area of the solar panels
        :param percentage_of_roof:
        :return:
        """

        return (
            self.insulation_floor_area * percentage_of_roof
            if self.roof["is_flat"]
            else self.pitched_roof_area * percentage_of_roof
        )

    def set_energy_source(self):
        """
        This method sets the energy source of the property, based on the mains gas flag and energy tariff.
        """
        # Default to "electricity_and_gas" to cover most scenarios including when mains_gas_flag is True
        energy_source = "electricity_and_gas"

        # If the tariff explicitly indicates electricity use without a dual indication and mains_gas_flag is not True
        # We check for the common electricity tariffs
        if not self.data["mains-gas-flag"] and self.data["energy-tariff"] in [
            "Single",
            "off-peak 7 hour",
            "off-peak 10 hour",
            "off-peak 18 hour",
            "standard tariff",
            "24 hour",
        ]:
            energy_source = "electricity"

        # Set the energy source based on the conditions above
        self.energy_source = energy_source