Model/recommendations/recommendation_utils.py

from copy import deepcopy
from model_data.Property import Property
from statistics import mean


def r_value_per_mm_to_u_value(depth_mm: int, r_value_per_mm: float):
    """
    Converts R-value per mm to U-value in W/m²K.

    Parameters
    ----------
    depth_mm : int
        Depth of the material in mm.
    r_value_per_mm : float
        R-value per mm.

    Returns
    -------
    float
        U-value in W/m²K.
    """
    return 1 / (depth_mm * r_value_per_mm)


def calculate_u_value_uplift(u_value, insulation_u_value):
    """
    Calculates the U-value uplift (improvement) when applying internal wall insulation to a wall.


    :param u_value: Float, Starting U-value of the wall (without insulation) in W/m²K.
    :param insulation_u_value: Float, U-value of the internal wall insulation in W/m²K.

    Returns:
        float: U-value uplift (improvement) achieved by applying internal wall insulation in W/m²K.

    Raises:
        ZeroDivisionError: If either u_value or iwi_u_value is zero.

    Notes:
        This function assumes 100% coverage of the internal wall insulation and does not account for other factors
        such as thermal bridging or the specific configuration of the wall.
    """

    inverse_u_value = 1 / u_value
    inverse_insulation_u_value = 1 / insulation_u_value

    inverse_u_total = inverse_u_value + inverse_insulation_u_value
    new_u_value = 1 / inverse_u_total

    u_value_uplift = u_value - new_u_value

    return u_value_uplift, new_u_value


def is_diminishing_returns(recommendations, new_u_value, lowest_selected_u_value, diminishing_returns_u_value):
    """
    What are defines diminishing returns?
    1) The new u value is lower than the lowest selected u value
    2) The new u value is below the diminishing returns threshold
    3) We already have some recommendations so there is no need to
       insert another recommendation in
    """

    # if we don't have anything selected, lowest_selected_u_value will be missing
    if lowest_selected_u_value is None:
        if recommendations:
            raise ValueError("Recommendations should be empty - investigate")
        # This means that nothing has been selected yet
        # the new u value is less than the threshold, however this MIGHT be the only
        # solution and so we consider it
        return False

    # We should already have recommendations
    if not recommendations:
        raise ValueError("Recommendations should not be empty - investigate")

    # We already have a solution that is suitable so we want to make sure that
    # any new solutin actually has a higher u-value as it will either be
    # 1) cheaper
    # 2) thinner with a more efficient material
    is_diminishing = (new_u_value < diminishing_returns_u_value) and (
        new_u_value < lowest_selected_u_value
    )

    return is_diminishing


def update_lowest_selected_u_value(lowest_selected_u_value, new_u_value):
    """
    Utility funciton which holds the logic for how we update the lowest selected u value
    :param lowest_selected_u_value:  current lowest selected u value, initialised as None
    :param new_u_value: new u value to compare against
    :return:
    """
    if lowest_selected_u_value is None:
        lowest_selected_u_value = new_u_value

    if new_u_value <= lowest_selected_u_value:
        lowest_selected_u_value = new_u_value

    return lowest_selected_u_value


def get_recommended_part(part, selected_depth):
    """
    Utility function to return a recommended part with the selected depth.
    :param part:
    :param selected_depth:
    :return:
    """
    recommended_part = deepcopy(part)
    recommended_part["depths"] = [selected_depth]

    return recommended_part


def get_uvalue_estimate(uvalue_estimates, property: Property):
    """
    Wrapper function which contains the methodology to extract a property's walls u-value estimate
    when we don't have a true value and if we can't base our assumption off of the material
    :return:
    """

    if not uvalue_estimates:
        raise ValueError("No U-value estimate found for the given property - investigate")

    # We try and filter on total_floor_area_group_decile
    floor_area_filter = [
        x for x in uvalue_estimates if
        x["total-floor-area_group"] == property.data["total_floor_area_group_decile"]
    ]

    if not floor_area_filter:
        # Take a mean of all the u-value estimates
        return mean(
            [x["median_thermal_transmittance"] for x in uvalue_estimates if x["median_thermal_transmittance"]]
        )

    # Because of how spuriously populated the data is for number-habitable-rooms and number-heated-rooms,
    # we will try and filter on these to see if we get a result

    habitable_rooms_filer = [
        x for x in floor_area_filter if
        x["number-habitable-rooms"] == property.data["number-habitable-rooms"]
    ]

    if not habitable_rooms_filer:
        # Take a mean of all the u-value estimates
        return mean(
            [x["median_thermal_transmittance"] for x in floor_area_filter if x["median_thermal_transmittance"]]
        )

    # Try perform a filter on heated rooms
    heated_rooms_filter = [
        x for x in habitable_rooms_filer if
        x["number-heated-rooms"] == property.data["number-heated-rooms"]
    ]

    if not heated_rooms_filter:
        # Take a mean of all the u-value estimates
        return mean(
            [x["median_thermal_transmittance"] for x in habitable_rooms_filer if x["median_thermal_transmittance"]]
        )

    return mean(
        [x["median_thermal_transmittance"] for x in heated_rooms_filter if x["median_thermal_transmittance"]]
    )