import backend.app.assumptions as assumptions


def prepare_input_measures(property_recommendations, goal, needs_ventilation):
    """
    Basic function to convert recommendations_to_upload to a format that is
    suitable for the optimiser - large
    :param property_recommendations:   object containing the recommendations, created in the plan trigger api
    :param goal:    goal to be optimised for, should be one of the keys in gain_map. E.g. if the gain is SAP points,
                    the goal should reflect that desired gain
    :param needs_ventilation:  boolean to indicate if the property needs ventilation
    :return:    Nested list of input measures
    """

    goal_map = {
        "Increasing EPC": "sap_points",
        "Energy Savings": "kwh_savings",
        "Reducing CO2 emissions": "co2_equivalent_savings",
    }

    goal_key = goal_map[goal]
    if not goal_key:
        raise NotImplementedError("Not implemented this gain type - investigate me")

    # We ony ever have one ventilation measure with now
    ventilation_recommendation = next(
        (measure[0] for measure in property_recommendations if measure[0]["type"] == "mechanical_ventilation"),
        {}
    )

    input_measures = []
    for recs in property_recommendations:

        if needs_ventilation and recs[0]["type"] == "mechanical_ventilation":
            # If we house needs ventilation, ventilation will be packaged with the fabric measure so
            # we don't need to optimise it independently
            continue

        if recs[0]["type"] == "solar_pv":
            # if the recommendation is a solar recommendation with a battery, we exclude it from the optimisation.
            recs = [r for r in recs if ~r["has_battery"]]

        recs_to_append = [rec for rec in recs if rec["energy_cost_savings"] >= 0]
        if not recs_to_append:
            continue

        to_append = []
        for rec in recs:
            # We bundle the impact of ventilation with the measure
            total = (
                rec["total"] + ventilation_recommendation["total"]
                if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                else rec["total"]
            )
            gain = (
                rec[goal_key] + ventilation_recommendation[goal_key]
                if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                else rec[goal_key]
            )

            rec_type = (
                "+".join(
                    [rec["type"], ventilation_recommendation["type"]]
                ) if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                else rec["type"]
            )

            to_append.append(
                {
                    "id": rec["recommendation_id"],
                    "cost": total,
                    "gain": gain,
                    "type": rec_type
                }
            )

        input_measures.append(to_append)

    return input_measures