Model/recommendations/Recommendations.py

class Recommendations:
    """
    High level recommendations class, which sits above the measure specific recommendation classes
    """

    @classmethod
    def calculate_recommendation_impact(cls, property_instance, all_predictions, recommendations):

        """
        Given predictions from the model apis, with method will update the recommendations with the predicted
        impact of the recommendation on the property

        :param property_instance: Instance of the Property class, for the home associated to property_id
        :param all_predictions: dictionary of predictions from the model apis
        :param recommendations: dictionary of recommendations for the property
        :return:
        """

        property_sap_predictions = all_predictions["sap_change_predictions"][
            all_predictions["sap_change_predictions"]["property_id"] == str(property_instance.id)
            ]
        property_heat_predictions = all_predictions["heat_demand_predictions"][
            all_predictions["heat_demand_predictions"]["property_id"] == str(property_instance.id)
            ]
        property_carbon_predictions = all_predictions["carbon_change_predictions"][
            all_predictions["carbon_change_predictions"]["property_id"] == str(property_instance.id)
            ]

        property_recommendations = recommendations[property_instance.id].copy()

        for recommendations_by_type in property_recommendations:
            for rec in recommendations_by_type:
                new_sap = property_sap_predictions[property_sap_predictions["recommendation_id"] == str(
                    rec["recommendation_id"]
                )]["predictions"].values[0]

                new_heat_demand = property_heat_predictions[property_heat_predictions["recommendation_id"] == str(
                    rec["recommendation_id"]
                )]["predictions"].values[0]

                new_carbon = property_carbon_predictions[property_carbon_predictions["recommendation_id"] == str(
                    rec["recommendation_id"]
                )]["predictions"].values[0]

                rec["sap_points"] = new_sap - float(property_instance.data["current-energy-efficiency"])
                rec["co2_equivalent_savings"] = float(property_instance.data["co2-emissions-current"]) - new_carbon
                rec["heat_demand"] = float(property_instance.data["co2-emissions-current"]) - new_heat_demand

                if rec["sap_points"] is None:
                    raise ValueError("Sap points missing")

        return property_recommendations