import pytest
from types import SimpleNamespace
from recommendations.optimiser import optimiser_functions


class TestPrepareInputMeasures:
    def test_returns_expected_structure_without_ventilation(self):
        recs = [
            [  # loft insulation measure
                {"recommendation_id": "loft1", "type": "loft_insulation", "total": 100, "kwh_savings": 200,
                 "energy_cost_savings": 10, "has_battery": False},
            ],
        ]
        measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=False)
        assert isinstance(measures, list)
        assert measures[0][0]["id"] == "loft1"
        assert measures[0][0]["cost"] == 100
        assert measures[0][0]["gain"] == 200

    def test_bundles_ventilation_when_needed(self, monkeypatch):
        # patch measures_needing_ventilation so that "wall_insulation" needs ventilation
        monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["wall_insulation"])
        recs = [
            [{"recommendation_id": "wall1", "type": "internal_wall_insulation", "total": 500, "kwh_savings": 300,
              "energy_cost_savings": 5, "has_battery": False}],
            [{"recommendation_id": "vent1", "type": "mechanical_ventilation", "total": 50, "kwh_savings": 30,
              "energy_cost_savings": 5, "has_battery": False}]
        ]
        measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=True)
        wall_option = measures[0][0]
        assert wall_option["cost"] == 550
        assert wall_option["gain"] == 330
        assert "+mechanical_ventilation" in wall_option["type"]

    def test_filters_out_negative_cost_savings(self):
        recs = [
            [{"recommendation_id": "bad1", "type": "loft_insulation", "total": 200, "kwh_savings": 100,
              "energy_cost_savings": -5, "has_battery": False}],
        ]
        measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=False)
        assert measures == []  # should skip negative cost saving recs


class TestCalculateFixedGain:
    def test_no_required_measures_returns_zero(self):
        fixed_gain = optimiser_functions.calculate_fixed_gain(
            [], {}, SimpleNamespace(id="P1"), needs_ventilation=False
        )
        assert fixed_gain == 0

    def test_sums_max_sap_points_per_type(self, monkeypatch):
        monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["wall_insulation"])
        required_measures = [
            [{"type": "internal_wall_insulation", "sap_points": 5},
             {"type": "internal_wall_insulation", "sap_points": 10}],
            [{"type": "loft_insulation", "sap_points": 3}]
        ]
        recommendations = {"P1": [[{"type": "mechanical_ventilation", "sap_points": 2}]]}
        prop = SimpleNamespace(id="P1")
        gain = optimiser_functions.calculate_fixed_gain(
            required_measures, recommendations, prop, needs_ventilation=True
        )
        # Should take max of wall (10) + loft (3) + ventilation (2)
        assert gain == 15


class TestCalculateGain:
    def test_returns_none_for_energy_savings_goal(self):
        body = SimpleNamespace(goal="Energy Savings")
        prop = SimpleNamespace(data={"current-energy-efficiency": "50"})
        gain = optimiser_functions.calculate_gain(body, prop, fixed_gain=0)
        assert gain is None

    def test_calculates_gain_for_epc(self, monkeypatch):
        # patch cost optimiser calculation
        monkeypatch.setattr(optimiser_functions.CostOptimiser, "calculate_sap_gain_with_slack", lambda x: x + 1)
        monkeypatch.setattr(optimiser_functions, "epc_to_sap_lower_bound", lambda goal_value: 69)

        body = SimpleNamespace(goal="Increasing EPC", goal_value="C", simulate_sap_10=False)
        prop = SimpleNamespace(data={"current-energy-efficiency": "50"})
        gain = optimiser_functions.calculate_gain(body, prop, fixed_gain=2)
        # epc_to_sap_lower_bound (69) - current (50) = 10 + slack (1) = 11 - fixed_gain (2) = 9
        assert gain == 18.5


class TestAddRequiredMeasures:
    def test_adds_cheapest_required_measure(self):
        property_id = "P1"
        required_measures = [
            [{"recommendation_id": "a", "total": 100, "sap_points": 5, "type": "loft_insulation"},
             {"recommendation_id": "b", "total": 80, "sap_points": 6, "type": "loft_insulation"}]
        ]
        recommendations = {
            "P1": [[{"recommendation_id": "a", "total": 100, "sap_points": 5, "type": "loft_insulation"},
                    {"recommendation_id": "b", "total": 80, "sap_points": 6, "type": "loft_insulation"}]]
        }
        selected = set()
        result = optimiser_functions.add_required_measures(property_id, required_measures, recommendations, selected)
        # cheapest should be b
        assert "b" in selected
        assert any(rec["id"] == "b" for rec in result)


class TestAddBestPracticeMeasures:
    def test_adds_ventilation_and_trickle_vents(self, monkeypatch):
        monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["wall_insulation"])
        property_id = "P1"
        solution = [{"type": "internal_wall_insulation", "id": "w1", "gain": 10, "cost": 100}]
        recommendations = {
            "P1": [
                [{"type": "mechanical_ventilation", "recommendation_id": "vent1"}],
                [{"type": "trickle_vents", "recommendation_id": "trickle1"}]
            ]
        }
        selected = set()
        updated = optimiser_functions.add_best_practice_measures(property_id, solution, recommendations, selected)
        assert "vent1" in updated
        assert "trickle1" in updated


class TestFlattenRecommendationsWithDefaults:
    def test_marks_selected_and_flattens(self):
        property_id = "P1"
        recommendations = {
            "P1": [
                [{"recommendation_id": "a", "foo": 1}, {"recommendation_id": "b", "foo": 2}],
                [{"recommendation_id": "c", "foo": 3}]
            ]
        }
        selected = {"b", "c"}
        result = optimiser_functions.flatten_recommendations_with_defaults(property_id, recommendations, selected)
        # All recs should now have a default key
        assert all("default" in rec for rec in result)
        assert next(r for r in result if r["recommendation_id"] == "b")["default"] is True
        assert next(r for r in result if r["recommendation_id"] == "a")["default"] is False