"""T02: generated from examples/tutorials/fundamentals.py; VAMOS 1.0.1, core dependencies only."""


def process_objectives(x):
    temperature, residence_time = x
    energy = ((temperature - 60.0) / 40.0) ** 2 + 0.25 * residence_time / 10.0
    shortfall = (100.0 - temperature) / 40.0 + 2.0 / residence_time
    return [energy, shortfall]


def process_constraints(x):
    temperature, residence_time = x
    return [450.0 - temperature * residence_time]


def solve_process(max_evaluations=400):
    import numpy as np

    from vamos import OptimizationResult, make_problem, optimize

    problem = make_problem(
        process_objectives,
        n_var=2,
        n_obj=2,
        bounds=[(60.0, 100.0), (2.0, 10.0)],
        encoding="real",
        constraints=process_constraints,
        n_constraints=1,
        name="process_design_surrogate",
    )
    result = optimize(
        problem,
        algorithm="nsgaii",
        pop_size=40,
        max_evaluations=max_evaluations,
        engine="numpy",
        seed=42,
    )
    # Recheck the original requirement, then keep all arrays aligned.
    G = np.array([process_constraints(x) for x in result.X])
    feasible_rows = np.flatnonzero(np.all(G <= 0.0, axis=1))
    if len(feasible_rows) == 0:
        raise ValueError("No feasible candidates: revisit the model or budget.")
    feasible = OptimizationResult(
        {
            "X": result.X[feasible_rows],
            "F": result.F[feasible_rows],
            "G": G[feasible_rows],
        }
    )
    front_F, local_indices = feasible.front(return_indices=True)
    return {
        "result": result,
        "G": G,
        "feasible_rows": feasible_rows,
        "feasible": feasible,
        "front_F": front_F,
        "front_indices": feasible_rows[local_indices],
    }


def run_t02(max_evaluations=400):
    probes = ([60.0, 2.0], [75.0, 6.0], [90.0, 8.0])
    for x in probes:
        f = process_objectives(x)
        g = process_constraints(x)[0]
        print(f"Check {x}: F=({f[0]:.6f}, {f[1]:.6f}), g={g:.1f}")
    data = solve_process(max_evaluations)
    result = data["result"]
    print(f"Feasible returned rows: {len(data['feasible_rows'])}/{len(result.F)}")
    print(f"Feasible non-dominated rows: {len(data['front_F'])}")
    print(f"Evaluations: {result.data['evaluations']}")
    return data


if __name__ == "__main__":
    run_t02()