#!/usr/bin/env python3
"""Tests for density evolution optimizer."""

import numpy as np
import pytest
import sys
import os

sys.path.insert(0, os.path.dirname(__file__))


class TestDensityEvolution:
    """Tests for the Monte Carlo DE engine."""

    def test_de_known_good_converges(self):
        """DE with original staircase profile at lam_s=10 should converge easily."""
        from density_evolution import run_de, ORIGINAL_STAIRCASE_PROFILE
        np.random.seed(42)
        converged, error_frac = run_de(
            ORIGINAL_STAIRCASE_PROFILE, lam_s=10.0, lam_b=0.1,
            z_pop=10000, max_iter=50
        )
        assert converged, f"DE should converge at lam_s=10, error_frac={error_frac}"

    def test_de_known_bad_fails(self):
        """DE at very low lam_s=0.1 should not converge."""
        from density_evolution import run_de, ORIGINAL_STAIRCASE_PROFILE
        np.random.seed(42)
        converged, error_frac = run_de(
            ORIGINAL_STAIRCASE_PROFILE, lam_s=0.1, lam_b=0.1,
            z_pop=10000, max_iter=50
        )
        assert not converged, f"DE should NOT converge at lam_s=0.1, error_frac={error_frac}"

    def test_de_population_shape(self):
        """Verify belief arrays have correct shapes after one step."""
        from density_evolution import de_channel_init, density_evolution_step
        np.random.seed(42)
        n_base = 8
        m_base = 7
        z_pop = 1000

        # Original staircase H_base profile
        from density_evolution import ORIGINAL_STAIRCASE_PROFILE
        beliefs, msg_memory = de_channel_init(ORIGINAL_STAIRCASE_PROFILE, z_pop, lam_s=5.0, lam_b=0.1)

        # beliefs should be (n_base, z_pop)
        assert beliefs.shape == (n_base, z_pop), f"Expected ({n_base}, {z_pop}), got {beliefs.shape}"

        # Run one step
        beliefs = density_evolution_step(beliefs, msg_memory, ORIGINAL_STAIRCASE_PROFILE, z_pop)
        assert beliefs.shape == (n_base, z_pop), f"Shape changed after step: {beliefs.shape}"