"""Mating-system tests (pure NumPy) — monogamy vs promiscuity as mate-pool breadth (E14).

Cover the diversity helpers and the two load-bearing behaviours: promiscuity (wide mate-pool breadth)
monotonically destroys standing diversity, and the run is deterministic and well-formed. The full
ruggedness crossover (intermediate breadth wins the champion on rugged landscapes) is a swept,
multi-replicate result asserted only in aggregate here to keep the test fast.
"""

from __future__ import annotations

import numpy as np

from knowledge.mating_system import _distinct_peaks, _diversity, run_mating_system
from knowledge.genotype import nk_fitness


def _run(breadth: float, K: int = 6, seed: int = 0, gens: int = 40, N: int = 32, L: int = 10):
    cfg = {"mating": {"L": L, "N": N, "breadth": breadth, "K": K, "recomb_rate": 0.5, "mu": 0.005},
           "generations": gens}
    return run_mating_system(cfg, seed=seed)


def test_diversity_zero_for_clones_and_positive_for_spread():
    clones = np.ones((5, 8), dtype=np.int8)
    assert _diversity(clones) == 0.0                              # identical -> no diversity
    spread = np.array([[0] * 8, [1] * 8], dtype=np.int8)
    assert np.isclose(_diversity(spread), 1.0)                    # opposite -> maximal diversity


def test_distinct_peaks_counts_basins():
    fitness = nk_fitness(6, 2, seed=0)
    pop = np.zeros((4, 6), dtype=np.int8)                         # all identical -> one basin
    assert _distinct_peaks(pop, fitness, 6) == 1


def test_schema_and_bounds():
    df = _run(0.5)
    for col in ["generation", "best_fitness", "mean_fitness", "diversity", "distinct_peaks", "global_opt"]:
        assert col in df.columns
    assert (df["best_fitness"] <= df["global_opt"] + 1e-9).all()  # nothing beats reality's optimum
    assert (df["diversity"] >= 0).all() and (df["diversity"] <= 1).all()
    assert df["distinct_peaks"].iloc[-1] >= 1


def test_deterministic_given_seed():
    a = _run(0.3, seed=7)
    b = _run(0.3, seed=7)
    assert np.allclose(a["best_fitness"], b["best_fitness"])      # pure function of the seed


def test_promiscuity_destroys_diversity():
    # Averaged over replicates, wide mate-pool breadth (promiscuity) leaves LESS standing diversity than
    # narrow breadth (monogamy) — the homogenisation effect, robust on a rugged landscape.
    def final_div(b):
        return np.mean([_run(b, K=8, seed=s, gens=40, N=32, L=10)["diversity"].iloc[-1]
                        for s in range(6)])
    assert final_div(1.0) < final_div(0.05)                        # panmixia < isolation-by-distance