"""Dynamic-society tests (pure NumPy) — the culminating vertical claim (E11 / C3).

Cover the finite-population operators (consensus, conformity, novelty) and the four ablation
behaviours: the full society climbs to near the optimum; removing grounding collapses it to an unfit
consensus (self-consumption); removing sex or diversity leaves it stuck below the full society.
"""

from __future__ import annotations

import numpy as np

from inheritance.dynamic_society import _conformity, _consensus, _novelty, run_dynamic_society


def _run(arm_overrides: dict, seed: int = 0, gens: int = 50):
    base = {"L": 10, "K": 6, "N": 50, "g": 0.85, "mu": 0.03, "novelty": 0.5,
            "n_off": 100, "recomb_rate": 0.2, "sex": True, "select": "qd"}
    base.update(arm_overrides)
    return run_dynamic_society({"society": base, "generations": gens}, seed=seed)


def test_consensus_and_conformity():
    pop = np.array([[1, 1, 0, 0], [1, 0, 0, 1], [1, 1, 1, 0]], dtype=np.int8)
    cons = _consensus(pop)
    assert np.array_equal(cons, [1, 1, 0, 0])            # majority vote per locus
    conf = _conformity(pop, cons)
    assert np.isclose(conf[0], 1.0)                       # agent 0 == consensus
    assert conf.min() >= 0.0 and conf.max() <= 1.0


def test_novelty_is_zero_for_clones_and_high_for_spread():
    clones = np.ones((4, 8), dtype=np.int8)
    assert np.allclose(_novelty(clones), 0.0)             # identical -> no diversity
    spread = np.array([[0] * 8, [1] * 8], dtype=np.int8)
    assert np.allclose(_novelty(spread), 1.0)             # opposite -> maximal diversity


def test_full_society_climbs_toward_optimum():
    df = _run({})
    go = df["global_opt"].iloc[0]
    assert df["best_fitness"].iloc[-1] > df["best_fitness"].iloc[0] + 0.05   # it climbs
    assert df["best_fitness"].iloc[-1] > 0.9 * go                            # ... to near the optimum


def test_no_grounding_collapses_to_unfit_consensus():
    full = _run({})["best_fitness"].iloc[-1]
    dry = _run({"g": 0.0})
    assert dry["best_fitness"].iloc[-1] < full - 0.1        # far below the grounded society
    assert dry["diversity"].iloc[-1] < 0.05                 # diversity collapsed
    assert dry["conformity_true_gap"].iloc[-1] > 0.3        # agreement >> real capability (delusion)


def test_ablations_stay_below_the_full_society():
    full = _run({})["best_fitness"].iloc[-1]
    no_sex = _run({"sex": False})["best_fitness"].iloc[-1]
    no_div = _run({"select": "greedy", "novelty": 0.0})["best_fitness"].iloc[-1]
    assert no_sex <= full + 1e-6 and no_div <= full + 1e-6   # neither beats the full society
    assert min(no_sex, no_div) < full                        # ... and at least one is strictly worse