#!/usr/bin/env python3
"""
example.py — Usage demonstrations for hv_falsification_os.

NumPy only.
"""

import numpy as np

from hv_falsification_os import (
    FalsificationOS,
    make_synthetic_workload,
    make_two_workloads,
    make_amortized_workload,
)


def demo_cv():
    print("=" * 72)
    print("Demo 1 — measure the CV of a metric")
    print("=" * 72)
    print()
    os_ = FalsificationOS()
    fn = make_synthetic_workload(base=1.0, cv=0.05, seed_offset=0)
    r = os_.measure_cv(fn, n_trials=500, seed=0)
    print(f"  mean:     {r['mean']:.4f}")
    print(f"  std:      {r['std']:.4f}")
    print(f"  CV:       {r['cv']:.4f}")
    print(f"  95% CI:   +/- {r['ci95_halfwidth']:.4f}")
    print()
    print("  Interpretation: a delta smaller than "
          f"{2*r['cv']:.2%} is indistinguishable from noise.")
    print()


def demo_same_code():
    print("=" * 72)
    print("Demo 2 — is this delta real?")
    print("=" * 72)
    print()
    os_ = FalsificationOS()
    for delta in [0.01, 0.05, 0.10, 0.15, 0.50]:
        fn_a, fn_b = make_two_workloads(delta, cv=0.05, seed_offset=0)
        r = os_.same_code_test(fn_a, fn_b, n_trials=500, seed=0)
        print(f"  delta={delta:>5.2f}  ratio={r['ratio_to_cv']:>5.2f}  "
              f"verdict={r['verdict']:<10s}  "
              f"(design CV = 5%)")
    print()


def demo_amortization():
    print("=" * 72)
    print("Demo 3 — amortization sweep")
    print("=" * 72)
    print()
    os_ = FalsificationOS()
    fn = make_amortized_workload(peak_param=1000.0, peak_speedup=0.78)
    params = [1, 10, 100, 500, 1000, 2000, 5000, 10000, 50000]
    r = os_.amortization_sweep(fn, params, n_trials=20, seed=0)
    print(f"  {'param':>8s}  {'value':>10s}  {'bar'}")
    print("  " + "-" * 50)
    for p, m in zip(r['param_values'], r['means']):
        bar = "#" * int(m * 40)
        print(f"  {p:>8.0f}  {m:>10.4f}  {bar}")
    print()
    print(f"  Peak: param = {r['peak_param']:.0f}, "
          f"value = {r['peak_value']:.3f}")
    if r['peak_value'] < 1.0:
        print("  Verdict: peak loses to baseline.")
    print()


def demo_precondition():
    print("=" * 72)
    print("Demo 4 — precondition testing")
    print("=" * 72)
    print()
    os_ = FalsificationOS()

    def clustered(seed):
        rng = np.random.default_rng(seed)
        K, d, N = 5, 64, 500
        centers = rng.standard_normal((K, d))
        centers /= np.linalg.norm(centers, axis=1, keepdims=True)
        a = rng.integers(0, K, size=N)
        items = centers[a] + 0.1 * rng.standard_normal((N, d))
        items /= np.linalg.norm(items, axis=1, keepdims=True)
        i = rng.integers(0, N, size=2000)
        j = rng.integers(0, N, size=2000)
        return float((np.einsum('ij,ij->i', items[i], items[j]) > 0.5).mean()) > 0.05

    def uniform(seed):
        rng = np.random.default_rng(seed)
        N, d = 500, 64
        items = rng.standard_normal((N, d))
        items /= np.linalg.norm(items, axis=1, keepdims=True)
        i = rng.integers(0, N, size=2000)
        j = rng.integers(0, N, size=2000)
        return float((np.einsum('ij,ij->i', items[i], items[j]) > 0.5).mean()) > 0.05

    for name, fn in [('clustered', clustered), ('uniform', uniform)]:
        r = os_.precondition_test(fn, n_trials=30, seed=0)
        print(f"  {name:<10s}  pass_rate = {r['pass_rate']:.1%}  "
              f"holds = {r['holds']}")
    print()
    print("  The same test passes on clustered data and fails on uniform.")
    print("  That is the precondition check.")
    print()


def demo_four_question():
    print("=" * 72)
    print("Demo 5 — four-question probe")
    print("=" * 72)
    print()
    os_ = FalsificationOS()
    rng = np.random.default_rng(42)
    N, d = 1000, 64

    print(f"  Case A: low-rank task in random H")
    H = rng.standard_normal((N, d))
    Y = H[:, :3].sum(axis=1) + 0.5 * rng.standard_normal(N)
    r = os_.four_question_probe(H, Y)
    print(f"    effective_rank_H = {r['effective_rank_H']:.2f}")
    print(f"    cross_cov_rank   = {r['cross_cov_rank']:.2f}")
    print(f"    fresh_probe_r2   = {r['fresh_probe_r2']:.4f}")
    print()

    print(f"  Case B: task unrelated to H")
    Y = rng.standard_normal(N)
    r = os_.four_question_probe(H, Y)
    print(f"    effective_rank_H = {r['effective_rank_H']:.2f}")
    print(f"    cross_cov_rank   = {r['cross_cov_rank']:.2f}")
    print(f"    fresh_probe_r2   = {r['fresh_probe_r2']:.4f}")
    print()
    print("  In case A the task signal is recoverable (R² > 0.9).")
    print("  In case B it is not, even though H is high-rank.")
    print()


def demo_reversal():
    print("=" * 72)
    print("Demo 6 — reversal detection")
    print("=" * 72)
    print()
    os_ = FalsificationOS()

    cases = [
        ('clear winner', (1.00, 1.10), (1.02, 1.11)),
        ('flipping winner', (1.00, 0.98), (0.99, 1.01)),
        ('tiny gap', (1.000, 1.001), (1.002, 1.000)),
    ]
    for name, r1, r2 in cases:
        r = os_.reversal_detection(r1, r2)
        status = "REVERSED" if r['reversal'] else "stable"
        print(f"  {name:<18s}  run1={r['winner_1']}  run2={r['winner_2']}  "
              f"→ {status}")
    print()


def main():
    demo_cv()
    demo_same_code()
    demo_amortization()
    demo_precondition()
    demo_four_question()
    demo_reversal()


if __name__ == "__main__":
    main()