"""Analytic claims are proved in MANUSCRIPT.md; these are independent checks."""
import json
import platform
from pathlib import Path
import numpy as np
from eve_reserve import Event, analyse, encode, gram_factor
from verify_exact import run as exact_run
from verify_relational import run as relational_run
from verify_continuous import run as continuous_run


def run():
    rng = np.random.default_rng(704107)
    cases = 400
    prefixes = 0
    largest_error = 0.
    for _ in range(cases):
        n = int(rng.integers(1, 8))
        steps = int(rng.integers(2, 12))
        events = []
        for t in range(steps):
            u = rng.normal(size=n) + 1j*rng.normal(size=n)
            u /= np.linalg.norm(u)
            z = rng.normal(size=(n, n)) + 1j*rng.normal(size=(n, n))
            unitary, _ = np.linalg.qr(z)
            c = float(rng.uniform(.55, .99))
            if t == 0 and rng.random() < .15:
                c = 0.
            if rng.random() < .1:
                c = 1.
            events.append(Event(u, c, int(rng.integers(2)), unitary))
        history = analyse(events, n)
        previous = [0, 0]
        for h in history:
            prefixes += 1
            p = h['product']
            total = sum(h['grams'])
            err = np.linalg.norm(total + p.conj().T@p - np.eye(n), 2)
            largest_error = max(largest_error, float(err))
            assert err < 1e-10
            factors = [gram_factor(g) for g in h['grams']]
            r = [c.shape[0] for c in factors]
            pooled = gram_factor(total).shape[0]
            active_rank = np.linalg.matrix_rank(h['active_normals'], tol=1e-8)
            assert pooled == active_rank
            assert 0 <= sum(r)-pooled <= pooled <= n
            assert all(a >= b for a, b in zip(r, previous))
            previous = r
            for c, s, g in zip(factors, h['transcripts'], h['grams']):
                assert np.allclose(c.conj().T@c, g, atol=1e-10)
                assert np.allclose(s.conj().T@s, g, atol=1e-10)
                # Transcript recovery from the colour's minimal factor.
                assert np.allclose(s@np.linalg.pinv(c)@c, s, atol=1e-8)
            x = rng.normal(size=n) + 1j*rng.normal(size=n)
            visible, memories, decoded = encode(h, x)
            assert np.allclose(decoded, x, atol=1e-9)
            assert abs(np.vdot(x, x) - np.vdot(visible, visible)
                       - sum(np.vdot(m, m) for m in memories)) < 1e-8
            # Non-unitary coordinate charts carry the transported metric.
            f0 = np.diag(rng.uniform(.5, 2., n))
            ft = np.diag(rng.uniform(.5, 2., n))
            inv0, invt = np.linalg.inv(f0), np.linalg.inv(ft)
            chart_product = ft@p@inv0
            chart_total = inv0.conj().T@total@inv0
            metric0, metrict = inv0.conj().T@inv0, invt.conj().T@invt
            assert np.allclose(chart_total + chart_product.conj().T@metrict@chart_product,
                               metric0, atol=1e-10)
            # Exact scanner factorization on the active subspace.
            hscan = gram_factor(total)
            hp = np.linalg.pinv(hscan)
            for g in h['grams']:
                k = hp.conj().T@g@hp
                assert np.allclose(hscan.conj().T@k@hscan, g, atol=1e-8)
            # Spectral truncation reaches the proved optimum for every k.
            values, vectors = np.linalg.eigh(total)
            order = np.argsort(values)[::-1]
            values, vectors = values[order], vectors[:, order]
            for k in range(n+1):
                approximation = (vectors[:, :k]*values[:k])@vectors[:, :k].conj().T
                optimum = max(0., float(values[k])) if k < n else 0.
                assert abs(np.linalg.norm(total-approximation, 2)-optimum) < 1e-9
    # Local rank-one unitary completion and topological chart transition.
    local_error = 0.
    for _ in range(96):
        n = 3
        u = rng.normal(size=n)+1j*rng.normal(size=n)
        u /= np.linalg.norm(u)
        c = float(rng.random()); s = np.sqrt(1-c*c)
        a = np.eye(n)+(c-1)*np.outer(u, u.conj())
        j = np.block([[a, -s*u[:, None]], [s*u.conj()[None, :], np.array([[c]])]])
        error = float(np.linalg.norm(j.conj().T@j-np.eye(n+1), 2))
        local_error = max(local_error, error)
        assert error < 1e-10
    phases = np.linspace(0, 2*np.pi, 513)
    transition = []
    for phase in phases:
        z = np.exp(1j*phase)
        un = np.array([1, z])/np.sqrt(2)
        us = np.array([1/z, 1])/np.sqrt(2)
        assert np.allclose(us, np.exp(-1j*phase)*un)
        projector = np.outer(un, un.conj())
        a, b = .5, .25
        # Continuous redundant global factors, in two fixed coordinates per colour.
        for g, factor in [(a*projector, np.sqrt(a)*projector),
                          (b*projector, np.sqrt(b)*projector)]:
            assert np.allclose(factor.conj().T@factor, g)
        transition.append(np.vdot(un, us))
    winding = (np.unwrap(np.angle(transition))[-1]-np.unwrap(np.angle(transition))[0])/(2*np.pi)
    assert abs(winding+1) < 1e-10
    # Deliberate counterexample to the incorrect, untransported colour-rank shortcut.
    e1 = np.array([1., 0.]); u = np.array([3/5, 4/5])
    chronology = analyse([Event(e1, .6, 0, np.eye(2)), Event(u, .6, 1, np.eye(2)),
                          Event(e1, .6, 0, np.eye(2))], 2)[-1]
    assert gram_factor(chronology['grams'][0]).shape[0] == 2
    assert np.linalg.matrix_rank(np.stack([e1, e1])) == 1
    return {
        **exact_run(), "complex_words": cases, "complex_prefixes": prefixes,
        "local_unitary_cases": 96, "topological_transition_samples": 513,
        "sampled_transition_winding": float(winding),
        "maximum_balance_error": largest_error,
        "maximum_local_unitarity_error": local_error,
        "python": platform.python_version(), "numpy": np.__version__,
        "seed": 704107,
        "version": "3.0.0",
        "relational_extension": relational_run(),
        "continuous_factorization_extension": continuous_run(),
        "scope": "finite checks support the analytic proofs; topology is not proved by sampling",
    }


if __name__ == '__main__':
    result = run()
    target = Path(__file__).with_name('VERIFICATION.json')
    target.write_text(json.dumps(result, indent=2)+'\n', encoding='utf-8')
    print(json.dumps(result, indent=2))