"""Independent finite checks of NØWÆ constructions and adverse cases.

Universal claims, particularly Borsuk-Ulam, are proved in MANUSCRIPT.md.
Synthetic sampling and numerical integration are consistency checks only.
"""
import itertools
import json
import numpy as np
from eve_reserve import analyse
from relational_geometry import (
    ray, global_factor, anchor_section, anchor_factor, recognition_margin,
    select_reference, pair_transport, cycle_holonomy, relational_direction,
    relational_factor, relational_event,
)


def run():
    rng = np.random.default_rng(26100429)
    maxima = {'factor': 0., 'distance': 0., 'pair': 0., 'decode': 0., 'vectorization': 0.}
    counts = {}

    def unit(n):
        z = rng.normal(size=n)+1j*rng.normal(size=n)
        return z/np.linalg.norm(z)

    def random_unitary(n):
        return np.linalg.qr(rng.normal(size=(n, n))+1j*rng.normal(size=(n, n)))[0]

    def check(name, residual, tolerance=2e-10):
        value = float(residual)
        maxima[name] = max(maxima[name], value)
        assert value < tolerance, (name, value)

    # Dense global factors and an explicit forced-zero family with known witnesses.
    for _ in range(192):
        n = int(rng.integers(2, 9)); m = int(rng.integers(1, n))
        p = ray(unit(n)); lam = float(rng.uniform(.2, 2.)); mu = .15*lam
        g = lam*p+mu*(np.eye(n)-p)
        c = global_factor(p, lam, mu)
        check('factor', np.linalg.norm(c.conj().T@c-g, 2))
        b = rng.normal(size=(m, n))+1j*rng.normal(size=(m, n))
        b /= np.linalg.norm(b, 2)
        witness = np.linalg.svd(b, full_matrices=True)[2][-1].conj()
        p0 = ray(witness)
        d = rng.normal(size=(m, n))+1j*rng.normal(size=(m, n))
        # C(P)=B P + D(P)(I-P), with a genuinely parameter-dependent D.
        d0 = d*(1+.2*p0[0, 0].real)
        c0 = b@p0+d0@(np.eye(n)-p0)
        assert np.linalg.norm(c0@witness) < 2e-12
        error = np.linalg.norm(c0.conj().T@c0-(lam*p0+mu*(np.eye(n)-p0)), 2)
        assert error >= lam-2e-10
    counts['global_factor_and_blind_witness_cases'] = 192

    # Closest blind projectors, exact anchor factors, and noise inequalities.
    noise_cases = 0
    for _ in range(384):
        n = int(rng.integers(2, 9)); u = unit(n); w = unit(n); p = ray(u)
        delta = recognition_margin(p, w)
        a = anchor_section(p, w); lam = float(rng.uniform(.2, 2.))
        c = anchor_factor(p, w, lam)
        check('factor', np.linalg.norm(c.conj().T@c-lam*p, 2))
        assert abs(np.vdot(w, a)-delta) < 2e-12
        v = u-w*np.vdot(w, u)
        v /= np.linalg.norm(v); blind = ray(v)
        check('distance', abs(np.linalg.norm(p-blind, 2)-delta))
        assert np.linalg.norm(blind@w) < 2e-12
        try:
            anchor_section(blind, w)
        except ValueError:
            pass
        else:
            raise AssertionError('blind anchor must reject')
        new = ray(u+float(rng.uniform(1e-4, .08))*unit(n))
        eta = np.linalg.norm(new-p, 2)
        if eta < delta:
            noise_cases += 1
            anew = anchor_section(new, w)
            assert np.linalg.norm(a-anew) <= 2*eta/delta+2e-12
    counts['anchor_and_exact_breakdown_cases'] = 384
    counts['recognized_perturbation_cases'] = noise_cases

    sensitivity = 0
    w = np.array([1., 0.]); v = np.array([0., 1.])
    for delta in [.01, .02, .05, .1, .2, .4, .7, .9]:
        for phi in np.linspace(.05, 2.8, 12):
            u0 = np.sqrt(1-delta**2)*v+delta*w
            u1 = np.sqrt(1-delta**2)*v+delta*np.exp(1j*phi)*w
            p0, p1 = ray(u0), ray(u1)
            quotient = np.linalg.norm(anchor_section(p1, w)-anchor_section(p0, w))/np.linalg.norm(p1-p0, 2)
            assert abs(quotient-1/delta) < 2e-8
            sensitivity += 1
    counts['sharp_inverse_margin_examples'] = sensitivity

    # Reference-atlas optimality: constructive sign-vector witnesses.
    atlas_cases = 0
    for n in range(2, 9):
        basis = random_unitary(n)
        balanced = basis@np.exp(1j*rng.uniform(0, 2*np.pi, n))/np.sqrt(n)
        overlaps = np.linalg.norm(ray(balanced)@basis, axis=0)**2
        assert np.allclose(overlaps, 1/n, atol=2e-12)
        for _ in range(12):
            refs = np.column_stack([unit(n) for _ in range(n)])
            a = refs.conj().T
            inv = np.linalg.inv(a)
            signs = np.array(list(itertools.product([-1., 1.], repeat=n)))
            inverse_signs = inv@signs.T
            distances = np.sum(np.abs(inverse_signs)**2, axis=0)
            mean = float(np.mean(distances))
            assert mean >= n-1e-9
            j = int(np.argmax(distances)); witness = inverse_signs[:, j]/np.sqrt(distances[j])
            tau = np.abs(a@witness)**2
            assert max(tau) <= 1/n+2e-10
            assert np.allclose(tau, 1/distances[j], atol=2e-10)
            index, section, margin = select_reference(ray(witness), refs)
            assert index in range(n) and margin > 0 and np.isclose(np.linalg.norm(section), 1)
            lower = np.linalg.eigvalsh(refs@refs.conj().T)[0]/n
            assert margin**2 >= lower-2e-10
            atlas_cases += 1
        deficient = np.column_stack([unit(n) for _ in range(n-1)])
        blind = np.linalg.svd(deficient.conj().T, full_matrices=True)[2][-1].conj()
        assert np.linalg.norm(deficient.conj().T@blind) < 2e-12
        try:
            select_reference(ray(blind), deficient)
        except ValueError:
            pass
        else:
            raise AssertionError('deficient atlas must have a blind ray')
    counts['nonorthogonal_atlas_witness_cases'] = atlas_cases
    counts['orthonormal_atlas_and_deficient_atlas_dimensions'] = 7

    # Partial-isometry identities, covariance, perturbation bounds, and loop phases.
    for _ in range(256):
        n = int(rng.integers(2, 8)); p, q = ray(unit(n)), ray(unit(n))
        t = pair_transport(p, q)
        check('pair', np.linalg.norm(t.conj().T@t-q, 'fro'))
        check('pair', np.linalg.norm(t@t.conj().T-p, 'fro'))
        u = random_unitary(n)
        transformed = pair_transport(u@p@u.conj().T, u@q@u.conj().T)
        check('pair', np.linalg.norm(transformed-u@t@u.conj().T, 'fro'))
        pp, qq = ray(unit(n)), ray(unit(n))
        tp = pair_transport(pp, qq)
        delta = min(np.linalg.norm(p@q, 'fro'), np.linalg.norm(pp@qq, 'fro'))
        assert np.linalg.norm(t-tp, 'fro') <= 2/delta*(np.linalg.norm(p-pp, 'fro')+np.linalg.norm(q-qq, 'fro'))+2e-12
        vectors = [unit(n) for _ in range(int(rng.integers(3, 8)))]
        phases = [np.vdot(vectors[i], vectors[(i+1)%len(vectors)]) for i in range(len(vectors))]
        expected = np.prod([z/abs(z) for z in phases])
        h = cycle_holonomy([ray(v) for v in vectors])
        check('pair', abs(h-expected))
        assert abs(abs(h)-1) < 2e-10
    counts['pair_transport_and_cycle_cases'] = 256
    triangle = [ray(np.array([1., 0.])), ray(np.array([1., 1.])), ray(np.array([1., 1j]))]
    triangle_phase = cycle_holonomy(triangle)
    assert abs(triangle_phase-np.exp(1j*np.pi/4)) < 2e-12
    positive_triangle = [ray(np.array([np.cos(t), np.sin(t)])) for t in [.1, .5, .9]]
    assert abs(cycle_holonomy(positive_triangle)-1) < 2e-12

    # Relational inputs are arbitrary complex matrices, not just paired rank-one states.
    for _ in range(192):
        n = int(rng.integers(2, 8)); u = unit(n); p = ray(u); r = relational_direction(p)
        x = rng.normal(size=(n, n))+1j*rng.normal(size=(n, n))
        z = x.reshape(-1, order='F')
        alpha = np.array([.5, .25]); total = float(sum(alpha))
        factor = relational_factor(p, total)
        check('factor', np.linalg.norm(factor.conj().T@factor-total*np.outer(r, r.conj()), 2))
        visible_matrix = x+(np.sqrt(1-total)-1)*np.trace(p@x)*p
        memories = np.sqrt(alpha)*np.trace(p@x)
        decoded_matrix = visible_matrix+(np.sqrt(1-total)-1)*np.trace(p@visible_matrix)*p
        decoded_matrix += np.dot(np.sqrt(alpha), memories)*p
        check('decode', np.linalg.norm(decoded_matrix-x, 'fro'))
        assert abs(np.linalg.norm(x, 'fro')**2-np.linalg.norm(visible_matrix, 'fro')**2-np.linalg.norm(memories)**2) < 2e-10
        events = [relational_event(p, 1/np.sqrt(2), 0), relational_event(p, 1/np.sqrt(2), 1)]
        prefix = analyse(events, n*n)[-1]
        check('vectorization', np.linalg.norm(prefix['product']@z-visible_matrix.reshape(-1, order='F')))
        for weight, g in zip(alpha, prefix['grams']):
            check('factor', np.linalg.norm(g-weight*np.outer(r, r.conj()), 2))
        transport = random_unitary(n)
        event = relational_event(p, .6, 0, transport)
        retained = x+(.6-1)*np.trace(p@x)*p
        transported = transport@retained@transport.conj().T
        check('vectorization', np.linalg.norm(analyse([event], n*n)[0]['product']@z-transported.reshape(-1, order='F')))
        a, b = complex(*rng.normal(size=2)), complex(*rng.normal(size=2))
        paired = np.outer(a*u, (b*u).conj())
        assert np.allclose(paired, a*b.conjugate()*p)
        assert np.allclose(paired/np.trace(paired), p)
        phase = np.exp(1j*float(rng.uniform(-np.pi, np.pi)))
        assert np.allclose(np.outer(phase*a*u, (phase*b*u).conj()), paired)
    counts['relational_matrix_completion_cases'] = 192

    # Nonlinear energy equality must not be mistaken for complex amplitude retention.
    p = ray(np.array([1., 0.])); active = np.array([1.+2j, 0.])
    readout = lambda x: np.sqrt(np.vdot(x, p@x).real)
    assert readout(active) == readout(1j*active)
    counts['nonlinear_phase_loss_counterexamples'] = 1

    # Nontrivial quantitative blind-region checks with C(P)=B P.
    for _ in range(192):
        n = int(rng.integers(2, 9)); m = int(rng.integers(1, n)); lam = float(rng.uniform(.2, 2.))
        b = rng.normal(size=(m, n))+1j*rng.normal(size=(m, n)); b /= np.linalg.norm(b, 2)
        u0 = np.linalg.svd(b, full_matrices=True)[2][-1].conj()
        v = unit(n); v -= u0*np.vdot(u0, v); v /= np.linalg.norm(v)
        aconstant = 1+np.sqrt(2)  # K=L=1
        radius = min(1., np.sqrt(.5*lam)/aconstant)
        d = float(rng.uniform(0, radius))
        u = np.sqrt(1-d*d)*u0+d*v; p = ray(u); c = b@p
        error = np.linalg.norm(c.conj().T@c-lam*p, 2)
        assert error >= lam-aconstant**2*d*d-2e-12
        assert error >= .5*lam-2e-12
    counts['quantitative_blind_region_cases'] = 192

    # Haar ball formula checked by reproducible synthetic sampling, never used as a proof.
    haar_checks = []
    for n in range(2, 9):
        samples = 12000; r = .25**(1/(2*(n-1)))
        raw = rng.normal(size=(samples, n))+1j*rng.normal(size=(samples, n))
        fidelity = np.abs(raw[:, 0])**2/np.sum(np.abs(raw)**2, axis=1)
        count = int(np.count_nonzero(fidelity >= 1-r*r))
        predicted = r**(2*(n-1)); measured = count/samples
        assert abs(measured-predicted) < 6*np.sqrt(predicted*(1-predicted)/samples)+2/samples
        haar_checks.append({'n': n, 'samples': samples, 'count': count, 'predicted_fraction': predicted, 'sampled_fraction': measured})
    counts['synthetic_haar_rays'] = sum(x['samples'] for x in haar_checks)

    # Integrate Kato's ODE; doubling resolution must exhibit second-order convergence.
    path_results = []
    for theta in [.3, np.pi/4, 1.1]:
        v0 = np.array([np.cos(theta), np.sin(theta)], dtype=complex)
        expected = np.exp(-2j*np.pi*np.sin(theta)**2)*v0
        errors = []
        for steps in [512, 1024]:
            h = 2*np.pi/steps; value = v0.copy()
            for j in range(steps):
                t = (j+.5)*h
                v = np.array([np.cos(theta), np.exp(1j*t)*np.sin(theta)])
                dv = np.array([0., 1j*np.exp(1j*t)*np.sin(theta)])
                p = np.outer(v, v.conj()); dp = np.outer(dv, v.conj())+np.outer(v, dv.conj())
                k = dp@p-p@dp
                value = np.linalg.solve(np.eye(2)-h*k/2, (np.eye(2)+h*k/2)@value)
            err = float(np.linalg.norm(value-expected)); errors.append(err)
            assert abs(np.linalg.norm(value)-1) < 3e-12
            assert err < 5e-5
        ratio = errors[0]/errors[1]
        assert 3.9 < ratio < 4.1
        path_results.append({'theta': float(theta), 'errors_512_1024': errors, 'convergence_ratio': ratio})
    counts['integrated_path_steps'] = 3*(512+1024)
    counts['path_resolution_comparisons'] = 3

    return {
        'seed': 26100429, 'counts': counts, 'maximum_residuals': maxima,
        'triangle_phase_radians': float(np.angle(triangle_phase)),
        'haar_ball_checks': haar_checks, 'path_integration_checks': path_results,
        'scope': 'Finite and synthetic checks support explicit constructions; proofs establish universal bounds. No empirical science or proof-assistant certification is claimed.',
    }


if __name__ == '__main__':
    print(json.dumps(run(), indent=2))