"""Regression checks for dephasing, photon loss, spectral gaps, and worked atlas."""
from pathlib import Path
import sys
import numpy as np
from scipy.linalg import expm
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "code"))
from orynthra import simplex_code, observable_closure, LinearEdge, permutation_matrix


def test_dephasing_destroys_exact_distinguishability():
    v, _, _ = simplex_code(6, 2)
    rho = []
    for j in range(6):
        pure = np.outer(v[:, j], v[:, j])
        pure[0, 1:] = 0
        pure[1:, 0] = 0
        rho.append(pure)
        assert np.isclose(np.trace(pure), 1)
    for i in range(6):
        for j in range(i):
            assert np.isclose(np.trace(rho[i] @ rho[j]), 1/18)


def test_h6_fails_one_photon_loss_knill_laflamme_condition():
    v, _, _ = simplex_code(6, 2)
    n1 = np.diag([0, 4, 3, 2, 1, 0])
    logical_error_gram = v.T @ n1 @ v
    eigenvalues = np.linalg.eigvalsh(logical_error_gram)
    assert np.allclose(eigenvalues, [0, 0, 1, 2, 3, 4])
    assert not np.allclose(logical_error_gram,
                           np.eye(6) * np.trace(logical_error_gram)/6)


def test_gap_sensitive_commutator_bound_on_seeded_unitaries():
    rng = np.random.default_rng(20261009)
    n = 6
    energies = np.cumsum(rng.uniform(0.5, 1.5, n))
    gap = np.min(np.diff(energies))
    h = np.diag(energies)
    a = permutation_matrix(list(range(1, n)) + [0])
    b = permutation_matrix([1, 0] + list(range(2, n)))
    assert np.isclose(np.linalg.norm(a @ b - b @ a, 2), np.sqrt(3))
    for t in [0, 0.001, 0.02, 0.1, 1]:
        us = []
        for _ in range(2):
            x = rng.normal(size=(n, n)) + 1j*rng.normal(size=(n, n))
            x = (x + x.conj().T)/2
            u = np.diag(np.exp(1j*rng.uniform(-1, 1, n))) @ expm(1j*t*x)
            us.append(u)
        ua, ub = us
        eta = sum(np.linalg.norm(h @ u - u @ h, "fro") for u in us)
        physical_comm = np.linalg.norm(ua @ ub - ub @ ua, 2)
        assert physical_comm <= 2*eta/gap + 1e-12
        eps = np.linalg.norm(ua-a, 2) + np.linalg.norm(ub-b, 2)
        assert np.sqrt(3) <= 2*eps + 2*eta/gap + 1e-12


def test_two_domain_worked_example():
    j = np.array([[0,1,0],[1,0,0],[0,0,1]])
    loop = np.array([[1,0,0],[0,0,1],[0,1,0]])
    obs = [np.array([[1,0,0]]), np.array([[1,0,0]])]
    edges = [LinearEdge(0,1,j), LinearEdge(1,0,j)]
    bases, _ = observable_closure(obs, edges)
    assert [len(b) for b in bases] == [2,2]
    bases, _ = observable_closure(obs, edges+[LinearEdge(0,0,loop)])
    assert [len(b) for b in bases] == [3,3]