"""Exactness checks for the SPDE formulation -- the machinery behind Claim 2.

These are *analytic* comparisons (not Monte Carlo): the SPDE propagation is
linear-Gaussian, so the joint law it induces over the field at future times can
be written in closed form and compared to the dense GP built from k_tHelm.
"""

import jax
import jax.numpy as jnp
import numpy as np

jax.config.update("jax_enable_x64", True)

from ballast.gp import posterior_ext_state
from ballast.kernels import HelmParams, k_helm_mat, k_thelm_mat
from ballast.spde import make_ops, temporal_matrices

P = HelmParams(
    phi_ls=0.8, phi_var=0.5, psi_ls=0.5, psi_var=0.5, time_ls=2.5, time_var=1.0
)


def _grid(n=3):
    xs = jnp.linspace(-1.0, 1.0, n)
    X, Y = jnp.meshgrid(xs, xs, indexing="ij")
    return jnp.stack([X.reshape(-1), Y.reshape(-1)], -1)


def _full_matrices(R, p, dt):
    """Explicit Phi_full = I_{2N} (x) Phi and Q_full = K_space (x) Q for the
    flattened state layout (i, c, b) -> i*4 + c*2 + b."""
    phi, q, pinf = temporal_matrices(p.time_ls, p.time_var, dt)
    Ks = k_helm_mat(R, R, p)
    n2 = Ks.shape[0]
    return (
        jnp.kron(jnp.eye(n2), phi),
        jnp.kron(Ks, q),
        jnp.kron(Ks, pinf),
    )


def _spde_joint_cov(R, p, dt, n_steps, Sigma0, mu0):
    """Joint mean/cov of the f-component at steps 0..n_steps under the SPDE.

    Propagates the Gaussian analytically:
        mu_{k+1}    = Phi_full mu_k
        Var_{k+1}   = Phi_full Var_k Phi_full^T + Q_full
        Cov(X_j,X_i) = Phi_full^{j-i} Var_i     (j > i)
    """
    Phi_f, Q_f, _ = _full_matrices(R, p, dt)
    d = Phi_f.shape[0]
    mus = [mu0]
    vars_ = [Sigma0]
    for _ in range(n_steps):
        mus.append(Phi_f @ mus[-1])
        vars_.append(Phi_f @ vars_[-1] @ Phi_f.T + Q_f)

    nT = n_steps + 1
    C = jnp.zeros((nT * d, nT * d))
    for i in range(nT):
        for j in range(nT):
            if j >= i:
                blk = jnp.linalg.matrix_power(Phi_f, j - i) @ vars_[i]
                C = C.at[j * d : (j + 1) * d, i * d : (i + 1) * d].set(blk)
            else:
                blk = vars_[j] @ jnp.linalg.matrix_power(Phi_f, i - j).T
                C = C.at[j * d : (j + 1) * d, i * d : (i + 1) * d].set(blk)
    mu = jnp.concatenate(mus)
    # select the f-component (b = 0) of every (location, velocity component)
    sel = jnp.arange(d).reshape(-1, 2)[:, 0]
    sel_all = jnp.concatenate([sel + k * d for k in range(nT)])
    return mu[sel_all], C[jnp.ix_(sel_all, sel_all)]


def test_prior_matches_dense_gp():
    """SPDE prior started at equilibrium reproduces k_tHelm exactly."""
    R = _grid(3)
    dt, n_steps = 0.37, 4
    _, _, Pinf_full = _full_matrices(R, P, dt)
    d = Pinf_full.shape[0]
    mu, C = _spde_joint_cov(R, P, dt, n_steps, Pinf_full, jnp.zeros(d))

    times = jnp.arange(n_steps + 1) * dt
    Rr = jnp.tile(R, (n_steps + 1, 1))
    tr = jnp.repeat(times, R.shape[0])
    K = k_thelm_mat(Rr, tr, Rr, tr, P)

    np.testing.assert_allclose(mu, 0.0, atol=1e-12)
    np.testing.assert_allclose(C, K, rtol=1e-8, atol=1e-10)


def test_posterior_sampling_is_exact_with_nongridded_observations():
    """The Sec. 4.1 scheme is exact: extended-state posterior at t_m + SPDE
    propagation == dense GP posterior at future times, even though the
    observations sit at non-gridded (Lagrangian) locations.

    This is the core correctness claim of BALLAST's sampler, and the reason it
    can avoid filtering over the observation locations.
    """
    key = jax.random.PRNGKey(0)
    R = _grid(3)
    sigma = 0.1
    dt, n_steps, t_m = 0.37, 4, 1.5

    # observations at random NON-grid locations, all strictly before t_m
    k1, k2, k3 = jax.random.split(key, 3)
    S_obs = jax.random.uniform(k1, (7, 2), minval=-1.4, maxval=1.4)
    t_obs = jax.random.uniform(k2, (7,), minval=0.0, maxval=t_m)
    y_obs = jax.random.normal(k3, (7, 2))

    # --- SPDE route: posterior of the extended state at t_m, then propagate
    mean, chol = posterior_ext_state(S_obs, t_obs, y_obs, R, t_m, P, sigma, jitter=0.0)
    Sigma0 = chol @ chol.T
    mu_s, C_s = _spde_joint_cov(R, P, dt, n_steps, Sigma0, mean)

    # --- dense route: GP posterior directly at R x future times
    times = t_m + jnp.arange(n_steps + 1) * dt
    Rr = jnp.tile(R, (n_steps + 1, 1))
    tr = jnp.repeat(times, R.shape[0])
    K_oo = k_thelm_mat(S_obs, t_obs, S_obs, t_obs, P) + sigma**2 * jnp.eye(14)
    K_ot = k_thelm_mat(S_obs, t_obs, Rr, tr, P)
    K_tt = k_thelm_mat(Rr, tr, Rr, tr, P)
    sol = jnp.linalg.solve(K_oo, K_ot)
    mu_d = sol.T @ y_obs.reshape(-1)
    C_d = K_tt - K_ot.T @ sol

    np.testing.assert_allclose(mu_s, mu_d, rtol=1e-6, atol=1e-9)
    np.testing.assert_allclose(C_s, C_d, rtol=1e-6, atol=1e-9)


def test_sampler_empirical_moments():
    """End-to-end: the actual sampling code path reproduces the analytic posterior."""
    from ballast.gp import sample_ext_state
    from ballast.spde import propagate

    key = jax.random.PRNGKey(2)
    R = _grid(2)
    sigma, dt, n_steps, t_m = 0.1, 0.25, 3, 1.0
    ops = make_ops(R, P, dt, jitter=0.0)

    k1, k2, k3 = jax.random.split(key, 3)
    S_obs = jax.random.uniform(k1, (5, 2), minval=-1.0, maxval=1.0)
    t_obs = jax.random.uniform(k2, (5,), minval=0.0, maxval=t_m)
    y_obs = jax.random.normal(k3, (5, 2))
    mean, chol = posterior_ext_state(S_obs, t_obs, y_obs, R, t_m, P, sigma, jitter=1e-12)

    n = 40000
    keys = jax.random.split(jax.random.PRNGKey(7), n)

    def one(k):
        ka, kb = jax.random.split(k)
        X0 = sample_ext_state(ka, mean, chol, R.shape[0])
        return propagate(X0, kb, ops, n_steps)[-1].reshape(-1)

    draws = jax.vmap(one)(keys)
    emp_mu = draws.mean(0)
    emp_cov = jnp.cov(draws.T)

    _, C_an = _spde_joint_cov(
        R, P, dt, n_steps, chol @ chol.T, mean
    )
    mu_an, _ = _spde_joint_cov(R, P, dt, n_steps, chol @ chol.T, mean)
    d = 2 * R.shape[0]
    mu_last = mu_an[-d:]
    C_last = C_an[-d:, -d:]

    # Statistical tolerances: fixed rtol/atol are meaningless here because the
    # small off-diagonal covariance entries are dominated by Monte Carlo noise.
    se_mu = jnp.sqrt(jnp.diag(C_last) / n)
    assert jnp.all(jnp.abs(emp_mu - mu_last) < 5 * se_mu), "sample mean off"

    # SE of an empirical covariance entry: sqrt((C_ii C_jj + C_ij^2)/n)
    d_ = jnp.diag(C_last)
    se_cov = jnp.sqrt((d_[:, None] * d_[None, :] + C_last**2) / n)
    z = jnp.abs(emp_cov - C_last) / se_cov
    assert z.max() < 5.0, f"max z-score {z.max():.2f} between empirical and analytic cov"