"""Kernels for the temporal Helmholtz GP of BALLAST (arXiv 2509.26005).

The surrogate is a separable, vector-output, spatio-temporal GP

    k_tHelm((s,t),(s',t')) = k_Helm(s,s') * k_time(t,t')

with k_Helm the Helmholtz kernel of Berlinghieri et al. (2023) (paper Sec. B.2)
built from two independent RBF kernels (potential Phi, stream Psi), and k_time a
Matern-3/2 kernel (paper Sec. 2.2).

Section 4.1 of the paper additionally needs the *extended* GP f = [f, d_t f]^T,
whose kernel is the 2x2 block matrix of temporal derivatives of k_tHelm. Because
k_tHelm is separable, all t-derivatives act on the Matern-3/2 factor only.

Everything is implemented **analytically**. The paper (Sec. H.2) warns that
autodiff through a Matern kernel written with a clipped distance
(`sqrt(max(sum((x-y)**2), 1e-36))`, as in GPJax) gives d^2_{tt'}k = 0 at t=t'
instead of the correct 3*sigma^2/l^2; the analytic form has no such problem.
`tests/test_kernels.py` checks these derivatives against finite differences.

Index layout
------------
Spatial-velocity blocks are flattened point-major / component-minor:
    row index of a velocity vector at point i, component c  ->  i*2 + c
The extended state adds the [f, d_t f] axis last:
    (i, c, a)  ->  i*4 + c*2 + a          with a=0 -> f, a=1 -> d_t f
"""

from __future__ import annotations

from typing import NamedTuple

import jax
import jax.numpy as jnp

SQRT3 = jnp.sqrt(3.0)


class HelmParams(NamedTuple):
    """Hyperparameters of the temporal Helmholtz GP."""

    phi_ls: jnp.ndarray  # potential kernel lengthscale
    phi_var: jnp.ndarray  # potential kernel variance
    psi_ls: jnp.ndarray  # stream kernel lengthscale
    psi_var: jnp.ndarray  # stream kernel variance
    time_ls: jnp.ndarray  # Matern-3/2 temporal lengthscale
    time_var: jnp.ndarray  # Matern-3/2 temporal variance

    def as_array(self) -> jnp.ndarray:
        return jnp.stack(
            [
                jnp.asarray(self.phi_ls),
                jnp.asarray(self.phi_var),
                jnp.asarray(self.psi_ls),
                jnp.asarray(self.psi_var),
                jnp.asarray(self.time_ls),
                jnp.asarray(self.time_var),
            ]
        )

    @staticmethod
    def from_array(a: jnp.ndarray) -> "HelmParams":
        return HelmParams(a[0], a[1], a[2], a[3], a[4], a[5])


# --------------------------------------------------------------------------
# Spatial: Helmholtz kernel
# --------------------------------------------------------------------------


def _rbf_hess(S: jnp.ndarray, S2: jnp.ndarray, ls, var) -> jnp.ndarray:
    """Mixed second derivatives of an RBF kernel.

    Returns H with H[i, j, a, b] = d^2 / (d x_a d x'_b) k(S_i, S2_j), which for
    k = var * exp(-|d|^2 / (2 l^2)), d = x - x', equals

        k * (delta_ab / l^2 - d_a d_b / l^4).
    """
    d = S[:, None, :] - S2[None, :, :]  # (N, M, 2)
    sq = jnp.sum(d**2, axis=-1)  # (N, M)
    k = var * jnp.exp(-0.5 * sq / ls**2)  # (N, M)
    eye = jnp.eye(2)
    outer = d[..., :, None] * d[..., None, :]  # (N, M, 2, 2)
    return k[..., None, None] * (eye / ls**2 - outer / ls**4)


def k_helm(S: jnp.ndarray, S2: jnp.ndarray, p: HelmParams) -> jnp.ndarray:
    """Helmholtz kernel (paper Sec. B.2), returned as (N, M, 2, 2).

    F = grad(Phi) + rot(Psi) with rot(Psi) = (d_2 Psi, -d_1 Psi), so

        K[0,0] = d^2_{x1 x1'} k_Phi + d^2_{x2 x2'} k_Psi
        K[0,1] = d^2_{x1 x2'} k_Phi - d^2_{x2 x1'} k_Psi
        K[1,0] = d^2_{x2 x1'} k_Phi - d^2_{x1 x2'} k_Psi
        K[1,1] = d^2_{x2 x2'} k_Phi + d^2_{x1 x1'} k_Psi
    """
    A = _rbf_hess(S, S2, p.phi_ls, p.phi_var)  # potential
    B = _rbf_hess(S, S2, p.psi_ls, p.psi_var)  # stream
    k00 = A[..., 0, 0] + B[..., 1, 1]
    k01 = A[..., 0, 1] - B[..., 1, 0]
    k10 = A[..., 1, 0] - B[..., 0, 1]
    k11 = A[..., 1, 1] + B[..., 0, 0]
    return jnp.stack(
        [jnp.stack([k00, k01], -1), jnp.stack([k10, k11], -1)], axis=-2
    )  # (N, M, 2, 2)


def k_helm_mat(S: jnp.ndarray, S2: jnp.ndarray, p: HelmParams) -> jnp.ndarray:
    """Helmholtz Gram matrix flattened to (2N, 2M), point-major/component-minor."""
    K = k_helm(S, S2, p)  # (N, M, 2, 2)
    N, M = K.shape[0], K.shape[1]
    return jnp.transpose(K, (0, 2, 1, 3)).reshape(2 * N, 2 * M)


# --------------------------------------------------------------------------
# Temporal: Matern-3/2 and its derivative blocks
# --------------------------------------------------------------------------


def matern32_blocks(t: jnp.ndarray, t2: jnp.ndarray, ls, var) -> jnp.ndarray:
    """Matern-3/2 kernel and its t/t' derivatives, as (N, M, 2, 2).

    With lam = sqrt(3)/l, tau = t - t':

        M[0,0] = k            = var (1 + lam|tau|) exp(-lam|tau|)
        M[0,1] = d_{t'} k     =  var lam^2 tau exp(-lam|tau|)
        M[1,0] = d_{t} k      = -var lam^2 tau exp(-lam|tau|)
        M[1,1] = d^2_{t t'} k =  var lam^2 (1 - lam|tau|) exp(-lam|tau|)

    Note M[1,1] at tau=0 is var*lam^2 = 3 var / l^2 (= 3 for var=l=1), the value
    the paper's Sec. H.2 flags as being silently zeroed by clipped-distance
    autodiff implementations. It also equals P_inf[1,1] in the SPDE formulation
    (spde.py), i.e. Var(d_t f) -- an internal consistency check of the two views.
    """
    lam = SQRT3 / ls
    tau = t[:, None] - t2[None, :]
    a = jnp.abs(tau)
    e = jnp.exp(-lam * a)
    k = var * (1.0 + lam * a) * e
    dk = var * lam**2 * tau * e  # d_{t'} k
    d2k = var * lam**2 * (1.0 - lam * a) * e
    return jnp.stack(
        [jnp.stack([k, dk], -1), jnp.stack([-dk, d2k], -1)], axis=-2
    )  # (N, M, 2, 2)


# --------------------------------------------------------------------------
# Full temporal-Helmholtz kernel (plain and extended)
# --------------------------------------------------------------------------


def k_thelm_mat(
    S: jnp.ndarray, t: jnp.ndarray, S2: jnp.ndarray, t2: jnp.ndarray, p: HelmParams
) -> jnp.ndarray:
    """Plain k_tHelm Gram matrix between (S,t) and (S2,t2). Shape (2N, 2M)."""
    KS = k_helm(S, S2, p)  # (N, M, 2, 2)
    kt = matern32_blocks(t, t2, p.time_ls, p.time_var)[..., 0, 0]  # (N, M)
    K = KS * kt[..., None, None]
    N, M = K.shape[0], K.shape[1]
    return jnp.transpose(K, (0, 2, 1, 3)).reshape(2 * N, 2 * M)


def k_ext_cross(
    S: jnp.ndarray, t: jnp.ndarray, S2: jnp.ndarray, t2: jnp.ndarray, p: HelmParams
) -> jnp.ndarray:
    """Cov between plain observations at (S,t) and the *extended* state at (S2,t2).

    Returns (2N, 4M): rows index (obs point, velocity component), columns index
    (test point, velocity component, [f, d_t f]).
    """
    KS = k_helm(S, S2, p)  # (N, M, 2, 2)
    Mt = matern32_blocks(t, t2, p.time_ls, p.time_var)  # (N, M, 2, 2)
    # observation is the f-component (a=0); test keeps both b in {f, d_t f}
    K = KS[..., :, :, None] * Mt[:, :, None, None, 0, :]  # (N, M, 2, 2, 2)
    N, M = K.shape[0], K.shape[1]
    # (N, c, M, c', b) -> (2N, 4M)
    return jnp.transpose(K, (0, 2, 1, 3, 4)).reshape(2 * N, 4 * M)


def k_ext_full(S: jnp.ndarray, t: jnp.ndarray, p: HelmParams) -> jnp.ndarray:
    """Covariance of the extended state f = [f, d_t f]^T at (S,t). Shape (4N, 4N)."""
    KS = k_helm(S, S, p)  # (N, N, 2, 2)
    Mt = matern32_blocks(t, t, p.time_ls, p.time_var)  # (N, N, 2, 2)
    K = KS[..., :, :, None, None] * Mt[:, :, None, None, :, :]  # (N,N,2,2,2,2)
    N = K.shape[0]
    # (i, c, a, j, c', b) -> (4N, 4N)
    return jnp.transpose(K, (0, 2, 4, 1, 3, 5)).reshape(4 * N, 4 * N)