"""
Anisotropic Network Model (ANM) for conformational path interpolation.

From-scratch implementation using scipy eigendecomposition.
Projects the apo→holo displacement onto low-frequency normal modes
to create physically motivated interpolation paths.
"""

import numpy as np
from scipy.linalg import eigh


def compute_anm_modes(ca_coords, cutoff=15.0, n_modes=10):
    """
    Build elastic network Hessian and compute normal modes via eigendecomposition.

    Args:
        ca_coords: [N, 3] CA atom coordinates
        cutoff: distance cutoff for spring connections (Angstroms)
        n_modes: number of non-trivial modes to return

    Returns:
        eigenvalues: [n_modes] array of eigenvalues (force constants)
        eigenvectors: [n_modes, N, 3] mode displacement vectors
    """
    N = len(ca_coords)
    if N < 4:
        return np.zeros(n_modes), np.zeros((n_modes, N, 3))

    # Build 3N x 3N Hessian with uniform spring constant (gamma=1)
    H = np.zeros((3 * N, 3 * N), dtype=np.float64)

    for i in range(N):
        for j in range(i + 1, N):
            diff = ca_coords[j] - ca_coords[i]
            dist = np.linalg.norm(diff)
            if dist > cutoff or dist < 1e-6:
                continue

            # Outer product of unit displacement vector
            unit = diff / dist
            block = np.outer(unit, unit)  # [3, 3]

            # Off-diagonal: H[i,j] = -gamma * (r_ij ⊗ r_ij) / |r_ij|^2
            # With uniform gamma=1 and unit vectors, this simplifies to:
            ii, jj = 3 * i, 3 * j
            H[ii:ii+3, jj:jj+3] = -block
            H[jj:jj+3, ii:ii+3] = -block

            # Diagonal: accumulate
            H[ii:ii+3, ii:ii+3] += block
            H[jj:jj+3, jj:jj+3] += block

    # Eigendecompose — first 6 modes are trivial (3 translation + 3 rotation)
    n_total = min(6 + n_modes, 3 * N)
    eigenvalues, eigvecs = eigh(H, subset_by_index=[0, n_total - 1])

    # Skip the 6 trivial zero-frequency modes
    start = min(6, len(eigenvalues) - 1)
    n_available = len(eigenvalues) - start
    n_return = min(n_modes, n_available)

    evals = eigenvalues[start:start + n_return]
    evecs = eigvecs[:, start:start + n_return]  # [3N, n_return]

    # Reshape eigenvectors to [n_modes, N, 3]
    mode_vectors = np.zeros((n_return, N, 3))
    for k in range(n_return):
        mode_vectors[k] = evecs[:, k].reshape(N, 3)

    # Pad if fewer modes available than requested
    if n_return < n_modes:
        pad_evals = np.zeros(n_modes)
        pad_evals[:n_return] = evals
        pad_modes = np.zeros((n_modes, N, 3))
        pad_modes[:n_return] = mode_vectors
        return pad_evals, pad_modes

    return evals, mode_vectors


def _kabsch_align(mobile_ca, ref_ca):
    """Kabsch alignment of mobile onto ref (CA atoms only)."""
    t_mobile = mobile_ca.mean(axis=0)
    t_ref = ref_ca.mean(axis=0)

    m = mobile_ca - t_mobile
    r = ref_ca - t_ref

    H = m.T @ r
    U, S, Vt = np.linalg.svd(H)
    d = np.linalg.det(Vt.T @ U.T)
    sign = np.array([1.0, 1.0, np.sign(d)])
    R = Vt.T @ np.diag(sign) @ U.T

    return R, t_mobile, t_ref


def _reconstruct_oxygen(coords):
    """Reconstruct O atom from N, CA, C with ideal C=O geometry."""
    C_pos = coords[:, 2, :]
    CA_pos = coords[:, 1, :]
    C_CA = C_pos - CA_pos
    C_CA_norm = np.linalg.norm(C_CA, axis=-1, keepdims=True)
    C_CA_norm = np.maximum(C_CA_norm, 1e-8)
    O_pos = C_pos + (C_CA / C_CA_norm) * 1.24
    coords[:, 3, :] = O_pos
    return coords


def anm_backbone_path(coords_x0, coords_x1, mask_x0, mask_x1,
                       n_frames=5, n_modes=10, cutoff=15.0):
    """
    Interpolate backbone along dominant ANM modes from X0 toward X1.

    Low-frequency modes capture global domain motions (e.g., CaM hinge bending),
    creating physically informed paths where large-scale motions precede local
    adjustments.

    Args:
        coords_x0: [N0, 4, 3] backbone coords (N, CA, C, O) for apo state
        coords_x1: [N1, 4, 3] backbone coords for holo state
        mask_x0: [N0] bool
        mask_x1: [N1] bool
        n_frames: number of intermediate frames (excluding endpoints)
        n_modes: number of ANM modes to use for projection
        cutoff: ANM spring cutoff in Angstroms

    Returns:
        path_frames: list of (coords_tau, mask_tau, tau) tuples
            Same interface as interpolate_backbone_path
    """
    n_common = min(len(coords_x0), len(coords_x1))
    c0 = coords_x0[:n_common].copy()
    c1 = coords_x1[:n_common].copy()
    m0 = mask_x0[:n_common]
    m1 = mask_x1[:n_common]

    common_mask = m0 & m1
    if common_mask.sum() < 5:
        return []

    # Kabsch-align X0 onto X1 using valid CA atoms
    ca0 = c0[common_mask, 1, :]
    ca1 = c1[common_mask, 1, :]
    R, t_mobile, t_ref = _kabsch_align(ca0, ca1)

    # Apply alignment to all X0 backbone atoms
    flat0 = c0.reshape(-1, 3)
    aligned0 = (flat0 - t_mobile) @ R.T + t_ref
    c0_aligned = aligned0.reshape(n_common, 4, 3)

    # Compute apo→holo displacement (CA atoms, valid residues only)
    ca0_aligned = c0_aligned[common_mask, 1, :]  # [N_valid, 3]
    ca1_valid = c1[common_mask, 1, :]

    displacement = ca1_valid - ca0_aligned  # [N_valid, 3]

    # Compute ANM modes of the aligned apo structure
    eigenvalues, mode_vectors = compute_anm_modes(
        ca0_aligned, cutoff=cutoff, n_modes=n_modes
    )  # mode_vectors: [n_modes, N_valid, 3]

    # Project displacement onto each mode
    # d_k = sum_i mode_k[i] . displacement[i]
    projections = np.zeros(n_modes)
    for k in range(n_modes):
        projections[k] = np.sum(mode_vectors[k] * displacement)

    # Reconstruct mode-projected displacement: d_mode = sum_k d_k * mode_k
    mode_displacement = np.zeros_like(displacement)  # [N_valid, 3]
    for k in range(n_modes):
        mode_displacement += projections[k] * mode_vectors[k]

    # Residual displacement not captured by modes
    residual = displacement - mode_displacement

    # Generate intermediate frames
    taus = np.linspace(0, 1, n_frames + 2)[1:-1]
    path_frames = []

    for tau in taus:
        # Apply mode-projected + residual displacement at each tau
        # Mode component applies smoothly; residual is linear
        ca_interp = ca0_aligned + tau * mode_displacement + tau * residual

        # Build full backbone by interpolating all 4 atom types
        coords_tau = (1.0 - tau) * c0_aligned + tau * c1
        # Override CA positions with ANM-interpolated values
        coords_tau[common_mask, 1, :] = ca_interp

        # Adjust N, C positions relative to CA shift
        # The N/CA/C triangle is preserved by blending the ANM CA shift
        # with the linear interpolation of N and C
        ca_shift = ca_interp - ((1.0 - tau) * ca0_aligned + tau * ca1_valid)
        coords_tau[common_mask, 0, :] += ca_shift  # N atoms
        coords_tau[common_mask, 2, :] += ca_shift  # C atoms

        # Reconstruct O from N, CA, C
        coords_tau = _reconstruct_oxygen(coords_tau)

        path_frames.append((
            coords_tau.astype(np.float32),
            common_mask.copy(),
            float(tau),
        ))

    return path_frames