"""noctilith/sim/quantized_field_ops.py — vectorized field operations (M12.2+)."""
from __future__ import annotations
import numpy as np
from typing import List, Tuple

SCHEMA_VERSION = "noctilith.schema.v1"
MODULE_NAME    = "noctilith.sim.quantized_field_ops"


def compute_overlap_eta(positions: np.ndarray, epsilon: float = 1e-12) -> float:
    """η(t) = 1 − |uniq β(𝒫)| / (N+ε)  — spatial overlap metric."""
    if len(positions) == 0:
        return 0.0
    beta = np.floor(positions).astype(int)
    unique = len({tuple(r) for r in beta})
    N = len(positions)
    return float(1.0 - unique / (N + epsilon))


def build_radius_kernel(radius: int, smooth: bool = True) -> np.ndarray:
    """Build ℱ! normalized Manhattan distance kernel of given radius."""
    r = max(0, int(radius))
    size = 2 * r + 1
    kernel = np.zeros((size, size, size), dtype=np.float64)
    for ix in range(size):
        for iy in range(size):
            for iz in range(size):
                dist = abs(ix-r) + abs(iy-r) + abs(iz-r)
                if dist <= r:
                    kernel[ix, iy, iz] = 1.0 / (dist + 1.0) if smooth else 1.0
    total = kernel.sum()
    if total > 0:
        kernel /= total
    else:
        kernel[r, r, r] = 1.0
    return kernel


def scatter_deposit(arr: np.ndarray, center: Tuple[int,int,int], delta: float,
                    kernel: np.ndarray, *, clamp: float = 1e9) -> None:
    """Vectorized kernel deposition at center voxel."""
    r = kernel.shape[0] // 2
    nx, ny, nz = arr.shape
    ix, iy, iz = int(round(center[0])), int(round(center[1])), int(round(center[2]))

    x0, x1 = max(0, ix-r), min(nx, ix+r+1)
    y0, y1 = max(0, iy-r), min(ny, iy+r+1)
    z0, z1 = max(0, iz-r), min(nz, iz+r+1)

    kx0 = x0 - (ix-r); kx1 = kx0 + (x1-x0)
    ky0 = y0 - (iy-r); ky1 = ky0 + (y1-y0)
    kz0 = z0 - (iz-r); kz1 = kz0 + (z1-z0)

    if x1 > x0 and y1 > y0 and z1 > z0:
        deposit = delta * kernel[kx0:kx1, ky0:ky1, kz0:kz1]
        arr[x0:x1, y0:y1, z0:z1] = np.clip(
            arr[x0:x1, y0:y1, z0:z1] + deposit, 0.0, clamp)


def batch_scatter_deposit(arr: np.ndarray, hits: list, kernel: np.ndarray,
                           *, clamp: float = 1e9) -> int:
    """β-quantize + uniq 𝒫 batch deposition. Returns unique voxel count."""
    if not hits:
        return 0
    acc: dict = {}
    for center, delta in hits:
        key = (int(round(center[0])), int(round(center[1])), int(round(center[2])))
        acc[key] = acc.get(key, 0.0) + float(delta)
    for key, delta in acc.items():
        scatter_deposit(arr, key, delta, kernel=kernel, clamp=clamp)
    return len(acc)


def vectorized_propagate(fracture_risk: np.ndarray, seeds: list, kernel: np.ndarray,
                          *, gain: float = 0.35, clamp: float = 1.0) -> np.ndarray:
    """Propagate fracture risk from seed candidates using kernel."""
    result = fracture_risk.astype(np.float64, copy=True)
    if not seeds:
        return result
    # β-quantize + uniq 𝒫 dedup on seed positions
    seen = set()
    unique_seeds = []
    for s in seeds:
        key = (int(s[0]), int(s[1]), int(s[2]))
        if key not in seen:
            seen.add(key)
            unique_seeds.append((key, float(s[3])))
    for (ix, iy, iz), risk_val in unique_seeds:
        scatter_deposit(result, (ix, iy, iz), gain * risk_val, kernel=kernel, clamp=clamp)
    return result


def vectorized_extract_candidates(fracture_risk: np.ndarray, threshold: float,
                                   max_candidates: int) -> List[Tuple[int,int,int,float]]:
    """O(n) β-quantized candidate extraction — no Python dedup loop needed."""
    flat = fracture_risk.ravel()
    flat_idx = np.flatnonzero(flat >= threshold)
    if flat_idx.size == 0:
        return []
    values = flat[flat_idx]
    n = len(values)
    if n <= max_candidates:
        top_flat = flat_idx[np.argsort(values)[::-1]]
    else:
        part     = np.argpartition(values, n - max_candidates)[-max_candidates:]
        top_flat = flat_idx[part[np.argsort(values[part])[::-1]]]
    top_flat = top_flat[:max_candidates]
    top_vals = flat[top_flat]
    ix, iy, iz = np.unravel_index(top_flat, fracture_risk.shape)
    return list(zip(ix.tolist(), iy.tolist(), iz.tolist(), top_vals.tolist()))


def vectorized_extract_candidates_local(fracture_risk: np.ndarray,
                                         threshold_field: np.ndarray,
                                         max_candidates: int) -> List[Tuple[int,int,int,float]]:
    """Per-cell threshold variant for LocalMaterialMap integration."""
    mask     = fracture_risk >= threshold_field
    flat     = fracture_risk.ravel()
    flat_idx = np.flatnonzero(mask.ravel())
    if flat_idx.size == 0:
        return []
    values = flat[flat_idx]
    n = len(values)
    if n <= max_candidates:
        top_flat = flat_idx[np.argsort(values)[::-1]]
    else:
        part     = np.argpartition(values, n - max_candidates)[-max_candidates:]
        top_flat = flat_idx[part[np.argsort(values[part])[::-1]]]
    top_flat = top_flat[:max_candidates]
    top_vals = flat[top_flat]
    ix, iy, iz = np.unravel_index(top_flat, fracture_risk.shape)
    return list(zip(ix.tolist(), iy.tolist(), iz.tolist(), top_vals.tolist()))