#!/usr/bin/env python3
"""
postprocess_to_hdf5.py — 将 OpenFOAM interFoam 原生输出转为 HDF5

直接解析 OpenFOAM 原生字段文件（不需要 foamToVTK），提取 u, v, alpha.water
场数据并写入 HDF5 格式，与训练 pipeline 兼容。

网格坐标从 system/blockMeshDict 解析（规则矩形网格）。
字段值从每个时间步目录的 U, p_rgh, alpha.water 文件解析。

输出 HDF5 格式:
  {
    'u':     float32, shape=(n_frames, nx, ny),   # X-速度
    'v':     float32, shape=(n_frames, nx, ny),   # Y-速度
    'p':     float32, shape=(n_frames, nx, ny),   # 压力 (p_rgh)
    'alpha': float32, shape=(n_frames, nx, ny),   # 相分数 (water=1)
    'x_grid': float64, shape=(nx, ny),            # X 坐标网格
    'y_grid': float64, shape=(nx, ny),            # Y 坐标网格
  }
  attrs:
    nx, ny, n_saved, times, case_name, mesh_Lx, mesh_Ly

用法:
  python3 postprocess_to_hdf5.py --input /opt/output/exp-openfoam-001
  python3 postprocess_to_hdf5.py --input /opt/output --from-processors
  python3 postprocess_to_hdf5.py --input /opt/output --from-processors --times 0,0.5,1,1.5,2

参考:
  [1] 数据加载兼容: Code/scripts/data_trans/openfoam_dataload.py 的 _read_field 方法
  [2] OpenFOAM Foundation. OpenFOAM v9 User Guide, 2021.
"""

import argparse
import os
import re
import struct
import logging

import numpy as np
import h5py


# ─── OpenFOAM 字段文件解析 ─────────────────────────────────────────────

def read_foam_field(path, n_cells=None):
    """解析 OpenFOAM 字段文件的 internalField

    支持:
      - ASCII: nonuniform List<scalar/vector> N (...数据...)
      - ASCII: uniform (值) 或 uniform 值;
      - Binary: nonuniform List<scalar/vector> N (binary marker + data)

    参数:
      path:     字段文件路径
      n_cells:  网格单元数（可选），uniform 场广播时必需；
                若未提供则尝试从同级 C 文件推断

    返回:
      np.ndarray: 标量场 shape=(N,), 向量场 shape=(N,3)
    """
    # 先以二进制模式读取，用于检测 ASCII/binary 格式
    with open(path, "rb") as fbin:
        raw = fbin.read()

    # 检测 internalField nonuniform List 头部
    # 尝试 UTF-8，失败则 latin-1
    try:
        header_text = raw.decode("utf-8")
    except UnicodeDecodeError:
        header_text = raw.decode("latin-1")

    m_head = re.search(
        r"internalField\s+nonuniform\s+List<(vector|scalar)>\s*(\d+)",
        header_text
    )
    if not m_head:
        # uniform 场处理
        return _read_uniform_field(header_text, path, n_cells)

    ftype = m_head.group(1)
    n_decl = int(m_head.group(2))

    # 找到 '(' 数据起始位置（在头部文本中的偏移 = 在 raw 中的偏移）
    paren_text_pos = header_text.find("(", m_head.end())
    if paren_text_pos == -1:
        raise ValueError(f"缺失数据起始 '(': {path}")
    # 转换为 raw 字节偏移（latin-1 下 1:1 映射）
    paren_raw_pos = len(header_text[:paren_text_pos].encode("latin-1"))

    # 检测 binary vs ASCII 格式
    # 策略: 检查 '(' 后的前 100 字节是否全部为可打印 ASCII 或空白
    # ASCII 格式: \n + 数字行 (全部为可打印字符)
    # Binary 格式: 包含非打印字节 (< 0x20 且不是 \n \r \t)
    check_start = paren_raw_pos + 1
    check_end = min(check_start + 200, len(raw))
    sample = raw[check_start:check_end]
    is_ascii = all(
        (0x20 <= b <= 0x7e) or b in (0x09, 0x0a, 0x0d)  # printable + whitespace
        for b in sample
    )

    if is_ascii:
        return _read_ascii_field(header_text, paren_text_pos, ftype, path)
    else:
        return _read_binary_field(raw, paren_text_pos, ftype, n_decl, path)


def _read_binary_field(raw, paren_pos, ftype, n_decl, path):
    """解析 OpenFOAM binary 格式字段数据

    OpenFOAM v1906 (ESI) binary format:
      '(' + N * data_bytes + ')'

    数据直接跟在 '(' 之后（无 marker / count），
    为 little-endian float64:
      vector: N * 3 * float64 (LE)
      scalar: N * float64 (LE)

    数据块以 ');\n' 结束。
    """
    # 数据起始：'(' 之后直接是二进制数据
    data_start = paren_pos + 1

    if ftype == "vector":
        n_bytes = n_decl * 3 * 8  # 3 * float64
        data = np.frombuffer(raw[data_start:data_start + n_bytes], dtype="<f8")
        data = data.reshape(n_decl, 3)
        return np.array(data, dtype=np.float64)
    else:
        n_bytes = n_decl * 8  # float64
        data = np.frombuffer(raw[data_start:data_start + n_bytes], dtype="<f8")
        return data.astype(np.float64)


def _read_ascii_field(header_text, paren_pos, ftype, path):
    """解析 OpenFOAM ASCII 格式字段数据"""
    # 提取括号块
    start = paren_pos
    m_end = re.search(r"\n\s*\)\s*;", header_text[start:])
    if m_end:
        end = start + m_end.start()
    else:
        end = header_text.rfind(")")
    block = header_text[start + 1:end].strip()
    lines = [ln.strip() for ln in block.splitlines() if ln.strip()]

    if ftype == "vector":
        arr = []
        for ln in lines:
            nums = re.findall(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", ln)
            arr.append([float(x) for x in nums[:3]])
        return np.array(arr, dtype=np.float64)
    else:
        vals = []
        for ln in lines:
            m = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", ln)
            vals.append(float(m.group(0)) if m else 0.0)
        return np.array(vals, dtype=np.float64)


def _read_uniform_field(header_text, path, n_cells=None):
    """解析 OpenFOAM uniform 格式字段"""
    m_uni = re.search(
        r"internalField\s+uniform\s*(.*?);", header_text, re.DOTALL
    )
    if not m_uni:
        raise ValueError(f"无法解析 internalField: {path}")

    token = m_uni.group(1).strip()
    if token.startswith("(") and token.endswith(")"):
        inner = token[1:-1].strip()
        items = inner.split()
    else:
        items = token.split()
    nums = []
    for it in items:
        try:
            nums.append(float(it))
        except ValueError:
            pass
    # 均匀场需要知道点数来广播
    if n_cells is not None:
        n_points = n_cells
    else:
        n_points = _try_read_n_points(path)
    if len(nums) == 3:
        vec = np.array(nums, dtype=np.float64)
        return np.tile(vec, (n_points, 1))
    elif len(nums) == 1:
        return np.full(n_points, nums[0], dtype=np.float64)
    else:
        raise ValueError(f"uniform 场解析异常 ({len(nums)} 个数值): {path}")


def _try_read_n_points(path):
    """尝试从同级目录的 C 文件或上下文推断网格点数"""
    parent = os.path.dirname(path)
    # 查找 C 文件
    c_path = os.path.join(parent, "C")
    if not os.path.exists(c_path):
        # 查找 processor* 目录（并行模式）
        for d in os.listdir(parent):
            if d.startswith("C"):
                c_path = os.path.join(parent, d)
                break
    if os.path.exists(c_path):
        with open(c_path, "r", encoding="latin-1") as f:
            txt = f.read()
        m = re.search(r"nonuniform\s+List<\w+>\s+(\d+)", txt)
        if m:
            return int(m.group(1))
    raise ValueError(f"无法推断网格点数: {path}")


# ─── blockMeshDict 解析 ─────────────────────────────────────────────────

def parse_block_mesh(case_dir):
    """从 system/blockMeshDict 解析网格参数

    返回: dict {"nx", "ny", "x0", "y0", "dx", "dy", "Lx", "Ly"}
    """
    bmd_path = os.path.join(case_dir, "system", "blockMeshDict")
    with open(bmd_path, "r", encoding="latin-1") as f:
        content = f.read()

    # 仅在 vertices 块内匹配顶点坐标，避免匹配 blocks 行的网格尺寸
    v_start = content.find("vertices")
    if v_start == -1:
        raise ValueError(f"blockMeshDict 中未找到 vertices: {bmd_path}")
    v_paren = content.find("(", v_start)
    depth, v_end = 0, v_paren
    for ci in range(v_paren, len(content)):
        if content[ci] == "(":
            depth += 1
        elif content[ci] == ")":
            depth -= 1
            if depth == 0:
                v_end = ci
                break
    v_block = content[v_paren:v_end + 1]

    verts = re.findall(
        r"\(\s*([\d.eE+\-]+)\s+([\d.eE+\-]+)\s+([\d.eE+\-]+)\s*\)", v_block
    )
    if len(verts) < 3:
        raise ValueError(f"blockMeshDict vertices 不足: {bmd_path}")
    # 取所有顶点的 min/max 作为域边界
    xs = [float(v[0]) for v in verts]
    ys = [float(v[1]) for v in verts]
    zs = [float(v[2]) for v in verts]
    x0, x1 = min(xs), max(xs)
    y0, y1 = min(ys), max(ys)
    z0, z1 = min(zs), max(zs)

    m = re.search(r"hex\s+\([^)]+\)\s+\(\s*(\d+)\s+(\d+)\s+(\d+)\s*\)", content)
    if not m:
        raise ValueError(f"无法解析 blockMeshDict blocks: {bmd_path}")
    nx, ny, nz = int(m.group(1)), int(m.group(2)), int(m.group(3))

    # 有效行维度：2D 案例用 ny，3D 薄方向案例（ny=1, nz>1）用 nz
    nrows = nz if (ny == 1 and nz > 1) else ny

    Lx = x1 - x0
    # 3D 薄方向案例用 z 方向尺寸作为 Ly
    if ny == 1 and nz > 1:
        Ly = z1 - z0
    else:
        Ly = y1 - y0
    dx = Lx / nx
    dy = Ly / nrows

    return {
        "nx": nx, "ny": nrows, "nz": nz,
        "ny_orig": ny, "nz_orig": nz,
        "x0": x0, "y0": y0,
        "dx": dx, "dy": dy,
        "Lx": Lx, "Ly": Ly,
    }


# ─── 时间步发现 ──────────────────────────────────────────────────────────

def find_time_dirs(case_dir):
    """发现 case 目录下的所有数值时间步目录（已 reconstructPar 合并）"""
    time_dirs = []
    for entry in os.listdir(case_dir):
        full_path = os.path.join(case_dir, entry)
        if os.path.isdir(full_path) and re.match(r"^\d+(\.\d+)?$", entry):
            # 确认包含 U 文件
            if os.path.exists(os.path.join(full_path, "U")):
                time_dirs.append(entry)
    time_dirs.sort(key=lambda x: float(x))
    return time_dirs


def find_processor_dirs(case_dir):
    """发现 case 下所有 processor 子目录

    每个 processor 目录必须包含至少一个数值时间目录且时间目录含 U 文件。
    返回按编号排序的目录名列表，如 ["processor0", "processor1"]。
    """
    proc_dirs = []
    for entry in os.listdir(case_dir):
        full_path = os.path.join(case_dir, entry)
        if not os.path.isdir(full_path) or not entry.startswith("processor"):
            continue
        # 验证至少有一个含 U 文件的数值时间目录
        for t_entry in os.listdir(full_path):
            t_path = os.path.join(full_path, t_entry)
            if (os.path.isdir(t_path) and
                    re.match(r"^\d+(\.\d+)?$", t_entry) and
                    os.path.exists(os.path.join(t_path, "U"))):
                proc_dirs.append(entry)
                break
    # 按 processor 编号排序（processor0 < processor1 < ...）
    proc_dirs.sort(key=lambda d: int(re.search(r"\d+", d).group()))
    return proc_dirs


def find_processor_time_dirs(case_dir, proc_dirs):
    """从 processor0 发现所有数值时间目录（并行模式）

    返回排序后的时间目录名列表（字符串）。
    """
    ref_dir = os.path.join(case_dir, proc_dirs[0])
    time_dirs = []
    for entry in os.listdir(ref_dir):
        full_path = os.path.join(ref_dir, entry)
        if os.path.isdir(full_path) and re.match(r"^\d+(\.\d+)?$", entry):
            if os.path.exists(os.path.join(full_path, "U")):
                time_dirs.append(entry)
    time_dirs.sort(key=lambda x: float(x))
    return time_dirs


def filter_time_dirs(time_dirs, times_str):
    """按用户指定的时间值筛选时间目录（最近匹配）

    参数:
      time_dirs: 可用时间目录名列表（字符串），如 ["0", "0.01", "0.02"]
      times_str: 逗号分隔的时间值，如 "0,0.5,1,1.5,2"

    返回:
      筛选后的时间目录名列表（保持原顺序）

    匹配容差: max(1e-4, 1e-6 * |t_requested|)（适配 OpenFOAM 输出精度）
    """
    requested = [float(t.strip()) for t in times_str.split(",")]
    available = [(float(t), t) for t in time_dirs]
    selected = set()
    for req in requested:
        best_dir = None
        best_diff = float("inf")
        for avail_val, avail_dir in available:
            diff = abs(avail_val - req)
            if diff < best_diff:
                best_diff = diff
                best_dir = avail_dir
        tol = max(1e-4, 1e-6 * abs(req))
        if best_dir is not None and best_diff < tol:
            selected.add(best_dir)
        else:
            logging.warning(f"  --times: 未找到接近 {req} 的时间目录 (最近距离={best_diff:.6g})")
    return [t for t in time_dirs if t in selected]


def _detect_decomposition(dict_path, nx, ny, n_procs, cells_per_proc):
    """推断 decomposePar 的 (n_x, n_y) 分解模式。

    优先从 decomposeParDict 读取；若不可用则根据 cell 数推断。
    返回 (n_x, n_y)，始终满足 n_x * n_y == n_procs。
    """
    # 尝试从文件读取
    if os.path.exists(dict_path):
        try:
            with open(dict_path, "r", encoding="latin-1") as f:
                content = f.read()
            m = re.search(r"n\s*\(\s*(\d+)\s+(\d+)\s+(\d+)\s*\)", content)
            if m:
                n_x_f, n_y_f = int(m.group(1)), int(m.group(2))
                if n_x_f * n_y_f == n_procs:
                    logging.debug(f"  decomposeParDict: n=({n_x_f},{n_y_f},1)")
                    return n_x_f, n_y_f
        except Exception:
            pass

    # 从 cell 数推断：尝试所有 (n_x, n_y) 对
    for cand_x in range(1, n_procs + 1):
        if n_procs % cand_x != 0:
            continue
        cand_y = n_procs // cand_x
        blk_nx = nx // cand_x if cand_x <= nx else None
        blk_ny = ny // cand_y if cand_y <= ny else None
        if blk_nx and blk_ny and blk_nx * blk_ny == cells_per_proc:
            return cand_x, cand_y

    # 退化：全部切 x
    logging.warning(f"  无法推断分解模式，默认 n=({n_procs},1,1)")
    return n_procs, 1


def _read_cell_addressing(path):
    """读取 cellProcAddressing 文件，返回全局 cell ID 数组。

    格式: ASCII labelList（每行一个整数），在 ( 和 ) 之间。
    """
    with open(path, "r", encoding="latin-1") as f:
        text = f.read()
    lines = text.split("\n")
    data_started = False
    ids = []
    for line in lines:
        s = line.strip()
        if s == "(":
            data_started = True
            continue
        if s == ")":
            break
        if data_started and s:
            ids.append(int(s))
    return np.array(ids, dtype=np.int64)


def _read_proc_field(path, ftype, pnx, pny):
    """读取单个 processor 的标量或向量场，返回 reshape 后的数组。

    标量: shape (pny, pnx)  向量: shape (pny, pnx, 3)
    """
    raw = read_foam_field(path, n_cells=pnx * pny)
    if ftype == "vector":
        return raw.reshape(pny, pnx, 3)
    else:
        return raw.reshape(pny, pnx)


# ─── 主处理流程 ──────────────────────────────────────────────────────────

def process_case(case_dir, h5_path, crop=1.0, from_processors=False, times=None,
                 case_type=None):
    """处理单个 case: 读取所有时间步，写入 HDF5

    参数:
      case_dir:        OpenFOAM case 根目录
      h5_path:         HDF5 输出文件完整路径
      crop:            水槽尾部裁剪比例 (0-1)，1.0=不裁剪
      from_processors: 若为 True，从 processor*/ 目录直接读取（跳过 reconstructPar）
      times:           逗号分隔的时间值字符串（如 "0,0.5,1"），None 表示全部时间步
      case_type:       案例类型名（如 "dam_break"），用于 HDF5 case_name 属性
    """
    case_name = case_type if case_type else os.path.basename(case_dir)

    # 解析网格
    mesh = parse_block_mesh(case_dir)
    nx, ny = mesh["nx"], mesh["ny"]
    # x-z 平面案例 (ny_orig=1, nz>1): 垂直速度是 Uz (index=2) 而非 Uy (index=1)
    is_xz = (mesh.get("ny_orig", ny) == 1 and mesh.get("nz_orig", 1) > 1)
    v_comp = 2 if is_xz else 1
    logging.info(f"  网格: {nx}x{ny}, 域尺寸: {mesh['Lx']:.4f}x{mesh['Ly']:.4f} m"
                 f"{' (x-z plane)' if is_xz else ''}")

    # 生成 cell 中心坐标网格 (indexing='ij' -> shape (nx, ny), 匹配旧数据约定)
    x_1d = np.array([mesh["x0"] + (i + 0.5) * mesh["dx"] for i in range(nx)])
    y_1d = np.array([mesh["y0"] + (j + 0.5) * mesh["dy"] for j in range(ny)])
    x_grid, y_grid = np.meshgrid(x_1d, y_1d, indexing="ij")  # shape (nx, ny)

    if from_processors:
        # -- 并行模式：从 processor*/ 目录直接读取 --
        proc_dirs = find_processor_dirs(case_dir)
        if not proc_dirs:
            logging.warning(f"  未发现 processor 目录: {case_dir}")
            return
        n_procs = len(proc_dirs)
        logging.info(f"  并行模式: {n_procs} 个 processor")

        # 发现时间步（从 processor0）并按 --times 筛选
        time_dirs = find_processor_time_dirs(case_dir, proc_dirs)
        if times is not None:
            time_dirs = filter_time_dirs(time_dirs, times)
        n_frames = len(time_dirs)
        if n_frames == 0:
            logging.warning(f"  未发现时间步: {case_dir}")
            return
        logging.info(f"  时间步数: {n_frames}")

        # 读取 cellProcAddressing 获取每个 processor 的 local→global 映射
        total_mesh = nx * ny
        proc_global_ids = {}  # {proc_dir: np.array of global cell IDs}
        for proc_dir in proc_dirs:
            addr_path = os.path.join(case_dir, proc_dir, "constant", "polyMesh",
                                     "cellProcAddressing")
            if os.path.exists(addr_path):
                proc_global_ids[proc_dir] = _read_cell_addressing(addr_path)
            else:
                proc_global_ids[proc_dir] = None

        # 检测每个 processor 的实际 cell 数（从第一个非 uniform 时间步）
        detect_t = time_dirs[1] if len(time_dirs) > 1 else time_dirs[0]
        first_u_path = os.path.join(case_dir, proc_dirs[0], detect_t, "U")
        first_u = read_foam_field(first_u_path, n_cells=nx * ny)
        first_n_cells = first_u.shape[0]

        if first_n_cells >= total_mesh:
            # 每个 processor 有完整 mesh（decomposePar 未实际分割）
            logging.info(f"  processor0 有完整 mesh ({first_n_cells} cells)，仅读 processor0")
            proc_dirs = [proc_dirs[0]]
            proc_ncells_list = [total_mesh]
            use_addressing = False
            proc_local_blocks = [(nx, ny, slice(0, ny), slice(0, nx))]

        elif all(v is not None for v in proc_global_ids.values()):
            # 使用 cellProcAddressing 做精确映射
            use_addressing = True
            proc_ncells_list = [len(proc_global_ids[pd]) for pd in proc_dirs]
            logging.info(f"  使用 cellProcAddressing 精确映射 ({n_procs} 个 processor)")

        else:
            # 回退: 从 decomposeParDict 推断分解模式
            use_addressing = False
            decomp_dict_path = os.path.join(case_dir, "system", "decomposeParDict")
            n_x, n_y = _detect_decomposition(decomp_dict_path, nx, ny, n_procs,
                                              first_n_cells)

            proc_local_blocks = []
            if n_y > 1:
                logging.info(f"  2D 分解: n=({n_x},{n_y},1)")
                block_nx = nx // n_x
                block_ny = ny // n_y
                for pid in range(n_procs):
                    gx = pid % n_x
                    gy = pid // n_x
                    bx0, by0 = gx * block_nx, gy * block_ny
                    bx1 = min(bx0 + block_nx, nx) if gx < n_x - 1 else nx
                    by1 = min(by0 + block_ny, ny) if gy < n_y - 1 else ny
                    proc_local_blocks.append(
                        (bx1 - bx0, by1 - by0, slice(by0, by1), slice(bx0, bx1))
                    )
            else:
                logging.info(f"  1D x-分解: n=({n_x},1,1)")
                boundaries = [0]
                base, rem = divmod(nx, n_x)
                for p in range(n_x):
                    boundaries.append(boundaries[-1] + base + (1 if p < rem else 0))
                for pid in range(n_procs):
                    pnx = boundaries[pid + 1] - boundaries[pid]
                    proc_local_blocks.append(
                        (pnx, ny, slice(0, ny), slice(boundaries[pid], boundaries[pid + 1]))
                    )
            proc_ncells_list = [bnx * bny for bnx, bny, _, _ in proc_local_blocks]

        # 预分配数组 — shape (n_frames, ny, nx)，与 OpenFOAM cell-major 行主序一致
        u_full = np.zeros((n_frames, ny, nx), dtype=np.float32)
        v_full = np.zeros((n_frames, ny, nx), dtype=np.float32)
        p_full = np.zeros((n_frames, ny, nx), dtype=np.float32)
        alpha_full = np.zeros((n_frames, ny, nx), dtype=np.float32)
        times_arr = np.zeros(n_frames, dtype=np.float64)

        for i, t_dir in enumerate(time_dirs):
            times_arr[i] = float(t_dir)

            for j, proc_dir in enumerate(proc_dirs):
                proc_base = os.path.join(case_dir, proc_dir, t_dir)
                n_local = proc_ncells_list[j]

                if use_addressing:
                    # cellProcAddressing 精确映射
                    global_ids = proc_global_ids[proc_dir]
                    g_ix = global_ids % nx
                    g_iy = global_ids // nx

                    u_raw = read_foam_field(os.path.join(proc_base, "U"),
                                            n_cells=n_local)
                    if u_raw.ndim == 1:
                        u_raw = u_raw.reshape(-1, 3)
                    u_full[i, g_iy, g_ix] = u_raw[:, 0].astype(np.float32)
                    v_full[i, g_iy, g_ix] = u_raw[:, v_comp].astype(np.float32)

                    p_raw = read_foam_field(os.path.join(proc_base, "p_rgh"),
                                            n_cells=n_local)
                    p_full[i, g_iy, g_ix] = p_raw.astype(np.float32)

                    a_raw = read_foam_field(os.path.join(proc_base, "alpha.water"),
                                            n_cells=n_local)
                    alpha_full[i, g_iy, g_ix] = a_raw.astype(np.float32)
                else:
                    # 块放置（已知块位置）
                    pnx, pny, rows, cols = proc_local_blocks[j]
                    u_local = _read_proc_field(os.path.join(proc_base, "U"),
                                               "vector", pnx, pny)
                    u_full[i, rows, cols] = u_local[:, :, 0].astype(np.float32)
                    v_full[i, rows, cols] = u_local[:, :, 1].astype(np.float32)

                    p_local = _read_proc_field(os.path.join(proc_base, "p_rgh"),
                                               "scalar", pnx, pny)
                    p_full[i, rows, cols] = p_local.astype(np.float32)

                    a_local = _read_proc_field(os.path.join(proc_base, "alpha.water"),
                                               "scalar", pnx, pny)
                    alpha_full[i, rows, cols] = a_local.astype(np.float32)

        # 转置为 (nx, ny) 输出约定
        u_data = u_full.transpose(0, 2, 1)     # (n_frames, nx, ny)
        v_data = v_full.transpose(0, 2, 1)
        p_data = p_full.transpose(0, 2, 1)
        alpha_data = alpha_full.transpose(0, 2, 1)
        times = times_arr

    else:
        # -- 重构模式：从根目录读取（原有逻辑） --
        time_dirs = find_time_dirs(case_dir)
        if times is not None:
            time_dirs = filter_time_dirs(time_dirs, times)
        n_frames = len(time_dirs)
        if n_frames == 0:
            logging.warning(f"  未发现时间步: {case_dir}")
            return
        logging.info(f"  时间步数: {n_frames}")

        # 预分配数组 - shape (T, nx, ny), 与旧数据 (data_generation.py) 轴序一致
        u_data = np.zeros((n_frames, nx, ny), dtype=np.float32)
        v_data = np.zeros((n_frames, nx, ny), dtype=np.float32)
        p_data = np.zeros((n_frames, nx, ny), dtype=np.float32)
        alpha_data = np.zeros((n_frames, nx, ny), dtype=np.float32)
        times = np.zeros(n_frames, dtype=np.float64)

        for i, t_dir in enumerate(time_dirs):
            t_path = os.path.join(case_dir, t_dir)
            times[i] = float(t_dir)

            # 读取速度场 U (vector)
            expected = ny * nx
            u_raw = read_foam_field(os.path.join(t_path, "U"), n_cells=expected)
            # u_raw shape: (nx*ny, 3)，行主序 (j*nx+i)
            if u_raw.shape[0] != expected:
                raise ValueError(
                    f"U field size {u_raw.shape[0]} != mesh {expected} ({ny}x{nx})"
                )
            # reshape -> (ny, nx, 3)，再转置为 (nx, ny, 3) 匹配旧约定
            u_vec = u_raw.reshape(ny, nx, 3).transpose(1, 0, 2)
            u_data[i] = u_vec[:, :, 0].astype(np.float32)  # X-速度
            v_data[i] = u_vec[:, :, v_comp].astype(np.float32)  # 垂直速度 (Y 或 Z)

            # 读取压力场 p_rgh (scalar)
            p_raw = read_foam_field(os.path.join(t_path, "p_rgh"), n_cells=expected)
            p_data[i] = p_raw.reshape(ny, nx).T.astype(np.float32)

            # 读取相分数 alpha.water (scalar)
            alpha_raw = read_foam_field(os.path.join(t_path, "alpha.water"), n_cells=expected)
            alpha_data[i] = alpha_raw.reshape(ny, nx).T.astype(np.float32)

    # 裁剪水槽尾部（仅对波浪案例有效）
    if crop < 1.0:
        nx_crop = max(1, int(nx * crop))
        logging.info(f"  裁剪: nx {nx} → {nx_crop} (crop={crop})")
        u_data = u_data[:, :nx_crop, :]
        v_data = v_data[:, :nx_crop, :]
        p_data = p_data[:, :nx_crop, :]
        alpha_data = alpha_data[:, :nx_crop, :]
        x_grid = x_grid[:nx_crop, :]
        y_grid = y_grid[:nx_crop, :]
        nx = nx_crop

    # 写入 HDF5（格式兼容训练 pipeline 的 MultiphaseFlowDataset）
    os.makedirs(os.path.dirname(h5_path), exist_ok=True)
    with h5py.File(h5_path, "w") as f:
        # 训练 pipeline 核心数据集: u, v, alpha, p
        f.create_dataset("u", data=u_data, compression="gzip", compression_opts=4)
        f.create_dataset("v", data=v_data, compression="gzip", compression_opts=4)
        f.create_dataset("p", data=p_data, compression="gzip", compression_opts=4)
        f.create_dataset("alpha", data=alpha_data, compression="gzip", compression_opts=4)
        f.create_dataset("x_grid", data=x_grid)
        f.create_dataset("y_grid", data=y_grid)
        # 元数据
        f.attrs["nx"] = nx
        f.attrs["ny"] = ny
        f.attrs["n_saved"] = n_frames
        f.attrs["times"] = times
        f.attrs["case_name"] = case_name
        f.attrs["mesh_Lx"] = mesh["Lx"]
        f.attrs["mesh_Ly"] = mesh["Ly"]

    logging.info(f"  HDF5 写入: {h5_path}")
    logging.info(f"  u 范围: [{u_data.min():.6f}, {u_data.max():.6f}]")
    logging.info(f"  alpha 范围: [{alpha_data.min():.6f}, {alpha_data.max():.6f}]")


def discover_trajectories(input_dir):
    """自动发现 input_dir 下的所有 trajectory，按 case_type 组织

    发现逻辑: {input_dir}/{case_type}/tNN/ 目录中含 Allrun + system/blockMeshDict

    返回:
      dict: {case_type: [(traj_idx, case_dir), ...], ...}
    """
    cases_by_type = {}
    case_types = ["dam_break", "rising_bubble", "droplet_impact", "stokes_wave"]

    for case_type in case_types:
        case_type_dir = os.path.join(input_dir, case_type)
        if not os.path.isdir(case_type_dir):
            continue
        trajectories = []
        for entry in sorted(os.listdir(case_type_dir)):
            traj_dir = os.path.join(case_type_dir, entry)
            if not os.path.isdir(traj_dir):
                continue
            # 检查是否为有效的 OpenFOAM case
            if (os.path.exists(os.path.join(traj_dir, "Allrun")) and
                os.path.exists(os.path.join(traj_dir, "system", "blockMeshDict"))):
                # 从目录名提取索引 (t00 → 0, t01 → 1, ...)
                try:
                    traj_idx = int(entry.lstrip("t"))
                except ValueError:
                    traj_idx = len(trajectories)
                trajectories.append((traj_idx, traj_dir))
        if trajectories:
            cases_by_type[case_type] = sorted(trajectories, key=lambda x: x[0])

    return cases_by_type


def main():
    parser = argparse.ArgumentParser(
        description="OpenFOAM interFoam 原生输出 → HDF5 转换"
    )
    parser.add_argument(
        "--input", type=str, default="/opt/output",
        help="包含 case 目录的根目录（含 dam_break/, rising_bubble/, droplet_impact/ 子目录）"
    )
    parser.add_argument(
        "--output", type=str, default=None,
        help="HDF5 输出根目录（默认 = 输入目录）"
    )
    parser.add_argument(
        "--crop", type=float, default=1.0,
        help="水槽尾部裁剪比例 (0-1)，默认 1.0 不裁剪。仅对波浪案例有效，裁剪 x 方向尾部区域"
    )
    parser.add_argument(
        "--from-processors", action="store_true", default=False,
        help="从 processor*/ 目录直接读取，跳过 reconstructPar（并行模式）"
    )
    parser.add_argument(
        "--times", type=str, default=None,
        help="仅处理指定时间值（逗号分隔，如 '0,0.5,1,1.5,2'），默认全部时间步"
    )
    parser.add_argument(
        "-v", "--verbose", action="store_true",
        help="详细日志输出"
    )
    args = parser.parse_args()

    logging.basicConfig(
        level=logging.DEBUG if args.verbose else logging.INFO,
        format="%(asctime)s [%(levelname)s] %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
    )

    output_root = args.output if args.output else args.input

    # 发现所有 trajectory
    cases_by_type = discover_trajectories(args.input)

    if not cases_by_type:
        logging.warning(f"未发现任何 case 目录: {args.input}")
        return

    total = sum(len(v) for v in cases_by_type.values())
    logging.info(f"发现 {total} 个 trajectory: " +
                 ", ".join(f"{k}={len(v)}" for k, v in cases_by_type.items()))

    success = 0
    failed = 0

    for case_type, trajectories in cases_by_type.items():
        # 输出目录: {output_root}/{case_type}/traj_{NNNN}.h5
        h5_dir = os.path.join(output_root, case_type)
        os.makedirs(h5_dir, exist_ok=True)

        for traj_idx, case_dir in trajectories:
            # HDF5 文件名: traj_{NNNN}.h5（兼容 MultiphaseFlowDataset）
            h5_path = os.path.join(h5_dir, f"traj_{traj_idx:04d}.h5")
            logging.info(f"处理: {case_type}/t{traj_idx:02d} → {os.path.basename(h5_path)}")
            try:
                process_case(case_dir, h5_path, crop=args.crop, from_processors=args.from_processors, times=args.times, case_type=case_type)
                success += 1
            except Exception as e:
                logging.error(f"  处理失败: {e}")
                failed += 1
                if args.verbose:
                    import traceback
                    traceback.print_exc()

    logging.info(f"=== 后处理完成: 成功 {success}, 失败 {failed} ===")


if __name__ == "__main__":
    main()