"""从开环扫点 CSV 拟合 A_eff(x) 表，输出 valve_model.json。

用法：
    python identify_valve.py --csv open_loop_data/open_loop_xxx.csv \\
        --out valve_model.json [--tail 20] [--plot]

流程：
1. 按 step_index 分组，每组取末尾 --tail 个采样点平均，得到稳态 (x, Q, P1, P2)；
2. 对每个稳态点反解 A_eff = Q / (P1_abs * F(r))；
3. 按行程升序构表，做单调性检查；
4. 用 ValveModel 存成 JSON，并打印摘要（可选画诊断图）。
"""

import argparse
import csv
import math
from pathlib import Path

import config
from valve_model import CRITICAL_RATIO, ValveModel, abs_pressure, f_ratio

DEFAULT_TAIL = 20


def _to_float(text):
    """把 CSV 单元格解析成有限浮点数；空/非法/非有限返回 None。"""
    if text is None:
        return None
    text = str(text).strip()
    if text == "":
        return None
    try:
        value = float(text)
    except (TypeError, ValueError):
        return None
    return value if math.isfinite(value) else None


def _average(values):
    """返回非 None 数值的平均；没有有效值时返回 None。"""
    valid = [value for value in values if value is not None]
    return None if not valid else sum(valid) / len(valid)


def load_steady_points(csv_path, tail=DEFAULT_TAIL):
    """提取每个 step_index 的稳态均值，返回按 step 升序的 (x, q, p1, p2) 列表。"""
    rows_by_step = {}
    with open(csv_path, "r", newline="", encoding="utf-8-sig") as file:
        for row in csv.DictReader(file):
            step_text = (row.get("step_index") or "").strip()
            if step_text == "":
                continue
            try:
                step = int(float(step_text))
            except (TypeError, ValueError):
                continue
            rows_by_step.setdefault(step, []).append(row)

    points = []
    for step in sorted(rows_by_step):
        rows = rows_by_step[step]
        rows.sort(key=lambda r: _to_float(r.get("time_s")) or 0.0)
        tail_rows = rows[-tail:] if tail > 0 else rows

        x = _average(_to_float(r.get("motor_position")) for r in tail_rows)
        q = _average(_to_float(r.get("flow_after_slm")) for r in tail_rows)
        p1 = _average(_to_float(r.get("pressure_before_kpa")) for r in tail_rows)
        p2 = _average(_to_float(r.get("pressure_after_kpa")) for r in tail_rows)

        if None in (x, q, p1, p2):
            continue
        points.append((x, q, p1, p2))
    return points


def compute_area_table(steady_points):
    """把稳态点换算成 (x, A_eff) 单调表，并返回丢弃点与阻塞/亚声速统计。

    返回 ``(table, dropped, stats)``。``table`` 为按 x 升序的 (x, A_eff) 列表，
    只保留最长的单调连续段；``dropped`` 为因非单调被丢弃的点。
    """
    area_points = []
    choked = subsonic = 0
    for x, q, p1, p2 in steady_points:
        p1_abs = abs_pressure(p1)
        p2_abs = abs_pressure(p2)
        if p1_abs <= 0.0:
            continue
        r = p2_abs / p1_abs
        f = f_ratio(r)
        denom = p1_abs * f
        if denom <= 0.0 or q < 0.0:
            continue
        if r <= CRITICAL_RATIO:
            choked += 1
        else:
            subsonic += 1
        area_points.append((x, q / denom))

    area_points.sort(key=lambda item: item[0])
    ys = [a for _, a in area_points]

    if _is_monotonic(ys):
        table = area_points
        dropped = []
    else:
        start, end = _longest_monotonic_run(ys)
        table = area_points[start:end + 1]
        dropped = area_points[:start] + area_points[end + 1:]

    stats = {"choked": choked, "subsonic": subsonic, "total": choked + subsonic}
    return table, dropped, stats


def _is_monotonic(ys):
    """序列是否单调（允许相等，但不允许中途反向）。"""
    direction = 0
    for i in range(1, len(ys)):
        delta = ys[i] - ys[i - 1]
        if delta == 0:
            continue
        sign = 1 if delta > 0 else -1
        if direction == 0:
            direction = sign
        elif direction != sign:
            return False
    return True


def _longest_monotonic_run(ys):
    """返回最长单调连续段的闭区间下标 ``(start, end)``。"""
    best_start = best_end = 0
    for start in range(len(ys)):
        direction = 0
        end = start
        for j in range(start + 1, len(ys)):
            delta = ys[j] - ys[j - 1]
            if delta == 0:
                end = j
                continue
            sign = 1 if delta > 0 else -1
            if direction == 0:
                direction = sign
                end = j
            elif direction == sign:
                end = j
            else:
                break
        if end - start > best_end - best_start:
            best_start, best_end = start, end
    return best_start, best_end


def identify(csv_path, tail=DEFAULT_TAIL, out_path=None):
    """端到端辨识；返回 ``(model, steady_points, table, dropped, stats)``。"""
    steady = load_steady_points(csv_path, tail)
    table, dropped, stats = compute_area_table(steady)
    if len(table) < 2:
        raise ValueError(
            f"有效稳态点不足（{len(table)} 个），无法构表。请确认 CSV 的 "
            "pressure_before_kpa / pressure_after_kpa / flow_after_slm 列读数合理。"
        )
    model = ValveModel(
        table,
        config.MOTOR_OPEN_POSITION,
        config.MOTOR_CLOSED_POSITION,
    )
    if out_path:
        model.save(out_path)
    return model, steady, table, dropped, stats


def create_diagnostic_plot(steady_points, table, dropped, image_path):
    """画 x vs A_eff（散点+插值线）与 x vs Q_ss（散点）两张子图。"""
    import matplotlib

    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    xs = [x for x, _ in table]
    areas = [a for _, a in table]

    figure, axes = plt.subplots(2, 1, figsize=(10, 8), constrained_layout=True)
    figure.suptitle("Identified valve characteristic")

    axes[0].scatter(xs, areas, color="#1565C0", label="measured A_eff")
    axes[0].plot(xs, areas, color="#1565C0", linewidth=1.2)
    if dropped:
        axes[0].scatter(
            [x for x, _ in dropped],
            [a for _, a in dropped],
            color="#D84315",
            marker="x",
            label="dropped (non-monotonic)",
        )
    axes[0].set_ylabel("A_eff (slm/kPa)")
    axes[0].set_title("Effective area vs stroke")
    axes[0].legend(loc="best")
    axes[0].grid(True, alpha=0.3, linestyle="--")

    qxs = [x for x, _, _, _ in steady_points]
    qs = [q for _, q, _, _ in steady_points]
    axes[1].scatter(qxs, qs, color="#2E7D32", label="measured Q_ss")
    axes[1].set_ylabel("Flow (SLM)")
    axes[1].set_xlabel("Motor stroke x")
    axes[1].set_title("Steady flow vs stroke")
    axes[1].legend(loc="best")
    axes[1].grid(True, alpha=0.3, linestyle="--")

    figure.savefig(image_path, dpi=config.PLOT_DPI, bbox_inches="tight")
    plt.close(figure)


def parse_args(argv=None):
    parser = argparse.ArgumentParser(
        description="从开环扫点 CSV 拟合 A_eff(x) 阀特性表"
    )
    parser.add_argument("--csv", required=True, help="开环扫点 CSV 路径")
    parser.add_argument("--out", default="valve_model.json", help="输出 JSON 路径")
    parser.add_argument(
        "--tail", type=int, default=DEFAULT_TAIL,
        help=f"每组末尾用于平均的采样点数（默认 {DEFAULT_TAIL}）",
    )
    parser.add_argument("--plot", action="store_true", help="生成诊断图 PNG")
    args = parser.parse_args(argv)
    if args.tail < 1:
        parser.error("--tail 必须 >= 1")
    return args


def main(argv=None):
    args = parse_args(argv)
    csv_path = Path(args.csv)
    if not csv_path.exists():
        print(f"CSV 不存在：{csv_path}")
        return 2

    try:
        model, steady, table, dropped, stats = identify(
            csv_path, tail=args.tail, out_path=args.out
        )
    except (ValueError, OSError) as exc:
        print(f"辨识失败：{exc}")
        return 2

    print(
        f"读取稳态点 {len(steady)} 个；阻塞 {stats['choked']} 个，"
        f"亚声速 {stats['subsonic']} 个，合计 {stats['total']} 个。"
    )
    print(
        f"有效行程范围 {table[0][0]:.1f} ~ {table[-1][0]:.1f}"
        f"（{len(table)} 点）。"
    )

    if dropped:
        print(
            "警告：A_eff(x) 非单调，以下点被丢弃"
            "（建议缩小扫点范围到单调段重扫）。"
        )
    print("行程 x -> A_eff：")
    for x, a in table:
        print(f"  x={x:8.3f}  A_eff={a:.6f}")
    if dropped:
        print("被丢弃的点：")
        for x, a in dropped:
            print(f"  x={x:8.3f}  A_eff={a:.6f}  [DROPPED]")

    print(f"已保存阀特性模型：{args.out}")

    if args.plot:
        image_path = Path(args.out).with_suffix(".png")
        try:
            create_diagnostic_plot(steady, table, dropped, image_path)
            print(f"诊断图已保存：{image_path}")
        except Exception as exc:
            print(f"生成诊断图失败（不影响 JSON）：{exc}")

    return 0


if __name__ == "__main__":
    import sys

    sys.exit(main())