# -*- coding: utf-8 -*-
"""自动拟合候选曲线目标函数。

本模块提供轻量级的曲线误差计算，用于比较目标曲线和代理模型预测曲线的匹配度。
目标函数同时考虑相对误差和绝对误差，并分别计算 log_pressure 与 log_derivative
两条曲线的贡献，适合在参数筛选、PSO 候选排序和局部邻域验证中复用。
"""

from __future__ import annotations

import numpy as np




def split_curve_by_layout(curve: np.ndarray, layout: dict) -> dict[str, np.ndarray]:
    """按照 curve_layout 拆分曲线。"""
    parts: dict[str, np.ndarray] = {}
    for part in layout["parts"]:
        start = int(part["start"])
        end = int(part["end"])
        parts[str(part["name"])] = np.asarray(curve[start:end], dtype=np.float64)
    return parts


def calculate_curve_objective_1d(target: np.ndarray, pred: np.ndarray) -> float:
    """计算单段曲线的自动拟合目标，兼顾相对误差和绝对误差。"""
    target = np.asarray(target, dtype=np.float64).reshape(-1)
    pred = np.asarray(pred, dtype=np.float64).reshape(-1)

    if target.size == 0 or pred.size != target.size:
        return float("inf")
    if not (np.isfinite(target).all() and np.isfinite(pred).all()):
        return float("inf")

    # 对较大的 log 值适当降权，避免其完全主导排序结果。
    weight_factor = np.minimum(100.0, np.abs(target) * 0.01)
    weight = 1.0 / (1.0 + weight_factor)

    scale = np.maximum(np.maximum(np.abs(target), np.abs(pred)), 1e-12)
    relative_error = np.abs(target - pred) / scale
    absolute_error = np.abs(target - pred)

    # 相对误差用于跨尺度比较曲线形态，绝对误差用于保留整体幅值差异。
    point_error = 0.7 * relative_error + 0.3 * absolute_error
    weighted_mse = np.sum(weight * (point_error**2)) / max(np.sum(weight), 1e-12)
    return float(np.sqrt(weighted_mse))


def dual_log_objective(
    curve_target: np.ndarray,
    curve_pred: np.ndarray,
    curve_layout: dict,
    w_pressure: float = 0.5,
    w_derivative: float = 0.5,
) -> dict[str, float]:
    """分别计算压力和导数目标，并按权重合成双对数自动拟合目标。"""
    parts_target = split_curve_by_layout(curve_target, curve_layout)
    parts_pred = split_curve_by_layout(curve_pred, curve_layout)

    p_obj = calculate_curve_objective_1d(parts_target["log_pressure"], parts_pred["log_pressure"])
    d_obj = calculate_curve_objective_1d(parts_target["log_derivative"], parts_pred["log_derivative"])

    total_w = max(float(w_pressure) + float(w_derivative), 1e-12)
    combined = (float(w_pressure) * p_obj + float(w_derivative) * d_obj) / total_w

    return {
        "log_pressure_objective": float(p_obj),
        "log_derivative_objective": float(d_obj),
        "dual_log_objective": float(combined),
    }