"""测量详细日志记录器 - 记录测量过程的所有详细信息"""
import sys
from pathlib import Path
from datetime import datetime
from typing import Optional


class MeasurementLogger:
    """测量过程详细日志记录器"""

    def __init__(self, output_dir: str = "results"):
        """
        初始化测量日志记录器

        Args:
            output_dir: 输出目录路径
        """
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

        # 生成日志文件名
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        self.log_file = self.output_dir / f"measurement_detail_{timestamp}.log"

        # 打开日志文件
        self.file_handle = open(self.log_file, 'w', encoding='utf-8')

        # 写入头部
        self._write_header()

    def _write_header(self):
        """写入日志头部"""
        self.log("=" * 80)
        self.log("点云测量系统 - 详细测量日志")
        self.log("=" * 80)
        self.log(f"开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        self.log("=" * 80)
        self.log("")

    def log(self, message: str, also_print: bool = True):
        """
        记录日志信息

        Args:
            message: 日志消息
            also_print: 是否同时输出到控制台
        """
        # 写入文件
        self.file_handle.write(message + "\n")
        self.file_handle.flush()  # 立即刷新到文件

        # 输出到控制台
        if also_print:
            print(message)

    def log_task_start(self, task_index: int, total_tasks: int, task_name: str):
        """记录任务开始"""
        self.log("")
        self.log("=" * 80)
        self.log(f"[{task_index}/{total_tasks}] 开始执行任务: {task_name}")
        self.log("=" * 80)

    def log_roi_extraction(self, roi_name: str, point_count: int, bbox_min: list, bbox_max: list):
        """
        记录ROI提取信息

        Args:
            roi_name: ROI名称（如"源平面ROI"、"目标平面ROI"）
            point_count: 提取到的点数
            bbox_min: ROI最小边界 [x, y, z]
            bbox_max: ROI最大边界 [x, y, z]
        """
        self.log(f"  {roi_name}:")
        self.log(f"    边界: X[{bbox_min[0]:.2f}, {bbox_max[0]:.2f}], "
                f"Y[{bbox_min[1]:.2f}, {bbox_max[1]:.2f}], "
                f"Z[{bbox_min[2]:.2f}, {bbox_max[2]:.2f}]")
        self.log(f"    提取点数: {point_count:,} 个点")

    def log_ransac_fitting(self, plane_name: str, total_points: int,
                          inlier_count: int, inlier_ratio: float,
                          plane_equation: Optional[tuple] = None):
        """
        记录RANSAC平面拟合信息

        Args:
            plane_name: 平面名称（如"源平面"、"目标平面"）
            total_points: 参与拟合的总点数
            inlier_count: 内点数量
            inlier_ratio: 内点率（0-1之间的浮点数）
            plane_equation: 平面方程 (a, b, c, d)，满足 ax + by + cz + d = 0
        """
        self.log(f"  {plane_name} RANSAC拟合:")
        self.log(f"    拟合点数: {total_points:,} 个点")
        self.log(f"    内点数量: {inlier_count:,} 个点")
        self.log(f"    内点率: {inlier_ratio:.2%}")

        if plane_equation:
            a, b, c, d = plane_equation
            self.log(f"    平面方程: {a:.6f}x + {b:.6f}y + {c:.6f}z + {d:.6f} = 0")
            # 计算法向量
            import numpy as np
            normal = np.array([a, b, c])
            norm = np.linalg.norm(normal)
            if norm > 0:
                normal = normal / norm
                self.log(f"    法向量: ({normal[0]:.6f}, {normal[1]:.6f}, {normal[2]:.6f})")

    def log_sampling(self, sample_count: int, method: str = "均匀采样"):
        """
        记录采样信息

        Args:
            sample_count: 采样点数
            method: 采样方法
        """
        self.log(f"  采样策略: {method}")
        self.log(f"  采样点数: {sample_count} 个点")

    def log_distance_result(self, mean_distance: float, std_distance: float,
                           min_distance: float, max_distance: float):
        """
        记录距离测量结果

        Args:
            mean_distance: 平均距离
            std_distance: 标准差
            min_distance: 最小距离
            max_distance: 最大距离
        """
        self.log(f"  测量结果:")
        self.log(f"    平均距离: {mean_distance:.4f} mm")
        self.log(f"    标准差: {std_distance:.4f} mm")
        self.log(f"    最小距离: {min_distance:.4f} mm")
        self.log(f"    最大距离: {max_distance:.4f} mm")
        self.log(f"    距离范围: [{min_distance:.4f}, {max_distance:.4f}] mm")

    def log_task_success(self, execution_time: float):
        """记录任务成功"""
        self.log(f"  任务状态: ✅ 成功")
        self.log(f"  执行时间: {execution_time:.2f} 秒")

    def log_task_failure(self, error_message: str, execution_time: float):
        """记录任务失败"""
        self.log(f"  任务状态: ❌ 失败")
        self.log(f"  错误信息: {error_message}")
        self.log(f"  执行时间: {execution_time:.2f} 秒")

    def log_circle_fitting(self, circle_name: str, center: tuple, radius: float,
                          point_count: int):
        """
        记录圆拟合信息

        Args:
            circle_name: 圆的名称
            center: 圆心坐标 (x, y, z)
            radius: 半径
            point_count: 拟合点数
        """
        self.log(f"  {circle_name}:")
        self.log(f"    圆心: ({center[0]:.4f}, {center[1]:.4f}, {center[2]:.4f})")
        self.log(f"    半径: {radius:.4f} mm")
        self.log(f"    拟合点数: {point_count:,} 个点")

    def log_kdtree_optimization(self, original_points: int, filtered_points: int,
                                percentage: float):
        """
        记录KDTree优化信息

        Args:
            original_points: 原始点数
            filtered_points: 筛选后点数
            percentage: 保留百分比
        """
        self.log(f"  KDTree优化:")
        self.log(f"    原始点数: {original_points:,} 个点")
        self.log(f"    筛选后: {filtered_points:,} 个点 ({percentage:.2f}%)")

    def close(self):
        """关闭日志文件"""
        self.log("")
        self.log("=" * 80)
        self.log(f"结束时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        self.log("=" * 80)

        if self.file_handle:
            self.file_handle.close()
            print(f"\n详细测量日志已保存到: {self.log_file}")

    def __del__(self):
        """析构函数，确保文件被关闭"""
        if hasattr(self, 'file_handle') and self.file_handle:
            self.file_handle.close()