import os
import matplotlib.pyplot as plt
import pandas as pd
import logging
from typing import List, Optional

class FilterVisualizer:
    """过滤结果可视化器"""
    
    def __init__(self, output_dir: str):
        self.output_dir = output_dir
        self.logger = logging.getLogger('UAV_Preprocess.Visualizer')
        
    def visualize_filter_step(self, 
                            current_points: pd.DataFrame,
                            previous_points: pd.DataFrame,
                            step_name: str,
                            save_name: Optional[str] = None):
        """
        可视化单个过滤步骤的结果
        
        Args:
            current_points: 当前步骤后的点
            previous_points: 上一步骤的点
            step_name: 步骤名称
            save_name: 保存文件名，默认为step_name
        """
        self.logger.info(f"开始生成{step_name}的可视化结果")
        
        # 找出被过滤掉的点
        filtered_files = set(previous_points['file']) - set(current_points['file'])
        filtered_points = previous_points[previous_points['file'].isin(filtered_files)]
        
        # 创建图形
        plt.figure(figsize=(20, 16))
        
        # 绘制保留的点
        plt.scatter(current_points['lon'], current_points['lat'],
                   color='blue', label='Retained Points',
                   alpha=0.6, s=50)
        
        # 绘制被过滤的点
        plt.scatter(filtered_points['lon'], filtered_points['lat'],
                   color='red', marker='x', label='Filtered Points',
                   alpha=0.6, s=100)
        
        # 设置图形属性
        plt.title(f"GPS Points After {step_name}", fontsize=14)
        plt.xlabel("Longitude", fontsize=12)
        plt.ylabel("Latitude", fontsize=12)
        plt.grid(True)
        
        # 添加统计信息
        stats_text = (f"Total Points: {len(previous_points)}\n"
                     f"Filtered Points: {len(filtered_points)}\n"
                     f"Remaining Points: {len(current_points)}")
        plt.figtext(0.02, 0.02, stats_text, fontsize=10,
                   bbox=dict(facecolor='white', alpha=0.8))
        
        plt.legend()
        
        # 保存图形
        save_name = save_name or step_name.lower().replace(' ', '_')
        save_path = os.path.join(self.output_dir, f'filter_{save_name}.png')
        plt.savefig(save_path, dpi=300, bbox_inches='tight')
        plt.close()
        
        self.logger.info(f"{step_name}过滤可视化结果已保存至 {save_path}")
        
    def visualize_all_steps(self, 
                           points_history: List[pd.DataFrame],
                           step_names: List[str]):
        """
        可视化所有过滤步骤的结果
        
        Args:
            points_history: 每个步骤的点数据列表
            step_names: 步骤名称列表
        """
        if len(points_history) != len(step_names):
            raise ValueError("点数据列表和步骤名称列表长度不匹配")
            
        plt.figure(figsize=(20, 16))
        
        # 使用不同的颜色表示不同步骤
        colors = plt.cm.rainbow(np.linspace(0, 1, len(points_history)))
        
        # 绘制每个步骤的点
        for i, (points, color) in enumerate(zip(points_history, colors)):
            plt.scatter(points['lon'], points['lat'],
                       color=color, label=f'After {step_names[i]}',
                       alpha=0.6, s=50)
        
        # 设置图形属性
        plt.title("GPS Points Filtering Process", fontsize=14)
        plt.xlabel("Longitude", fontsize=12)
        plt.ylabel("Latitude", fontsize=12)
        plt.grid(True)
        
        # 添加统计��息
        stats_text = "Points Count:\n" + "\n".join(
            f"{step_names[i]}: {len(points)}"
            for i, points in enumerate(points_history)
        )
        plt.figtext(0.02, 0.02, stats_text, fontsize=10,
                   bbox=dict(facecolor='white', alpha=0.8))
        
        plt.legend()
        
        # 保存图形
        save_path = os.path.join(self.output_dir, 'filter_all_steps.png')
        plt.savefig(save_path, dpi=300, bbox_inches='tight')
        plt.close()
        
        self.logger.info(f"所有过滤步骤的可视化结果已保存至 {save_path}")