import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
from algorithms import detect_outliers_3sigma, detect_outliers_iqr, detect_outliers_grubbs, detect_outliers_gesd

class OutlierDetector:
    def __init__(self):
        self.data = None
        self.results = None
        self.current_method = None
        
    def load_data(self, file_path):
        """加载时间序列数据"""
        try:
            self.data = pd.read_csv(file_path, parse_dates=['timestamp'])
            self.data.set_index('timestamp', inplace=True)
            return True, "数据加载成功"
        except Exception as e:
            return False, f"数据加载失败: {str(e)}"
    
    def detect_outliers(self, method, column, **kwargs):
        """使用指定方法检测离群点"""
        if self.data is None:
            return False, "请先加载数据"
            
        if column not in self.data.columns:
            return False, f"列'{column}'不存在"
            
        series = self.data[column].dropna()
        if len(series) == 0:
            return False, "所选列没有有效数据"
            
        self.current_method = method
        
        try:
            if method == '3sigma':
                results = detect_outliers_3sigma(series, **kwargs)
            elif method == 'iqr':
                results = detect_outliers_iqr(series, **kwargs)
            elif method == 'grubbs':
                results = detect_outliers_grubbs(series, **kwargs)
            elif method == 'gesd':
                results = detect_outliers_gesd(series, **kwargs)
            else:
                return False, "不支持的检测方法"
                
            self.results = results
            return True, "离群点检测成功"
        except Exception as e:
            return False, f"离群点检测失败: {str(e)}"
    
    def get_detection_results(self):
        """获取检测结果"""
        if self.results is None:
            return None
            
        return {
            'method': self.current_method,
            'series_name': self.results['series_name'],
            'upper_bound': self.results['upper_bound'],
            'lower_bound': self.results['lower_bound'],
            'mean': self.results['mean'],
            'outliers': self.results['outliers'],
            'outlier_indices': self.results['outlier_indices']
        }
    
    def plot_results(self):
        """绘制检测结果"""
        if self.results is None:
            return False, "没有可用的检测结果"
            
        plt.figure(figsize=(12, 6))
        
        # 绘制原始数据
        plt.plot(self.results['series'], 'b-', label='原始数据')
        
        # 绘制均值线
        plt.axhline(self.results['mean'], color='g', linestyle='--', label='均值')
        
        # 绘制边界线
        plt.axhline(self.results['upper_bound'], color='r', linestyle='--', label='上边界')
        if 'lower_bound' in self.results:
            plt.axhline(self.results['lower_bound'], color='r', linestyle='--', label='下边界')
        
        # 标记离群点
        outlier_dates = self.results['series'].index[self.results['outlier_indices']]
        outlier_values = self.results['series'].iloc[self.results['outlier_indices']]
        plt.plot(outlier_dates, outlier_values, 'ro', markersize=8, label='离群点')
        
        plt.title(f"离群点检测 - {self.current_method.upper()}方法")
        plt.xlabel('时间')
        plt.ylabel('数值')
        plt.legend()
        plt.grid(True)
        plt.tight_layout()
        
        return True, plt