删除 1.py

1.py

@@ -1,98 +0,0 @@
-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