data = pd.Series(index=pd.date_range(start='20190701', periods=24, freq='H'), data=rangprint('5'.ljust(30, '=')) print(data[:5]) print('3'.ljust(30, '=')) print(data.resample('3H').mean()) print('5'.ljust(30, '=')) print(data.resample('5H').sum()) 5========================= 2019-07-01 00:00:00 0 2019-07-01 01:00:00 1 2019-07-01 02:00:00 2 2019-07-01 03:00:00 3 2019-07-01 04:00:00 4 Freq: H, dtype: int64 3=================== 2019-07-01 00:00:00 1 2019-07-01 03:00:00 4 2019-07-01 06:00:00 7 2019-07-01 09:00:00 10 2019-07-01 12:00:00 13 2019-07-01 15:00:00 16 2019-07-01 18:00:00 19 2019-07-01 21:00:00 22 Freq: 3H, dtype: int64 5===================== 2019-07-01 00:00:00 10 2019-07-01 05:00:00 35 2019-07-01 10:00:00 60 2019-07-01 15:00:00 85 2019-07-01 20:00:00 86 Freq: 5H, dtype: int64

data=pd.Series(index=pd.date_range(start='20190701',periods=24,freq='H'),data=range) print('5'.ljust(30,'=')) print(data[:5]) print('3'.ljust(30,'=')) print(data.resample('3H').mean()) print('5'.ljust(30,'=')) print(data.resample('5H').sum())

index=pd.date_range(start='20190701', periods=24, freq='H'), data=range(24) ) print('5'.ljust(30,'=')) print("前5小时原始数据:") print(data[:5]) print('\n3'.ljust(30,'=')) # 改进分隔符格式 print(...

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idx = pd.date_range(start=data.index[-1], periods=12, freq='M') forecast = pd.Series(forecast, index=idx) plt.figure(figsize=(9.62,8.62)) plt.plot(data, label='Original Data') plt.plot(forecast, label...

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date_index = pd.date_range(start='2023-01-01', periods=10, freq='D') # 生成10个按日排列的日期此处生成了一个从 '2023-01-01' 开始的长度为 10 的日期序列[^2]。 #### 构造随机数据利用 NumPy 提供的...

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import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码，数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') print(data)这个代码继续怎么写

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10/1440 ORDER BY anomalies_time DESC FETCH FIRST 1 ROWS ONLY """ # 执行查询 cursor.execute(query) result = cursor.fetchall() if not result: print("没有需要分析的异常记录") return # 提取数据 obj_name = result[0][0] anom_time = result[0][1] anom_inst = int(result[0][2]) print(f"分析记录: {obj_name} at {anom_time} (实例 {anom_inst})") # 3. 更新状态为doing update_query = f""" UPDATE RT_RESULT SET alert_analysis_status = 'doing' WHERE object_name = '{escape_sql(obj_name)}' AND anomalies_time = TO_TIMESTAMP('{format_timestamp(anom_time)}', 'YYYY-MM-DD HH24:MI:SS.FF3') AND anomalies_inst_number = {anom_inst} """ cursor.execute(update_query) cursor.connection.commit() print("状态更新为doing") # 4. 获取异常详情 detail_query = f""" SELECT ANOMALYDETAILS FROM "{obj_name}" WHERE snap_time = TO_TIMESTAMP('{format_timestamp(anom_time)}', 'YYYY-MM-DD HH24:MI:SS.FF3') AND instance_number = {anom_inst} """ # 执行查询 cursor.execute(detail_query) details_result = cursor.fetchall() if not details_result: raise Exception(f"{obj_name}表中未找到匹配记录") # 获取XML数据 xml_data = details_result[0][0] # 处理XML命名空间问题 xml_data = re.sub(r'\sxmlns="[^"]+"', '', xml_data, count=1) root = ET.fromstring(xml_data) attributes = [] for attr in root.findall('.//Attribute'): try: attr_data = { 'name': attr.get('name'), 'value': float(attr.get('actualValue')), 'weight': float(attr.get('weight')), 'rank': int(attr.get('rank')) } attributes.append(attr_data) except (TypeError, ValueError) as e: print(f"解析属性时出错: {e}") # 按rank排序 attributes.sort(key=lambda x: x['rank']) # 5. 交叉验证 - 获取异常点前10个采样点 print("获取异常点前10个采样点数据...") anomaly_df, B_total_std, C_col_std = get_data_samples(cursor, obj_name, anom_inst, anom_time) if anomaly_df is None: raise Exception(f"未找到异常点前10个采样点数据: {obj_name} at {anom_time} (实例 {anom_inst})") # 6. 获取正常模式数据 print("获取正常模式数据...") # P1: 一天前相同时间点（跳过休息日） p1_time = anom_time - timedelta(days=1) if is_holiday(p1_time): p1_time = find_previous_workday(p1_time) day_ago_df, A1_total_std, A2_col_std = get_data_samples(cursor, obj_name, anom_inst, p1_time) # P2: 一周前相同时间点 p2_time = anom_time - timedelta(weeks=1) week_ago_df, B1_total_std, B2_col_std = get_data_samples(cursor, obj_name, anom_inst, p2_time) # P3: 一个月前相同日期（跳过休息日） p3_time = anom_time - timedelta(days=30) if is_holiday(p3_time): p3_time = find_previous_workday(p3_time) month_ago_df, C1_total_std, C2_col_std = get_data_samples(cursor, obj_name, anom_inst, p3_time) # 如果正常模式数据为空，使用默认值 if day_ago_df is None: print("警告: 未找到一天前相同时间点数据") day_ago_df = pd.DataFrame() A1_total_std, A2_col_std = 0, {} if week_ago_df is None: print("警告: 未找到一周前相同时间点数据") week_ago_df = pd.DataFrame() B1_total_std, B2_col_std = 0, {} if month_ago_df is None: print("警告: 未找到一个月前相同日期数据") month_ago_df = pd.DataFrame() C1_total_std, C2_col_std = 0, {} # 7. 生成验证结果 validation_results = "\n\n===== 交叉验证结果 =====\n" m4_changes = [] m4_indicator_set = set() # 用于存储m4级别的指标名称 # 第一轮验证：总标准差比较 validation_results += "\n第一轮验证：总标准差比较\n" validation_results += f"异常模式总标准差 (B): {B_total_std:.4f}\n" # 比较1：B vs A1 if A1_total_std is not None: change_rate = get_relative_change(B_total_std, A1_total_std) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一天前相同时间点 (A1: {A1_total_std:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(comp_result.strip()) #m4_indicator_set.add("总标准差") # 比较2：B vs B1 if B1_total_std is not None: change_rate = get_relative_change(B_total_std, B1_total_std) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一周前相同时间点 (B1: {B1_total_std:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(comp_result.strip()) #m4_indicator_set.add("总标准差") # 比较3：B vs C1 if C1_total_std is not None: change_rate = get_relative_change(B_total_std, C1_total_std) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一个月前相同日期 (C1: {C1_total_std:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(comp_result.strip()) #m4_indicator_set.add("总标准差") # 第二轮验证：各列标准差比较 validation_results += "\n第二轮验证：各列标准差比较\n" # 只分析排名前3的指标 top_attributes = attributes[:3] for attr in top_attributes: col_name = attr['name'] validation_results += f"\n指标: {col_name} (当前值: {attr['value']}, 权重: {attr['weight']:.2f}, 排名: {attr['rank']})\n" # 异常模式该列标准差 col_std_b = C_col_std.get(col_name, 0) validation_results += f"异常模式标准差 (C): {col_std_b:.4f}\n" # 比较1：C vs A2 if A2_col_std and col_name in A2_col_std: col_std_a2 = A2_col_std[col_name] change_rate = get_relative_change(col_std_b, col_std_a2) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一天前相同时间点 (A2: {col_std_a2:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(f"指标 {col_name}: {comp_result.strip()}") m4_indicator_set.add(col_name) # 比较2：C vs B2 if B2_col_std and col_name in B2_col_std: col_std_b2 = B2_col_std[col_name] change_rate = get_relative_change(col_std_b, col_std_b2) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一周前相同时间点 (B2: {col_std_b2:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(f"指标 {col_name}: {comp_result.strip()}") m4_indicator_set.add(col_name) # 比较3：C vs C2 if C2_col_std and col_name in C2_col_std: col_std_c2 = C2_col_std[col_name] change_rate = get_relative_change(col_std_b, col_std_c2) change_desc, change_class = classify_change(change_rate) rate_str = format_change_rate(change_rate) comp_result = f"与一个月前相同日期 (C2: {col_std_c2:.4f}) 比较: 变化率 {rate_str} - {change_desc} ({change_class})\n" validation_results += comp_result if change_class == "m4": m4_changes.append(f"指标 {col_name}: {comp_result.strip()}") m4_indicator_set.add(col_name) # 7. 数据可视化 # 创建特定目录保存图片 timestamp_str = anom_time.strftime("%Y%m%d_%H%M%S") save_dir = os.path.join(PIC_DIR, f"{obj_name}_{anom_inst}_{timestamp_str}") if not os.path.exists(save_dir): os.makedirs(save_dir) # 准备所有样本数据用于聚类 all_data = [] labels = [] # 添加正常模式样本（只添加非空数据） if day_ago_df is not None and not day_ago_df.empty: all_data.append(day_ago_df) labels.extend(['Day Ago'] * len(day_ago_df)) if week_ago_df is not None and not week_ago_df.empty: all_data.append(week_ago_df) labels.extend(['Week Ago'] * len(week_ago_df)) if month_ago_df is not None and not month_ago_df.empty: all_data.append(month_ago_df) labels.extend(['Month Ago'] * len(month_ago_df)) # 添加异常样本 if anomaly_df is not None and not anomaly_df.empty: all_data.append(anomaly_df) labels.extend(['Anomaly'] * len(anomaly_df)) # 检查是否有足够数据 if len(all_data) == 0: print("警告: 没有足够的数据进行可视化") else: # 合并所有数据 combined_df = pd.concat(all_data, ignore_index=True) # 排除非数值列 exclude_cols = ['SNAP_TIME', 'INSTANCE_NUMBER', 'PRED', 'PROB', 'ANOMALYDETAILS'] numeric_cols = [col for col in combined_df.columns if col not in exclude_cols and combined_df[col].dtype in [np.int64, np.float64]] if not numeric_cols: print("警告: 没有数值列可用于可视化") else: # 提取数值数据 data = combined_df[numeric_cols].values # 标准化数据 scaler = StandardScaler() scaled_data = scaler.fit_transform(data) # 使用K-Means聚类 kmeans = KMeans(n_clusters=2, random_state=42) clusters = kmeans.fit_predict(scaled_data) # 使用PCA降维可视化 from sklearn.decomposition import PCA pca = PCA(n_components=2) reduced_data = pca.fit_transform(scaled_data) # 创建聚类图 plt.figure(figsize=(12, 8)) # 为不同标签使用不同颜色 colors = {'Day Ago': 'blue', 'Week Ago': 'green', 'Month Ago': 'purple', 'Anomaly': 'red'} # 绘制所有点 for label in set(labels): idx = [i for i, l in enumerate(labels) if l == label] plt.scatter(reduced_data[idx, 0], reduced_data[idx, 1], c=colors[label], label=label, alpha=0.6) # 标记异常点（红色） anomaly_idx = [i for i, l in enumerate(labels) if l == 'Anomaly'] plt.scatter(reduced_data[anomaly_idx, 0], reduced_data[anomaly_idx, 1], c='red', marker='x', s=100, label='Anomaly Points') plt.title('K-Means Clustering of System Metrics') plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.legend() plt.grid(True) # 保存聚类图 kmeans_path = os.path.join(save_dir, 'kmeans_clustering.png') plt.savefig(kmeans_path) plt.close() pic_paths.append(kmeans_path) print(f"保存K-Means聚类图到: {kmeans_path}") # 创建时间序列图 plt.figure(figsize=(15, 10)) # 为每个指标创建子图 num_plots = min(5, len(numeric_cols)) # 最多显示5个指标 fig, axes = plt.subplots(num_plots, 1, figsize=(15, 5num_plots)) if num_plots == 1: axes = [axes] # 确保单图时axes是列表 for i, col in enumerate(numeric_cols[:num_plots]): ax = axes[i] # 绘制正常模式（只绘制非空数据） if day_ago_df is not None and not day_ago_df.empty: day_ago_df['SNAP_TIME'] = pd.to_datetime(day_ago_df['SNAP_TIME']) ax.plot(day_ago_df['SNAP_TIME'], day_ago_df[col], 'b-', label='Day Ago', alpha=0.7) if week_ago_df is not None and not week_ago_df.empty: week_ago_df['SNAP_TIME'] = pd.to_datetime(week_ago_df['SNAP_TIME']) ax.plot(week_ago_df['SNAP_TIME'], week_ago_df[col], 'g-', label='Week Ago', alpha=0.7) if month_ago_df is not None and not month_ago_df.empty: month_ago_df['SNAP_TIME'] = pd.to_datetime(month_ago_df['SNAP_TIME']) ax.plot(month_ago_df['SNAP_TIME'], month_ago_df[col], 'm-', label='Month Ago', alpha=0.7) # 绘制异常点 if anomaly_df is not None and not anomaly_df.empty: anomaly_df['SNAP_TIME'] = pd.to_datetime(anomaly_df['SNAP_TIME']) # 只绘制异常点 ax.scatter(anomaly_df['SNAP_TIME'], anomaly_df[col], c='red', s=50, label='Anomaly', zorder=5) ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M')) ax.xaxis.set_major_locator(mdates.AutoDateLocator()) ax.set_title(f'Time Series: {col}') ax.set_xlabel('Time') ax.set_ylabel('Value') ax.legend(loc='upper right') ax.grid(True, linestyle='--', alpha=0.7) plt.setp(ax.get_xticklabels(), rotation=45, ha='right') plt.tight_layout() # 保存时间序列图 timeseries_path = os.path.join(save_dir, 'timeseries_comparison.png') plt.savefig(timeseries_path, bbox_inches='tight') plt.close() pic_paths.append(timeseries_path) print(f"保存时间序列图到: {timeseries_path}") # 评估哪种可视化更直观 evaluation = """ === 可视化方法评估 === 1. K-Means聚类图: - 优点: 展示数据点在高维空间的分布，可以清晰看到异常点与正常模式的分离 - 缺点: 需要降维处理，可能丢失部分信息；不直观展示时间变化 2. 时间序列图: - 优点: 直观展示指标随时间的变化趋势，容易识别异常点 - 缺点: 当指标过多时难以在同一图中展示结论: 时间序列图更直观展示异常点信息，特别是当需要分析指标随时间的变化趋势时。建议将时间序列图作为主要可视化工具，聚类图作为辅助分析工具。 """ print(evaluation) # 保存评估结论 with open(os.path.join(save_dir, 'visualization_evaluation.txt'), 'w') as f: f.write(evaluation) # 8. 生成分析报告 analysis_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") summary = f"检测到{len(attributes)}个异常指标" if m4_changes: summary += "，检测到指标有重大变化，请及时关注！" m4_warning = "\n\n===== 重大变化警告 =====\n" for i, change in enumerate(m4_changes, 1): m4_warning += f"{i}. {change}\n" summary += m4_warning details = f"异常分析报告 ({analysis_time})\n" details += f"目标对象: {obj_name}\n" details += f"异常时间: {anom_time}\n" details += f"实例编号: {anom_inst}\n" details += "="50 + "\n" # 添加指标详情 for attr in attributes: details += ( f"指标 {attr['rank']}: {attr['name']}\n" f" 当前值: {attr['value']}\n" f" 影响权重: {attr['weight']:.2f}\n" f" 问题描述: {generate_description(attr)}\n\n" ) # 添加交叉验证结果 details += validation_results # 添加数据样本信息 details += "\n\n===== 数据样本详情 =====\n" # 定义要排除的列 exclude_columns = ['ANOMALYDETAILS'] # 异常点前10个采样点 if anomaly_df is not None: # 排除指定列 anomaly_df_display = anomaly_df.drop(columns=exclude_columns, errors='ignore') details += f"异常点前10个采样点:\n{anomaly_df_display.to_string()}\n\n" else: details += "异常点前10个采样点: 无数据\n\n" # 一天前相同时间点采样点 if day_ago_df is not None: # 排除指定列 day_ago_df_display = day_ago_df.drop(columns=exclude_columns, errors='ignore') details += f"一天前相同时间点采样点:\n{day_ago_df_display.to_string()}\n\n" else: details += "一天前相同时间点采样点: 无数据\n\n" # 一周前相同时间点采样点 if week_ago_df is not None: # 排除指定列 week_ago_df_display = week_ago_df.drop(columns=exclude_columns, errors='ignore') details += f"一周前相同时间点采样点:\n{week_ago_df_display.to_string()}\n\n" else: details += "一周前相同时间点采样点: 无数据\n\n" # 一个月前相同日期采样点 if month_ago_df is not None: # 排除指定列 month_ago_df_display = month_ago_df.drop(columns=exclude_columns, errors='ignore') details += f"一个月前相同日期采样点:\n{month_ago_df_display.to_string()}\n" else: details += "一个月前相同日期采样点: 无数据\n" print(details) # 9. 更新分析结果 # 根据是否有重大变化设置alert_status alert_status = "sending" if m4_changes else "normal" # 准备图片路径字符串 pic_paths_str = ",".join(pic_paths) if pic_paths else "无图片" # 准备m4指标字符串 m4_indicators_str = ",".join(m4_indicator_set) if m4_indicator_set else "无" update_query = """ UPDATE RT_RESULT SET alert_analysis_status = :status, alert_analysis_summary = :summary, alert_analysis_detail = :detail, alert_status = :alert_status, alert_analysis_pic = :pic_paths, alert_analysis_dar = :m4_indicators WHERE object_name = :obj_name AND anomalies_time = TO_TIMESTAMP(:anom_time, 'YYYY-MM-DD HH24:MI:SS.FF3') AND anomalies_inst_number = :inst_number """ params = { 'status': 'done', 'summary': summary, 'detail': details, 'alert_status': alert_status, 'pic_paths': pic_paths_str, 'm4_indicators': m4_indicators_str, 'obj_name': obj_name, 'anom_time': format_timestamp(anom_time), 'inst_number': anom_inst } cursor.execute(update_query, params) cursor.connection.commit() print("分析结果保存成功") print(f"图片路径: {pic_paths_str}") print(f"m4指标: {m4_indicators_str}") print(details) except Exception as e: print(f"处理失败: {str(e)}") # 更新状态为error if obj_name and anom_time and anom_inst is not None: error_update = f""" UPDATE RT_RESULT SET alert_analysis_status = 'error' WHERE object_name = '{escape_sql(obj_name)}' AND anomalies_time = TO_TIMESTAMP('{format_timestamp(anom_time)}', 'YYYY-MM-DD HH24:MI:SS.FF3') AND anomalies_inst_number = {anom_inst} """ try: if cursor: cursor.execute(error_update) cursor.connection.commit() except Exception as inner_e: print(f"更新错误状态时出错: {str(inner_e)}") finally: # 关闭游标 if cursor: cursor.close() # 关闭连接 if conn: conn.close() print("数据库连接关闭") if name == "main": analyze_anomalies() ====================== 以上代码在《创建时间序列图》这段代码中画出来的图上图所示都是按天，我的采样点比较密集，就容易混到一起，是否能按照我采集的数据来做图，没有日期显示的就不用画出图了，也就是绘制时间序列时跳过无数据的区间。请修改以上的相关代码段

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Traceback (most recent call last): File "C:\Users\ASUS\AppData\Roaming\JetBrains\PyCharmCE2023.3\scratches\scratch_17.py", line 72, in <module> adf_test(ts['发病率'], '原始数据') NameError: name 'adf_test' is not defined. Did you mean: 'ts_test'?

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