208 lines
9.0 KiB
Python
208 lines
9.0 KiB
Python
# ...existing code...
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from pykalman import KalmanFilter
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import os
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def clean_flow_data_kf(input_csv_path: str, show_plot: bool = False) -> str:
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"""
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读取 input_csv_path 中的每列时间序列，使用一维 Kalman 滤波平滑并用预测值替换基于 3σ 检测出的异常点。
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保存输出为：<input_filename>_cleaned.xlsx（与输入同目录），并返回输出文件的绝对路径。
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仅保留输入文件路径作为参数（按要求）。
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"""
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# 读取 CSV
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data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
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# 存储 Kalman 平滑结果
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data_kf = pd.DataFrame(index=data.index, columns=data.columns)
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# 平滑每一列
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for col in data.columns:
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observations = pd.Series(data[col].values).ffill().bfill()
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if observations.isna().any():
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observations = observations.fillna(observations.mean())
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obs = observations.values.astype(float)
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kf = KalmanFilter(
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transition_matrices=[1],
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observation_matrices=[1],
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initial_state_mean=float(obs[0]),
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initial_state_covariance=1,
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observation_covariance=1,
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transition_covariance=0.01
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)
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# 跳过EM学习，使用固定参数以提高性能
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state_means, _ = kf.smooth(obs)
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data_kf[col] = state_means.flatten()
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# 计算残差并用IQR检测异常（更稳健的方法）
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residuals = data - data_kf
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residual_thresholds = {}
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for col in data.columns:
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res_values = residuals[col].dropna().values # 移除NaN以计算IQR
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q1 = np.percentile(res_values, 25)
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q3 = np.percentile(res_values, 75)
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iqr = q3 - q1
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lower_threshold = q1 - 1.5 * iqr
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upper_threshold = q3 + 1.5 * iqr
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residual_thresholds[col] = (lower_threshold, upper_threshold)
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cleaned_data = data.copy()
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anomalies_info = {}
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for col in data.columns:
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lower, upper = residual_thresholds[col]
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sensor_residuals = residuals[col]
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anomaly_mask = (sensor_residuals < lower) | (sensor_residuals > upper)
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anomaly_idx = data.index[anomaly_mask.fillna(False)]
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anomalies_info[col] = pd.DataFrame({
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'Observed': data.loc[anomaly_idx, col],
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'Kalman_Predicted': data_kf.loc[anomaly_idx, col],
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'Residual': sensor_residuals.loc[anomaly_idx]
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})
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cleaned_data.loc[anomaly_idx, f'{col}_cleaned'] = data_kf.loc[anomaly_idx, col]
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# 构造输出文件名：在输入文件名基础上加后缀 _cleaned.xlsx
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input_dir = os.path.dirname(os.path.abspath(input_csv_path))
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input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
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output_filename = f"{input_base}_cleaned.xlsx"
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output_path = os.path.join(input_dir, output_filename)
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# 覆盖同名文件
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if os.path.exists(output_path):
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os.remove(output_path)
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cleaned_data.to_excel(output_path, index=False)
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# 可选可视化（第一个传感器）
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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if show_plot and len(data.columns) > 0:
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sensor_to_plot = data.columns[0]
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plt.figure(figsize=(12, 6))
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plt.plot(data.index, data[sensor_to_plot], label="监测值", marker='o', markersize=3, alpha=0.7)
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plt.plot(data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2)
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anomaly_idx = anomalies_info[sensor_to_plot].index
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if len(anomaly_idx) > 0:
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plt.plot(anomaly_idx, data[sensor_to_plot].loc[anomaly_idx], 'ro', markersize=8, label="监测值异常点")
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plt.plot(anomaly_idx, data_kf[sensor_to_plot].loc[anomaly_idx], 'go', markersize=8, label="Kalman修复值")
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plt.xlabel("时间点（序号）")
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plt.ylabel("监测值")
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plt.title(f"{sensor_to_plot}：观测值与Kalman滤波预测值（异常点标记）")
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plt.legend()
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plt.show()
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# 返回输出文件的绝对路径
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return os.path.abspath(output_path)
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def clean_flow_data_dict(data_dict: dict, show_plot: bool = False) -> dict:
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"""
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接收一个字典数据结构，其中键为列名，值为时间序列列表，使用一维 Kalman 滤波平滑并用预测值替换基于 3σ 检测出的异常点。
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返回清洗后的字典数据结构。
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"""
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# 将字典转换为 DataFrame
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data = pd.DataFrame(data_dict)
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# 存储 Kalman 平滑结果
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data_kf = pd.DataFrame(index=data.index, columns=data.columns)
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# 平滑每一列
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for col in data.columns:
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observations = pd.Series(data[col].values).ffill().bfill()
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if observations.isna().any():
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observations = observations.fillna(observations.mean())
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obs = observations.values.astype(float)
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kf = KalmanFilter(
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transition_matrices=[1],
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observation_matrices=[1],
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initial_state_mean=float(obs[0]),
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initial_state_covariance=1,
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observation_covariance=10,
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transition_covariance=10
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)
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# 跳过EM学习，使用固定参数以提高性能
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state_means, _ = kf.smooth(obs)
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data_kf[col] = state_means.flatten()
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# 计算残差并用IQR检测异常（更稳健的方法）
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residuals = data - data_kf
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residual_thresholds = {}
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for col in data.columns:
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res_values = residuals[col].dropna().values # 移除NaN以计算IQR
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q1 = np.percentile(res_values, 25)
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q3 = np.percentile(res_values, 75)
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iqr = q3 - q1
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lower_threshold = q1 - 1.5 * iqr
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upper_threshold = q3 + 1.5 * iqr
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residual_thresholds[col] = (lower_threshold, upper_threshold)
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cleaned_data = data.copy()
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anomalies_info = {}
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for col in data.columns:
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lower, upper = residual_thresholds[col]
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sensor_residuals = residuals[col]
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anomaly_mask = (sensor_residuals < lower) | (sensor_residuals > upper)
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anomaly_idx = data.index[anomaly_mask.fillna(False)]
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anomalies_info[col] = pd.DataFrame({
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'Observed': data.loc[anomaly_idx, col],
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'Kalman_Predicted': data_kf.loc[anomaly_idx, col],
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'Residual': sensor_residuals.loc[anomaly_idx]
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})
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cleaned_data.loc[anomaly_idx, f'{col}_cleaned'] = data_kf.loc[anomaly_idx, col]
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# 可选可视化（第一个传感器）
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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if show_plot and len(data.columns) > 0:
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sensor_to_plot = data.columns[0]
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plt.figure(figsize=(12, 6))
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plt.plot(data.index, data[sensor_to_plot], label="监测值", marker='o', markersize=3, alpha=0.7)
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plt.plot(data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2)
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anomaly_idx = anomalies_info[sensor_to_plot].index
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if len(anomaly_idx) > 0:
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plt.plot(anomaly_idx, data[sensor_to_plot].loc[anomaly_idx], 'ro', markersize=8, label="监测值异常点")
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plt.plot(anomaly_idx, data_kf[sensor_to_plot].loc[anomaly_idx], 'go', markersize=8, label="Kalman修复值")
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plt.xlabel("时间点（序号）")
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plt.ylabel("监测值")
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plt.title(f"{sensor_to_plot}：观测值与Kalman滤波预测值（异常点标记）")
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plt.legend()
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plt.show()
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# 返回清洗后的字典
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return cleaned_data.to_dict(orient='list')
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# # 测试
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# if __name__ == "__main__":
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# # 默认：脚本目录下同名 CSV 文件
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# script_dir = os.path.dirname(os.path.abspath(__file__))
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# default_csv = os.path.join(script_dir, "pipe_flow_data_to_clean2.0.csv")
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# out = clean_flow_data_kf(default_csv)
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# print("清洗后的数据已保存到:", out)
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# 测试 clean_flow_data_dict 函数
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if __name__ == "__main__":
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import random
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# 读取 szh_flow_scada.csv 文件
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script_dir = os.path.dirname(os.path.abspath(__file__))
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csv_path = os.path.join(script_dir, "szh_flow_scada.csv")
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data = pd.read_csv(csv_path, header=0, index_col=None, encoding="utf-8")
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# 排除 Time 列，随机选择 5 列
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columns_to_exclude = ['Time']
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available_columns = [col for col in data.columns if col not in columns_to_exclude]
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selected_columns = random.sample(available_columns, 1)
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# 将选中的列转换为字典
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data_dict = {col: data[col].tolist() for col in selected_columns}
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print("选中的列:", selected_columns)
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print("原始数据长度:", len(data_dict[selected_columns[0]]))
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# 调用函数进行清洗
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cleaned_dict = clean_flow_data_dict(data_dict, show_plot=True)
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# 将清洗后的字典写回 CSV
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out_csv = os.path.join(script_dir, f"{selected_columns[0]}_clean.csv")
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pd.DataFrame(cleaned_dict).to_csv(out_csv, index=False, encoding='utf-8-sig')
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print("已保存清洗结果到:", out_csv)
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print("清洗后的字典键:", list(cleaned_dict.keys()))
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print("清洗后的数据长度:", len(cleaned_dict[selected_columns[0]]))
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print("测试完成：函数运行正常")
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