390 lines
14 KiB
Python
390 lines
14 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 fill_time_gaps(
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data: pd.DataFrame,
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time_col: str = "time",
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freq: str = "1min",
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short_gap_threshold: int = 10,
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) -> pd.DataFrame:
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"""
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补齐缺失时间戳并填补数据缺口。
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Args:
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data: 包含时间列的 DataFrame
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time_col: 时间列名（默认 'time'）
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freq: 重采样频率（默认 '1min'）
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short_gap_threshold: 短缺口阈值（分钟），<=此值用线性插值，>此值用前向填充
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Returns:
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补齐时间后的 DataFrame（保留原时间列格式）
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"""
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if time_col not in data.columns:
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raise ValueError(f"时间列 '{time_col}' 不存在于数据中")
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# 解析时间列并设为索引
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data = data.copy()
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data[time_col] = pd.to_datetime(data[time_col], utc=True)
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data_indexed = data.set_index(time_col)
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# 生成完整时间范围
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full_range = pd.date_range(
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start=data_indexed.index.min(), end=data_indexed.index.max(), freq=freq
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)
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# 重索引以补齐缺失时间点
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data_reindexed = data_indexed.reindex(full_range)
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# 按列处理缺口
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for col in data_reindexed.columns:
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# 识别缺失值位置
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is_missing = data_reindexed[col].isna()
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# 计算连续缺失的长度
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missing_groups = (is_missing != is_missing.shift()).cumsum()
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gap_lengths = is_missing.groupby(missing_groups).transform("sum")
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# 短缺口：线性插值
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short_gap_mask = is_missing & (gap_lengths <= short_gap_threshold)
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if short_gap_mask.any():
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data_reindexed.loc[short_gap_mask, col] = (
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data_reindexed[col]
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.interpolate(method="linear", limit_area="inside")
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.loc[short_gap_mask]
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)
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# 长缺口：前向填充
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long_gap_mask = is_missing & (gap_lengths > short_gap_threshold)
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if long_gap_mask.any():
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data_reindexed.loc[long_gap_mask, col] = (
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data_reindexed[col].ffill().loc[long_gap_mask]
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)
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# 重置索引并恢复时间列（保留原格式）
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data_result = data_reindexed.reset_index()
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data_result.rename(columns={"index": time_col}, inplace=True)
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# 保留时区信息
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data_result[time_col] = data_result[time_col].dt.strftime("%Y-%m-%dT%H:%M:%S%z")
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# 修正时区格式（Python的%z输出为+0000，需转为+00:00）
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data_result[time_col] = data_result[time_col].str.replace(
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r"(\+\d{2})(\d{2})$", r"\1:\2", regex=True
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)
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return data_result
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def clean_flow_data_kf(
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input_csv_path: str, show_plot: bool = False, fill_gaps: bool = True
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) -> 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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Args:
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input_csv_path: CSV 文件路径
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show_plot: 是否显示可视化
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fill_gaps: 是否先补齐时间缺口（默认 True）
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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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# 补齐时间缺口（如果数据包含 time 列）
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if fill_gaps and "time" in data.columns:
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data = fill_time_gaps(
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data, time_col="time", freq="1min", short_gap_threshold=10
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)
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# 分离时间列和数值列
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time_col_data = None
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if "time" in data.columns:
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time_col_data = data["time"]
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data = data.drop(columns=["time"])
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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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{
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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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)
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cleaned_data.loc[anomaly_idx, f"{col}_cleaned"] = data_kf.loc[anomaly_idx, col]
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# 如果原始数据包含时间列，将其添加回结果
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if time_col_data is not None:
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cleaned_data.insert(0, "time", time_col_data)
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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(
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data.index,
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data[sensor_to_plot],
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label="监测值",
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marker="o",
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markersize=3,
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alpha=0.7,
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)
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plt.plot(
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data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
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)
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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(
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anomaly_idx,
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data[sensor_to_plot].loc[anomaly_idx],
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"ro",
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markersize=8,
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label="监测值异常点",
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)
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plt.plot(
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anomaly_idx,
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data_kf[sensor_to_plot].loc[anomaly_idx],
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"go",
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markersize=8,
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label="Kalman修复值",
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)
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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_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
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"""
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接收一个 DataFrame 数据结构，使用一维 Kalman 滤波平滑并用预测值替换基于 IQR 检测出的异常点。
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区分合理的0值（流量转换）和异常的0值（连续多个0或孤立0）。
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返回完整的清洗后的字典数据结构。
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Args:
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data: 输入 DataFrame（可包含 time 列）
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show_plot: 是否显示可视化
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"""
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# 使用传入的 DataFrame
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data = data.copy()
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# 补齐时间缺口（如果启用且数据包含 time 列）
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data_filled = fill_time_gaps(
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data, time_col="time", freq="1min", short_gap_threshold=10
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)
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# 移除 time 列用于后续清洗
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data_filled = data_filled.drop(columns=["time"])
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# 存储 Kalman 平滑结果
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data_kf = pd.DataFrame(index=data_filled.index, columns=data_filled.columns)
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# 平滑每一列
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for col in data_filled.columns:
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observations = pd.Series(data_filled[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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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
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residuals = data_filled - data_kf
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residual_thresholds = {}
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for col in data_filled.columns:
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res_values = residuals[col].dropna().values
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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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# 创建完整的修复数据
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cleaned_data = data_filled.copy()
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anomalies_info = {}
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for col in data_filled.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_filled.index[anomaly_mask.fillna(False)]
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anomalies_info[col] = pd.DataFrame(
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{
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"Observed": data_filled.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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)
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# 直接在原列上替换异常值为 Kalman 预测值
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cleaned_data.loc[anomaly_idx, col] = data_kf.loc[anomaly_idx, col]
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# 可选可视化
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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, 8))
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plt.subplot(2, 1, 1)
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plt.plot(
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data.index,
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data[sensor_to_plot],
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label="原始监测值",
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marker="o",
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markersize=3,
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alpha=0.7,
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)
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abnormal_zero_idx = data.index[data_filled[sensor_to_plot].isna()]
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if len(abnormal_zero_idx) > 0:
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plt.plot(
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abnormal_zero_idx,
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data[sensor_to_plot].loc[abnormal_zero_idx],
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"mo",
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markersize=8,
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label="异常0值",
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)
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plt.plot(
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data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
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)
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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(
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anomaly_idx,
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data_filled[sensor_to_plot].loc[anomaly_idx],
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"ro",
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markersize=8,
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label="IQR异常点",
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)
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plt.xlabel("时间点（序号）")
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plt.ylabel("流量值")
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plt.title(f"{sensor_to_plot}：原始数据与异常检测")
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plt.legend()
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plt.subplot(2, 1, 2)
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plt.plot(
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data.index,
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cleaned_data[sensor_to_plot],
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label="修复后监测值",
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marker="o",
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markersize=3,
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color="green",
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)
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plt.xlabel("时间点（序号）")
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plt.ylabel("流量值")
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plt.title(f"{sensor_to_plot}：修复后数据")
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plt.legend()
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plt.tight_layout()
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plt.show()
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# 返回完整的修复后字典
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return cleaned_data
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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_df_kf(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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