DataNalysis/DataClean/turbine_cleaning.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2024/6/21 13:49
# file: turbine_cleaning.py
# author: David
# company: shenyang JY
import os
import pandas as pd
from datetime import timedelta


# ——————————————————————————机头风速-99和连续异常值清洗代码——————————————————————————————
def mark_abnormal_streaks(df, columns, min_streak):
    abnormal_mask = pd.Series(False, index=df.index)
    streak_start = None
    for i in range(len(df)):
        if i == 0 or any(df.at[i - 1, col] != df.at[i, col] for col in columns):
            streak_start = i

        if i - streak_start >= min_streak - 1:
            abnormal_mask[i - min_streak + 1:i + 1] = True

    return abnormal_mask


def remove_abnormal_values(df, N):
    # 标记C_ACTIVE_POWER为-99的行为异常值
    abnormal_mask1 = df['C_ACTIVE_POWER'] == -99
    count_abnormal1 = abnormal_mask1.sum()

    # 标记C_WS, A, B连续5行不变的行为异常值
    columns = ['C_WS', 'C_WD', 'C_ACTIVE_POWER']
    abnormal_mask2 = mark_abnormal_streaks(df, columns, N)
    count_abnormal2 = abnormal_mask2.sum()
    # 获得所有异常值的布尔掩码
    abnormal_mask = abnormal_mask1 | abnormal_mask2
    # 获取连续异常值具体数值
    removed_continuous_values = {column: df.loc[abnormal_mask2, column].unique() for column in columns}
    # 剔除异常值
    df_clean = df[~abnormal_mask]
    total_removed = abnormal_mask.sum()
    return df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values


def process_csv_files(input_dir, output_dir, turbines_id, M,N):  # MBD:没有考虑时间重复
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    for i in turbines_id:
        input_file = os.path.join(input_dir, f"turbine-{i}.csv")
        output_file = os.path.join(output_dir, f"turbine-{i}.csv")

        # 读取csv文件
        df = pd.read_csv(input_file)

        # 剔除异常值，并获取异常值统计信息
        df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values = remove_abnormal_values(df,N)

        # 输出异常值统计信息
        print(f"处理文件：{input_file}")
        print(f"剔除 -99 点异常值数量：{count_abnormal1}")
        print(f"剔除连续异常值数量：{count_abnormal2}")
        print(f"总共剔除数据量：{total_removed}")
        print(f"剔除的连续异常值具体数值：{removed_continuous_values}\n")

        # 保存处理过的CSV文件
        df_clean.to_csv(output_file, index=False)


# ——————————————————————————风机单机时间对齐——————————————————————————————
def TimeMerge(input_dir, output_dir, turbines_id, M):
    # 读取所有CSV文件
    files = [os.path.join(input_dir, f"turbine-{i}.csv") for i in turbines_id]
    dataframes = [pd.read_csv(f) for f in files]

    # 获取C_TIME列的交集
    c_time_intersection = set(dataframes[0]["C_TIME"])
    for df in dataframes[1:]:
        c_time_intersection.intersection_update(df["C_TIME"])

    # 只保留C_TIME交集中的数据
    filtered_dataframes = [df[df["C_TIME"].isin(c_time_intersection)] for df in dataframes]

    # 将每个过滤后的DataFrame写入新的CSV文件
    os.makedirs(output_dir, exist_ok=True)
    turbines_all, names = [], ['C_TIME']
    for (filtered_df, i) in zip(filtered_dataframes, turbines_id):
        # if i == 144:
        #     filtered_df['C_ACTIVE_POWER'] /= 1000
        filtered_df.to_csv(os.path.join(output_dir, f"turbine-{i}.csv"), index=False)
        names.append('C_ACTIVE_POWER_{}'.format(i))
        turbines_all.append(filtered_df['C_ACTIVE_POWER'].reset_index(drop=True))
    turbines_all.insert(0, filtered_dataframes[0]['C_TIME'].reset_index(drop=True))
    turbines_all = pd.concat(turbines_all, axis=1)
    turbines_all.columns = names
    turbines_all.to_csv(os.path.join(output_dir, f"turbines.csv"), index=False)


# ——————————————————————————风机缺失点处理——————————————————————————————
def MissingPointProcessing(input_dir,output_dir, turbines_id, M,N):

    # 存储数据的列表

    # 读取M个文件
    for k in turbines_id:
        file_name = input_dir + '/' + f"turbine-{k}.csv"
        # file_name = os.path.join(input_dir, f"turbine-{k}.csv")
    # 读取CSV文件
        data = pd.read_csv(file_name, parse_dates=['C_TIME'])

    # 计算时间差
        data['time_diff'] = data['C_TIME'].diff().dt.total_seconds()

    # 找出缺失的时间点
        missing_data_points = data[data['time_diff'] > 900]

    # 存储填充的时间和值
        filled_data = []

    # 输出缺失的开始时刻和数量
        print("缺失的开始时刻:")
        for index, row in missing_data_points.iterrows():
            missing_start = row['C_TIME'] - timedelta(seconds=row['time_diff'])
            missing_count = int(row['time_diff'] // 900) - 1

            # 如果缺失的点数小于N个，则进行填充 MBD:填充代码比较啰嗦
            if missing_count <= N:
                prev_values = data.iloc[index - 1][['C_WS', 'C_WD', 'C_ACTIVE_POWER']]
                next_values = row[['C_WS', 'C_WD', 'C_ACTIVE_POWER']]

                for i in range(1, missing_count + 1):
                    t = i / (missing_count + 1)
                    filled_time = missing_start + timedelta(minutes=15 * i)

                    filled_values = {
                        'C_TIME': filled_time,
                        'C_WS': prev_values['C_WS'] + (next_values['C_WS'] - prev_values['C_WS']) * t,
                        'C_WD': prev_values['C_WD']+(next_values['C_WD']-prev_values['C_WD'])*t,
                        'C_ACTIVE_POWER': prev_values['C_ACTIVE_POWER'] + (
                                    next_values['C_ACTIVE_POWER'] - prev_values['C_ACTIVE_POWER']) * t,
                    }

                    # 将角度值限制在-180到180的范围内
                    filled_values['C_WD'] = (filled_values['C_WD'] + 180) % 360 - 180

                    filled_data.append(filled_values)
                    print(f"填充的时间: {filled_time}, 填充的值: {filled_values}")

            print(f"{missing_start} - 缺失的点的数量: {missing_count}")

        # 将填充的数据插入原始数据中
        filled_df = pd.DataFrame(filled_data)
        data = pd.concat([data, filled_df], ignore_index=True)
        # 对数据按时间排序并重置索引
        data = data.sort_values(by='C_TIME').reset_index(drop=True)

        # 输出总缺失点数
        missing_data_points = data[data['time_diff'] > 900]
        print(f"总缺失点数: {int(missing_data_points['time_diff'].sum() // 900) - len(missing_data_points)}")
        data.drop(columns=['time_diff'], inplace=True)
        os.makedirs(output_dir, exist_ok=True)
        output_path_name = os.path.join(output_dir, f"turbine-{k}.csv")
        print(output_path_name)
    # 保存插值后的文件
        data.to_csv(output_path_name, index=False)