DataNalysis/DataBase/db-wind/inputData.py

import pandas as pd
import datetime, time
import pytz
from savedata import saveData, readData
from Arg import Arg
from sqlalchemy import create_engine
import pytz
from data_cleaning import cleaning, rm_duplicated

# def readData(name):
#     """
#     读取数据
#     :param name: 名字
#     :return:
#     """
#     path = dataloc + r"/" + name
#     return pd.read_csv(path)
#
#
# def saveData(name, data):
#     """
#     存放数据
#     :param name: 名字
#     :param data: 数据
#     :return:
#     """
#     path = dataloc + r"/" + name
#     os.makedirs(os.path.dirname(path), exist_ok=True)
#     data.to_csv(path, index=False)


def timestamp_to_datetime(ts):
    local_timezone = pytz.timezone('Asia/Shanghai')
    if type(ts) is not int:
        raise ValueError("timestamp-时间格式必须是整型")
    if len(str(ts)) == 13:
        dt = datetime.datetime.fromtimestamp(ts/1000, tz=pytz.utc).astimezone(local_timezone)
        return dt
    elif len(str(ts)) == 10:
        dt = datetime.datetime.fromtimestamp(ts, tz=pytz.utc).astimezone(local_timezone)
        return dt
    else:
        raise ValueError("timestamp-时间格式错误")


def timestr_to_timestamp(time_str):
    """
    将时间戳或时间字符串转换为datetime.datetime类型
    :param time_data: int or str
    :return:datetime.datetime
    """
    if isinstance(time_str, str):
        if len(time_str) == 10:
            dt = datetime.datetime.strptime(time_str, '%Y-%m-%d')
            return int(round(time.mktime(dt.timetuple())) * 1000)
        elif len(time_str) in {17, 18, 19}:
            dt = datetime.datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S')   # strptime字符串解析必须严格按照字符串中的格式
            return int(round(time.mktime(dt.timetuple())) * 1000)   # 转换成毫秒级的时间戳
        else:
            raise ValueError("时间字符串长度不满足要求！")
    else:
        return time_str


class DataBase(object):
    def __init__(self, arg):
        self.begin = datetime.datetime.strptime(arg.begin, '%Y-%m-%d')
        self.end = datetime.datetime.strptime(arg.end, '%Y-%m-%d') - pd.Timedelta(minutes=15)
        self.begin_stamp = timestr_to_timestamp(str(arg.begin))
        self.end_stamp = timestr_to_timestamp(str(self.end))
        self.database = arg.database
        self.towerloc = arg.towerloc
        self.turbineloc = arg.turbineloc
        self.dataloc = arg.dataloc

    def clear_data(self):
        """
        删除所有csv
        :return:
        """
        # 设置文件夹路径
        import glob
        import os
        folder_path = self.dataloc

        # 使用 glob 获取所有的 .csv 文件路径
        csv_files = glob.glob(os.path.join(folder_path, '**/*.csv'), recursive=True)

        # 遍历所有 .csv 文件并删除
        for file_path in csv_files:
            os.remove(file_path)
        print("清除所有csv文件")

    def create_database(self):
        """
        创建数据库连接
        :param database: 数据库地址
        :return:
        """
        engine = create_engine(self.database)
        return engine

    def exec_sql(self, sql, engine):
        """
        从数据库获取数据
        :param sql: sql语句
        :param engine: 数据库对象
        :return:
        """
        df = pd.read_sql_query(sql, engine)
        return df

    def split_time(self, data):
        data.set_index('C_TIME', inplace=True)
        data = data.sort_index().loc[self.begin: self.end]
        data.reset_index(drop=False, inplace=True)
        return data

    def get_process_NWP(self):
        """
        从数据库中获取NWP数据，并进行简单处理
        :param database:
        :return:
        """
        # NPW数据
        engine = self.create_database()
        sql_NWP = "select C_PRE_TIME,C_T,C_RH,C_PRESSURE, C_SWR," \
                  "C_DIFFUSE_RADIATION, C_DIRECT_RADIATION,  " \
                  "C_WD10,C_WD30,C_WD50,C_WD70,C_WD80,C_WD90,C_WD100,C_WD170," \
                  "C_WS10,C_WS30,C_WS50,C_WS70,C_WS80,C_WS90,C_WS100,C_WS170 from t_nwp" \
                  " where C_PRE_TIME between {} and {}".format(self.begin_stamp, self.end_stamp) # 风的NWP字段
        NWP = self.exec_sql(sql_NWP, engine)

        NWP['C_PRE_TIME'] = NWP['C_PRE_TIME'].apply(timestamp_to_datetime)

        NWP = NWP.rename(columns={'C_PRE_TIME': 'C_TIME'})
        NWP = cleaning(NWP, 'NWP')
        # NWP = self.split_time(NWP)
        NWP['C_TIME'] = NWP['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("NWP.csv", NWP)
        return NWP

    def get_process_tower(self):
        """
        获取环境检测仪数据
        :param database:
        :return:
        """
        engine = self.create_database()
        print("提取测风塔：{}".format(self.towerloc))
        for i in self.towerloc:
            # 删除没用的列
            drop_colmns = ["C_DATA1","C_DATA2","C_DATA3","C_DATA4","C_DATA5","C_DATA6","C_DATA7","C_DATA8","C_DATA9","C_DATA10","C_IS_GENERATED","C_ABNORMAL_CODE"]

            get_colmns = []
            # 查询表的所有列名
            result_set = self.exec_sql("SHOW COLUMNS FROM t_wind_tower_status_data", engine)
            for name in result_set.iloc[:, 0]:
                if name not in drop_colmns:
                    get_colmns.append(name)

            all_columns_str = ", ".join([f'{col}' for col in get_colmns])
            tower_sql = "select " + all_columns_str + " from t_wind_tower_status_data where C_EQUIPMENT_NO="+str(i) + " and C_TIME between '{}' and '{}'".format(self.begin, self.end)
            tower = self.exec_sql(tower_sql, engine)
            tower['C_TIME'] = pd.to_datetime(tower['C_TIME'])
            saveData("/tower-{}.csv".format(i), tower)
            print("测风塔{}导出数据".format(i))


    def get_process_power(self):
        """
        获取整体功率数据
        :param database:
        :return:
        """
        engine = self.create_database()
        sql_cap = "select C_CAPACITY from t_electric_field"
        cap = self.exec_sql(sql_cap, engine)['C_CAPACITY']
        sql_power = "select C_TIME,C_REAL_VALUE, C_ABLE_VALUE, C_IS_RATIONING_BY_MANUAL_CONTROL, C_IS_RATIONING_BY_AUTO_CONTROL" \
                    " from t_power_station_status_data where C_TIME between '{}' and '{}'".format(self.begin, self.end)
        # if self.opt.usable_power["clean_power_by_signal"]:
        #     sql_power += " and C_IS_RATIONING_BY_MANUAL_CONTROL=0 and  C_IS_RATIONING_BY_AUTO_CONTROL=0"
        powers = self.exec_sql(sql_power, engine)
        powers['C_TIME'] = pd.to_datetime(powers['C_TIME'])
        mask1 = powers['C_REAL_VALUE'].astype(float) > float(cap)
        mask = powers['C_REAL_VALUE'] == -99

        mask = mask | mask1
        print("实际功率共{}条，要剔除功率有{}条".format(len(powers), mask.sum()))
        powers = powers[~mask]
        print("剔除完后还剩{}条".format(len(powers)))
        binary_map = {b'\x00': 0, b'\x01': 1}
        powers['C_IS_RATIONING_BY_AUTO_CONTROL'] = powers['C_IS_RATIONING_BY_AUTO_CONTROL'].map(binary_map)
        powers = rm_duplicated(powers)
        saveData("power.csv", powers)

    def get_process_dq(self):
        """
        获取短期预测结果
        :param database:
        :return:
        """
        engine = self.create_database()
        sql_dq = "select C_FORECAST_TIME AS C_TIME, C_FP_VALUE from t_forecast_power_short_term " \
                 "where C_FORECAST_TIME between {} and {}".format(self.begin_stamp, self.end_stamp)
        dq = self.exec_sql(sql_dq, engine)
        # dq['C_TIME'] = pd.to_datetime(dq['C_TIME'], unit='ms')
        dq['C_TIME'] = dq['C_TIME'].apply(timestamp_to_datetime)
        # dq = dq[dq['C_FORECAST_HOW_LONG_AGO'] == 1]
        # dq.drop('C_FORECAST_HOW_LONG_AGO', axis=1, inplace=True)
        dq = cleaning(dq, 'dq', cols=['C_FP_VALUE'])
        dq['C_TIME'] = dq['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("dq.csv", dq)

    def indep_process(self):
        """
        进一步数据处理：时间统一处理等
        :return:
        """
        # 测风塔数据处理
        for i in self.towerloc:
            tower = readData("/tower-{}.csv".format(i))
            tower['C_TIME'] = pd.to_datetime(tower['C_TIME'])
            # 判断每一列是否全是 -99
            all_minus_99 = (tower == -99).all()

            # 获取全是 -99 的列的列名
            cols_to_drop = all_minus_99[all_minus_99 == True].index.tolist()

            # 使用 drop() 方法删除列
            tower = tower.drop(cols_to_drop, axis=1)
            tower = cleaning(tower, 'tower', ['C_WS_INST_HUB_HEIGHT'])
            saveData("/tower-{}-process.csv".format(i), tower)

    def get_process_cdq(self):
        """
        获取超短期预测结果
        :param database:
        :return:
        """
        engine = self.create_database()
        sql_cdq = "select C_FORECAST_TIME AS C_TIME, C_ABLE_VALUE, C_FORECAST_HOW_LONG_AGO from t_forecast_power_ultra_short_term_his" \
                  " where C_FORECAST_TIME between {} and {}".format(self.begin_stamp, self.end_stamp)
        cdq = self.exec_sql(sql_cdq, engine)
        cdq['C_TIME'] = cdq['C_TIME'].apply(timestamp_to_datetime)
        cdq = cleaning(cdq, 'cdq', cols=['C_ABLE_VALUE'], dup=False)
        # cdq = cdq[cdq['C_FORECAST_HOW_LONG_AGO'] == int(str(self.opt.predict_point)[1:])]
        cdq['C_TIME'] = cdq['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("cdq.csv", cdq)

    def get_process_turbine(self):
        """
        从数据库中获取风头数据，并进行简单处理
        :param database:
        :return:
        """
        for number in self.turbineloc:
            # number = self.opt.usable_power['turbine_id']
            # 机头数据
            engine = self.create_database()

            print("导出风机{}的数据".format(number))
            sql_turbine = "select C_TIME, C_WS, C_WD, C_ACTIVE_POWER from t_wind_turbine_status_data " \
                          "WHERE C_EQUIPMENT_NO=" + str(number) + " and C_TIME between '{}' and '{}'".format(self.begin, self.end)    # + " and C_WS>0 and C_ACTIVE_POWER>0"
            turbine = self.exec_sql(sql_turbine, engine)
            turbine = cleaning(turbine, 'cdq', cols=['C_WS', 'C_ACTIVE_POWER'], dup=False)
            turbine['C_TIME'] = pd.to_datetime(turbine['C_TIME'])
            turbine = turbine[turbine['C_TIME'].dt.strftime('%M').isin(['00', '15', '30', '45'])]
            # 直接导出所有数据
            saveData("turbine-15/turbine-{}.csv".format(number), turbine)



    def data_process(self):
        """
        数据导出+初步处理的总操控代码
        :param database:
        :return:
        """
        self.clear_data()
        try:
            self.get_process_power()
            self.get_process_dq()
            self.get_process_cdq()
            self.get_process_NWP()
            self.get_process_tower()
            # self.get_process_turbine()
            self.indep_process()
        except Exception as e:
            print("导出数据出错：{}".format(e.args))