energy-data-trans/etl/wind_power/wave/WaveTrans.py

import json
import multiprocessing

from service.plt_service import get_all_wind
from service.trans_service import get_wave_conf, save_df_to_db, get_or_create_wave_table, \
    get_wave_data, delete_exist_wave_data
from utils.file.trans_methods import *
from utils.systeminfo.sysinfo import get_available_cpu_count_with_percent


class WaveTrans(object):

    def __init__(self, field_code, read_path, save_path: str):
        self.field_code = field_code
        self.read_path = read_path
        self.save_path = save_path
        self.begin = datetime.datetime.now()

    def get_data_exec(self, func_code, arg):
        exec(func_code)
        return locals()['get_data'](arg)

    def del_exists_data(self, df):
        min_date, max_date = df['time_stamp'].min(), df['time_stamp'].max()
        db_df = get_wave_data(self.field_code + '_wave', min_date, max_date)

        exists_df = pd.merge(db_df, df,
                             on=['wind_turbine_name', 'time_stamp', 'sampling_frequency', 'mesure_point_name'],
                             how='inner')
        ids = [int(i) for i in exists_df['id'].to_list()]
        if ids:
            delete_exist_wave_data(self.field_code + "_wave", ids)

    def run(self):
        all_files = read_files(self.read_path, ['csv'])
        print(len)
        # 最大取系统cpu的 1/2
        split_count = get_available_cpu_count_with_percent(1 / 2)

        all_wind, _ = get_all_wind(self.field_code, False)

        get_or_create_wave_table(self.field_code + '_wave')

        wave_conf = get_wave_conf(self.field_code)

        base_param_exec = wave_conf['base_param_exec']
        map_dict = {}
        if base_param_exec:
            base_param_exec = base_param_exec.replace('\r\n', '\n').replace('\t', '    ')
            print(base_param_exec)
            if 'import ' in base_param_exec:
                raise Exception("方法不支持import方法")

        mesure_poins = [key for key, value in wave_conf.items() if str(key).startswith('conf_') and value]
        for point in mesure_poins:
            map_dict[wave_conf[point]] = point.replace('conf_', '')

        map_dict['rotational_speed'] = 'rotational_speed'

        with multiprocessing.Pool(split_count) as pool:
            file_datas = pool.starmap(self.get_data_exec, [(base_param_exec, i) for i in all_files])

        print("读取文件耗时:", datetime.datetime.now() - self.begin)

        result_list = list()
        for file_data in file_datas:
            wind_turbine_name, time_stamp, sampling_frequency, rotational_speed, mesure_point_name, mesure_data = \
                file_data[0], file_data[1], file_data[2], file_data[3], file_data[4], file_data[5]

            if mesure_point_name in map_dict.keys():
                result_list.append(
                    [wind_turbine_name, time_stamp, sampling_frequency, 'rotational_speed', [float(rotational_speed)]])

                result_list.append(
                    [wind_turbine_name, time_stamp, sampling_frequency, mesure_point_name, mesure_data])

        df = pd.DataFrame(result_list,
                          columns=['wind_turbine_name', 'time_stamp', 'sampling_frequency', 'mesure_point_name',
                                   'mesure_data'])
        df['time_stamp'] = pd.to_datetime(df['time_stamp'], errors='coerce')
        df['mesure_point_name'] = df['mesure_point_name'].map(map_dict)
        df.dropna(subset=['mesure_point_name'], inplace=True)

        df['wind_turbine_number'] = df['wind_turbine_name'].map(all_wind).fillna(df['wind_turbine_name'])

        df['mesure_data'] = df['mesure_data'].apply(lambda x: json.dumps(x))

        df.sort_values(by=['time_stamp', 'mesure_point_name'], inplace=True)

        self.del_exists_data(df)

        save_df_to_db(self.field_code + '_wave', df, batch_count=1000)

        print("总耗时:", datetime.datetime.now() - self.begin)