zlwl-algos-new/USER/limingze/REFANGZHEN/temp_curve.py

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import datetime
from tqdm import tqdm
from scipy.interpolate import interp1d
from pathlib import Path
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression

from ZlwlAlgosCommon.utils.ProUtils import *
from ZlwlAlgosCommon.service.iotp.IotpAlgoService import IotpAlgoService
from ZlwlAlgosCommon.service.iotp.Beans import DataField
from ZlwlAlgosCommon.orm.models import *
   
from Data_Cleaning_oop_thr import DataCleaning  

sn_list = pd.read_csv('/home/limingze/zlwl-algos/USER/limingze/REFANGZHEN/丰田通商.csv')
sn_list = sn_list['pack_code'].tolist()

start_time = datetime.datetime.strptime("2023-7-1 00:00:00", '%Y-%m-%d %H:%M:%S').strftime('%Y-%m-%d %H:%M:%S')
end_time = datetime.datetime.strptime("2023-7-8 00:00:00", '%Y-%m-%d %H:%M:%S').strftime('%Y-%m-%d %H:%M:%S')

cur_env = 'dev' # 设置运行环境
app_path = "/home/limingze/zlwl-algos/" # 设置app绝对路径           
sysUtils = SysUtils(cur_env, app_path)
hbase_params = sysUtils.get_cf_param('hbase-datafactory')
iotp_service = IotpAlgoService(hbase_params=hbase_params)
columns = [ DataField.time, DataField.sn, DataField.pack_crnt, DataField.pack_soc,  
            DataField.cell_voltage, DataField.cell_temp, DataField.bms_sta]

for sn in sn_list:
    df_data = iotp_service.get_data(sn_list=[sn], columns=columns, start_time=start_time, end_time=end_time)
    data_clean = DataCleaning(df_data)
    data_clean = data_clean.revise_status_codes()
    data_clean['time'] = pd.to_datetime(df_data['time'])
    data_clean['bms_sta_change'] = data_clean['bms_sta'].shift(-1) - data_clean['bms_sta']
    charge_index = data_clean[(data_clean['bms_sta_change'] == 18) | (data_clean['bms_sta_change'] == 21)].index
    discharge_index = data_clean[(data_clean['bms_sta_change'] == -18) | (data_clean['bms_sta_change'] == -21)].index

    data_clean.to_csv('/home/limingze/zlwl-algos/USER/limingze/REFANGZHEN/ceshi.py')

    if (len(charge_index) != 0) & (len(discharge_index) != 0):
        if len(charge_index) > len(discharge_index):
            num_cycle = len(discharge_index)
        elif len(charge_index) == len(discharge_index):
            num_cycle = len(discharge_index) -1
        if num_cycle > 0:
            for j in tqdm(range(num_cycle)):
                result_df = data_clean[data_clean['time'].between(data_clean.loc[charge_index[j], 'time'], data_clean.loc[charge_index[j+1], 'time'])]
                result_df = result_df.reset_index(drop=True)