zlwl-algos-new/USER/cez/hz_user/get_dataframes.py

import pandas as pd
import os
import numpy as np
from tqdm import tqdm
from datetime import datetime

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)

path = './data/'
out_path = './dataframes/'


temp_thresh_max_c = 35
temp_thresh_min_c = 10
mean_crnt_thresh_c = 1

temp_thresh_max_d = 35
temp_thresh_min_d = 10
mean_crnt_thresh_d = 0.1

if __name__ == '__main__':
    test_vin_list = os.listdir(path)
    
    for test_vin in test_vin_list:
        if not os.path.exists(out_path+test_vin+'/charge/'):
            os.makedirs(out_path+test_vin+'/charge/')
        
        if not os.path.exists(out_path+test_vin+'/drive/'):
            os.makedirs(out_path+test_vin+'/drive/')
        
        crg_file_list = [file for file in os.listdir(path+test_vin+'/') if file.endswith('charge_proc_di.csv')]
        
        for crg_file in tqdm(crg_file_list):
            df_user = pd.DataFrame(columns=['vin', 'pack_model', 'week', 'temp_max_25', 'temp_max_75', 'temp_35', 'temp_min_25', 'temp_min_75', 'temp_10', \
                'chrgah', 'meancrnt', 'sts_flg', 'full_chrg_flg', 'cellvol_max_25', 'cellvol_max_75', 'packvol_max_25', 'packvol_max_75', 'temp_time_15', 'temp_time_30'])
        
            vin = crg_file.split('_')[0]
            df_data = pd.read_csv(path+test_vin+'/' + crg_file)
            # convert dt from int to datetime
            df_data['dt'] = df_data['dt'].apply(lambda x: datetime.strptime(str(x), '%Y%m%d'))
            df_data = df_data.loc[df_data['dt'] >= df_data['dt'].max() - pd.Timedelta(days=90)]
            if len(df_data) == 0:
                continue
            df_data['wk'] = df_data['dt'].dt.isocalendar().week
            pack_model = df_data['pack_model'][0]
            df_data_dt_list = list(df_data.groupby('wk'))
            for date, df_data_dt in df_data_dt_list:
                temp_max_list = df_data_dt['temp_max'].tolist()
                temp_max_25 = np.quantile(temp_max_list, 0.25)
                temp_max_75 = np.quantile(temp_max_list, 0.75)
                temp_35 = len([temp for temp in temp_max_list if temp > temp_thresh_max_c])
                
                temp_min_list = df_data_dt['temp_min'].tolist()
                temp_min_25 = np.quantile(temp_min_list, 0.25)
                temp_min_75 = np.quantile(temp_min_list, 0.75)
                temp_10 = len([temp for temp in temp_min_list if temp < temp_thresh_min_c])
                
                chrgah = df_data_dt['chrgah'].sum()
                
                meancrnt = len([crnt for crnt in df_data_dt['meancrnt'].tolist() if crnt > mean_crnt_thresh_c])
                
                sts_flg = len([sts for sts in df_data_dt['sts_flg'].tolist() if sts == 1])
                
                full_chrg_flg = len([full_chrg for full_chrg in df_data_dt['full_chrg_flg'].tolist() if full_chrg == 1])
                
                cellvol_max_list = df_data_dt.loc[df_data_dt['full_chrg_flg'] == 1]['cellvol_max'].tolist()
                if len(cellvol_max_list) == 0:
                    cellvol_max_25 = 0
                    cellvol_max_75 = 0
                else:
                    cellvol_max_25 = np.quantile(cellvol_max_list, 0.25)
                    cellvol_max_75 = np.quantile(cellvol_max_list, 0.75)
                
                packvol_max_list = df_data_dt.loc[df_data_dt['full_chrg_flg'] == 1]['packvol_max'].tolist()
                if len(packvol_max_list) == 0:
                    packvol_max_25 = 0
                    packvol_max_75 = 0
                else:
                    packvol_max_25 = np.quantile(packvol_max_list, 0.25)
                    packvol_max_75 = np.quantile(packvol_max_list, 0.75)
                
                temp_time_15 = (df_data_dt['temp_time_1']+df_data_dt['temp_time_2']+df_data_dt['temp_time_3']+df_data_dt['temp_time_4']).sum()
                temp_time_30 = (df_data_dt['temp_time_6']+df_data_dt['temp_time_7']).sum()
                
                df_user.loc[len(df_user)] = [vin, pack_model, date, temp_max_25, temp_max_75, temp_35, temp_min_25, temp_min_75, temp_10, \
                    chrgah, meancrnt, sts_flg, full_chrg_flg, cellvol_max_25, cellvol_max_75, packvol_max_25, packvol_max_75, temp_time_15, temp_time_30]
                
            df_user.to_csv(out_path+test_vin+'/charge/' +f'{vin}.csv', index=False)
                
        
        
        drv_file_list = [file for file in os.listdir(path+test_vin+'/') if file.endswith('drive_proc_di.csv')]
        
        
        for drv_file in tqdm(drv_file_list):
            df_user = pd.DataFrame(columns=['vin', 'pack_model', 'week', 'temp_max_25', 'temp_max_75', 'temp_35', 'temp_min_25', 'temp_min_75', 'temp_10', \
                'delta_odo', 'dschrgah', 'meancrnt','temp_time_15', 'temp_time_30', 'spd_mean', 'accon_mean', 'fst_acc', 'maxspd'])

            vin = drv_file.split('_')[0]
            df_data = pd.read_csv(path+test_vin+'/' + drv_file)
            # convert dt from int to datetime
            df_data['dt'] = df_data['dt'].apply(lambda x: datetime.strptime(str(x), '%Y%m%d'))
            df_data = df_data.loc[df_data['dt'] >= df_data['dt'].max() - pd.Timedelta(days=90)]
            if len(df_data) == 0:
                continue
            df_data['wk'] = df_data['dt'].dt.isocalendar().week
            pack_model = df_data['pack_model'][0]
            df_data_dt_list = list(df_data.groupby('wk'))
            for date, df_data_dt in df_data_dt_list:
                temp_max_list = df_data_dt['temp_max'].tolist()
                temp_max_25 = np.quantile(temp_max_list, 0.25)
                temp_max_75 = np.quantile(temp_max_list, 0.75)
                temp_35 = len([temp for temp in temp_max_list if temp > temp_thresh_max_d])
                
                temp_min_list = df_data_dt['temp_min'].tolist()
                temp_min_25 = np.quantile(temp_min_list, 0.25)
                temp_min_75 = np.quantile(temp_min_list, 0.75)
                temp_10 = len([temp for temp in temp_min_list if temp < temp_thresh_min_d])
                
                delta_odo = df_data_dt['delta_odo'].sum()
                
                dschrg_ah = df_data_dt['dschrg_ah'].sum()
                
                spd_mean = df_data_dt['spd_mean'].max()
                
                accon_mean = df_data_dt['accon_mean'].max()
                
                fst_acc = (df_data_dt['fst_acc_pls']+df_data_dt['fst_acc_mus']+df_data_dt['fst_acc_trn']).sum()
                
                meancrnt = len([crnt for crnt in df_data_dt['meancrnt'].tolist() if crnt > mean_crnt_thresh_d])
                
                maxspd = df_data_dt['maxspd'].max()
                
                temp_time_15 = (df_data_dt['temp_time_1']+df_data_dt['temp_time_2']+df_data_dt['temp_time_3']+df_data_dt['temp_time_4']).sum()
                temp_time_30 = (df_data_dt['temp_time_6']+df_data_dt['temp_time_7']).sum()
                
                df_user.loc[len(df_user)] = [vin, pack_model, date, temp_max_25, temp_max_75, temp_35, temp_min_25, temp_min_75, temp_10, \
                    delta_odo, dschrg_ah, meancrnt, temp_time_15, temp_time_30, spd_mean, accon_mean, fst_acc, maxspd]
            
            df_user.to_csv(out_path+test_vin+'/drive/' +f'{vin}.csv', index=False)