def main(argv):
print('starting....')
para_dict = init_data_para()
para_dict = fc.deal_argv(argv, para_dict)
mode = para_dict['run_mode']
print('starting processing the data...')
bat_list = rwd.get_bat_list(para_dict, mode)
if bat_list is not None:
for bat_name in bat_list:
para_dict['bat_config'] = fc.get_bat_config(
para_dict['bat_info_config'][mode], bat_name, fuzzy=False)
if 'pro_info' in para_dict['mission'] or 'all' in para_dict[
'mission']:
pro_info = rwd.get_pro_info(para_dict, mode, bat_name)
rwd.save_pro_info(pro_info,
para_dict['log_info'] + '_' + bat_name,
para_dict, mode)
if 'cell_v_drop' in para_dict['mission'] or 'all' in para_dict[
'mission']:
#获得cell_v_drop
pro_info = rwd.read_bat_data(
para_dict, mode, para_dict['log_info'] + '_' + bat_name)
feature = cvd.get_feature_vdrop(para_dict, mode, bat_name,
pro_info)
rwd.save_pro_info(feature, 'cell_v_drop_' + bat_name,
para_dict, mode)
if 'cell_stdv' in para_dict['mission'] or 'all' in para_dict[
'mission']:
#获得cell_v_drop
pro_info = rwd.read_bat_data(
para_dict, mode, para_dict['log_info'] + '_' + bat_name)
feature = cs.get_feature_stdv(para_dict, mode, bat_name,
pro_info)
rwd.save_pro_info(feature, 'cell_stdv_' + bat_name, para_dict,
mode)
if 'soh_feature' in para_dict['mission'] or 'all' in para_dict[
'mission']:
pro_info = rwd.read_bat_data(
para_dict, mode, para_dict['log_info'] + '_' + bat_name)
feature = sf.get_feature_soh(para_dict, mode, bat_name,
pro_info)
rwd.save_pro_info(feature, 'cell_soh_' + bat_name, para_dict,
mode)
if 'dqdv_calc' in para_dict['mission'] or 'all' in para_dict[
'mission']:
pro_info = rwd.read_bat_data(
para_dict, mode, para_dict['log_info'] + '_' + bat_name)
feature = dc.get_dqdv_data(para_dict, mode, bat_name, pro_info)
rwd.save_pro_info(feature, 'cell_dqdv_' + bat_name, para_dict,
mode)
else:
print('there is no bat!')
def get_1_pro_data(para_dict, mode, table_name, pro_info, pro_no, condition1='start_time', condition2='end_time', t_keywords='stime'):
str_value1 = pro_info[condition1].iloc[pro_no]
str_value1 = str_value1.strftime("%Y-%m-%d %H:%M:%S")
str_value2 = pro_info[condition2].iloc[pro_no]
str_value2 = str_value2.strftime("%Y-%m-%d %H:%M:%S")
df = rwd.read_bat_data(para_dict, mode, table_name, start_time=str_value1, end_time=str_value2)
return df
def main(argv):
sys.path.append(app_dir)
import rw_bat_data as rwd
import preprocess_data as ppd
import func as fc
import scale_data as sd
para_dict = init_data_para()
para_dict = fc.deal_argv(argv, para_dict)
mode = para_dict['run_mode']
#读取所需的数据，并进行处理后存储到指定位置
print('starting processing the data...')
bat_list = rwd.get_bat_list(para_dict, mode)
regx, mask_filename = fc.get_filename_regx(para_dict['log_pro'],
**para_dict)
if bat_list is not None:
for bat_name in bat_list:
raw_data = rwd.read_bat_data(para_dict,
mode,
bat_name,
limit=para_dict['data_limit'][mode])
data = ppd.preprocess_data(bat_name, raw_data)
rwd.save_bat_data(data, para_dict['log_pro'] + '_' + bat_name,
para_dict, mode) #存储处理后的数据
else:
print('there is no bat!')
#将处理好的数据按工作状态划分后存储到指定位置
print('save the processed data...')
result = fc.save_workstate_data(regx, mask_filename,
para_dict['processed_data_dir'][mode],
para_dict['processed_data_dir'][mode])
if not result:
print(
'there is not any files included the data which would been scaled.'
)
return
else:
#进行扩充
print('to be scaled...')
for state in para_dict['states']:
file_name = r'%s_' % state + mask_filename
processed_data = sd.get_processed_data(
os.path.join(para_dict['processed_data_dir'][mode], file_name))
scale_data = sd.generate_data(processed_data, **para_dict)
sd.save_scale_data(scale_data,
para_dict['log_scale'] + '_' + file_name,
para_dict['scale_data_dir'][mode])
print('finished scaling the %s data' % state)
#训练模型
print('starting training the model...')
for state in para_dict['states']:
file_name = r'%s_%s_%s' % (para_dict['log_scale'], state,
mask_filename)
para_dict['pkl_dir'] = {
'run':
os.path.normpath('/raid/data/processed_data/pkl/' + save_dir +
'/%s_pkl' % state),
'debug':
os.path.normpath(app_dir + '/%s_pkl' % state)
}
import build_model as bm
bm.train_model(file_name, state, **para_dict)
'db': 'test_bat',
'port': 3306
}
}
para_dict['processed_data_dir'] = {'debug': './data'}
mode = 'debug'
table_list = rwd.get_bat_list(para_dict, mode)
if 'cell_v_drop' in table_list[0]:
file_name = 'cell_v_drop'
cut = 12
elif 'cell_stdv' in table_list[0]:
file_name = 'cell_stdv'
cut = 10
elif 'cell_soh' in table_list[0]:
file_name = 'cell_soh'
cut = 9
if table_list is not None:
data = []
data_dir = para_dict['processed_data_dir'][mode]
if not os.path.exists(data_dir):
os.mkdir(data_dir)
writer = pd.ExcelWriter(os.path.join(data_dir, file_name + '.xlsx'))
for table_name in table_list:
v = rwd.read_bat_data(para_dict, mode, table_name)
v['bat_id'] = table_name[cut:]
v = v.drop(columns='index')
v.to_excel(writer, table_name[cut:])
rwd.save_bat_data(v, table_name, para_dict, mode)
data.append(v)
df = pd.concat(tuple(data))
rwd.save_bat_data(df, file_name, para_dict, mode)