def oldMain(factory, line, device, measurePoint='三相总有功功率'):
# print(factory)
# print(line)
# print(device)
#
# dataDir = "data/"+factory+"/"
# # measurePoint = '' # 所分析数据项
# # device = '低压总出' # 所要预测设备(全建筑总出)
# # if os.path.exists('data\\tmp\\P_total.csv'):
# # P_total = pd.read_csv('data\\tmp\\P_total.csv', index_col=0)
# # else:
# P_total = readData(dataDir, (measurePoint))
# P_total.to_csv('data/tmp/P_total.csv')
# P_total.index = pd.to_datetime(P_total.index)
# # 补全device名并得到device_index
# for i in range(P_total.shape[1]):
# if device in P_total.columns[i]:
# device = P_total.columns[i]
# device_index = i
# if device_index == -1:
# raise NameError('Check the device!')
# 不需要时间参数
# 功能一：基于自适应时滞pearson相关系数找最相关设备
P_total, device_index = Tool.getP_total(factory, line, device,
measurePoint)
print("—————————————————一、时空相关性分析(图1)—————————————————————")
corr_device = correlation(P_total, device_index, 3)
print('corr_device:', corr_device)
# 功能二：进行负荷预测模型的训练与测试
# 需要返回什么数据/模型可自行修改函数
print("—————————————————二、用户负荷建模与预测(图2)—————————————————————")
a, b = train_forecast(P_total, corr_device, device_index)
return a.tolist(), b.tolist()
