import pandas as pd
from variance_x import variance_x
import ray

ray.init()

RAY_DISABLE_MEMORY_MONITOR=1
filepath = "D:/python_project_data/1.csv"
origin_data = pd.read_csv(filepath)
row_len = origin_data.shape[0]
cow_len = origin_data.shape[1]
variance_data = origin_data
algorithm_variance_x = variance_x.remote()

cow_name = "G1.TTXD1_3"
contrast_data = pd.DataFrame()
contrast_data[cow_name+'_origin'] = origin_data[cow_name]
algorithm_variance_x.set_config.remote('{"WINDOW_LENGTH": 5 }')
# algorithm_step.set_config('{"CYCLE_TIME_BASE": 5 }')
for i in range(0,row_len):
    futures=algorithm_variance_x.eval.remote(origin_data.loc[i,cow_name])
    variance_data.loc[i, cow_name]=ray.get(futures)

print(variance_data.loc[:, cow_name])
contrast_data[cow_name+'_variance_x'] = variance_data[cow_name]
# average_data.to_csv("D:/python_project_data/1_disturb.csv", index=False)
contrast_data.to_csv("D:/python_project_data/variance_data_x.csv", index=False)


# 以下均为测试性能用
# print(algorithm_step.config_)