import pandas as pd
import random
import numpy
from white_noise import white_noise
import ray

ray.init()
filepath = "E:/Data_sum/ttxd200.csv"
origin_data = pd.read_csv(filepath)
row_len = origin_data.shape[0]
cow_len = origin_data.shape[1]
columns_index=origin_data.columns
columns_list=columns_index.tolist()

disturb_data = origin_data
real_origin_data = origin_data
algorithm_white_noise = white_noise.remote()
algorithm_white_noise.set_config.remote('{"WHITE_NOISE_STANDARD_DEVIATION_BASE": 30}')

#cow_name = "ttxd_12_1"
contrast_data = pd.DataFrame()


for i in range(0,row_len-50,10):
    col_number = random.randint(0,cow_len)
    cow_name = columns_list[col_number]
    for j in range(i,i+50):
        futures = algorithm_white_noise.eval.remote(origin_data.loc[j, cow_name])
        disturb_data.loc[i, cow_name] = ray.get(futures)
    contrast_data[cow_name+'_disturb'+str(i)] = disturb_data[cow_name]
    disturb_data = real_origin_data
    origin_data = real_origin_data

# print(columns_list)
contrast_data.to_csv("E:/Data_sum/ttxd200_disturb.csv", index=False)
contrast_data_only = contrast_data.values
# contrast_data_only.to_csv("E:/Data_sum/ttxd200_disturb_data.csv", index=False)
print(contrast_data_only)
# pd.DataFrame(contrast_data_only).to_csv("E:/Data_sum/ttxd200_disturb_data.csv", index=False)
numpy.savetxt("E:/Data_sum/ttxd200_disturb_data.csv", contrast_data_only, delimiter=",")