40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import pandas as pd
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import random
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import numpy
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from white_noise import white_noise
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import ray
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ray.init()
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filepath = "E:/Data_sum/ttxd200.csv"
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origin_data = pd.read_csv(filepath)
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row_len = origin_data.shape[0]
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cow_len = origin_data.shape[1]
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columns_index=origin_data.columns
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columns_list=columns_index.tolist()
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disturb_data = origin_data
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real_origin_data = origin_data
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algorithm_white_noise = white_noise.remote()
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algorithm_white_noise.set_config.remote('{"WHITE_NOISE_STANDARD_DEVIATION_BASE": 30}')
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#cow_name = "ttxd_12_1"
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contrast_data = pd.DataFrame()
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for i in range(0,row_len-50,10):
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col_number = random.randint(0,cow_len)
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cow_name = columns_list[col_number]
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for j in range(i,i+50):
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futures = algorithm_white_noise.eval.remote(origin_data.loc[j, cow_name])
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disturb_data.loc[i, cow_name] = ray.get(futures)
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contrast_data[cow_name+'_disturb'+str(i)] = disturb_data[cow_name]
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disturb_data = real_origin_data
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origin_data = real_origin_data
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# print(columns_list)
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contrast_data.to_csv("E:/Data_sum/ttxd200_disturb.csv", index=False)
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contrast_data_only = contrast_data.values
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# contrast_data_only.to_csv("E:/Data_sum/ttxd200_disturb_data.csv", index=False)
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print(contrast_data_only)
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# pd.DataFrame(contrast_data_only).to_csv("E:/Data_sum/ttxd200_disturb_data.csv", index=False)
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numpy.savetxt("E:/Data_sum/ttxd200_disturb_data.csv", contrast_data_only, delimiter=",") |