写了一个白噪声干扰算法的实际应用案例（指读取文件然后随机选列最后在规定位置输出）

disturb_data_to_csv.py

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+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=",")