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）

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (python_vitual_3.85)" project-jdk-type="Python SDK" />
 </project>

.idea/python_project.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (python_vitual_3.85)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">

disturb_data_to_csv.py

@@ -0,0 +1,40 @@
+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=",")

variance_x.py

@@ -2,6 +2,7 @@ from algorithm import algorithm
 import json
 import ray
 
+# @ray.remote
 @ray.remote
 class variance_x(algorithm):
     def __init__(self):

variance_x_test_csv.py

@@ -26,6 +26,6 @@ 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)
 
-
+ray.shutdown()
 # 以下均为测试性能用
 # print(algorithm_step.config_)