Changes:增加了自动生成修改数据变csv的格式

cycle.py

@@ -1,8 +1,9 @@
 from algorithm import algorithm
 import json
 import math
+import ray
 
-
+@ray.remote
 class cycle(algorithm):
     def __init__(self):
         self.config_dict_ = None

cycle_test_csv.py

@@ -1,25 +1,29 @@
 import pandas as pd
 from cycle import cycle
+import ray
 
+ray.init()
 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]
 disturb_data = origin_data
-algorithm_step = cycle()
+algorithm_step = cycle.remote()
 
 cow_name = "G1.TTXD1_3"
 contrast_data = pd.DataFrame()
 contrast_data[cow_name+'_origin'] = origin_data[cow_name]
-algorithm_step.set_config('{"CYCLE_AMPLITUDE_BASE": 200 , "CYCLE_TIME_BASE": 5 ,"CYCLE_START_ANGLE_BASE": 3.14}')
+algorithm_step.set_config.remote('{"CYCLE_AMPLITUDE_BASE": 200 , "CYCLE_TIME_BASE": 5 ,"CYCLE_START_ANGLE_BASE": 3.14}')
 # algorithm_step.set_config('{"CYCLE_TIME_BASE": 5 }')
 for i in range(0,row_len):
-    disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name],i)
+    futures=algorithm_step.eval.remote(origin_data.loc[i,cow_name],i)
-print(disturb_data)
+    disturb_data.loc[i,cow_name]=ray.get(futures)
+
+print(disturb_data.loc[:,cow_name])
 contrast_data[cow_name+'_disturb'] = disturb_data[cow_name]
 disturb_data.to_csv("D:/python_project_data/1_disturb.csv", index=False)
 contrast_data.to_csv("D:/python_project_data/contrast_data.csv", index=False)
 
 
 # 以下均为测试性能用
-print(algorithm_step.config_)
+# print(algorithm_step.config_)

excursion.py

@@ -1,7 +1,8 @@
 from algorithm import algorithm
 import json
+import ray
 
-
+@ray.remote
 class excursion(algorithm):
     def __init__(self):
         self.config_dict_ = None

ray_example.py

@@ -0,0 +1,18 @@
+import ray
+ray.init()
+
+@ray.remote
+class Counter(object):
+    def __init__(self):
+        self.n = 0
+
+    def increment(self):
+        self.n += 1
+
+    def read(self):
+        return self.n
+
+counters = [Counter.remote() for i in range(4)]
+[c.increment.remote() for c in counters]
+futures = [c.read.remote() for c in counters]
+print(ray.get(futures))

step.py

@@ -1,7 +1,8 @@
 from algorithm import algorithm
 import json
+import ray
 
-
+@ray.remote
 class step(algorithm):
     def __init__(self):
         self.config_dict_ = None

step_test.py

@@ -1,14 +1,17 @@
 import pandas as pd
 from step import step
+import ray
+ray.init()
 
-filepath = "D:/python_project/TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx"
+filepath = "D:/python_project_data/1.csv"
-origin_data = pd.read_excel(filepath,sheet_name='s4_ttxd')
+origin_data = pd.read_csv(filepath)
 row_len = origin_data.shape[0]
 cow_len = origin_data.shape[1]
 disturb_data = origin_data
-algorithm_step = step()
+algorithm_step = step.remote()
-cow_name = "ttxd_12_1"
+cow_name = "G1.TTXD1_3"
-algorithm_step.set_config('{"STEP_AMPLITUDE_BASE": 1.3}')
+algorithm_step.set_config.remote('{"STEP_AMPLITUDE_BASE": 1.3}')
 for i in range(0,row_len):
-    disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name])
+    futures=algorithm_step.eval.remote(origin_data.loc[i,cow_name])
-print(disturb_data)
+    disturb_data.loc[i, cow_name] = ray.get(futures)
+print(disturb_data.loc[:,cow_name])

white_noise.py

@@ -2,8 +2,9 @@ from random import random
 import numpy as np
 from algorithm import algorithm
 import json
+import ray
 
-
+@ray.remote
 class white_noise(algorithm):
     def __init__(self):
         self.config_dict_ = None