diff --git a/code/comparison experiments/IGBT/DPA/DPA.py b/code/comparison experiments/IGBT/DPA/DPA.py
new file mode 100644
index 0000000..b94cca3
--- /dev/null
+++ b/code/comparison experiments/IGBT/DPA/DPA.py	
@@ -0,0 +1,445 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 16:46:05 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 22:07:49 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 20:41:41 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 18 13:04:56 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:49:17 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:26:02 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 14:28:22 2022
+
+@author: Administrator
+"""
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 31 18:17:31 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 14:50:53 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 28 14:55:56 2022
+
+@author: Administrator
+
+"""
+
+########相比fitting1 多了一个纵轴的自由度
+#########采用  新曲线 计算阈值到达时间
+ ########################################   小于门槛值    到达寿命
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-5.5
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+
+    
+    CS2_35=[1.0522460048000002, 2.219000064, 2.2502500608, 2.2502500608, 2.219000064, 2.219000064, 2.1877500671999996, 2.7502500095999998, 2.7502500095999998, 2.7190000128, 3.2189999616, 3.2502499584, 3.2189999616, 3.2502499584, 3.7189999104, 3.6877499136, 3.7189999104, 3.7189999104, 3.7189999104, 3.6877499136, 3.7189999104, 3.6564999167999996, 3.6877499136, 3.6877499136, 4.1877498624, 4.1877498624, 4.2189998592, 4.1877498624, 4.1877498624, 4.1877498624, 4.2189998592, 4.1877498624, 4.2189998592, 4.1877498624, 4.1877498624, 4.1877498624, 4.1877498624, 4.1877498624, 4.6877498112, 4.6564998144, 4.6877498112, 4.7189998079999995, 4.6877498112, 4.6252498176, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6252498176, 5.156499763199999, 5.156499763199999, 5.156499763199999, 5.1252497664, 5.0939997696, 5.156499763199999, 5.156499763199999, 5.1252497664, 5.0627497728, 5.0627497728, 4.8439997952, 5.0939997696, 5.1249997824, 5.1562497791999995, 5.2187497728, 5.187499776, 5.2187497728, 5.4062497536, 5.4687497472, 5.374999756799999, 4.6249998336, 5.187499776, 5.1562497791999995, 5.1249997824, 5.2499997696000005, 5.1249997824, 5.187499776, 5.2812497664, 5.2812497664, 5.1562497791999995, 5.2812497664, 5.2187497728, 5.3124997632, 5.2187497728, 5.1562497791999995, 5.2812497664, 5.34374976, 5.2187497728, 5.34374976, 5.2499997696000005, 5.2187497728, 5.4374997504, 5.374999756799999, 5.2812497664, 5.2812497664, 5.374999756799999, 5.34374976, 5.812499711999999, 5.374999756799999, 5.2499997696000005, 5.2812497664, 5.2499997696000005, 5.2499997696000005, 5.2499997696000005, 5.2499997696000005, 5.4062497536, 5.374999756799999, 5.374999756799999, 1.750000128]
+    CS2_36=[1.0473631920000002, 2.250000064, 2.2187500672000002, 2.1875000704, 2.250000064, 2.2187500672000002, 2.2812500608, 2.2187500672000002, 2.2187500672000002, 2.2187500672000002, 2.1875000704, 2.1875000704, 3.2187499647999998, 3.187499968, 3.3124999552, 3.187499968, 3.2187499647999998, 3.1562499712, 3.1562499712, 3.1562499712, 3.65624992, 3.65624992, 3.6874999168, 3.6874999168, 3.6249999232, 4.1562498688, 4.124999872, 4.124999872, 4.124999872, 4.5624998272, 4.593749824, 4.593749824, 4.5624998272, 4.593749824, 4.593749824, 4.593749824, 4.624999820799999, 4.593749824, 4.593749824, 5.0937497728, 5.062499776, 4.9999997824, 5.062499776, 5.0937497728, 5.1874997632, 5.1249997696000005, 5.1249997696000005, 5.062499776, 5.062499776, 5.062499776, 5.0312497791999995, 5.062499776, 5.0937497728, 4.7187498112, 5.249999756799999, 5.1562497664, 5.062499776, 5.5624997248, 5.6249997184, 5.3124997504, 5.249999756799999, 5.249999756799999, 5.1562497664, 5.1874997632, 6.1249996672, 5.6999997184, 5.1749997312, 5.4499996928000005, 6.2557499904, 6.2557499904, 6.1932499968, 6.5994999552, 6.3494999808, 6.3182499839999995, 6.4119999744, 6.3807499776, 6.6932499456, 6.2869999872, 6.6307499519999995, 6.4119999744, 6.4432499712, 6.2244999936, 6.2869999872, 6.2244999936, 6.4119999744, 6.3807499776, 6.3494999808, 6.162, 6.162, 6.5994999552, 6.1932499968, 6.1307500032, 6.1307500032, 6.1307500032, 6.1307500032, 6.1307500032, 2.7557503488, 2.7557503488, 2.6932503552]
+    CS2_37=[1.0864256944000001, 3.2965001088, 2.79650016, 2.7340001664, 2.6715001728, 2.6090001792, 2.6090001792, 2.6090001792, 2.5465001856000002, 2.5465001856000002, 2.9840001408, 3.6090000768, 3.2965001088, 3.421500096, 3.3590001024, 3.3590001024, 3.3590001024, 3.2965001088, 3.3590001024, 3.421500096, 3.421500096, 3.3590001024, 3.8590000512, 3.9840000384, 3.9215000448, 3.8590000512, 4.359, 4.4214999936, 4.359, 4.2965000064, 4.2965000064, 4.1715000192, 4.2965000064, 4.2965000064, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.7964999552, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.6089999744, 4.7339999616, 4.7964999552, 4.7339999616, 4.7339999616, 4.7339999616, 4.7964999552, 4.7339999616, 4.7339999616, 4.7964999552, 4.6089999744, 4.671499968, 4.671499968, 4.7339999616, 4.6089999744, 4.6089999744, 4.7339999616, 4.7964999552, 4.7339999616, 4.983999936, 4.983999936, 4.7964999552, 4.9214999424, 5.2339999104, 4.983999936, 5.0464999295999995, 4.983999936, 4.9685000576, 4.9377500544, 5.5002499968, 5.7189999744, 5.805000038399999, 6.5237499647999995, 5.7737500416, 6.5237499647999995, 6.5237499647999995, 6.0550000128, 5.4925000704, 5.7425000448, 6.4612499712, 6.7112499456, 6.0550000128, 5.9612500224, 6.0862500096, 6.336249984, 6.2737499904, 6.492499968, 5.9300000256, 5.9925000191999995, 5.9925000191999995, 6.023750015999999, 5.9925000191999995, 5.805000038399999, 6.117500006399999, 6.1487500032, 6.1487500032, 6.1487500032, 6.1487500032, 2.8362503423999996, 2.8050003456, 2.8362503423999996, 3.2737502976]
+    CS2_38=[2.4536132784, 2.4609374976, 2.2505000704, 2.1880000768000003, 2.15675008, 2.4380000512000004, 2.2817500672, 2.2505000704, 2.781750016, 2.7505000192, 2.5942500352, 2.7505000192, 2.7505000192, 2.6567500288000003, 2.781750016, 2.7505000192, 2.6880000256, 3.250499968, 3.250499968, 3.250499968, 3.250499968, 3.2817499648000004, 3.250499968, 3.250499968, 3.2192499712, 3.2192499712, 3.250499968, 3.250499968, 3.71924992, 3.71924992, 3.6879999232, 4.2504998656, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.6879998208, 4.7192498176, 4.7192498176, 4.6879998208, 4.7192498176, 4.7192498176, 4.7192498176, 4.656749824, 4.6879998208, 4.5942498304, 4.7192498176, 4.6879998208, 4.6879998208, 5.2192497664, 5.2192497664, 5.1879997696, 5.0004997888, 5.3129997568, 5.1879997696, 5.250499763200001, 5.0629997824, 5.3129997568, 5.28174976, 5.28174976, 5.250499763200001, 5.3129997568, 5.3129997568, 5.250499763200001, 5.1567497728, 5.3129997568, 5.594249727999999, 5.9379996928, 6.281749657600001, 6.0004996863999995, 5.906749696, 5.906749696, 6.0942496768000005, 5.9692496896, 5.7817497088, 5.906749696, 5.7817497088, 5.875499699200001, 5.656749721600001, 5.125499776, 6.0942496768000005, 5.8129997056, 5.875499699200001, 5.9379996928, 5.875499699200001, 5.906749696, 5.8442497024, 5.9379996928, 5.8129997056, 5.906749696, 5.8442497024, 5.8442497024, 5.906749696, 6.0942496768000005, 5.9379996928, 5.9379996928, 5.9379996928, 5.9379996928, 6.3129996544, 6.281749657600001, 5.9692496896, 2.7505000192, 2.7505000192, 2.7505000192]
+    
+    
+     
+
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="Device2")
+    ax.plot(CS2_36, linewidth=1,c='g',label="Device3")
+    ax.plot(CS2_37, linewidth=1,c='y',label="Device4")
+    ax.plot(CS2_38, linewidth=1,c='r',label="Device5")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35[:-5],CS2_36[:-5],CS2_37[:-5],CS2_38[:-5]
+
+
+
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+print(CS2_35)
+
+CS2_35=list(-(np.array(CS2_35)-CS2_35[0]))
+CS2_36=list(-(np.array(CS2_36)-CS2_36[0]))
+CS2_37=list(-(np.array(CS2_37)-CS2_37[0]))
+CS2_38=list(-(np.array(CS2_38)-CS2_38[0]))
+
+print(CS2_35)
+
+
+print(CS2_35[0])
+print(CS2_36[0])
+print(CS2_37[0])
+print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return CS235
+
+
+
+
+def get_input_out(CS235,CS237,CS236):
+    min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    input_list=[]
+    output_list=[]
+    
+    true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS238=get_health_list(CS2_38,shed)  
+    # print(len(CS236_health))   
+    
+    # print(min(len(CS235),len(CS236),len(CS237)))
+    
+    # print("ccccccccccccccccccccccccc")
+    
+    
+    
+    for i in range(2,len(CS236_health)):
+
+        
+        
+        
+        # one_input.append(np.array([len(CS235_health),len(CS237_health)]))
+        
+        
+
+
+
+
+
+
+
+
+        y = [CS235[0], CS235[min_len-1]]
+        x = [0, (min_len-1)*(min_len-1)]
+        slope_0, intercept_0, r_value, p_value, std_err = linregress(x, y)
+
+        
+        y = [CS237[0], CS237[min_len-1]]
+        x = [0,(min_len-1)*(min_len-1)]
+        slope_1, intercept_1, r_value, p_value, std_err = linregress(x, y)
+        
+        
+        
+        
+        
+        
+
+        one_input=[]
+        one_input.append(np.array(CS235[:i]))
+        one_input.append(np.array(CS237[:i]))
+        
+        one_input.append("CS238")
+    
+        min_len_train=min(len(CS235),len(CS237))
+            
+        one_input.append(np.array(CS235[:min_len_train]))
+            
+      
+        one_input.append(np.array(CS237[:min_len_train]))
+        
+        one_input.append(np.array(CS236[:i]))
+        
+        
+        
+        
+        
+        input_list.append(one_input)
+        
+        one_out=[]
+        one_out.append(np.array(CS236[:i]))
+        
+        output_list.append(one_out)
+        
+        true_out_list.append(max(len(CS236_health)-i-1,0))
+
+
+
+
+
+    
+    true_out_array=np.array(true_out_list)
+    return input_list[:len(CS236_health)],output_list[:len(CS236_health)],true_out_array[:len(CS236_health)]
+
+
+
+
+import random
+
+pred_list=[]
+error_list=[]
+
+for xxx in range(1):
+
+    for i in range(1):
+        
+
+    
+    
+        if i==0:
+            
+            input_list,output_list,true_out_array=get_input_out(CS2_36,CS2_36,CS2_37)######第三个变量是测试集 ，前两个是训练集
+
+        
+        rul_pred_list=[]
+        
+        regress_error_list=[]
+        
+        for j in range(len(input_list)):
+            
+            # print(j)
+            # print("jjjjjjjjjjjjjjjjjjjjjjjjjj")
+            index=j
+            def objective(x):
+                
+                integrate_array=input_list[index][0]*x[0]+input_list[index][1]*x[1]+x[2]
+                
+                error = np.sum(np.square(integrate_array - output_list[index][0]))/len(integrate_array)
+                
+                return error    
+            
+
+    
+            
+            x0=np.ones(3)
+            x0[2]=0
+     
+            
+            
+    
+            # sol = minimize(objective, x0 ) 
+            sol = minimize(objective, x0 ,method='BFGS', options={'maxiter':(1+1)*200,'gtol':1e-5 }  )
+            u1=sol.x[0] 
+            u2=sol.x[1]
+            c1=input_list[j][3]
+    
+            c2=input_list[j][4]
+            
+            offset_vertical=sol.x[2]
+    
+    
+    
+            
+ 
+            
+            ######补偿误差
+            pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1] +offset_vertical
+  
+            pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve) [int(-min(j*0.2,20)):] )       
+     
+    #############预测曲线          
+            # plt.plot(range(len(pred_curve)), pred_curve)
+            # plt.show()
+     
+    #############
+    
+            
+            ful_pred=len(pred_curve)
+            for ii in range(len(pred_curve)):
+                
+                # if i == len(pred_curve)-1:
+                    
+                #     ful_pred==i
+                
+                if pred_curve[ii]<shed:
+                    
+                    ful_pred=ii
+                    
+                    break
+    
+    
+            rul_pred_list.append(ful_pred-j-1) #######(-1  because of  begining from 2)
+    
+    
+    
+    
+    
+    #############实时的拟合曲线与实际曲线的所有点的误差        
+            # pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1]                
+            # plt.plot(range(len(input_list[j][5])), input_list[j][5] - pred_online_cruve)
+            # plt.show()
+    
+    #################
+    
+    
+                    
+        
+        rul_pred_array=np.array(rul_pred_list)
+        
+        error_pred_array=rul_pred_array-true_out_array
+        
+        error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+            # print(sol.x)
+            
+        # print(error_pred_array.sum())
+        # print("xxxxx")
+        # print(error_pred_array)
+            
+            
+        fig, ax = plt.subplots()
+        # 在生成的坐标系下画折线图
+        ax.plot(error_pred_array, linewidth=1)
+        
+        
+        
+        # 显示图形
+        plt.show()   
+         
+        pred_list.append(rul_pred_array)
+        error_list.append(error_pred_array)
+        print(xxx)
+print(pred_list)
+print(error_list) 
+
+
+# 0
+# rul_pred_array
+# [19, 23, 22, 21, 24, 23, 22, 21, 20, 19, 14, 27, 44, 50, 49, 48, 47, 46, 45, 51, 73, 49, 41, 40, 43, 42, 37, 36, 35, 41, 40, 39, 38, 30, 29, 28, 27, 20, 25, 28, 23, 22, 25, 24, 23, 25, 24, 20, 19, 18, 17, 16, 11, 14, 16, 15, 19, 36, 35, 11, 15, 9, 8, 30, 29, 5, 4, 26, 25, 24, 23, 22, 21, 20]
+# true_out_array
+# [73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+# error_pred_array
+# [54, 49, 49, 49, 45, 45, 45, 45, 45, 45, 49, 35, 17, 10, 10, 10, 10, 10, 10, 3, 20, 3, 10, 10, 6, 6, 10, 10, 10, 3, 3, 3, 3, 10, 10, 10, 10, 16, 10, 6, 10, 10, 6, 6, 6, 3, 3, 6, 6, 6, 6, 6, 10, 6, 3, 3, 2, 20, 20, 3, 2, 3, 3, 20, 20, 3, 3, 20, 20, 20, 20, 20, 20, 20]
+# 1
+# rul_pred_array
+# [19, 23, 22, 21, 24, 23, 22, 21, 20, 19, 14, 27, 44, 50, 49, 48, 47, 46, 45, 51, 73, 49, 41, 40, 43, 42, 37, 36, 35, 41, 40, 39, 38, 30, 29, 28, 27, 20, 25, 28, 23, 22, 25, 24, 23, 25, 24, 20, 19, 18, 17, 16, 11, 14, 16, 15, 19, 36, 35, 11, 15, 9, 8, 30, 29, 5, 4, 26, 25, 24, 23, 22, 21, 20]
+# true_out_array
+# [73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+# error_pred_array
+# [54, 49, 49, 49, 45, 45, 45, 45, 45, 45, 49, 35, 17, 10, 10, 10, 10, 10, 10, 3, 20, 3, 10, 10, 6, 6, 10, 10, 10, 3, 3, 3, 3, 10, 10, 10, 10, 16, 10, 6, 10, 10, 6, 6, 6, 3, 3, 6, 6, 6, 6, 6, 10, 6, 3, 3, 2, 20, 20, 3, 2, 3, 3, 20, 20, 3, 3, 20, 20, 20, 20, 20, 20, 20]
\ No newline at end of file
diff --git a/code/comparison experiments/IGBT/prediction_value_and_error_IGBT_3_4_error_band.py b/code/comparison experiments/IGBT/prediction_value_and_error_IGBT_3_4_error_band.py
new file mode 100644
index 0000000..8342996
--- /dev/null
+++ b/code/comparison experiments/IGBT/prediction_value_and_error_IGBT_3_4_error_band.py	
@@ -0,0 +1,274 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 26 22:25:42 2023
+
+@author: Administrator
+"""
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+Si=[57, 25, 32, 35, 37, 40, 42, 43, 46, 48, 41, 32, 38, 37, 40, 41, 43, 46, 46, 46, 47, 51, 40, 38, 40, 43, 32, 32, 34, 36, 37, 42, 39, 40, 28, 30, 29, 30, 30, 31, 33, 32, 33, 35, 34, 35, 42, 38, 37, 39, 40, 41, 39, 42, 43, 41, 49, 47, 48, 46, 53, 53, 48, 46, 49, 38, 39, 48, 43, 30, 41, 38, 41, 43, 44, 21, 14, 11, 0, 13, 0, 0, 5, 22, 14, 0]
+     
+Zhang=[14, 15, 41, 52, 54, 55, 57, 57, 57, 58, 58, 48, 35, 41, 39, 40, 40, 40, 41, 40, 39, 39, 40, 30, 28, 29, 30, 21, 20, 21, 22, 22, 24, 22, 22, 14, 15, 14, 14, 14, 14, 15, 14, 14, 15, 14, 14, 16, 15, 14, 15, 15, 15, 14, 15, 15, 14, 16, 15, 15, 15, 16, 16, 15, 14, 15, 11, 11, 14, 12, 8, 11, 10, 11, 11, 12, 5, 3, 2, 0, 2, 0, 0, 0, 5, 2]
+DCNN=[53.293327, 53.308586, 52.25776, 50.868267, 49.517498, 48.15722, 46.78237, 45.45514, 44.5608, 44.213245, 43.093254, 41.84089, 40.156425, 38.357307, 36.545918, 34.712627, 33.02111, 31.47141, 30.372427, 29.987188, 29.831493, 29.653675, 29.180141, 28.398905, 27.815289, 27.04422, 26.0629, 25.120876, 24.234377, 23.240973, 22.323774, 21.454617, 21.090029, 20.588352, 19.938559, 19.128391, 18.018799, 16.789806, 15.533107, 14.494667, 13.608917, 12.647665, 11.8576765, 11.224333, 10.598362, 10.096522, 9.655268, 9.160848, 8.693614, 8.226995, 8.004075, 7.8854227, 7.9010925, 7.9804306, 7.901428, 7.9004693, 7.910248, 7.9891663, 8.015012, 8.05662, 8.236889, 8.441938, 8.570276, 8.882413, 9.109388, 9.081394, 9.071549, 9.237994, 9.096155, 8.986002, 8.719963, 8.331375, 7.945025, 7.982029]
+        
+TaFCN=[44.21796, 42.805122, 42.043377, 41.26773, 40.437565, 39.301758, 38.085163, 36.953697, 35.64536, 34.2916, 32.98562, 31.647074, 30.127924, 28.324146, 26.934727, 26.329594, 25.737942, 24.74506, 23.52901, 23.882565, 23.427698, 23.251583, 22.276297, 21.29385, 20.627192, 20.35797, 19.921137, 19.38269, 18.840317, 18.60413, 18.263926, 17.95653, 17.503567, 16.980068, 16.237957, 15.419586, 14.483716, 13.468272, 12.544446, 11.787228, 11.157798, 10.658507, 10.224942, 9.760351, 9.311747, 8.85345, 8.351404, 7.8764696, 7.483403, 7.159368, 6.891464, 6.7258024, 6.7476144, 6.784125, 6.873828, 6.945498, 7.0724053, 7.1963983, 7.3313003, 7.4637733, 7.6273794, 7.703505, 7.714101, 7.6956434, 7.61364, 7.5363793, 7.443631, 7.3159614, 7.177247, 6.9668517, 6.8047357, 6.5526075, 6.2302856, 5.7974715]
+
+Our=[24, 27, 26, 35, 34, 33, 32, 36, 35, 29, 18, 52, 63, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10]
+
+groud_truth=[822.0, 821.0, 820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+
+
+
+
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our))-1
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+ax.plot(Si[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='-.')
+ax.plot(Zhang[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='-.')
+ax.plot(DCNN[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='-.')
+ax.plot(TaFCN[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='-.')
+
+
+ax.plot(Our[-min_len:], linewidth=2,c='r',label="Our",linestyle='-.')
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+error_rate=0.3
+error_rate_1=0.8
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1-error_rate),color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1+error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1-error_rate), np.array(groud_truth[-min_len:])*(1-error_rate_1),color="#E7E7E7")# color="#CCEEFF")
+    
+plt.xlabel('Number of cycle period (cycle)',font1) #X轴标签
+plt.ylabel("Predicted RUL (cycle)",font1) #Y轴标签
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+# plt.legend(loc="upper left")
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_IGBT_3_4.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_IGBT_3_4.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
+# Si_error=[13.0, 57.0, 48.0, 44.0, 40.0, 36.0, 32.0, 29.0, 22.0, 18.0, 28.0, 40.0, 30.0, 31.0, 26.0, 22.0, 18.0, 11.0, 10.0, 8.0, 3.0, 6.0, 17.0, 18.0, 12.0, 5.0, 24.0, 24.0, 19.0, 14.0, 11.0, 0.0, 4.0, 1.0, 22.0, 18.0, 18.0, 16.0, 14.0, 12.0, 6.0, 7.0, 5.0, 2.0, 0.0, 3.0, 21.0, 13.0, 11.0, 19.0, 23.0, 26.0, 23.0, 33.0, 37.0, 32.0, 68.0, 60.0, 66.0, 58.0, 100.0, 111.0, 73.0, 64.0, 86.0, 38.0, 41.0, 85.0, 58.0, 18.0, 53.0, 45.0, 60.0, 66.0, 78.0, 3.0]
+Si_error=[13, 57, 48, 44, 40, 36, 32, 29, 22, 18, 28, 40, 30, 31, 26, 22, 18, 11, 10, 8, 3, 6, 17, 18, 12, 5, 24, 24, 19, 14, 11, 0, 4, 1, 22, 18, 18, 16, 14, 12, 6, 7, 5, 2, 0, 3, 21, 13, 11, 19, 23, 26, 23, 33, 37, 32, 68, 60, 66, 58, 100, 111, 73, 64, 86, 38, 41, 85, 58, 18, 53, 45, 60, 66, 78, 3]
+Zhang_error=[64.0, 62.0, 51.0, 41.0, 39.0, 36.0, 34.0, 33.0, 32.0, 30.0, 29.0, 36.0, 47.0, 40.0, 41.0, 39.0, 38.0, 37.0, 35.0, 35.0, 35.0, 34.0, 32.0, 39.0, 40.0, 38.0, 36.0, 42.0, 42.0, 40.0, 38.0, 37.0, 35.0, 35.0, 34.0, 38.0, 36.0, 36.0, 35.0, 34.0, 33.0, 31.0, 31.0, 30.0, 28.0, 28.0, 27.0, 24.0, 24.0, 24.0, 22.0, 21.0, 20.0, 20.0, 18.0, 17.0, 17.0, 14.0, 14.0, 13.0, 12.0, 10.0, 9.0, 9.0, 9.0, 7.0, 8.0, 7.0, 5.0, 5.0, 5.0, 3.0, 2.0, 1.0, 0.0, 1.0]
+        
+TaFCN_error=[28.904346084594728, 29.088148498535155, 28.850256729125977, 29.03882713317871, 29.027362060546874, 28.875647354125977, 28.96324234008789, 28.948114013671876, 29.236447906494142, 29.611362075805665, 29.895995712280275, 30.18790512084961, 30.679544830322264, 31.477498245239257, 31.88624801635742, 31.549593353271483, 31.18598289489746, 31.153302955627442, 31.486330795288087, 30.10425853729248, 29.580868530273438, 28.77017765045166, 28.797579193115233, 28.786879062652588, 28.43111734390259, 27.762622356414795, 27.217262935638427, 26.64298372268677, 26.131721210479736, 25.385798168182372, 24.750729084014893, 24.0867169380188, 23.52749910354614, 23.062818431854247, 22.799000644683836, 22.57734899520874, 22.467468738555908, 22.442122745513917, 22.369387340545654, 22.09353494644165, 21.755588054656982, 21.286892795562743, 20.738014221191406, 20.22369728088379, 19.715774822235108, 19.25242805480957, 18.781087112426757, 18.280701446533204, 17.66046538352966, 16.933815145492552, 16.169437599182128, 15.302395915985107, 14.28380331993103, 13.239881658554078, 12.15295238494873, 11.084389257431031, 9.963778877258301, 8.83853440284729, 7.713970947265625, 6.5862836837768555, 5.41293683052063, 4.3293591976165775, 3.4914825916290284, 2.8876953125, 2.754207944869995, 3.096543788909912, 3.4230144023895264, 3.7711595058441163, 4.213962411880493, 4.601145696640015, 5.0251784324646, 5.370839071273804, 5.632096529006958, 5.776178312301636]
+# TaFCN_error=[28.71866, 29.088146000000002, 28.840629999999997, 28.612740000000002, 28.455150000000003, 28.532111999999998, 28.77243, 28.921154, 29.236446, 29.611362, 29.895995999999997, 30.187906, 30.679544, 31.477497, 31.88625, 31.549595, 31.185984, 31.153301, 31.486332, 30.104258, 29.580868, 28.770178, 28.79758, 28.786879, 28.431118, 27.762623, 27.217264, 26.642984, 26.13172, 25.385798, 24.750729, 24.086718, 23.5275, 23.062817, 22.799, 22.57735, 22.467468, 22.442123000000002, 22.369388, 22.093534, 21.755588, 21.286893, 20.738014, 20.223697, 19.7157755, 19.252428000000002, 18.781088, 18.2807016, 17.6604652, 16.933815, 16.1694384, 15.3023963, 14.283803500000001, 13.239882, 12.1529527, 11.0843897, 9.9637785, 8.8385344, 7.713971, 6.586283, 5.412936, 4.3293586, 3.3097205, 2.3202423999999997, 1.3805246000000002, 0.4471179999999997, 0.4637184000000003, 1.3536786999999997, 2.2139626000000003, 3.0011460000000003, 3.8251786, 4.570839, 5.2320967, 5.7761784]
+Our_error=[54, 49, 49, 49, 49, 45, 45, 45, 45, 49, 49, 35, 17, 10, 10, 10, 10, 10, 10, 3, 3, 3, 3, 10, 6, 10, 10, 10, 10, 10, 10, 6, 3, 6, 6, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 20, 20, 20, 20, 20, 20, 20]
+
+
+
+Si_error=[28.0, 59.0, 51.0, 47.0, 44.0, 40.0, 37.0, 35.0, 31.0, 28.0, 34.0, 42.0, 35.0, 35.0, 31.0, 29.0, 26.0, 22.0, 21.0, 20.0, 18.0, 13.0, 23.0, 24.0, 21.0, 17.0, 27.0, 26.0, 23.0, 20.0, 18.0, 12.0, 14.0, 12.0, 23.0, 20.0, 20.0, 18.0, 17.0, 15.0, 12.0, 12.0, 10.0, 7.0, 7.0, 5.0, 3.0, 0.0, 0.0, 3.0, 5.0, 7.0, 6.0, 10.0, 12.0, 11.0, 20.0, 19.0, 21.0, 20.0, 28.0, 29.0, 25.0, 24.0, 28.0, 18.0, 20.0, 30.0, 26.0, 14.0, 26.0, 24.0, 28.0, 31.0, 33.0, 11.0, 5.0, 3.0, 7.0, 7.0, 5.0, 4.0, 2.0, 20.0, 13.0, 0.0]
+
+Zhang_error=[71.0, 69.0, 42.0, 30.0, 27.0, 25.0, 22.0, 21.0, 20.0, 18.0, 17.0, 26.0, 38.0, 31.0, 32.0, 30.0, 29.0, 28.0, 26.0, 26.0, 26.0, 25.0, 23.0, 32.0, 33.0, 31.0, 29.0, 37.0, 37.0, 35.0, 33.0, 32.0, 29.0, 30.0, 29.0, 36.0, 34.0, 34.0, 33.0, 32.0, 31.0, 29.0, 29.0, 28.0, 26.0, 26.0, 25.0, 22.0, 22.0, 22.0, 20.0, 19.0, 18.0, 18.0, 16.0, 15.0, 15.0, 12.0, 12.0, 11.0, 10.0, 8.0, 7.0, 7.0, 7.0, 5.0, 8.0, 7.0, 3.0, 4.0, 7.0, 3.0, 3.0, 1.0, 0.0, 2.0, 4.0, 5.0, 5.0, 6.0, 3.0, 4.0, 3.0, 2.0, 4.0, 2.0]
+        
+TaFCN_error=[28.96772880554199, 29.194879150390626, 28.966249465942383, 29.15835647583008, 29.13464698791504, 29.041773986816406, 29.105652236938475, 29.073262405395507, 29.35464210510254, 29.708401107788085, 30.01438217163086, 30.35292854309082, 30.872076416015624, 31.675856399536134, 32.065272903442384, 31.670408248901367, 31.262058448791503, 31.254939079284668, 31.470988464355468, 30.11743507385254, 29.572300910949707, 28.748418045043945, 28.723702430725098, 28.706149959564208, 28.372809314727782, 27.642030429840087, 27.07886266708374, 26.617309093475342, 26.159684276580812, 25.395871257781984, 24.736073780059815, 24.04347105026245, 23.496433162689208, 23.01993246078491, 22.762042903900145, 22.580413150787354, 22.51628370285034, 22.53172674179077, 22.455554294586182, 22.2127724647522, 21.84220199584961, 21.341492938995362, 20.775057792663574, 20.239649295806885, 19.6882532119751, 19.146550273895265, 18.648596000671386, 18.123530387878418, 17.51659712791443, 16.840631437301635, 16.108535957336425, 15.27419695854187, 14.25238585472107, 13.215874862670898, 12.126171779632568, 11.054502153396607, 9.927595043182373, 8.80360131263733, 7.668699455261231, 6.536226558685303, 5.372620534896851, 4.2964949131011965, 3.4676616191864014, 2.871809387207031, 2.748372507095337, 3.0800405502319337, 3.4029272556304933, 3.733441686630249, 4.177246809005737, 4.5668517589569095, 5.004735660552979, 5.35260796546936, 5.630285406112671, 5.797471189498902]
+#TaFCN_error=[28.71866, 29.088146000000002, 28.840629999999997, 28.612740000000002, 28.455150000000003, 28.532111999999998, 28.77243, 28.921154, 29.236446, 29.611362, 29.895995999999997, 30.187906, 30.679544, 31.477497, 31.88625, 31.549595, 31.185984, 31.153301, 31.486332, 30.104258, 29.580868, 28.770178, 28.79758, 28.786879, 28.431118, 27.762623, 27.217264, 26.642984, 26.13172, 25.385798, 24.750729, 24.086718, 23.5275, 23.062817, 22.799, 22.57735, 22.467468, 22.442123000000002, 22.369388, 22.093534, 21.755588, 21.286893, 20.738014, 20.223697, 19.7157755, 19.252428000000002, 18.781088, 18.2807016, 17.6604652, 16.933815, 16.1694384, 15.3023963, 14.283803500000001, 13.239882, 12.1529527, 11.0843897, 9.9637785, 8.8385344, 7.713971, 6.586283, 5.412936, 4.3293586, 3.3097205, 2.3202423999999997, 1.3805246000000002, 0.4471179999999997, 0.4637184000000003, 1.3536786999999997, 2.2139626000000003, 3.0011460000000003, 3.8251786, 4.570839, 5.2320967, 5.7761784]
+
+DCNN_error=[19.706673431396485, 18.69141082763672, 18.742241668701173, 19.131729125976562, 19.482498931884766, 19.842776489257812, 20.217630004882814, 20.544860076904296, 20.439202880859376, 19.786754608154297, 19.90674819946289, 20.159110260009765, 20.843572235107423, 21.642693328857423, 22.45408477783203, 23.287371826171874, 23.97888946533203, 24.528590393066406, 24.62757263183594, 24.012812042236327, 23.168506240844728, 22.34632453918457, 21.81985740661621, 21.601093292236328, 21.184711837768553, 20.955782318115233, 20.937101364135742, 20.879124450683594, 20.765622711181642, 20.759026336669923, 20.67622604370117, 20.545381546020508, 19.909970855712892, 19.411648178100585, 19.061442184448243, 18.871609497070313, 18.981203079223633, 19.21019287109375, 19.466893577575682, 19.50533256530762, 19.391082763671875, 19.352335739135743, 19.142323112487794, 18.77566776275635, 18.40163860321045, 17.903476905822753, 17.344732666015624, 16.83915195465088, 16.306385898590086, 15.77300386428833, 14.995925617218017, 14.114577579498292, 13.098907279968262, 12.019569969177246, 11.098571586608887, 10.099530887603759, 9.089752578735352, 8.010833835601806, 6.984987735748291, 5.943381023406983, 4.763111114501953, 3.5580615997314453, 2.42972469329834, 1.4623234748840332, 0.7683503150939941, 1.0971601486206055, 2.071548843383789, 3.237994575500488, 4.09615421295166, 4.9860025405883786, 5.719962692260742, 6.331375503540039, 6.945025062561035, 7.982028770446777]    
+
+
+Our_error=[59, 55, 55, 45, 45, 45, 45, 40, 40, 45, 55, 20, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
+
+# min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our))
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+
+ax.plot(np.arange(min_len,0,-1),Si_error[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Zhang_error[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),DCNN_error[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),TaFCN_error[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Our_error[-min_len:], linewidth=2,c='r',label="Our",linestyle='--')
+# ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+
+
+def rmse(x):
+    s=0
+    for i in x:
+        s+=i*i
+    r=sqrt(s/len(x))
+    return r
+
+def aae(x):
+    return np.mean(x)
+
+
+def score(Y_test,Y_pred) :
+      
+    s=0    
+    for i in range(len(Y_pred)):
+        if Y_pred[i]>Y_test[i]:
+            s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+        else:
+            s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+    # print('unbalanced_penalty_score{}'.format(s))
+    return s  
+  
+def error_range(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+     
+    error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return error_range
+
+        
+def error_list(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+
+    error_list=Y_test-Y_pred
+    # Y_test =np.array(Y_test)  
+    # Y_pred =np.array(Y_pred) 
+     
+    # error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return list(error_list)
+
+
+
+# def predcition(targets,predictions):
+#     targets=np.array(targets)
+#     predictions=np.array(predictions)
+
+#     smape = (1 / len(predictions)) * np.sum(2 * np.abs(predictions - targets) / (np.abs(predictions) + np.abs(targets))) * 100
+    
+#     return 100-smape
+
+def predcition(targets,predictions):
+    targets=np.array(targets)
+    predictions=np.array(predictions)
+    
+    
+    # targets=np.array(targets)
+    # predictions=np.array(predictions)
+
+    
+
+    smape = np.sum( np.abs(predictions - targets)) /np.sum( np.abs( targets)) * 100
+    print(np.sum( np.abs(predictions - targets)))
+    
+    print(np.sum( np.abs( targets)))
+    
+    print(smape)
+    y=100-smape
+    
+    # print(y)
+    return y   
+
+print(list((error_list(groud_truth[-min_len:],Si[-min_len:]),error_list(groud_truth[-min_len:],Zhang[-min_len:]),error_list(groud_truth[-min_len:],DCNN[-min_len:]),error_list(groud_truth[-min_len:],TaFCN[-min_len:]),error_list(groud_truth[-min_len:],Our[-min_len:]))))
+        
+print(list((rmse(Si_error[-min_len:]),rmse(Zhang_error[-min_len:]),rmse(DCNN_error[-min_len:]),rmse(TaFCN_error[-min_len:]),rmse(Our_error[-min_len:]))))
+print(list((aae(Si_error[-min_len:]),aae(Zhang_error[-min_len:]),aae(DCNN_error[-min_len:]),aae(TaFCN_error[-min_len:]),aae(Our_error[-min_len:]))))
+print(list((score(groud_truth[-min_len:],Si[-min_len:]),score(groud_truth[-min_len:],Zhang[-min_len:]),score(groud_truth[-min_len:],DCNN[-min_len:]),score(groud_truth[-min_len:],TaFCN[-min_len:]),score(groud_truth[-min_len:],Our[-min_len:]))))
+print(list((error_range(groud_truth[-min_len:],Si[-min_len:]),error_range(groud_truth[-min_len:],Zhang[-min_len:]),error_range(groud_truth[-min_len:],DCNN[-min_len:]),error_range(groud_truth[-min_len:],TaFCN[-min_len:]),error_range(groud_truth[-min_len:],Our[-min_len:]))))
+
+
+
+
+
+print(list((predcition(groud_truth[-min_len:],Si[-min_len:]),predcition(groud_truth[-min_len:],Zhang[-min_len:]),predcition(groud_truth[-min_len:],DCNN[-min_len:]),predcition(groud_truth[-min_len:],TaFCN[-min_len:]),predcition(groud_truth[-min_len:],Our[-min_len:]))))
+
+# print
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+# error_rate=0.2
+# plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC")# color="#CCEEFF")
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.array(np.arange(min_len,0,-1))*(error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")    
+plt.xlabel('Actual RUL (cycle)',font1) #X轴标签
+plt.ylabel("Absolute prediction error (cycle)",font1) #Y轴标签
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+plt.gca().invert_xaxis()
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_IGBT_3_4.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_IGBT_3_4.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
diff --git a/code/comparison experiments/IGBT/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt b/code/comparison experiments/IGBT/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt
new file mode 100644
index 0000000..c84ed61
--- /dev/null
+++ b/code/comparison experiments/IGBT/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt	
@@ -0,0 +1,806 @@
+2023-03-22 19:18:03,735 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,736 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,825 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,826 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,460 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,461 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:20:00,213 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+2023-03-22 19:20:00,235 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+2023-03-22 19:20:00,241 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+2023-03-22 19:20:00,258 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+2023-03-22 19:20:00,312 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+2023-03-22 19:20:00,313 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+2023-03-22 19:20:00,314 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+2023-03-22 19:20:02,670 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+2023-03-22 19:20:02,671 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+2023-03-22 19:20:02,901 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+2023-03-22 19:20:02,940 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+2023-03-22 19:20:02,990 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+2023-03-22 19:20:03,741 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+2023-03-22 19:20:03,983 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+2023-03-22 19:20:04,011 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+2023-03-22 19:20:04,216 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
+
+2023-03-22 19:22:47,451 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,452 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,545 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,546 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:52,870 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,872 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
+2023-03-22 19:22:52,873 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,879 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,888 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'YouYuan' (SIMYOU.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal heavy normal>) = 10.43
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrak.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibril.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Deng.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STFangsong' (STFANGSO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal black normal>) = 11.525
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal demi condensed>) = 10.44
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'icomoon' (iqiyi_logov5.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXihei' (STXIHEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrakb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextrab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal medium normal>) = 11.145
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,929 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXinwei' (STXINWEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal demi normal>) = 11.24
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra' (mtextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,938 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariblk.ttf) normal normal black normal>) = 6.888636363636364
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,949 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STHupo' (STHUPO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal semibold normal>) = 11.24
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger' (Symbol Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclid.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZYaoTi' (FZYTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal bold condensed>) = 7.8986363636363635
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger' (Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger Expert' (Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'KaiTi' (simkai.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZShuTi' (FZSTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal heavy normal>) = 11.43
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'LiSu' (SIMLI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,978 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Unicode MS' (ARIALUNI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STLiti' (STLITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,994 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STKaiti' (STKAITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger Expert' (Symbol Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal semibold normal>) = 10.24
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal medium condensed>) = 10.344999999999999
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal black normal>) = 11.525
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXingkai' (STXINGKA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STZhongsong' (STZHONGS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Fences' (fences.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 7.613636363636363
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal bold condensed>) = 6.8986363636363635
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FangSong' (simfang.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 6.613636363636363
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STSong' (STSONG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Mincho' (MSMINCHO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra Tiger' (Mt Extra Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,040 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STCaiyun' (STCAIYUN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,042 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,057 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal medium normal>) = 10.145
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWAD.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,063 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal regular normal>) = 10.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal bold condensed>) = 11.535
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWDB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimHei' (simhei.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,071 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('C:\\Users\\Administrator\\anaconda3\\envs\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000.
+2023-03-22 19:23:05,774 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_428:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,776 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_427:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,778 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_426:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,779 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_425:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,781 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_424:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,782 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_423:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,783 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_422:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,784 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_421:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,785 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_420:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,786 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_419:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,787 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_418:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,788 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_417:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,789 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_416:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,791 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_415:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,792 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_414:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,793 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_413:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,794 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_412:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,795 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_411:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,796 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_410:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,798 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_409:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,799 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_408:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:24:47,221 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,222 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,315 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,316 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,162 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,163 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,082 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,176 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,177 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,332 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,333 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,407 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,505 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,506 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,215 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,216 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,143 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,243 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,244 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,337 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,338 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,432 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,433 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,264 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,265 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,578 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,579 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,674 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,675 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,053 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,054 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,145 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,912 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,913 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,422 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,423 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,515 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,517 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,455 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,456 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,550 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,551 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,409 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,496 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,497 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,883 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,884 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,970 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,971 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,988 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,989 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,080 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,943 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,944 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,041 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,042 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,890 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,891 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,985 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,984 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,071 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,072 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,916 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,917 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,073 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,074 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,166 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,168 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,894 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,897 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,005 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
diff --git a/code/comparison experiments/IGBT/tafcn2022/TaFCN.py b/code/comparison experiments/IGBT/tafcn2022/TaFCN.py
new file mode 100644
index 0000000..0c80943
--- /dev/null
+++ b/code/comparison experiments/IGBT/tafcn2022/TaFCN.py	
@@ -0,0 +1,647 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 22 16:37:59 2023
+
+@author: Administrator
+"""
+########正负 门槛值  大于小于
+
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+
+timestep=1
+
+shed=5.5
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+
+    
+    CS2_35=[1.0522460048000002, 2.219000064, 2.2502500608, 2.2502500608, 2.219000064, 2.219000064, 2.1877500671999996, 2.7502500095999998, 2.7502500095999998, 2.7190000128, 3.2189999616, 3.2502499584, 3.2189999616, 3.2502499584, 3.7189999104, 3.6877499136, 3.7189999104, 3.7189999104, 3.7189999104, 3.6877499136, 3.7189999104, 3.6564999167999996, 3.6877499136, 3.6877499136, 4.1877498624, 4.1877498624, 4.2189998592, 4.1877498624, 4.1877498624, 4.1877498624, 4.2189998592, 4.1877498624, 4.2189998592, 4.1877498624, 4.1877498624, 4.1877498624, 4.1877498624, 4.1877498624, 4.6877498112, 4.6564998144, 4.6877498112, 4.7189998079999995, 4.6877498112, 4.6252498176, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6564998144, 4.6252498176, 5.156499763199999, 5.156499763199999, 5.156499763199999, 5.1252497664, 5.0939997696, 5.156499763199999, 5.156499763199999, 5.1252497664, 5.0627497728, 5.0627497728, 4.8439997952, 5.0939997696, 5.1249997824, 5.1562497791999995, 5.2187497728, 5.187499776, 5.2187497728, 5.4062497536, 5.4687497472, 5.374999756799999, 4.6249998336, 5.187499776, 5.1562497791999995, 5.1249997824, 5.2499997696000005, 5.1249997824, 5.187499776, 5.2812497664, 5.2812497664, 5.1562497791999995, 5.2812497664, 5.2187497728, 5.3124997632, 5.2187497728, 5.1562497791999995, 5.2812497664, 5.34374976, 5.2187497728, 5.34374976, 5.2499997696000005, 5.2187497728, 5.4374997504, 5.374999756799999, 5.2812497664, 5.2812497664, 5.374999756799999, 5.34374976, 5.812499711999999, 5.374999756799999, 5.2499997696000005, 5.2812497664, 5.2499997696000005, 5.2499997696000005, 5.2499997696000005, 5.2499997696000005, 5.4062497536, 5.374999756799999, 5.374999756799999, 1.750000128]
+    CS2_36=[1.0473631920000002, 2.250000064, 2.2187500672000002, 2.1875000704, 2.250000064, 2.2187500672000002, 2.2812500608, 2.2187500672000002, 2.2187500672000002, 2.2187500672000002, 2.1875000704, 2.1875000704, 3.2187499647999998, 3.187499968, 3.3124999552, 3.187499968, 3.2187499647999998, 3.1562499712, 3.1562499712, 3.1562499712, 3.65624992, 3.65624992, 3.6874999168, 3.6874999168, 3.6249999232, 4.1562498688, 4.124999872, 4.124999872, 4.124999872, 4.5624998272, 4.593749824, 4.593749824, 4.5624998272, 4.593749824, 4.593749824, 4.593749824, 4.624999820799999, 4.593749824, 4.593749824, 5.0937497728, 5.062499776, 4.9999997824, 5.062499776, 5.0937497728, 5.1874997632, 5.1249997696000005, 5.1249997696000005, 5.062499776, 5.062499776, 5.062499776, 5.0312497791999995, 5.062499776, 5.0937497728, 4.7187498112, 5.249999756799999, 5.1562497664, 5.062499776, 5.5624997248, 5.6249997184, 5.3124997504, 5.249999756799999, 5.249999756799999, 5.1562497664, 5.1874997632, 6.1249996672, 5.6999997184, 5.1749997312, 5.4499996928000005, 6.2557499904, 6.2557499904, 6.1932499968, 6.5994999552, 6.3494999808, 6.3182499839999995, 6.4119999744, 6.3807499776, 6.6932499456, 6.2869999872, 6.6307499519999995, 6.4119999744, 6.4432499712, 6.2244999936, 6.2869999872, 6.2244999936, 6.4119999744, 6.3807499776, 6.3494999808, 6.162, 6.162, 6.5994999552, 6.1932499968, 6.1307500032, 6.1307500032, 6.1307500032, 6.1307500032, 6.1307500032, 2.7557503488, 2.7557503488, 2.6932503552]
+    CS2_37=[1.0864256944000001, 3.2965001088, 2.79650016, 2.7340001664, 2.6715001728, 2.6090001792, 2.6090001792, 2.6090001792, 2.5465001856000002, 2.5465001856000002, 2.9840001408, 3.6090000768, 3.2965001088, 3.421500096, 3.3590001024, 3.3590001024, 3.3590001024, 3.2965001088, 3.3590001024, 3.421500096, 3.421500096, 3.3590001024, 3.8590000512, 3.9840000384, 3.9215000448, 3.8590000512, 4.359, 4.4214999936, 4.359, 4.2965000064, 4.2965000064, 4.1715000192, 4.2965000064, 4.2965000064, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.7964999552, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.7339999616, 4.7964999552, 4.7964999552, 4.6089999744, 4.7339999616, 4.7964999552, 4.7339999616, 4.7339999616, 4.7339999616, 4.7964999552, 4.7339999616, 4.7339999616, 4.7964999552, 4.6089999744, 4.671499968, 4.671499968, 4.7339999616, 4.6089999744, 4.6089999744, 4.7339999616, 4.7964999552, 4.7339999616, 4.983999936, 4.983999936, 4.7964999552, 4.9214999424, 5.2339999104, 4.983999936, 5.0464999295999995, 4.983999936, 4.9685000576, 4.9377500544, 5.5002499968, 5.7189999744, 5.805000038399999, 6.5237499647999995, 5.7737500416, 6.5237499647999995, 6.5237499647999995, 6.0550000128, 5.4925000704, 5.7425000448, 6.4612499712, 6.7112499456, 6.0550000128, 5.9612500224, 6.0862500096, 6.336249984, 6.2737499904, 6.492499968, 5.9300000256, 5.9925000191999995, 5.9925000191999995, 6.023750015999999, 5.9925000191999995, 5.805000038399999, 6.117500006399999, 6.1487500032, 6.1487500032, 6.1487500032, 6.1487500032, 2.8362503423999996, 2.8050003456, 2.8362503423999996, 3.2737502976]
+    CS2_38=[2.4536132784, 2.4609374976, 2.2505000704, 2.1880000768000003, 2.15675008, 2.4380000512000004, 2.2817500672, 2.2505000704, 2.781750016, 2.7505000192, 2.5942500352, 2.7505000192, 2.7505000192, 2.6567500288000003, 2.781750016, 2.7505000192, 2.6880000256, 3.250499968, 3.250499968, 3.250499968, 3.250499968, 3.2817499648000004, 3.250499968, 3.250499968, 3.2192499712, 3.2192499712, 3.250499968, 3.250499968, 3.71924992, 3.71924992, 3.6879999232, 4.2504998656, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.2192498688, 4.6879998208, 4.7192498176, 4.7192498176, 4.6879998208, 4.7192498176, 4.7192498176, 4.7192498176, 4.656749824, 4.6879998208, 4.5942498304, 4.7192498176, 4.6879998208, 4.6879998208, 5.2192497664, 5.2192497664, 5.1879997696, 5.0004997888, 5.3129997568, 5.1879997696, 5.250499763200001, 5.0629997824, 5.3129997568, 5.28174976, 5.28174976, 5.250499763200001, 5.3129997568, 5.3129997568, 5.250499763200001, 5.1567497728, 5.3129997568, 5.594249727999999, 5.9379996928, 6.281749657600001, 6.0004996863999995, 5.906749696, 5.906749696, 6.0942496768000005, 5.9692496896, 5.7817497088, 5.906749696, 5.7817497088, 5.875499699200001, 5.656749721600001, 5.125499776, 6.0942496768000005, 5.8129997056, 5.875499699200001, 5.9379996928, 5.875499699200001, 5.906749696, 5.8442497024, 5.9379996928, 5.8129997056, 5.906749696, 5.8442497024, 5.8442497024, 5.906749696, 6.0942496768000005, 5.9379996928, 5.9379996928, 5.9379996928, 5.9379996928, 6.3129996544, 6.281749657600001, 5.9692496896, 2.7505000192, 2.7505000192, 2.7505000192]
+    
+    
+     
+
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="Device2")
+    ax.plot(CS2_36, linewidth=1,c='g',label="Device3")
+    ax.plot(CS2_37, linewidth=1,c='y',label="Device4")
+    ax.plot(CS2_38, linewidth=1,c='r',label="Device5")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35[:-5],CS2_36[:-5],CS2_37[:-5],CS2_38[:-5]
+
+
+
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+shed=4.5
+
+# print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+# print(CS2_35)
+
+
+# print(CS2_35[0])
+# print(CS2_36[0])
+# print(CS2_37[0])
+# print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]>shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return list(CS235)
+
+# CS235=get_health_list(CS2_35,shed) 
+# CS236=get_health_list(CS2_36,shed) 
+# CS237=get_health_list(CS2_37,shed) 
+# CS238=get_health_list(CS2_38,shed)   
+
+# fig, ax = plt.subplots()
+# # 在生成的坐标系下画折线图
+# ax.plot(CS235, linewidth=1)
+# ax.plot(CS236, linewidth=1)
+# ax.plot(CS237, linewidth=1)
+# ax.plot(CS238, linewidth=1)
+
+# # 显示图形
+# plt.show()
+
+def get_input_out_2(CS236,CS237,time_windows):
+
+    print(CS237)
+    # CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS235_health[::-1] 
+    # print(CS236_health)
+    
+    # print(CS235_health)
+    
+    # CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    # for i in range(time_windows-1-2):
+    #     CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    # CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    # for i in range(len(CS235_health)-time_windows+1):        
+    #     x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+    #     y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+
+
+
+
+
+
+
+
+
+def get_input_out_3(CS235,CS236,CS237,time_windows):
+    # min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    # input_list=[]
+    # output_list=[]
+    
+    # true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    CS237_health=get_health_list(CS237,shed)     
+    
+    print(len(CS237_health))
+    print("hhhhhhhhhhhh")
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    for i in range(time_windows-1-2):
+        CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    for i in range(len(CS235_health)-time_windows+1):        
+        x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+        y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    print(y_test_array.shape)
+    print("jjjjjjjjjj")
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+    
+    
+    
+    
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_3(CS2_35,CS2_36,CS2_37,20)      
+
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+
+
+
+
+
+
+
+#import tensorflow as tf
+import os
+import logging
+import numpy as np
+#from numpy import trans
+import matplotlib.pyplot as plt
+#import tensorflow as tf
+# import CMAPSSDataset
+import pandas as pd
+import datetime
+import keras
+from keras.layers import Lambda
+import math
+import keras.backend as K
+import tensorflow.compat.v1 as tf
+# tf.disable_v2_behavior()
+from tfdeterminism import patch
+from sklearn.model_selection import train_test_split
+from keras.utils.vis_utils import plot_model
+# from tf import keras
+patch()
+# tf.random.set_seed(0)
+#import keras
+#flags = tf.flags
+#flags.DEFINE_string("weights", None, 'weights of the network')################# the file path of weights
+#flags.DEFINE_integer("epochs", 100, 'train epochs')
+#flags.DEFINE_integer("batch_size", 32, 'batch size for train/test')
+#flags.DEFINE_integer("sequence_length", 32, 'sequence length')
+#flags.DEFINE_boolean('debug', False, 'debugging mode or not')
+#FLAGS = flags.FLAGS
+
+def root_mean_squared_error(y_true, y_pred):
+        return K.sqrt(K.mean(K.square(y_pred - y_true),axis=0))##################  axis=0
+
+def rmse(predictions, targets):
+    return np.sqrt(((predictions - targets) ** 2).mean())
+
+
+
+
+    
+segment=3
+
+
+
+run_times=10
+
+
+
+nb_epochs=2000           #200
+batch_size=64    ## 64        #####300
+# sequence_length=31    ############# min31  max303
+
+patience=50
+patience_reduce_lr=20
+
+
+
+
+
+seed=2
+
+
+
+num_filter1=64
+num_filter2=128
+num_filter3=64
+
+
+
+kernel1_size=8
+kernel2_size=5
+kernel3_size=3
+
+
+
+
+
+
+
+
+
+
+
+             
+
+
+
+# X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_38,sequence_length) 
+
+
+sequence_length=20
+
+
+
+X_train , Y_train , X_test ,  Y_test =get_input_out_2(CS2_36,CS2_37,sequence_length)
+
+
+for FD in [1]: ######['1','2','3','4']
+    # if max_life==110 and FD=='1':
+    #     continue
+    # if max_life==110 and FD=='2':
+        # continue
+    
+    FD_feature_columns=[]
+ 
+
+
+
+
+
+
+
+
+
+
+
+    method_name='grid_FD{}_TaFCN_IGBT_npseed{}_segment_{}'.format(FD,seed,segment)
+    # method_name='FCN_RUL_1out_train_split_test'
+    dataset='cmapssd'
+    
+    
+    def unbalanced_penalty_score_1out(Y_test,Y_pred) :
+          
+        s=0    
+        for i in range(len(Y_pred)):
+            if Y_pred[i]>Y_test[i]:
+                s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+            else:
+                s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+        print('unbalanced_penalty_score{}'.format(s))
+        return s  
+      
+    def error_range_1out(Y_test,Y_pred) :           
+        error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+        print('error range{}'.format(error_range))
+        return error_range
+    
+ 
+    print(X_train.shape)
+    X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1,1)
+    
+    
+    X_test=X_test.reshape(X_test.shape[0],X_train.shape[1],1,1)
+    
+    # x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+    
+    
+    import six
+    
+    import keras.backend as K
+    from keras.utils.generic_utils import deserialize_keras_object
+    from keras.utils.generic_utils import serialize_keras_object
+    from tensorflow.python.ops import math_ops
+    from tensorflow.python.util.tf_export import tf_export
+    
+    
+    
+    
+    
+    from tensorflow.python.ops import math_ops
+    
+
+    
+
+    
+    
+
+
+    
+    
+
+
+    # reshape_size=len(FD_feature_columns)*int((sequence_length/3))
+    def FCN_model():
+    #    in0 = keras.Input(shape=(sequence_length,train_feature_slice.shape[1]))  # shape: (batch_size, 3, 2048)
+    #    in0_shaped= keras.layers.Reshape((train_feature_slice.shape[1],sequence_length,1))(in0)   
+    
+        in0 = keras.Input(shape=(X_train.shape[1],X_train.shape[2],X_train.shape[3]),name='layer_13')  # shape: (batch_size, 3, 2048)
+    #    begin_senet=SeBlock()(in0)
+        x = keras.layers.AveragePooling2D(pool_size=(int(sequence_length/segment), 1), strides=int(sequence_length/segment),name='layer_12')(in0)
+        # x = keras.layers.Reshape((-1,1))(x) 
+        
+        # x = keras.layers.Reshape((len(FD_feature_columns)*int((sequence_length/3)),))(x)             
+        x = keras.layers.Reshape((-1,))(x)               
+        # x = keras.layers.GlobalAveragePooling2D()(in0)
+        x = keras.layers.Dense(1, use_bias=False,activation=keras.activations.relu)(x)
+        kernel = keras.layers.Dense(1, use_bias=False,activation=keras.activations.hard_sigmoid,name='layer_11')(x)
+        begin_senet= keras.layers.Multiply(name='layer_10')([in0,kernel])    #给通道加权重
+     
+    
+       
+    
+    #     conv0 = keras.layers.
+        
+        
+        conv0 = keras.layers.Conv2D(num_filter1, kernel1_size, strides=1, padding='same',name='layer_9')(begin_senet)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_8')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter2, kernel2_size, strides=1, padding='same',name='layer_7')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_6')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter3, kernel3_size, strides=1, padding='same',name='layer_5')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_4')(conv0)
+        conv0 = keras.layers.GlobalAveragePooling2D(name='layer_3')(conv0)
+        conv0 = keras.layers.Dense(64, activation='relu',name='layer_2')(conv0)
+        out = keras.layers.Dense(1, activation='relu',name='layer_1')(conv0)
+    
+        
+        
+    
+    
+    
+        model = keras.models.Model(inputs=in0, outputs=[out])    
+    
+        return model
+    
+    
+    # ##############shuaffle the data
+    np.random.seed(seed)
+    index=np.arange(X_train.shape[0])
+    np.random.shuffle(index,)
+    
+     
+    X_train=X_train[index]#X_train是训练集，y_train是训练标签
+    Y_train=Y_train[index]
+    
+    #X_train, Xtest, Y_train, ytest = train_test_split(X_train, Y_train, test_size=0.7, random_state=0)
+    
+    
+    if __name__ == '__main__':
+    
+        error_record=[]
+        index_record=[]
+        unbalanced_penalty_score_record=[]
+        error_range_left_record=[]
+        error_range_right_record=[]
+        index_min_val_loss_record,min_val_loss_record=[],[]
+        
+        if os.path.exists(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name)):os.remove(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name))
+    
+     
+    
+
+    
+        rul_pred_array_list=[]
+        true_out_array_list=[]          
+        error_pred_array_list=[]
+                    
+    #######             single  output                
+    
+        for i in range(run_times):
+            print('xxx')
+ 
+            model=FCN_model()
+            plot_model(model, to_file=r"F:\桌面11.17\project\RUL\Flatten.png", show_shapes=True)#########to_file='Flatten.png',r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5
+            
+            optimizer = keras.optimizers.Adam()
+            model.compile(loss='mse',#loss=root_mean_squared_error,
+                          optimizer=optimizer,
+                          metrics=[root_mean_squared_error])
+             
+            reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = 'loss', factor=0.5,
+                              patience=patience_reduce_lr, min_lr=0.0001) 
+            
+
+    #                  verbose=1, validation_split=VALIDATION_SPLIT, callbacks = [reduce_lr])   
+            model_name='{}_dataset_{}_log{}_time{}'.format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+            earlystopping=keras.callbacks.EarlyStopping(monitor='loss',patience=patience,verbose=1)
+            modelcheckpoint=keras.callbacks.ModelCheckpoint(monitor='loss',filepath=r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),save_best_only=True,verbose=1)
+            hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+                      verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+    #        hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+    #                  verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+            log = pd.DataFrame(hist.history)
+            log.to_excel(r"F:\桌面11.17\project\RUL\experiments_result\log\{}_dataset_{}_log{}_time{}.xlsx".format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S')))
+            
+            print(hist.history.keys())
+            epochs=range(len(hist.history['loss']))
+            plt.figure()
+            plt.plot(epochs,hist.history['loss'],'b',label='Training loss')
+            plt.plot(epochs,hist.history['val_loss'],'r',label='Validation val_loss')
+            plt.title('Traing and Validation loss')
+            plt.legend()
+            plt.show()
+
+    
+            
+    #        model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error,'Smooth':Smooth,'SeBlock':SeBlock})
+            model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error})
+            for layer in model.layers:
+                layer.trainable=False        
+    #        score = model.evaluate(X_test, Y_test)  ############forbid evaluate!!!!!!!!!!!!!!!!!!
+    #        print('score[1]:{}'.format(score[1]))    ############forbid evaluate!!!!!!!!!!!!!!!!!!    
+            
+            Y_pred=model.predict(X_test)
+    #        rmse=root_mean_squared_error(Y_test,Y_pred)
+    #        with tf.Session() as sess:
+    #            print(rmse.eval())
+            rmse_value=rmse(Y_test,Y_pred)
+            # print('rmse:{}'.format(rmse_value))
+            
+
+            rul_pred_array=np.array(Y_pred)
+            rul_pred_array=rul_pred_array.reshape(rul_pred_array.shape[0])
+            
+            # print(rul_pred_array.shape)
+            
+            true_out_array=np.array(Y_test)
+            
+            error_pred_array=rul_pred_array-true_out_array
+            
+            error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+                # print(sol.x)
+                
+            # print(error_pred_array.sum())
+            # print("xxxxx")
+            # print(error_pred_array)
+                
+                
+            fig, ax = plt.subplots()
+            # 在生成的坐标系下画折线图
+            ax.plot(error_pred_array, linewidth=1)
+            
+            
+            
+            # 显示图形
+            plt.show()   
+             
+            
+            # print(i)
+            # print("rul_pred_array") 
+            # print(list(rul_pred_array))
+            # print("true_out_array") 
+            # print(list(true_out_array))
+            # print("error_pred_array") 
+            # print(list(error_pred_array))  
+            
+            rul_pred_array_list.append(rul_pred_array)
+            true_out_array_list.append(true_out_array)            
+            error_pred_array_list.append(error_pred_array)
+        rul_pred_array=np.mean(rul_pred_array_list,axis=0)
+        true_out_array=np.mean(true_out_array_list,axis=0)
+        error_pred_array=np.mean(error_pred_array_list,axis=0)
+
+
+        print(i)
+        print("rul_pred_array") 
+        print(list(rul_pred_array))
+        print("true_out_array") 
+        print(list(true_out_array))
+        print("error_pred_array") 
+        print(list(error_pred_array))  
+
+
+
diff --git a/code/comparison experiments/cacle/DPA_our_method/DPA.py b/code/comparison experiments/cacle/DPA_our_method/DPA.py
new file mode 100644
index 0000000..69efd13
--- /dev/null
+++ b/code/comparison experiments/cacle/DPA_our_method/DPA.py	
@@ -0,0 +1,456 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 16:46:05 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 22:07:49 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 20:41:41 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 18 13:04:56 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:49:17 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:26:02 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 14:28:22 2022
+
+@author: Administrator
+"""
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 31 18:17:31 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 14:50:53 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 28 14:55:56 2022
+
+@author: Administrator
+
+"""
+
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-0.5
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_35_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_35=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_35 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+
+
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_36_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_36=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_36 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_37_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_37=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_37 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+        
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_38_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_38=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_38 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="CS2_35")
+    ax.plot(CS2_36, linewidth=1,c='g',label="CS2_36")
+    ax.plot(CS2_37, linewidth=1,c='y',label="CS2_37")
+    ax.plot(CS2_38, linewidth=1,c='r',label="CS2_38")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_cycle_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_cycle_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35,CS2_36,CS2_37,CS2_38
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+print(CS2_35)
+
+
+print(CS2_35[0])
+print(CS2_36[0])
+print(CS2_37[0])
+print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return CS235
+
+
+
+def get_input_out(CS235,CS237,CS236):
+    min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    input_list=[]
+    output_list=[]
+    
+    true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS238=get_health_list(CS2_38,shed)  
+    # print(len(CS236_health))   
+    
+    # print(min(len(CS235),len(CS236),len(CS237)))
+    
+    # print("ccccccccccccccccccccccccc")
+    
+    
+    
+    for i in range(2,len(CS236_health)):
+
+        
+        
+        
+        # one_input.append(np.array([len(CS235_health),len(CS237_health)]))
+        
+        
+
+
+
+
+
+
+
+
+        y = [CS235[0], CS235[min_len-1]]
+        x = [0, (min_len-1)*(min_len-1)]
+        slope_0, intercept_0, r_value, p_value, std_err = linregress(x, y)
+
+        
+        y = [CS237[0], CS237[min_len-1]]
+        x = [0,(min_len-1)*(min_len-1)]
+        slope_1, intercept_1, r_value, p_value, std_err = linregress(x, y)
+        
+        
+        
+        
+        
+        
+
+        one_input=[]
+        one_input.append(np.array(CS235[:i]))
+        one_input.append(np.array(CS237[:i]))
+        
+        one_input.append("CS238")
+    
+        min_len_train=min(len(CS235),len(CS237))
+            
+        one_input.append(np.array(CS235[:min_len_train]))
+            
+      
+        one_input.append(np.array(CS237[:min_len_train]))
+        
+        one_input.append(np.array(CS236[:i]))
+        
+        
+        
+        
+        
+        input_list.append(one_input)
+        
+        one_out=[]
+        one_out.append(np.array(CS236[:i]))
+        
+        output_list.append(one_out)
+        
+        true_out_list.append(max(len(CS236_health)-i-1,0))
+
+
+
+
+
+    
+    true_out_array=np.array(true_out_list)
+    return input_list[:len(CS236_health)],output_list[:len(CS236_health)],true_out_array[:len(CS236_health)]
+
+
+
+
+
+
+for i in range(1):
+    
+    
+
+        
+    if i==0:
+        
+        input_list,output_list,true_out_array=get_input_out(CS2_35,CS2_36,CS2_38)
+
+
+   
+    
+    rul_pred_list=[]
+    
+    regress_error_list=[]
+    
+    for j in range(len(input_list)):
+        
+        # print(j)
+        # print("jjjjjjjjjjjjjjjjjjjjjjjjjj")
+        index=j
+        def objective(x):
+            
+            integrate_array=input_list[index][0]*x[0]+input_list[index][1]*x[1]+x[2]
+            
+            error = np.sum(np.square(integrate_array - output_list[index][0]))/len(integrate_array)
+            
+            return error    
+        
+
+
+        
+        x0=np.ones(3)
+        x0[2]=0
+ 
+        
+        
+
+        # sol = minimize(objective, x0 ) 
+        sol = minimize(objective, x0 ,method='BFGS', options={'maxiter':(2+1)*200,'gtol':1e-5 }  )
+        
+        u1=sol.x[0] 
+        u2=sol.x[1]
+        c1=input_list[j][3]
+
+        c2=input_list[j][4]
+        
+        offset_vertical=sol.x[2]
+
+
+
+
+        
+        ######补偿误差
+        pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1] +offset_vertical
+        pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve)[int(-min(j*0.2,20)):] )      
+
+
+#############预测曲线          
+        # plt.plot(range(len(pred_curve)), pred_curve)
+        # plt.show()
+ 
+#############
+
+        
+        ful_pred=len(pred_curve)
+        for ii in range(len(pred_curve)):
+            
+            # if i == len(pred_curve)-1:
+                
+            #     ful_pred==i
+            
+            if pred_curve[ii]<shed:
+                
+                ful_pred=ii
+                
+                break
+
+
+        rul_pred_list.append(ful_pred-j-1) #######(-1  because of  begining from 2)
+
+
+
+
+
+#############实时的拟合曲线与实际曲线的所有点的误差        
+        # pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1]                
+        # plt.plot(range(len(input_list[j][5])), input_list[j][5] - pred_online_cruve)
+        # plt.show()
+
+#################
+
+
+                
+    
+    rul_pred_array=np.array(rul_pred_list)
+    
+    error_pred_array=rul_pred_array-true_out_array
+    
+    error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+        # print(sol.x)
+        
+    # print(error_pred_array.sum())
+    # print("xxxxx")
+    # print(error_pred_array)
+        
+        
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(error_pred_array, linewidth=1)
+    
+    
+    
+    # 显示图形
+    plt.show()   
+     
+    
+    print(i)
+    print("rul_pred_array") 
+    print(list(rul_pred_array))
+    print("true_out_array") 
+    print(list(true_out_array))
+    print("error_pred_array") 
+    print(list(error_pred_array))    
+
+
+
+
+
+# 0
+# rul_pred_array
+# [849, 847, 846, 845, 844, 752, 727, 724, 723, 722, 721, 720, 721, 720, 719, 719, 718, 721, 720, 719, 718, 717, 716, 716, 715, 714, 717, 716, 713, 710, 709, 708, 706, 708, 707, 704, 703, 702, 700, 699, 700, 699, 697, 696, 695, 693, 692, 692, 691, 689, 688, 687, 686, 685, 684, 683, 682, 681, 680, 679, 678, 677, 676, 671, 670, 669, 668, 666, 665, 664, 663, 662, 661, 660, 657, 656, 655, 656, 655, 654, 653, 652, 651, 650, 649, 648, 647, 646, 645, 644, 644, 643, 646, 645, 644, 643, 637, 636, 635, 634, 633, 633, 636, 630, 629, 618, 613, 612, 611, 610, 609, 608, 607, 606, 599, 598, 603, 602, 601, 600, 599, 593, 591, 590, 589, 588, 587, 586, 585, 584, 582, 581, 580, 579, 579, 578, 576, 576, 580, 580, 579, 578, 577, 575, 574, 574, 573, 572, 571, 570, 569, 568, 567, 566, 565, 564, 563, 563, 562, 561, 560, 559, 558, 557, 559, 558, 557, 556, 555, 554, 553, 552, 551, 550, 549, 548, 547, 546, 545, 544, 543, 542, 541, 540, 539, 538, 537, 536, 535, 534, 533, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 522, 521, 520, 520, 518, 517, 517, 516, 515, 514, 513, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 497, 496, 495, 494, 493, 492, 491, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 411, 410, 409, 408, 407, 406, 405, 404, 403, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 387, 386, 385, 384, 383, 382, 382, 381, 381, 380, 379, 378, 379, 378, 382, 382, 381, 380, 379, 378, 376, 375, 375, 373, 368, 366, 365, 364, 363, 362, 361, 360, 357, 356, 355, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 342, 341, 340, 339, 338, 337, 337, 336, 335, 334, 333, 333, 332, 331, 332, 331, 330, 330, 333, 333, 332, 331, 330, 329, 328, 327, 326, 324, 324, 322, 321, 316, 315, 313, 312, 311, 310, 309, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 298, 297, 296, 295, 294, 293, 293, 296, 296, 295, 294, 293, 292, 292, 292, 291, 290, 290, 289, 288, 288, 287, 292, 291, 292, 293, 292, 291, 290, 289, 289, 288, 287, 286, 285, 284, 283, 282, 282, 281, 280, 279, 277, 276, 276, 275, 274, 273, 273, 273, 272, 271, 271, 271, 270, 285, 285, 284, 284, 283, 284, 283, 286, 286, 285, 295, 294, 293, 292, 291, 290, 289, 288, 287, 287, 286, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 273, 272, 271, 270, 269, 268, 267, 266, 265, 264, 263, 262, 262, 261, 261, 260, 260, 259, 259, 258, 258, 258, 262, 261, 260, 260, 259, 258, 257, 256, 256, 255, 254, 253, 252, 251, 250, 249, 248, 247, 246, 245, 244, 243, 242, 241, 239, 238, 237, 236, 235, 234, 233, 232, 231, 230, 229, 228, 227, 226, 225, 224, 223, 222, 221, 220, 219, 218, 217, 216, 216, 215, 215, 214, 213, 212, 211, 210, 209, 208, 207, 206, 204, 203, 202, 201, 199, 198, 197, 196, 195, 193, 192, 191, 190, 189, 186, 182, 181, 180, 178, 177, 176, 176, 174, 173, 171, 170, 169, 168, 166, 165, 164, 163, 161, 160, 158, 157, 156, 155, 154, 153, 152, 150, 149, 149, 148, 147, 147, 147, 146, 146, 146, 149, 150, 150, 149, 149, 149, 156, 155, 155, 155, 157, 156, 155, 155, 154, 153, 152, 152, 151, 150, 149, 148, 147, 146, 144, 143, 142, 141, 140, 138, 137, 136, 132, 130, 129, 128, 127, 125, 119, 115, 114, 113, 111, 110, 109, 108, 107, 106, 105, 105, 104, 103, 102, 101, 102, 102, 105, 105, 104, 103, 103, 102, 101, 101, 100, 99, 98, 97, 96, 95, 97, 96, 95, 94, 93, 92, 92, 92, 91, 91, 90, 89, 89, 89, 89, 90, 89, 89, 88, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 80, 79, 79, 79, 79, 78, 77, 76, 76, 75, 75, 76, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 62, 54, 61, 60, 59, 58, 57, 56, 55, 54, 53, 51, 42, 40, 39, 38, 36, 35, 33, 32, 31, 30, 27, 25, 24, 23, 22, 21, 19, 18, 17, 16, 15, 14]
+# true_out_array
+# [820, 819, 818, 817, 816, 815, 814, 813, 812, 811, 810, 809, 808, 807, 806, 805, 804, 803, 802, 801, 800, 799, 798, 797, 796, 795, 794, 793, 792, 791, 790, 789, 788, 787, 786, 785, 784, 783, 782, 781, 780, 779, 778, 777, 776, 775, 774, 773, 772, 771, 770, 769, 768, 767, 766, 765, 764, 763, 762, 761, 760, 759, 758, 757, 756, 755, 754, 753, 752, 751, 750, 749, 748, 747, 746, 745, 744, 743, 742, 741, 740, 739, 738, 737, 736, 735, 734, 733, 732, 731, 730, 729, 728, 727, 726, 725, 724, 723, 722, 721, 720, 719, 718, 717, 716, 715, 714, 713, 712, 711, 710, 709, 708, 707, 706, 705, 704, 703, 702, 701, 700, 699, 698, 697, 696, 695, 694, 693, 692, 691, 690, 689, 688, 687, 686, 685, 684, 683, 682, 681, 680, 679, 678, 677, 676, 675, 674, 673, 672, 671, 670, 669, 668, 667, 666, 665, 664, 663, 662, 661, 660, 659, 658, 657, 656, 655, 654, 653, 652, 651, 650, 649, 648, 647, 646, 645, 644, 643, 642, 641, 640, 639, 638, 637, 636, 635, 634, 633, 632, 631, 630, 629, 628, 627, 626, 625, 624, 623, 622, 621, 620, 619, 618, 617, 616, 615, 614, 613, 612, 611, 610, 609, 608, 607, 606, 605, 604, 603, 602, 601, 600, 599, 598, 597, 596, 595, 594, 593, 592, 591, 590, 589, 588, 587, 586, 585, 584, 583, 582, 581, 580, 579, 578, 577, 576, 575, 574, 573, 572, 571, 570, 569, 568, 567, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 555, 554, 553, 552, 551, 550, 549, 548, 547, 546, 545, 544, 543, 542, 541, 540, 539, 538, 537, 536, 535, 534, 533, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 522, 521, 520, 519, 518, 517, 516, 515, 514, 513, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 497, 496, 495, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 411, 410, 409, 408, 407, 406, 405, 404, 403, 402, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 382, 381, 380, 379, 378, 377, 376, 375, 374, 373, 372, 371, 370, 369, 368, 367, 366, 365, 364, 363, 362, 361, 360, 359, 358, 357, 356, 355, 354, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 341, 340, 339, 338, 337, 336, 335, 334, 333, 332, 331, 330, 329, 328, 327, 326, 325, 324, 323, 322, 321, 320, 319, 318, 317, 316, 315, 314, 313, 312, 311, 310, 309, 308, 307, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 273, 272, 271, 270, 269, 268, 267, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 253, 252, 251, 250, 249, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 238, 237, 236, 235, 234, 233, 232, 231, 230, 229, 228, 227, 226, 225, 224, 223, 222, 221, 220, 219, 218, 217, 216, 215, 214, 213, 212, 211, 210, 209, 208, 207, 206, 205, 204, 203, 202, 201, 200, 199, 198, 197, 196, 195, 194, 193, 192, 191, 190, 189, 188, 187, 186, 185, 184, 183, 182, 181, 180, 179, 178, 177, 176, 175, 174, 173, 172, 171, 170, 169, 168, 167, 166, 165, 164, 163, 162, 161, 160, 159, 158, 157, 156, 155, 154, 153, 152, 151, 150, 149, 148, 147, 146, 145, 144, 143, 142, 141, 140, 139, 138, 137, 136, 135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+# error_pred_array
+# [29, 28, 28, 28, 28, 63, 87, 89, 89, 89, 89, 89, 87, 87, 87, 86, 86, 82, 82, 82, 82, 82, 82, 81, 81, 81, 77, 77, 79, 81, 81, 81, 82, 79, 79, 81, 81, 81, 82, 82, 80, 80, 81, 81, 81, 82, 82, 81, 81, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 86, 86, 86, 86, 87, 87, 87, 87, 87, 87, 87, 89, 89, 89, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 86, 86, 82, 82, 82, 82, 87, 87, 87, 87, 87, 86, 82, 87, 87, 97, 101, 101, 101, 101, 101, 101, 101, 101, 107, 107, 101, 101, 101, 101, 101, 106, 107, 107, 107, 107, 107, 107, 107, 107, 108, 108, 108, 108, 107, 107, 108, 107, 102, 101, 101, 101, 101, 102, 102, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 100, 100, 100, 100, 100, 100, 100, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 96, 97, 97, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 91, 91, 91, 91, 91, 91, 90, 90, 89, 89, 89, 89, 87, 87, 82, 81, 81, 81, 81, 81, 82, 82, 81, 82, 86, 87, 87, 87, 87, 87, 87, 87, 89, 89, 89, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 90, 90, 90, 90, 90, 89, 89, 89, 87, 87, 87, 86, 82, 81, 81, 81, 81, 81, 81, 81, 81, 82, 81, 82, 82, 86, 86, 87, 87, 87, 87, 87, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 87, 87, 87, 87, 87, 87, 86, 82, 81, 81, 81, 81, 81, 80, 79, 79, 79, 78, 78, 78, 77, 77, 71, 71, 69, 67, 67, 67, 67, 67, 66, 66, 66, 66, 66, 66, 66, 66, 65, 65, 65, 65, 66, 66, 65, 65, 65, 65, 64, 63, 63, 63, 62, 61, 61, 45, 44, 44, 43, 43, 41, 41, 37, 36, 36, 25, 25, 25, 25, 25, 25, 25, 25, 25, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 23, 23, 22, 22, 21, 21, 20, 20, 19, 18, 13, 13, 13, 12, 12, 12, 12, 12, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 15, 18, 18, 18, 19, 19, 19, 18, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 23, 23, 23, 23, 23, 23, 23, 24, 24, 23, 23, 23, 22, 21, 21, 20, 19, 15, 13, 12, 12, 11, 10, 2, 2, 1, 0, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 3, 3, 0, 1, 1, 1, 1, 2, 7, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 8, 7, 3, 2, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6, 7, 8, 9, 11, 11, 12, 12, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 16, 17, 18, 18, 18, 18, 19, 19, 20, 22, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 22, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 21, 20, 20, 20, 19, 19, 18, 18, 18, 18, 16, 15, 15, 15, 15, 15, 14, 14, 14, 14, 14, 14]
+# 1
+# rul_pred_array
+# [848, 847, 846, 845, 844, 770, 728, 724, 723, 721, 723, 723, 726, 726, 726, 726, 726, 726, 725, 730, 729, 728, 727, 728, 727, 726, 717, 713, 711, 709, 708, 707, 706, 701, 699, 696, 695, 694, 693, 694, 691, 690, 689, 688, 687, 686, 685, 684, 682, 681, 680, 679, 678, 677, 676, 675, 674, 673, 672, 671, 670, 669, 668, 667, 666, 665, 663, 662, 662, 660, 659, 658, 657, 656, 655, 655, 654, 653, 652, 652, 651, 650, 649, 650, 649, 648, 647, 647, 646, 645, 648, 642, 641, 640, 639, 643, 642, 641, 641, 640, 639, 638, 637, 635, 609, 618, 617, 613, 612, 610, 609, 608, 607, 607, 606, 604, 603, 602, 601, 600, 599, 599, 598, 597, 596, 595, 594, 593, 592, 594, 593, 592, 591, 590, 589, 588, 587, 583, 582, 581, 580, 582, 581, 577, 576, 578, 574, 573, 572, 571, 570, 569, 568, 566, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 554, 553, 552, 551, 550, 549, 548, 547, 547, 545, 544, 544, 543, 542, 541, 540, 538, 537, 537, 536, 535, 534, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 523, 522, 520, 520, 519, 518, 517, 516, 518, 514, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 498, 497, 496, 495, 494, 496, 495, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 412, 411, 410, 409, 407, 406, 405, 404, 403, 402, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 382, 381, 381, 380, 379, 378, 377, 380, 379, 378, 377, 376, 375, 375, 374, 373, 372, 371, 370, 369, 368, 367, 366, 365, 364, 363, 362, 361, 360, 359, 358, 357, 355, 354, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 341, 340, 335, 338, 337, 336, 335, 334, 333, 332, 331, 331, 330, 329, 328, 327, 326, 325, 325, 324, 323, 323, 322, 320, 319, 318, 317, 316, 315, 314, 313, 311, 310, 309, 308, 307, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 293, 292, 291, 290, 289, 289, 288, 287, 288, 287, 286, 286, 289, 288, 288, 287, 286, 285, 284, 285, 285, 285, 284, 289, 288, 287, 286, 285, 284, 285, 284, 284, 284, 283, 283, 282, 281, 280, 279, 278, 277, 276, 275, 275, 274, 273, 272, 272, 272, 271, 271, 271, 270, 285, 284, 283, 283, 283, 283, 283, 286, 286, 285, 295, 294, 293, 292, 292, 291, 290, 289, 288, 287, 286, 285, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 272, 272, 271, 270, 269, 268, 267, 266, 266, 265, 265, 264, 264, 264, 264, 263, 265, 266, 266, 265, 264, 264, 264, 263, 270, 269, 268, 268, 267, 266, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 252, 251, 250, 249, 248, 247, 246, 244, 243, 242, 241, 240, 239, 238, 238, 237, 236, 236, 235, 236, 235, 234, 233, 232, 231, 231, 230, 230, 229, 229, 229, 228, 227, 226, 225, 224, 222, 221, 219, 218, 216, 215, 214, 213, 211, 210, 209, 208, 207, 206, 203, 202, 201, 199, 198, 197, 196, 194, 193, 192, 191, 190, 189, 188, 187, 186, 177, 176, 175, 174, 172, 172, 171, 170, 168, 167, 166, 164, 163, 163, 163, 162, 169, 168, 168, 170, 169, 169, 170, 171, 171, 173, 172, 172, 173, 172, 171, 171, 171, 170, 169, 169, 169, 168, 167, 168, 167, 166, 165, 164, 163, 162, 161, 160, 160, 158, 157, 156, 155, 154, 153, 151, 150, 149, 147, 146, 145, 144, 143, 142, 141, 139, 138, 137, 136, 135, 135, 134, 133, 132, 131, 130, 129, 128, 128, 127, 127, 126, 125, 124, 123, 122, 121, 120, 119, 119, 126, 126, 125, 124, 123, 122, 121, 121, 122, 121, 123, 129, 129, 128, 128, 127, 128, 129, 136, 135, 136, 135, 135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 71, 68, 67, 57, 55, 53, 52, 50, 49, 48, 46, 44, 34, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 23, 24, 23, 30, 30, 31, 32, 41, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6]
+# true_out_array
+# [874, 873, 872, 871, 870, 869, 868, 867, 866, 865, 864, 863, 862, 861, 860, 859, 858, 857, 856, 855, 854, 853, 852, 851, 850, 849, 848, 847, 846, 845, 844, 843, 842, 841, 840, 839, 838, 837, 836, 835, 834, 833, 832, 831, 830, 829, 828, 827, 826, 825, 824, 823, 822, 821, 820, 819, 818, 817, 816, 815, 814, 813, 812, 811, 810, 809, 808, 807, 806, 805, 804, 803, 802, 801, 800, 799, 798, 797, 796, 795, 794, 793, 792, 791, 790, 789, 788, 787, 786, 785, 784, 783, 782, 781, 780, 779, 778, 777, 776, 775, 774, 773, 772, 771, 770, 769, 768, 767, 766, 765, 764, 763, 762, 761, 760, 759, 758, 757, 756, 755, 754, 753, 752, 751, 750, 749, 748, 747, 746, 745, 744, 743, 742, 741, 740, 739, 738, 737, 736, 735, 734, 733, 732, 731, 730, 729, 728, 727, 726, 725, 724, 723, 722, 721, 720, 719, 718, 717, 716, 715, 714, 713, 712, 711, 710, 709, 708, 707, 706, 705, 704, 703, 702, 701, 700, 699, 698, 697, 696, 695, 694, 693, 692, 691, 690, 689, 688, 687, 686, 685, 684, 683, 682, 681, 680, 679, 678, 677, 676, 675, 674, 673, 672, 671, 670, 669, 668, 667, 666, 665, 664, 663, 662, 661, 660, 659, 658, 657, 656, 655, 654, 653, 652, 651, 650, 649, 648, 647, 646, 645, 644, 643, 642, 641, 640, 639, 638, 637, 636, 635, 634, 633, 632, 631, 630, 629, 628, 627, 626, 625, 624, 623, 622, 621, 620, 619, 618, 617, 616, 615, 614, 613, 612, 611, 610, 609, 608, 607, 606, 605, 604, 603, 602, 601, 600, 599, 598, 597, 596, 595, 594, 593, 592, 591, 590, 589, 588, 587, 586, 585, 584, 583, 582, 581, 580, 579, 578, 577, 576, 575, 574, 573, 572, 571, 570, 569, 568, 567, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 555, 554, 553, 552, 551, 550, 549, 548, 547, 546, 545, 544, 543, 542, 541, 540, 539, 538, 537, 536, 535, 534, 533, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 522, 521, 520, 519, 518, 517, 516, 515, 514, 513, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 497, 496, 495, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 411, 410, 409, 408, 407, 406, 405, 404, 403, 402, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 382, 381, 380, 379, 378, 377, 376, 375, 374, 373, 372, 371, 370, 369, 368, 367, 366, 365, 364, 363, 362, 361, 360, 359, 358, 357, 356, 355, 354, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 341, 340, 339, 338, 337, 336, 335, 334, 333, 332, 331, 330, 329, 328, 327, 326, 325, 324, 323, 322, 321, 320, 319, 318, 317, 316, 315, 314, 313, 312, 311, 310, 309, 308, 307, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 273, 272, 271, 270, 269, 268, 267, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 253, 252, 251, 250, 249, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 238, 237, 236, 235, 234, 233, 232, 231, 230, 229, 228, 227, 226, 225, 224, 223, 222, 221, 220, 219, 218, 217, 216, 215, 214, 213, 212, 211, 210, 209, 208, 207, 206, 205, 204, 203, 202, 201, 200, 199, 198, 197, 196, 195, 194, 193, 192, 191, 190, 189, 188, 187, 186, 185, 184, 183, 182, 181, 180, 179, 178, 177, 176, 175, 174, 173, 172, 171, 170, 169, 168, 167, 166, 165, 164, 163, 162, 161, 160, 159, 158, 157, 156, 155, 154, 153, 152, 151, 150, 149, 148, 147, 146, 145, 144, 143, 142, 141, 140, 139, 138, 137, 136, 135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+# error_pred_array
+# [26, 26, 26, 26, 26, 99, 140, 143, 143, 144, 141, 140, 136, 135, 134, 133, 132, 131, 131, 125, 125, 125, 125, 123, 123, 123, 131, 134, 135, 136, 136, 136, 136, 140, 141, 143, 143, 143, 143, 141, 143, 143, 143, 143, 143, 143, 143, 143, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 145, 145, 144, 145, 145, 145, 145, 145, 145, 144, 144, 144, 144, 143, 143, 143, 143, 141, 141, 141, 141, 140, 140, 140, 136, 141, 141, 141, 141, 136, 136, 136, 135, 135, 135, 135, 135, 136, 161, 151, 151, 154, 154, 155, 155, 155, 155, 154, 154, 155, 155, 155, 155, 155, 155, 154, 154, 154, 154, 154, 154, 154, 154, 151, 151, 151, 151, 151, 151, 151, 151, 154, 154, 154, 154, 151, 151, 154, 154, 151, 154, 154, 154, 154, 154, 154, 154, 155, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 155, 155, 155, 155, 155, 155, 155, 155, 154, 155, 155, 154, 154, 154, 154, 154, 155, 155, 154, 154, 154, 154, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 154, 154, 155, 154, 154, 154, 154, 154, 151, 154, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 154, 154, 154, 154, 154, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 150, 150, 150, 150, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 150, 150, 150, 150, 150, 146, 146, 146, 146, 146, 146, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 150, 146, 146, 146, 146, 146, 146, 146, 146, 145, 145, 145, 145, 145, 145, 145, 144, 144, 144, 143, 143, 144, 144, 144, 144, 144, 144, 144, 144, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 144, 144, 144, 144, 144, 143, 143, 143, 141, 141, 141, 140, 136, 136, 135, 135, 135, 135, 135, 133, 132, 131, 131, 125, 125, 125, 125, 125, 125, 123, 123, 122, 121, 121, 120, 120, 120, 120, 120, 120, 120, 120, 120, 119, 119, 119, 119, 118, 117, 117, 116, 115, 115, 99, 99, 99, 98, 97, 96, 95, 91, 90, 90, 79, 79, 79, 79, 78, 78, 78, 78, 78, 78, 78, 78, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 78, 77, 77, 77, 77, 77, 77, 77, 76, 76, 75, 75, 74, 73, 72, 72, 69, 67, 66, 66, 66, 65, 64, 64, 56, 56, 56, 55, 55, 55, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 55, 55, 55, 54, 54, 52, 52, 52, 52, 52, 52, 51, 51, 50, 50, 49, 48, 48, 48, 48, 48, 48, 49, 49, 50, 50, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 54, 54, 54, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 64, 64, 64, 64, 65, 64, 64, 64, 65, 65, 65, 66, 66, 65, 64, 64, 56, 56, 55, 52, 52, 51, 49, 47, 46, 43, 43, 42, 40, 40, 40, 39, 38, 38, 38, 37, 36, 36, 36, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 34, 34, 34, 34, 34, 34, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 36, 35, 35, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 25, 24, 24, 24, 24, 24, 24, 23, 21, 21, 18, 11, 10, 10, 9, 9, 7, 5, 3, 3, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 3, 3, 6, 7, 8, 8, 9, 9, 9, 10, 11, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 20, 18, 18, 10, 9, 7, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]     
diff --git a/code/comparison experiments/cacle/prediction_value_and_error_cacle_3536_38_error_band_bian.py b/code/comparison experiments/cacle/prediction_value_and_error_cacle_3536_38_error_band_bian.py
new file mode 100644
index 0000000..d0b1218
--- /dev/null
+++ b/code/comparison experiments/cacle/prediction_value_and_error_cacle_3536_38_error_band_bian.py	
@@ -0,0 +1,283 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 27 11:37:45 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 26 22:25:42 2023
+
+@author: Administrator
+"""
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+Si=[792, 790, 789, 787, 786, 784, 780, 774, 771, 769, 766, 768, 768, 767, 766, 765, 764, 763, 762, 760, 759, 758, 756, 755, 754, 753, 751, 744, 740, 738, 736, 734, 738, 738, 731, 727, 726, 725, 723, 728, 728, 720, 718, 717, 716, 713, 718, 718, 712, 709, 707, 706, 704, 725, 723, 723, 722, 715, 713, 711, 708, 706, 705, 701, 698, 696, 693, 691, 690, 690, 688, 684, 683, 681, 679, 681, 680, 678, 677, 675, 675, 673, 672, 670, 669, 667, 666, 665, 664, 662, 660, 659, 652, 650, 648, 647, 654, 654, 652, 655, 654, 649, 647, 645, 644, 642, 645, 645, 639, 636, 634, 633, 630, 636, 635, 630, 626, 625, 625, 622, 621, 626, 625, 626, 625, 625, 624, 624, 623, 622, 621, 620, 619, 618, 617, 616, 615, 605, 604, 602, 601, 603, 605, 605, 595, 596, 597, 597, 600, 599, 590, 589, 589, 586, 584, 590, 590, 582, 580, 580, 579, 574, 580, 581, 578, 570, 569, 569, 567, 569, 572, 572, 571, 571, 570, 569, 569, 568, 567, 566, 565, 564, 564, 563, 562, 562, 561, 552, 551, 549, 548, 545, 551, 551, 545, 541, 544, 549, 549, 543, 539, 539, 539, 537, 539, 541, 541, 533, 532, 531, 530, 528, 534, 534, 529, 525, 526, 529, 530, 529, 528, 528, 527, 526, 526, 525, 524, 523, 522, 522, 521, 520, 519, 518, 517, 516, 516, 508, 507, 506, 503, 500, 506, 506, 501, 497, 498, 497, 493, 498, 498, 494, 489, 489, 487, 486, 488, 489, 489, 481, 480, 481, 478, 477, 482, 482, 476, 473, 472, 471, 475, 471, 470, 475, 474, 466, 465, 463, 462, 462, 461, 460, 464, 465, 461, 455, 456, 453, 452, 453, 455, 454, 447, 446, 445, 443, 441, 446, 446, 445, 444, 444, 444, 443, 442, 442, 441, 440, 439, 438, 437, 437, 436, 435, 428, 423, 425, 422, 420, 424, 425, 425, 415, 415, 414, 413, 410, 409, 415, 416, 414, 416, 417, 416, 408, 407, 406, 404, 402, 407, 406, 406, 405, 405, 404, 403, 402, 401, 400, 399, 398, 397, 397, 396, 395, 394, 387, 383, 382, 380, 377, 382, 383, 383, 374, 373, 371, 371, 369, 374, 374, 368, 365, 365, 362, 362, 364, 366, 366, 362, 360, 360, 362, 362, 359, 353, 352, 351, 350, 350, 349, 352, 352, 351, 351, 351, 351, 350, 349, 349, 348, 347, 347, 346, 345, 345, 344, 339, 333, 331, 330, 329, 329, 332, 333, 326, 324, 324, 323, 321, 324, 325, 325, 318, 318, 317, 316, 314, 319, 317, 316, 314, 312, 310, 309, 308, 307, 305, 309, 310, 309, 309, 309, 308, 307, 303, 302, 304, 304, 304, 300, 300, 299, 299, 298, 300, 300, 297, 296, 293, 293, 292, 291, 293, 293, 289, 286, 286, 285, 284, 282, 286, 285, 281, 279, 279, 278, 278, 279, 280, 279, 279, 278, 278, 277, 277, 276, 276, 275, 274, 273, 272, 272, 271, 270, 269, 267, 268, 269, 268, 263, 262, 262, 261, 258, 261, 261, 258, 255, 255, 254, 253, 252, 254, 254, 250, 248, 247, 246, 245, 246, 247, 247, 242, 240, 240, 239, 238, 240, 240, 240, 239, 239, 238, 238, 237, 236, 236, 235, 234, 233, 233, 232, 231, 231, 229, 228, 229, 229, 228, 223, 222, 220, 219, 218, 221, 221, 219, 215, 215, 214, 212, 211, 214, 214, 210, 207, 207, 206, 201, 200, 205, 205, 198, 194, 194, 192, 190, 193, 196, 197, 197, 197, 196, 196, 196, 196, 194, 194, 193, 192, 191, 191, 190, 190, 184, 181, 185, 186, 186, 177, 176, 174, 173, 170, 174, 176, 177, 168, 166, 165, 167, 165, 163, 162, 168, 169, 164, 160, 160, 158, 157, 156, 161, 161, 157, 153, 152, 150, 149, 149, 153, 154, 148, 146, 146, 145, 145, 145, 148, 149, 144, 141, 141, 140, 139, 140, 143, 143, 143, 142, 142, 142, 141, 141, 141, 140, 140, 139, 139, 138, 135, 133, 134, 136, 136, 130, 127, 127, 126, 125, 126, 128, 128, 123, 120, 121, 119, 119, 118, 122, 121, 119, 115, 114, 114, 112, 110, 115, 115, 115, 115, 107, 108, 107, 106, 108, 110, 110, 110, 109, 109, 109, 108, 108, 108, 107, 107, 107, 106, 106, 101, 98, 98, 101, 101, 99, 94, 94, 93, 93, 92, 96, 96, 96, 90, 89, 89, 89, 87, 90, 91, 90, 84, 84, 83, 83, 82, 84, 86, 86, 83, 82, 81, 81, 81, 80, 83, 84, 83, 83, 83, 82, 82, 82, 82, 81, 81, 80, 80, 79, 82, 80, 78, 81, 81, 78, 75, 74, 74, 73, 72, 75, 75, 75, 71, 69, 67, 67, 67, 67, 67, 69, 69, 65, 64, 72, 68, 59, 53, 58, 59, 58, 49, 44, 42, 39, 38, 35, 43, 43, 45, 36, 33, 32, 30, 29, 26, 32, 35, 36, 27, 25, 25, 23, 23, 21, 20, 19, 18, 17, 16, 18, 20, 22, 21, 21, 21, 21, 21, 21, 20, 20, 20, 20, 19, 30, 27, 24, 22, 21, 20, 19, 18, 20, 19, 19, 17, 16, 15, 15, 14, 14, 14, 16, 15, 13, 11, 10, 9, 9, 8, 7, 8, 8, 8, 8, 6, 5, 3, 2, 1, 0, 1, 1, 0, 0]
+     
+Zhang=[138, 139, 737, 736, 735, 734, 733, 732, 731, 730, 729, 728, 727, 726, 725, 724, 723, 722, 721, 720, 719, 718, 717, 716, 715, 714, 713, 712, 711, 710, 709, 708, 707, 706, 705, 704, 703, 702, 700, 699, 699, 698, 697, 692, 692, 692, 685, 692, 691, 690, 684, 681, 680, 674, 685, 684, 683, 682, 681, 680, 679, 678, 677, 676, 675, 674, 673, 672, 671, 670, 669, 668, 667, 666, 665, 658, 663, 662, 661, 660, 659, 658, 657, 656, 655, 654, 653, 652, 651, 650, 649, 647, 646, 619, 616, 614, 614, 642, 641, 640, 639, 638, 637, 636, 635, 634, 629, 632, 631, 630, 623, 617, 617, 609, 625, 624, 619, 610, 609, 611, 605, 605, 617, 616, 615, 614, 613, 612, 611, 610, 609, 608, 607, 606, 605, 604, 603, 602, 601, 600, 599, 598, 597, 596, 595, 593, 593, 592, 591, 590, 589, 588, 587, 586, 585, 584, 583, 582, 581, 580, 579, 578, 573, 576, 575, 574, 571, 572, 571, 570, 569, 568, 567, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 555, 554, 553, 552, 551, 550, 549, 548, 547, 546, 545, 544, 543, 542, 541, 540, 539, 538, 537, 536, 535, 534, 533, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 522, 521, 520, 519, 518, 517, 516, 515, 514, 513, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 497, 496, 495, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 411, 410, 409, 408, 407, 406, 405, 404, 403, 402, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 382, 381, 380, 379, 378, 377, 376, 375, 374, 373, 372, 371, 370, 369, 368, 367, 366, 365, 364, 363, 362, 361, 360, 359, 358, 357, 356, 355, 354, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 341, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 330, 329, 328, 327, 326, 336, 335, 323, 322, 321, 320, 319, 329, 328, 327, 315, 314, 313, 322, 311, 321, 320, 319, 318, 317, 316, 315, 314, 313, 312, 311, 310, 309, 309, 308, 307, 306, 304, 303, 303, 302, 301, 299, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 273, 272, 272, 271, 270, 269, 268, 267, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 254, 253, 251, 250, 249, 248, 247, 246, 246, 244, 243, 242, 241, 240, 239, 238, 238, 236, 235, 234, 233, 232, 231, 231, 230, 228, 227, 226, 225, 224, 223, 223, 222, 221, 220, 219, 218, 217, 216, 215, 214, 213, 212, 211, 210, 209, 208, 207, 206, 205, 204, 203, 202, 201, 200, 199, 198, 197, 196, 195, 194, 193, 192, 191, 190, 189, 188, 187, 185, 185, 184, 182, 181, 181, 180, 178, 177, 176, 174, 173, 173, 172, 172, 171, 170, 169, 168, 167, 166, 165, 164, 163, 162, 161, 160, 159, 158, 157, 155, 155, 154, 153, 151, 150, 149, 148, 147, 146, 146, 145, 143, 142, 140, 140, 139, 138, 137, 137, 136, 134, 133, 132, 131, 130, 128, 128, 128, 126, 125, 124, 122, 121, 120, 120, 119, 117, 116, 116, 114, 113, 113, 112, 111, 110, 108, 108, 107, 106, 105, 104, 103, 102, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 89, 89, 88, 87, 85, 84, 83, 82, 81, 80, 80, 79, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 64, 63, 63, 62, 61, 60, 58, 57, 56, 55, 55, 54, 53, 52, 51, 50, 50, 49, 48, 47, 46, 45, 44, 43, 42, 40, 39, 38, 38, 37, 35, 34, 33, 32, 31, 30, 30, 29, 28, 26, 25, 24, 23, 22, 22, 21, 20, 18, 17, 16, 15, 14, 14, 13, 67, 64, 63, 62, 63, 62, 62, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 70, 68, 66, 70, 70, 68, 64, 63, 63, 62, 61, 66, 66, 67, 61, 59, 57, 58, 57, 59, 59, 62, 62, 57, 56, 68, 63, 50, 43, 50, 51, 51, 39, 34, 31, 28, 27, 24, 33, 33, 36, 25, 22, 22, 20, 19, 16, 23, 25, 27, 17, 15, 15, 14, 13, 11, 10, 9, 8, 8, 8, 9, 12, 14, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11, 24, 21, 18, 15, 14, 13, 12, 11, 13, 12, 13, 11, 9, 9, 8, 8, 7, 7, 10, 9, 6, 5, 4, 4, 3, 2, 2, 3, 2, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+Hu=[749, 748, 747, 746, 744, 743, 737, 726, 724, 723, 721, 729, 730, 729, 729, 728, 728, 727, 726, 725, 724, 723, 722, 721, 720, 719, 718, 715, 713, 712, 710, 709, 710, 710, 707, 705, 704, 703, 702, 703, 702, 700, 698, 697, 697, 695, 696, 695, 693, 691, 690, 689, 688, 693, 692, 691, 690, 688, 686, 685, 684, 683, 681, 680, 678, 677, 676, 675, 674, 673, 672, 670, 669, 668, 667, 666, 665, 664, 663, 662, 661, 660, 659, 658, 657, 656, 655, 654, 653, 652, 650, 649, 647, 646, 645, 644, 644, 644, 642, 642, 641, 640, 638, 637, 636, 635, 635, 634, 632, 631, 629, 628, 627, 627, 626, 625, 623, 622, 621, 620, 619, 619, 618, 617, 616, 616, 615, 614, 613, 612, 611, 610, 609, 608, 607, 606, 605, 602, 601, 600, 599, 599, 598, 597, 595, 594, 593, 593, 592, 591, 589, 588, 587, 586, 585, 585, 584, 582, 581, 580, 579, 577, 577, 577, 575, 573, 572, 571, 570, 570, 569, 568, 567, 566, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 555, 554, 552, 551, 550, 549, 547, 547, 546, 545, 543, 543, 543, 542, 540, 539, 538, 537, 536, 535, 535, 534, 532, 531, 530, 529, 528, 527, 527, 525, 524, 523, 523, 522, 521, 520, 519, 518, 517, 516, 515, 514, 513, 512, 511, 510, 509, 508, 507, 506, 505, 504, 502, 501, 500, 499, 498, 497, 497, 495, 494, 493, 492, 490, 490, 489, 488, 486, 485, 484, 483, 483, 482, 481, 479, 478, 477, 476, 475, 475, 474, 472, 471, 470, 469, 469, 467, 466, 466, 465, 463, 462, 461, 460, 459, 458, 457, 457, 456, 454, 453, 452, 451, 450, 449, 448, 447, 445, 444, 443, 442, 441, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 423, 422, 421, 420, 419, 419, 418, 417, 415, 414, 413, 412, 410, 409, 409, 409, 407, 407, 406, 405, 403, 402, 401, 400, 399, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 381, 379, 378, 377, 376, 376, 375, 374, 372, 371, 370, 369, 368, 367, 367, 365, 364, 363, 361, 360, 360, 359, 358, 357, 356, 355, 354, 353, 352, 350, 349, 348, 347, 346, 345, 345, 344, 343, 342, 341, 340, 339, 338, 337, 336, 335, 334, 333, 332, 331, 330, 329, 327, 326, 325, 324, 323, 323, 322, 320, 319, 318, 317, 316, 315, 315, 314, 312, 311, 310, 309, 308, 307, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 292, 290, 289, 288, 287, 287, 285, 284, 283, 282, 281, 281, 280, 278, 277, 276, 275, 274, 273, 272, 272, 270, 269, 268, 267, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 253, 252, 251, 250, 249, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 238, 238, 237, 235, 234, 233, 232, 231, 230, 229, 228, 227, 226, 225, 224, 223, 222, 221, 220, 218, 217, 216, 215, 214, 214, 213, 211, 210, 209, 208, 207, 206, 205, 205, 204, 203, 202, 201, 200, 199, 198, 197, 196, 194, 193, 192, 191, 190, 189, 188, 187, 187, 186, 184, 183, 182, 181, 180, 179, 178, 177, 176, 175, 173, 172, 171, 171, 170, 168, 167, 166, 165, 164, 163, 162, 161, 159, 158, 157, 156, 155, 154, 154, 153, 152, 151, 150, 149, 148, 147, 146, 145, 144, 143, 142, 141, 140, 139, 137, 136, 135, 135, 134, 132, 131, 129, 128, 127, 127, 126, 125, 123, 122, 121, 120, 119, 118, 117, 116, 116, 114, 113, 112, 111, 110, 108, 108, 107, 106, 104, 103, 102, 101, 100, 100, 99, 97, 96, 95, 94, 93, 92, 91, 91, 89, 88, 87, 86, 85, 84, 83, 82, 81, 69, 69, 69, 69, 70, 70, 70, 70, 70, 69, 69, 68, 66, 67, 68, 68, 65, 64, 64, 64, 62, 63, 64, 64, 61, 60, 61, 61, 60, 60, 61, 61, 59, 58, 58, 58, 56, 56, 58, 58, 57, 57, 55, 55, 54, 54, 55, 55, 54, 54, 55, 55, 54, 54, 54, 54, 53, 53, 53, 53, 52, 50, 49, 49, 49, 50, 49, 47, 47, 47, 46, 46, 47, 47, 47, 45, 44, 44, 44, 44, 44, 44, 44, 42, 41, 41, 41, 41, 41, 41, 42, 41, 39, 39, 39, 39, 38, 39, 40, 40, 39, 39, 39, 39, 38, 38, 38, 38, 37, 37, 37, 38, 36, 35, 37, 37, 35, 34, 34, 34, 32, 32, 33, 33, 32, 31, 30, 30, 29, 29, 29, 29, 29, 29, 28, 27, 30, 28, 25, 24, 24, 24, 24, 22, 20, 19, 19, 18, 17, 18, 18, 19, 16, 16, 16, 15, 14, 14, 15, 15, 14, 13, 13, 13, 11, 11, 11, 11, 10, 10, 10, 10, 9, 9, 9, 9, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 8, 7, 6, 6, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 0]
+
+DCNN= [810.0135, 809.73895, 809.28986, 808.6551, 807.39856, 805.1235, 801.94995, 797.9386, 793.14856, 788.4023, 783.7584, 779.362, 775.1576, 771.0814, 767.0613, 763.0133, 758.8994, 754.70264, 750.45294, 746.26843, 742.22906, 738.5307, 735.383, 732.9147, 731.3576, 729.718, 727.6029, 724.69885, 721.0792, 716.3884, 712.05304, 708.07214, 703.71783, 698.96844, 693.8812, 688.4459, 682.78534, 677.736, 673.1267, 668.1578, 663.0049, 657.7403, 652.60345, 647.68805, 643.98206, 641.72076, 639.5765, 637.31464, 634.6608, 631.7324, 628.14325, 627.7377, 629.38806, 632.90594, 637.0918, 640.9564, 644.31433, 646.7606, 648.8445, 651.24036, 653.3373, 654.8333, 655.60455, 656.0061, 655.60785, 655.11804, 655.0667, 654.83246, 653.79553, 650.975, 646.7193, 639.55, 632.2343, 625.39935, 619.17145, 613.96906, 609.2101, 604.7988, 600.82117, 597.1158, 593.7106, 590.7154, 588.05664, 585.48785, 583.07764, 580.8139, 578.634, 576.5159, 574.7003, 573.2001, 570.7153, 567.5908, 563.8811, 559.48615, 556.03357, 553.2941, 551.1089, 549.8705, 549.1758, 548.4717, 547.6078, 546.47815, 545.0975, 543.376, 542.20386, 541.4406, 540.6498, 539.9732, 539.33093, 538.842, 538.56195, 538.7095, 538.68036, 537.8021, 535.5092, 532.69165, 529.7816, 526.33624, 523.047, 520.99243, 519.45154, 518.5287, 517.938, 517.76086, 517.6056, 518.0675, 519.4079, 521.0353, 522.7655, 524.29407, 525.8471, 526.9796, 528.55005, 530.9335, 533.61487, 535.04065, 535.64685, 535.2517, 533.91876, 531.912, 530.2241, 528.7428, 526.35, 523.8218, 521.033, 518.3157, 516.08966, 514.1869, 511.5229, 508.43976, 505.11118, 501.4708, 497.82578, 495.49213, 494.1884, 493.6275, 492.69052, 491.4709, 489.76788, 487.06122, 484.9633, 483.5241, 483.0673, 481.32324, 478.9392, 476.01215, 472.90372, 470.41455, 469.52744, 469.18637, 469.1898, 469.55704, 470.05728, 470.2381, 470.91064, 472.4533, 474.12238, 475.6421, 477.0783, 478.6394, 479.67886, 481.13495, 483.29898, 486.43204, 489.45386, 491.1297, 491.77753, 491.1717, 489.5524, 487.26825, 485.54523, 484.15292, 482.48163, 480.3961, 478.3504, 476.97998, 476.1097, 475.0074, 473.4576, 471.63144, 469.59937, 467.381, 465.76065, 465.075, 465.46402, 466.2902, 467.00644, 467.3287, 467.20517, 466.81122, 466.55225, 466.95502, 467.68524, 467.90268, 467.53873, 467.15582, 467.5406, 468.92715, 470.77545, 472.55713, 474.21964, 475.8585, 477.19418, 478.7666, 480.70224, 483.5196, 486.2203, 488.7763, 491.1085, 493.23468, 494.85223, 496.6049, 498.77582, 500.88824, 502.37036, 502.3015, 501.57755, 500.24542, 498.27637, 495.71173, 493.61514, 491.81635, 489.87442, 487.44415, 485.05145, 482.48837, 479.41187, 476.89062, 474.611, 472.1618, 469.2455, 466.29126, 463.1319, 460.3047, 458.11456, 457.28305, 456.8903, 456.01947, 454.9305, 453.55502, 451.33682, 449.22333, 448.29736, 448.0917, 447.27924, 446.06958, 444.67963, 442.62567, 441.35953, 440.53027, 440.16202, 440.30194, 440.80914, 440.52182, 439.78882, 438.58008, 437.35776, 436.76617, 435.95517, 434.86945, 434.434, 434.35236, 434.02246, 433.4616, 433.48932, 433.08807, 432.2203, 431.10767, 429.84164, 429.0794, 427.66925, 425.75537, 423.9599, 422.77432, 421.2902, 420.39728, 419.67685, 419.09164, 418.60587, 418.19147, 417.5866, 417.47333, 418.04254, 419.20898, 420.17627, 421.13364, 421.96768, 422.6687, 423.51157, 424.81738, 427.01895, 429.33487, 430.68115, 430.90146, 430.4415, 428.61078, 426.13632, 423.9572, 422.1315, 420.68954, 418.3968, 415.82422, 412.7413, 409.33606, 405.3802, 401.14648, 397.56653, 394.63004, 392.23138, 390.8902, 390.67334, 391.5283, 392.61533, 393.6722, 393.8688, 393.6126, 392.69727, 391.82272, 391.4909, 392.08978, 394.05698, 396.31183, 398.82635, 401.31235, 403.51288, 405.04443, 405.45245, 405.38312, 405.22498, 404.76593, 404.59247, 404.90637, 406.1913, 407.50537, 407.96762, 407.3898, 405.98126, 403.15747, 399.67017, 396.53613, 393.75812, 391.43112, 388.4078, 385.0929, 381.31244, 377.2659, 372.90765, 369.16202, 365.84213, 362.33804, 358.61368, 355.02286, 351.4443, 348.3523, 346.53513, 345.54895, 344.95984, 344.18817, 343.2768, 341.73813, 340.81036, 340.6629, 341.42117, 341.41736, 340.9737, 339.8153, 338.23416, 336.27838, 334.67883, 334.4822, 334.9555, 335.65085, 336.58102, 337.4028, 337.9107, 338.3931, 338.98615, 339.906, 340.7433, 341.51773, 342.2068, 343.29034, 345.11386, 347.67944, 350.2155, 352.17368, 353.00354, 352.63776, 351.29547, 348.9107, 346.3236, 344.11655, 342.25693, 339.87775, 337.11884, 334.17554, 330.90463, 327.29736, 324.02682, 321.08884, 318.61264, 315.72357, 312.92218, 310.28915, 308.11734, 306.61154, 306.43066, 306.365, 306.4427, 306.28253, 305.66772, 304.63702, 303.70306, 303.32544, 302.87103, 301.9484, 301.34445, 301.00323, 300.67236, 300.85248, 301.57864, 303.2044, 304.8471, 305.89856, 306.46576, 306.92947, 307.04352, 307.7032, 308.2634, 309.15, 310.23456, 311.5811, 312.8732, 314.51996, 316.2168, 317.56845, 318.23358, 318.19107, 317.8856, 317.40295, 316.8154, 316.715, 316.96936, 317.2901, 317.12622, 316.50705, 315.68597, 314.8296, 314.01538, 313.4461, 312.94604, 311.90778, 310.59497, 308.94525, 307.31085, 306.1316, 305.31363, 305.0525, 304.94638, 304.9518, 305.1372, 305.31702, 305.90308, 307.11417, 308.59186, 310.0263, 311.46704, 312.79443, 314.14935, 315.2104, 316.51807, 318.3102, 320.1127, 321.5567, 322.59482, 323.50446, 324.2282, 324.9221, 324.9371, 324.64728, 324.00067, 323.07037, 321.71576, 320.64322, 319.73135, 318.83344, 317.51193, 316.08478, 314.46124, 312.78827, 310.93207, 309.4693, 308.25262, 306.85477, 305.17035, 303.1468, 301.09653, 299.19992, 298.0351, 297.13614, 296.482, 295.37177, 294.04788, 292.20428, 290.4525, 288.85114, 287.92914, 287.09705, 286.54105, 286.11066, 285.89804, 285.6412, 285.7337, 286.36053, 287.1551, 287.79816, 288.3399, 288.73224, 288.94415, 289.22333, 289.8015, 290.9511, 292.15558, 293.1705, 293.77313, 294.4071, 294.6832, 294.94882, 294.56894, 293.73825, 292.32373, 290.5606, 288.44284, 286.73746, 285.34448, 284.1419, 282.56802, 280.8444, 278.83096, 276.59943, 274.09174, 272.0542, 270.29056, 268.38995, 266.07773, 263.4901, 260.95786, 258.1891, 255.88359, 254.1385, 252.72473, 250.67168, 247.94174, 244.49985, 240.9347, 237.59685, 235.03174, 233.00703, 231.51904, 230.29697, 229.27676, 228.07964, 227.38513, 227.26115, 227.58032, 227.94331, 228.38997, 229.00679, 229.42188, 229.5756, 230.08493, 231.4292, 233.15044, 234.15176, 234.65523, 235.14299, 235.22998, 235.21062, 234.0876, 232.48344, 230.14417, 227.57979, 224.64717, 222.10323, 219.96973, 218.36356, 216.09464, 213.3601, 210.22209, 207.31633, 204.40411, 201.7803, 199.22311, 197.75694, 196.60086, 194.90762, 193.02473, 191.09839, 189.81992, 188.58365, 187.44193, 186.80705, 186.31766, 185.16782, 183.62125, 182.06111, 181.03276, 180.08525, 179.09581, 178.4467, 178.30075, 177.59273, 176.36931, 174.49792, 172.72751, 171.58725, 170.76828, 170.44788, 170.52324, 170.19905, 169.37599, 167.90187, 166.51411, 165.68149, 165.21045, 165.20328, 165.58897, 166.03699, 166.26012, 166.18265, 166.25978, 166.84842, 167.6221, 168.32451, 169.14656, 169.79185, 170.18863, 170.3207, 170.58415, 170.98541, 171.29372, 171.6005, 172.19638, 172.89188, 172.9136, 172.1493, 170.98628, 169.75676, 168.52567, 167.4731, 166.6619, 166.01462, 164.87364, 163.32706, 161.68526, 159.98909, 158.40544, 156.85364, 155.81793, 155.19955, 154.54935, 153.15323, 151.48343, 149.99702, 148.92422, 147.96144, 147.41946, 147.13733, 146.9803, 146.52715, 145.28748, 144.09448, 143.21541, 142.51003, 141.94559, 141.73947, 141.59395, 141.40938, 140.8898, 140.54744, 140.4404, 140.65784, 140.96295, 141.33286, 141.77165, 142.16983, 142.08418, 141.83633, 141.78625, 141.4305, 141.30862, 141.02266, 140.98206, 141.15842, 140.98952, 140.18454, 139.17014, 138.02672, 136.91226, 135.78818, 134.97641, 134.33649, 133.81625, 132.76057, 131.4954, 130.05502, 128.60051, 127.185486, 126.22441, 125.87488, 125.75459, 124.95831, 123.69973, 122.34629, 121.317955, 120.63981, 120.11055, 119.94065, 119.91048, 119.56156, 118.62584, 117.66588, 116.85526, 116.52122, 116.16343, 116.18913, 116.30129, 116.57285, 116.49385, 116.5305, 116.73307, 117.41654, 118.09355, 118.80817, 119.47786, 120.038956, 120.31653, 120.39895, 120.53851, 121.22123, 121.89435, 122.47439, 123.239296, 124.00625, 124.62765, 124.51558, 124.088234, 123.56044, 122.94707, 122.24126, 121.86204, 121.64364, 121.58534, 121.12974, 120.29108, 119.18874, 118.0861, 116.883835, 115.77517, 114.522446, 113.63374, 112.8651, 111.554344, 110.14752, 109.81931, 109.647804, 108.803, 107.06856, 105.26821, 103.603226, 101.70085, 98.95575, 95.63401, 92.205215, 88.525314, 84.742966, 80.727356, 77.434296, 74.11883, 71.18396, 67.6345, 64.111435, 60.46526, 56.473614, 52.019207, 48.345795, 46.11303, 44.3263, 42.18865, 39.809628, 37.820908, 36.49629, 35.45804, 34.653965, 34.126583, 33.52999, 32.865128, 31.498913, 30.207733, 29.077316, 28.589014, 28.429913, 28.437775, 28.654745, 28.846949, 28.856853, 28.231983, 27.508093, 26.921045, 26.67891, 26.491613, 26.263401, 26.087132, 25.92953, 26.522055, 26.981379, 27.463451, 27.719051, 27.80247, 27.771328, 27.60975, 27.341358, 27.223145, 27.112442, 27.125885, 27.030773, 26.887302, 26.736597, 26.6179, 26.551447, 26.548285, 26.785046, 26.982386, 26.928507, 26.252087, 25.624765, 25.105682, 24.70178, 24.381, 24.11279, 23.87659, 23.679417, 23.430803, 23.247875, 23.064426, 22.886173, 22.717466, 22.478914, 22.244003, 22.02676, 21.792555, 21.547846, 21.22738, 20.99084, 20.801714]
+        
+TaFCN=[640.2638, 640.2338, 639.9586, 639.7268, 640.84216, 647.54083, 652.7472, 656.3685, 656.0379, 653.2189, 650.63525, 647.35394, 646.74023, 646.2994, 645.967, 646.7759, 646.9007, 644.8055, 641.3334, 641.2361, 640.8529, 640.24817, 638.7047, 628.2843, 615.6909, 616.3092, 623.40295, 630.21814, 637.97595, 639.6832, 634.8037, 633.9588, 633.9271, 640.6551, 646.8053, 652.35376, 653.7205, 651.6853, 648.9877, 647.9951, 651.1446, 655.8116, 663.2464, 660.78815, 638.84784, 630.7402, 629.5962, 628.8661, 628.7279, 643.1181, 642.4058, 636.3036, 646.7684, 649.50793, 644.0365, 634.14075, 657.13275, 658.9554, 638.60614, 630.6212, 624.0784, 619.1483, 617.79, 634.18585, 638.1526, 623.6941, 625.8482, 632.69324, 630.76843, 624.3636, 638.5076, 639.1909, 647.0736, 643.13684, 627.7255, 623.14026, 620.3446, 618.44666, 618.1465, 615.35486, 605.2337, 594.71844, 587.5086, 582.7233, 579.35706, 578.5424, 578.6277, 569.4335, 560.06976, 560.4929, 563.69403, 566.1092, 572.91296, 571.27527, 575.6464, 581.0604, 586.76984, 585.7844, 580.9946, 582.1033, 582.10535, 573.2013, 566.63605, 558.4759, 552.0082, 544.89526, 543.4496, 545.8635, 534.4691, 518.75305, 522.22876, 531.6213, 541.661, 553.08856, 553.6462, 553.6373, 558.2322, 557.37305, 541.78986, 533.1878, 532.3795, 529.32166, 526.2583, 529.6368, 525.2346, 511.15952, 501.70605, 498.596, 493.95575, 492.2085, 496.13828, 493.81097, 486.27228, 486.0689, 491.16992, 498.06805, 505.36823, 513.4586, 523.25635, 530.13196, 538.5374, 546.8972, 548.99817, 558.4176, 562.1979, 567.71783, 572.96375, 577.60693, 570.38165, 577.80444, 573.47345, 574.4366, 569.8109, 552.8644, 529.6376, 528.7592, 527.00037, 523.987, 532.5606, 538.78845, 522.3634, 508.80414, 505.24692, 503.12054, 509.13068, 516.51025, 513.6226, 498.3265, 504.3452, 510.8496, 509.21362, 507.7521, 511.0731, 513.97845, 504.02173, 499.69238, 496.06573, 489.41632, 480.48657, 481.90277, 483.61407, 474.0331, 463.40497, 464.2339, 462.61273, 462.0252, 468.70303, 475.86377, 480.5666, 479.6668, 483.5506, 487.28656, 486.41373, 493.2198, 496.83658, 503.49335, 512.8161, 509.4002, 502.90448, 506.77655, 506.58545, 511.90274, 511.2707, 504.2413, 491.4357, 489.33798, 490.70734, 489.34286, 492.33926, 505.55054, 499.31708, 497.0835, 487.24332, 484.39722, 491.78452, 495.2733, 496.42163, 496.12915, 500.3675, 494.3894, 494.65048, 498.0219, 501.43393, 496.3316, 483.21353, 484.75128, 479.4645, 471.1014, 467.45792, 481.74503, 477.97974, 467.94098, 463.17865, 461.9284, 464.46085, 470.62256, 476.23444, 479.73575, 482.6792, 481.48544, 483.05624, 484.17108, 484.5329, 487.01068, 489.96542, 491.13446, 490.04865, 491.02997, 485.22476, 477.84784, 481.22485, 481.73047, 481.9773, 481.98447, 470.4698, 467.61246, 463.4672, 458.22818, 462.604, 467.8462, 471.77164, 460.61386, 458.95404, 459.91904, 455.74106, 459.5796, 464.71515, 468.38388, 469.63858, 460.14252, 449.16293, 444.27826, 443.76236, 447.3166, 452.84766, 452.7376, 445.21655, 442.5839, 431.78192, 415.34024, 412.72617, 419.64755, 421.38354, 418.88104, 423.14526, 424.8672, 422.4918, 417.27036, 414.80304, 413.22934, 415.54843, 422.11392, 421.70923, 426.85123, 423.6957, 422.74707, 417.81982, 412.1022, 408.76538, 411.26694, 415.15732, 414.73398, 409.2032, 405.057, 402.32043, 402.14383, 407.43707, 410.52734, 412.95132, 409.79468, 413.17535, 412.41534, 410.86713, 411.69766, 411.6022, 412.4091, 403.97662, 403.38318, 401.85547, 402.51788, 403.78046, 409.05154, 411.28662, 410.58038, 399.17526, 380.80463, 376.16284, 379.73822, 385.8676, 391.46393, 389.52484, 387.54117, 387.92856, 384.87598, 380.58917, 374.9804, 369.43365, 380.19073, 386.92178, 390.07794, 394.24655, 395.74628, 396.19858, 395.90125, 390.4209, 383.24313, 377.8222, 375.43515, 376.8251, 378.949, 378.7485, 372.21613, 373.96478, 371.1656, 366.01675, 362.96307, 360.91858, 360.1914, 351.57617, 351.23563, 351.17902, 353.82114, 351.8738, 357.89536, 359.3686, 362.0889, 355.52118, 358.3515, 358.4753, 356.77142, 365.7575, 374.5812, 374.08307, 365.95465, 362.02023, 356.5419, 354.1161, 348.439, 351.4124, 358.84036, 362.55664, 361.03192, 354.6839, 343.9208, 344.7818, 343.72946, 346.9714, 347.37952, 331.97348, 326.1955, 325.60944, 324.91083, 320.50085, 321.21783, 330.21835, 334.08908, 333.76212, 333.30173, 336.7456, 341.75433, 346.26276, 346.91678, 344.00238, 342.12582, 340.54324, 333.37512, 326.79498, 320.1485, 314.75955, 308.2007, 302.9378, 301.63278, 300.05005, 297.07227, 298.0666, 301.19278, 303.4389, 301.95526, 303.7937, 304.2053, 308.95242, 312.3003, 322.60187, 327.9004, 323.64246, 314.7433, 316.98785, 310.21466, 309.18655, 311.67163, 311.73376, 312.3441, 305.07538, 301.4581, 295.06714, 285.73108, 284.49734, 289.14273, 293.80087, 296.52664, 294.35727, 299.17294, 303.7364, 302.29968, 298.50922, 300.64117, 305.99185, 311.399, 307.54236, 306.1403, 305.52365, 306.63443, 307.55414, 307.9188, 306.06265, 304.3891, 306.2422, 305.61264, 309.77768, 309.24323, 305.542, 306.10242, 306.42316, 301.3056, 300.90707, 299.48492, 298.9393, 297.1369, 289.0808, 287.398, 287.47614, 284.70462, 285.68076, 286.676, 287.3287, 288.267, 288.97162, 288.3819, 291.08728, 297.2618, 302.59906, 307.12482, 309.80234, 310.92337, 313.53418, 313.95224, 315.4682, 318.26096, 318.66464, 318.29492, 314.25577, 311.49307, 308.80206, 304.38208, 298.45718, 296.48993, 297.74344, 299.24347, 292.62473, 289.33026, 289.2372, 290.9333, 287.94498, 287.6911, 284.98032, 284.73434, 282.4622, 282.62454, 283.8222, 284.27377, 281.1516, 280.375, 280.1167, 279.19553, 280.50528, 281.0497, 281.4603, 277.53494, 277.63248, 276.65063, 277.03842, 275.65884, 277.23236, 279.40073, 278.38727, 274.98886, 274.88223, 272.4888, 274.01944, 279.17767, 281.7685, 284.55255, 283.29382, 285.07004, 286.07825, 284.91818, 285.29626, 287.52393, 289.19022, 289.76205, 286.0762, 284.91254, 283.1488, 280.43204, 276.49094, 275.6474, 277.26642, 278.54736, 271.879, 269.17273, 269.33856, 269.027, 266.9915, 267.7809, 265.08588, 262.6461, 262.53412, 265.46048, 266.663, 265.73782, 262.37576, 261.65118, 259.85248, 259.09006, 260.47076, 265.52972, 266.02524, 256.44424, 250.4626, 248.81084, 247.52242, 241.57173, 241.35312, 246.63506, 247.21346, 244.69987, 244.80486, 239.15488, 237.39653, 243.46646, 247.79227, 250.7613, 253.03067, 254.20036, 252.79118, 247.41643, 246.90974, 246.40056, 249.61848, 250.22993, 247.17586, 247.70438, 247.27214, 241.34372, 238.64388, 239.24373, 242.01343, 244.54631, 232.10718, 233.48633, 238.26775, 241.89021, 238.77524, 235.61197, 228.75716, 229.25945, 226.73706, 232.06848, 235.30806, 239.40845, 236.68562, 224.73486, 211.88496, 209.10373, 212.09798, 213.30124, 212.01022, 209.06088, 209.78511, 210.44771, 203.33046, 194.93349, 195.00192, 192.52267, 189.37708, 189.96054, 193.43857, 196.59232, 195.27422, 192.98373, 186.39822, 180.45514, 185.68733, 187.67262, 184.29446, 184.02881, 180.72716, 178.37918, 173.18723, 171.26605, 175.92984, 178.53595, 174.76259, 174.2713, 171.51642, 168.84618, 164.16306, 166.55136, 171.89232, 169.731, 169.20828, 171.67773, 172.86893, 169.19283, 164.68008, 165.1112, 168.79129, 168.32639, 168.06638, 166.47404, 166.45807, 168.44812, 170.4491, 169.53763, 163.21129, 163.42114, 167.01724, 168.8037, 168.98457, 167.63408, 165.19437, 163.40018, 159.97339, 160.21921, 163.72925, 165.0165, 164.39273, 158.71034, 156.7968, 154.23215, 156.21693, 156.93181, 157.12157, 153.25601, 151.9354, 148.80302, 147.72913, 145.14273, 148.04793, 151.60886, 147.11131, 148.862, 149.52013, 149.85309, 148.54092, 147.5966, 145.0571, 147.72308, 148.46829, 148.1262, 147.74438, 148.41335, 147.5111, 145.47958, 143.00839, 140.07011, 142.98965, 143.14236, 140.3899, 135.46097, 136.4108, 136.88956, 139.889, 140.13638, 138.98143, 133.47171, 134.82881, 134.91263, 136.93362, 135.30351, 136.85081, 136.53214, 136.7127, 134.46521, 137.40521, 139.52519, 139.05637, 132.66377, 131.38629, 129.85545, 129.79276, 129.59128, 131.22156, 128.41982, 128.75102, 126.912476, 125.193016, 125.59227, 124.71133, 127.18767, 127.0663, 125.46922, 123.89679, 123.56704, 120.39409, 121.7784, 121.842995, 125.83663, 126.58734, 123.67979, 123.33916, 124.38237, 123.02716, 121.642685, 120.045395, 119.38037, 120.91838, 120.72276, 121.45174, 124.646835, 123.54921, 122.009445, 123.33126, 123.695595, 123.83147, 121.88664, 121.97478, 122.3905, 122.54915, 120.21039, 121.301956, 121.540634, 124.082016, 121.616196, 120.58209, 121.040016, 122.86418, 121.6075, 117.64175, 115.074196, 117.29848, 119.86841, 117.482506, 117.134926, 121.29883, 118.269936, 113.25096, 115.31633, 113.92439, 115.376915, 118.1176, 99.96849, 87.0545, 86.29067, 83.66188, 80.1526, 73.401215, 77.13469, 77.50792, 82.38699, 79.67123, 77.248795, 74.50481, 69.46887, 58.84305, 54.8236, 58.191063, 58.09458, 59.661205, 51.18214, 45.628365, 38.512123, 37.501026, 36.654037, 35.646675, 36.88641, 35.933464, 34.11808, 29.195211, 26.121723, 26.218542, 27.781153, 28.60563, 29.77327, 32.797783, 34.074177, 33.85972, 31.739878, 31.3724, 31.009726, 30.468176, 29.717815, 29.43154, 28.975616, 32.378258, 33.803936, 36.207096, 34.22335, 33.71507, 32.762592, 32.39533, 31.58137, 30.899832, 30.385223, 31.015766, 29.988968, 30.316645, 28.30693, 28.13478, 27.212555, 25.407167, 25.386671, 28.091946, 27.89758, 25.64541, 22.687765, 20.89239, 19.815756, 18.983147, 18.37227, 18.187893, 17.8709, 17.846947, 17.80736, 17.591717, 17.167192, 16.387264, 15.4728365, 14.916109, 14.191609, 14.188156, 14.088129, 13.551312, 13.45193, 12.974179]
+
+Our=[848, 847, 846, 845, 844, 770, 727, 724, 723, 721, 723, 727, 727, 726, 726, 726, 727, 726, 731, 730, 729, 728, 727, 728, 727, 727, 717, 713, 711, 709, 704, 707, 706, 700, 699, 696, 695, 694, 693, 694, 691, 690, 689, 688, 687, 686, 685, 684, 682, 681, 680, 679, 678, 677, 676, 675, 674, 673, 672, 671, 670, 669, 668, 667, 666, 665, 663, 662, 661, 660, 659, 658, 657, 656, 655, 655, 654, 653, 653, 652, 651, 650, 651, 650, 649, 648, 647, 647, 646, 649, 648, 643, 641, 640, 639, 643, 642, 641, 641, 640, 639, 638, 637, 635, 609, 618, 617, 613, 612, 610, 609, 608, 607, 607, 606, 604, 603, 602, 601, 600, 599, 599, 598, 597, 596, 595, 594, 593, 592, 594, 593, 592, 591, 590, 589, 588, 587, 583, 582, 581, 580, 582, 581, 577, 576, 578, 574, 573, 572, 571, 570, 569, 568, 566, 566, 565, 564, 563, 562, 561, 560, 559, 558, 557, 556, 554, 553, 552, 551, 550, 549, 548, 547, 547, 545, 544, 544, 543, 542, 541, 540, 538, 537, 537, 536, 535, 534, 532, 531, 530, 529, 528, 527, 526, 525, 524, 523, 523, 522, 520, 520, 519, 518, 517, 516, 518, 514, 512, 511, 510, 509, 508, 507, 506, 505, 504, 503, 502, 501, 500, 499, 498, 498, 497, 496, 495, 494, 496, 495, 494, 493, 492, 491, 490, 489, 488, 487, 486, 485, 484, 483, 482, 481, 480, 479, 478, 477, 476, 475, 474, 473, 472, 471, 470, 469, 468, 467, 466, 465, 464, 463, 462, 461, 460, 459, 458, 457, 456, 455, 454, 453, 452, 451, 450, 449, 448, 447, 446, 445, 444, 443, 442, 441, 440, 439, 438, 437, 436, 435, 434, 433, 432, 431, 430, 429, 428, 427, 426, 425, 424, 423, 422, 421, 420, 419, 418, 417, 416, 415, 414, 413, 412, 412, 411, 410, 409, 407, 406, 405, 404, 403, 402, 401, 400, 399, 398, 397, 396, 395, 394, 393, 392, 391, 390, 389, 388, 387, 386, 385, 384, 383, 382, 381, 381, 380, 379, 378, 377, 380, 379, 378, 377, 376, 375, 375, 374, 373, 372, 371, 370, 369, 368, 367, 366, 365, 364, 363, 362, 361, 360, 359, 358, 357, 355, 354, 353, 352, 351, 350, 349, 348, 347, 346, 345, 344, 343, 342, 341, 340, 335, 338, 337, 336, 335, 334, 333, 332, 331, 331, 330, 329, 328, 327, 326, 325, 325, 324, 323, 323, 322, 320, 319, 318, 317, 316, 315, 314, 313, 311, 310, 309, 308, 307, 306, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 293, 292, 291, 290, 289, 289, 288, 287, 288, 287, 286, 286, 289, 288, 288, 287, 286, 285, 284, 285, 285, 285, 284, 289, 288, 287, 286, 285, 284, 285, 284, 284, 284, 283, 283, 282, 281, 280, 279, 278, 277, 276, 275, 275, 274, 273, 272, 272, 272, 271, 271, 271, 270, 285, 284, 283, 283, 283, 283, 283, 286, 286, 285, 295, 294, 293, 292, 292, 291, 290, 289, 288, 287, 286, 285, 285, 284, 283, 282, 281, 280, 279, 278, 277, 276, 275, 274, 272, 272, 271, 270, 269, 268, 267, 266, 266, 265, 265, 264, 264, 264, 264, 263, 265, 266, 266, 265, 264, 264, 264, 263, 270, 269, 268, 268, 267, 266, 266, 265, 264, 263, 262, 261, 260, 259, 258, 257, 256, 255, 254, 252, 251, 250, 249, 248, 247, 246, 244, 243, 242, 241, 240, 239, 238, 238, 237, 236, 236, 235, 236, 235, 234, 233, 232, 231, 231, 230, 230, 229, 229, 229, 228, 227, 226, 225, 224, 222, 221, 219, 218, 216, 215, 214, 213, 211, 210, 209, 208, 207, 206, 203, 202, 201, 199, 198, 197, 196, 194, 193, 192, 191, 190, 189, 188, 187, 186, 177, 176, 175, 174, 172, 172, 171, 170, 168, 167, 166, 164, 163, 163, 163, 162, 169, 168, 168, 170, 169, 169, 170, 171, 171, 173, 172, 172, 173, 172, 171, 171, 171, 170, 169, 169, 169, 168, 167, 168, 167, 166, 165, 164, 163, 162, 161, 160, 160, 158, 157, 156, 155, 154, 153, 151, 150, 149, 147, 146, 145, 144, 143, 142, 141, 139, 138, 137, 136, 135, 135, 134, 133, 132, 131, 130, 129, 128, 128, 127, 127, 126, 125, 124, 123, 122, 121, 120, 119, 119, 126, 126, 125, 124, 123, 122, 121, 121, 122, 121, 123, 129, 129, 128, 128, 127, 128, 129, 136, 135, 136, 135, 135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 71, 68, 67, 57, 55, 53, 52, 50, 49, 48, 46, 44, 34, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 23, 24, 23, 30, 30, 31, 32, 41, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6]
+
+
+
+groud_truth=[876.0, 875.0, 874.0, 873.0, 872.0, 871.0, 870.0, 869.0, 868.0, 867.0, 866.0, 865.0, 864.0, 863.0, 862.0, 861.0, 860.0, 859.0, 858.0, 857.0, 856.0, 855.0, 854.0, 853.0, 852.0, 851.0, 850.0, 849.0, 848.0, 847.0, 846.0, 845.0, 844.0, 843.0, 842.0, 841.0, 840.0, 839.0, 838.0, 837.0, 836.0, 835.0, 834.0, 833.0, 832.0, 831.0, 830.0, 829.0, 828.0, 827.0, 826.0, 825.0, 824.0, 823.0, 822.0, 821.0, 820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+
+
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our),len(Hu))-10
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+
+ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+ax.plot(Si[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='-.')
+ax.plot(Zhang[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='-.')
+ax.plot(Hu[-min_len:], linewidth=2,c='#A67D3D',label="Hu' model",linestyle='-.')
+ax.plot(DCNN[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='-.')
+ax.plot(TaFCN[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='-.')
+
+
+# plt.figure(figsize=(7,4))  
+# # 在生成的坐标系下画折线图
+
+# plt.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+# plt.plot(Si[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='-.')
+# plt.plot(Zhang[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='-.')
+# plt.plot(TaFCN[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='-.')
+
+
+plt.plot(Our[-min_len:], linewidth=2,c='r',label="Our",linestyle='-.')
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+error_rate=0.25
+error_rate_1=0.4
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1-error_rate),color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1+error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1-error_rate), np.array(groud_truth[-min_len:])*(1-error_rate_1),color="#E7E7E7")# color="#CCEEFF")
+
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看    
+plt.xlabel('Charging cycle times (cycle)',font1) #X轴标签
+plt.ylabel("Predicted RUL (cycle)",font1) #Y轴标签
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_cacle_3536_38.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_cacle_3536_38.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
+Si_error=[84.0, 85.0, 85.0, 86.0, 86.0, 87.0, 90.0, 95.0, 97.0, 98.0, 100.0, 97.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 97.0, 97.0, 97.0, 98.0, 98.0, 98.0, 98.0, 99.0, 105.0, 108.0, 109.0, 110.0, 111.0, 106.0, 105.0, 111.0, 114.0, 114.0, 114.0, 115.0, 109.0, 108.0, 115.0, 116.0, 116.0, 116.0, 118.0, 112.0, 111.0, 116.0, 118.0, 119.0, 119.0, 120.0, 98.0, 99.0, 98.0, 98.0, 104.0, 105.0, 106.0, 108.0, 109.0, 109.0, 112.0, 114.0, 115.0, 117.0, 118.0, 118.0, 117.0, 118.0, 121.0, 121.0, 122.0, 123.0, 120.0, 120.0, 121.0, 121.0, 122.0, 121.0, 122.0, 122.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 125.0, 126.0, 126.0, 132.0, 133.0, 134.0, 134.0, 126.0, 125.0, 126.0, 122.0, 122.0, 126.0, 127.0, 128.0, 128.0, 129.0, 125.0, 124.0, 129.0, 131.0, 132.0, 132.0, 134.0, 127.0, 127.0, 131.0, 134.0, 134.0, 133.0, 135.0, 135.0, 129.0, 129.0, 127.0, 127.0, 126.0, 126.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 134.0, 134.0, 135.0, 135.0, 132.0, 129.0, 128.0, 137.0, 135.0, 133.0, 132.0, 128.0, 128.0, 136.0, 136.0, 135.0, 137.0, 138.0, 131.0, 130.0, 137.0, 138.0, 137.0, 137.0, 141.0, 134.0, 132.0, 134.0, 141.0, 141.0, 140.0, 141.0, 138.0, 134.0, 133.0, 133.0, 132.0, 132.0, 132.0, 131.0, 131.0, 131.0, 131.0, 131.0, 131.0, 130.0, 130.0, 130.0, 129.0, 129.0, 137.0, 137.0, 138.0, 138.0, 140.0, 133.0, 132.0, 137.0, 140.0, 136.0, 130.0, 129.0, 134.0, 137.0, 136.0, 135.0, 136.0, 133.0, 130.0, 129.0, 136.0, 136.0, 136.0, 136.0, 137.0, 130.0, 129.0, 133.0, 136.0, 134.0, 130.0, 128.0, 128.0, 128.0, 127.0, 127.0, 127.0, 126.0, 126.0, 126.0, 126.0, 126.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 124.0, 131.0, 131.0, 131.0, 133.0, 135.0, 128.0, 127.0, 131.0, 134.0, 132.0, 132.0, 135.0, 129.0, 128.0, 131.0, 135.0, 134.0, 135.0, 135.0, 132.0, 130.0, 129.0, 136.0, 136.0, 134.0, 136.0, 136.0, 130.0, 129.0, 134.0, 136.0, 136.0, 136.0, 131.0, 134.0, 134.0, 128.0, 128.0, 135.0, 135.0, 136.0, 136.0, 135.0, 135.0, 135.0, 130.0, 128.0, 131.0, 136.0, 134.0, 136.0, 136.0, 134.0, 131.0, 131.0, 137.0, 137.0, 137.0, 138.0, 139.0, 133.0, 132.0, 132.0, 132.0, 131.0, 130.0, 130.0, 130.0, 129.0, 129.0, 129.0, 129.0, 129.0, 129.0, 128.0, 128.0, 128.0, 134.0, 138.0, 135.0, 137.0, 138.0, 133.0, 131.0, 130.0, 139.0, 138.0, 138.0, 138.0, 140.0, 140.0, 133.0, 131.0, 132.0, 129.0, 127.0, 127.0, 134.0, 134.0, 134.0, 135.0, 136.0, 130.0, 130.0, 129.0, 129.0, 128.0, 128.0, 128.0, 128.0, 128.0, 128.0, 128.0, 128.0, 128.0, 127.0, 127.0, 127.0, 127.0, 133.0, 136.0, 136.0, 137.0, 139.0, 133.0, 131.0, 130.0, 138.0, 138.0, 139.0, 138.0, 139.0, 133.0, 132.0, 137.0, 139.0, 138.0, 140.0, 139.0, 136.0, 133.0, 132.0, 135.0, 136.0, 135.0, 132.0, 131.0, 133.0, 138.0, 138.0, 138.0, 138.0, 137.0, 137.0, 133.0, 132.0, 132.0, 131.0, 130.0, 129.0, 129.0, 129.0, 128.0, 128.0, 128.0, 127.0, 127.0, 127.0, 126.0, 126.0, 130.0, 135.0, 136.0, 136.0, 136.0, 135.0, 131.0, 129.0, 135.0, 136.0, 135.0, 135.0, 136.0, 132.0, 130.0, 129.0, 135.0, 134.0, 134.0, 134.0, 135.0, 129.0, 130.0, 130.0, 131.0, 132.0, 133.0, 133.0, 133.0, 133.0, 134.0, 129.0, 127.0, 127.0, 126.0, 125.0, 125.0, 125.0, 128.0, 128.0, 125.0, 124.0, 123.0, 126.0, 125.0, 125.0, 124.0, 124.0, 121.0, 120.0, 122.0, 122.0, 124.0, 123.0, 123.0, 123.0, 120.0, 119.0, 122.0, 124.0, 123.0, 123.0, 123.0, 124.0, 119.0, 119.0, 122.0, 123.0, 122.0, 122.0, 121.0, 119.0, 117.0, 117.0, 116.0, 116.0, 115.0, 115.0, 114.0, 114.0, 113.0, 113.0, 113.0, 113.0, 113.0, 112.0, 112.0, 112.0, 112.0, 113.0, 111.0, 109.0, 109.0, 113.0, 113.0, 112.0, 112.0, 114.0, 110.0, 109.0, 111.0, 113.0, 112.0, 112.0, 112.0, 112.0, 109.0, 108.0, 111.0, 112.0, 112.0, 112.0, 112.0, 110.0, 108.0, 107.0, 111.0, 112.0, 111.0, 111.0, 111.0, 108.0, 107.0, 106.0, 106.0, 105.0, 105.0, 104.0, 104.0, 104.0, 103.0, 103.0, 103.0, 103.0, 102.0, 102.0, 102.0, 101.0, 102.0, 102.0, 100.0, 99.0, 99.0, 103.0, 103.0, 104.0, 104.0, 104.0, 100.0, 99.0, 100.0, 103.0, 102.0, 102.0, 103.0, 103.0, 99.0, 98.0, 101.0, 103.0, 102.0, 102.0, 106.0, 106.0, 100.0, 99.0, 105.0, 108.0, 107.0, 108.0, 109.0, 105.0, 101.0, 99.0, 98.0, 97.0, 97.0, 96.0, 95.0, 94.0, 95.0, 94.0, 94.0, 94.0, 94.0, 93.0, 93.0, 92.0, 97.0, 99.0, 94.0, 92.0, 91.0, 99.0, 99.0, 100.0, 100.0, 102.0, 97.0, 94.0, 92.0, 100.0, 101.0, 101.0, 98.0, 99.0, 100.0, 100.0, 93.0, 91.0, 95.0, 98.0, 97.0, 98.0, 98.0, 98.0, 92.0, 91.0, 94.0, 97.0, 97.0, 98.0, 98.0, 97.0, 92.0, 90.0, 95.0, 96.0, 95.0, 95.0, 94.0, 93.0, 89.0, 87.0, 91.0, 93.0, 92.0, 92.0, 92.0, 90.0, 86.0, 85.0, 84.0, 84.0, 83.0, 82.0, 82.0, 81.0, 80.0, 80.0, 79.0, 79.0, 78.0, 78.0, 80.0, 81.0, 79.0, 76.0, 75.0, 80.0, 82.0, 81.0, 81.0, 81.0, 79.0, 76.0, 75.0, 79.0, 81.0, 79.0, 80.0, 79.0, 79.0, 74.0, 74.0, 75.0, 78.0, 78.0, 77.0, 78.0, 79.0, 73.0, 72.0, 71.0, 70.0, 77.0, 75.0, 75.0, 75.0, 72.0, 69.0, 68.0, 67.0, 67.0, 66.0, 65.0, 65.0, 64.0, 63.0, 63.0, 62.0, 61.0, 61.0, 60.0, 64.0, 66.0, 65.0, 61.0, 60.0, 61.0, 65.0, 64.0, 64.0, 63.0, 63.0, 58.0, 57.0, 56.0, 61.0, 61.0, 60.0, 59.0, 60.0, 56.0, 54.0, 54.0, 59.0, 58.0, 58.0, 57.0, 57.0, 54.0, 51.0, 50.0, 52.0, 52.0, 52.0, 51.0, 50.0, 50.0, 46.0, 44.0, 44.0, 43.0, 42.0, 42.0, 41.0, 40.0, 39.0, 39.0, 38.0, 38.0, 37.0, 37.0, 33.0, 34.0, 35.0, 31.0, 30.0, 32.0, 34.0, 34.0, 33.0, 33.0, 33.0, 29.0, 28.0, 27.0, 30.0, 31.0, 32.0, 31.0, 30.0, 29.0, 28.0, 25.0, 24.0, 27.0, 27.0, 18.0, 21.0, 29.0, 34.0, 28.0, 26.0, 26.0, 34.0, 38.0, 39.0, 41.0, 41.0, 43.0, 34.0, 33.0, 30.0, 38.0, 40.0, 40.0, 41.0, 41.0, 43.0, 36.0, 32.0, 30.0, 38.0, 39.0, 38.0, 39.0, 38.0, 39.0, 39.0, 39.0, 39.0, 39.0, 39.0, 36.0, 33.0, 30.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 25.0, 24.0, 23.0, 22.0, 22.0, 10.0, 12.0, 14.0, 15.0, 15.0, 15.0, 15.0, 15.0, 12.0, 12.0, 11.0, 12.0, 12.0, 12.0, 11.0, 11.0, 10.0, 9.0, 6.0, 6.0, 7.0, 8.0, 8.0, 8.0, 7.0, 7.0, 7.0, 5.0, 4.0, 3.0, 2.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 2.0, 1.0, 1.0, 0.0]
+     
+# Zhang_error=[738.0, 736.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 191.0, 203.0, 203.0, 203.0, 203.0, 190.0, 190.0, 190.0, 203.0, 203.0, 203.0, 203.0, 203.0, 203.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 190.0, 190.0, 190.0, 190.0, 191.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 190.0, 190.0, 190.0, 190.0, 191.0, 190.0, 190.0, 189.0, 190.0, 190.0, 191.0, 190.0, 190.0, 190.0, 190.0, 190.0, 191.0, 190.0, 191.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 189.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 190.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 190.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 189.0, 190.0, 190.0, 190.0, 190.0, 190.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 189.0, 188.0, 188.0, 188.0, 188.0, 189.0, 189.0, 188.0, 188.0, 188.0, 189.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 187.0, 186.0, 186.0, 187.0, 187.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 188.0, 187.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 187.0, 186.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 187.0, 187.0, 187.0, 188.0, 188.0, 188.0, 187.0, 187.0, 187.0, 188.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 187.0, 187.0, 187.0, 187.0, 187.0, 187.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 186.0, 185.0, 185.0, 185.0, 185.0, 185.0, 185.0, 185.0, 186.0, 185.0, 185.0, 185.0, 185.0, 186.0, 186.0, 186.0, 186.0, 185.0, 185.0, 185.0, 122.0, 124.0, 121.0, 121.0, 120.0, 119.0, 114.0, 113.0, 114.0, 117.0, 117.0, 115.0, 116.0, 116.0, 110.0, 108.0, 107.0, 106.0, 112.0, 110.0, 109.0, 109.0, 106.0, 103.0, 101.0, 100.0, 99.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 92.0, 94.0, 93.0, 88.0, 86.0, 88.0, 91.0, 90.0, 89.0, 88.0, 88.0, 82.0, 81.0, 79.0, 85.0, 84.0, 83.0, 81.0, 82.0, 77.0, 75.0, 74.0, 80.0, 79.0, 79.0, 78.0, 77.0, 74.0, 70.0, 68.0, 70.0, 70.0, 70.0, 68.0, 68.0, 67.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 44.0, 45.0, 46.0, 41.0, 40.0, 41.0, 44.0, 44.0, 43.0, 43.0, 43.0, 37.0, 36.0, 34.0, 39.0, 40.0, 41.0, 39.0, 39.0, 36.0, 35.0, 31.0, 30.0, 34.0, 34.0, 21.0, 25.0, 37.0, 43.0, 35.0, 33.0, 32.0, 43.0, 47.0, 49.0, 51.0, 51.0, 53.0, 43.0, 42.0, 38.0, 48.0, 50.0, 49.0, 50.0, 50.0, 52.0, 44.0, 41.0, 38.0, 47.0, 48.0, 47.0, 47.0, 47.0, 48.0, 48.0, 48.0, 48.0, 47.0, 46.0, 44.0, 40.0, 37.0, 37.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 29.0, 15.0, 17.0, 19.0, 21.0, 21.0, 21.0, 21.0, 21.0, 18.0, 18.0, 16.0, 17.0, 18.0, 17.0, 17.0, 16.0, 16.0, 15.0, 11.0, 11.0, 13.0, 13.0, 13.0, 12.0, 12.0, 12.0, 11.0, 9.0, 8.0, 7.0, 6.0, 7.0, 6.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+Zhang_error=[738.0, 736.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 138.0, 138.0, 137.0, 137.0, 137.0, 141.0, 140.0, 139.0, 145.0, 137.0, 137.0, 137.0, 142.0, 144.0, 144.0, 149.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 143.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 138.0, 138.0, 164.0, 166.0, 167.0, 166.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 141.0, 137.0, 137.0, 137.0, 143.0, 148.0, 147.0, 154.0, 137.0, 137.0, 141.0, 149.0, 149.0, 146.0, 151.0, 150.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 138.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 141.0, 137.0, 137.0, 137.0, 139.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 137.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 137.0, 137.0, 137.0, 137.0, 137.0, 126.0, 126.0, 137.0, 137.0, 137.0, 137.0, 137.0, 126.0, 126.0, 126.0, 137.0, 137.0, 137.0, 127.0, 137.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 126.0, 125.0, 125.0, 125.0, 125.0, 126.0, 126.0, 125.0, 125.0, 125.0, 126.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 124.0, 123.0, 123.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 125.0, 125.0, 124.0, 124.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 124.0, 123.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 125.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 125.0, 124.0, 123.0, 124.0, 124.0, 124.0, 125.0, 125.0, 125.0, 124.0, 124.0, 125.0, 125.0, 124.0, 125.0, 125.0, 124.0, 124.0, 124.0, 124.0, 125.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 124.0, 123.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 125.0, 125.0, 124.0, 124.0, 124.0, 124.0, 125.0, 125.0, 125.0, 125.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 123.0, 124.0, 124.0, 124.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 123.0, 124.0, 124.0, 124.0, 124.0, 124.0, 123.0, 123.0, 68.0, 70.0, 70.0, 70.0, 68.0, 68.0, 67.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 44.0, 45.0, 46.0, 41.0, 40.0, 41.0, 44.0, 44.0, 43.0, 43.0, 43.0, 37.0, 36.0, 34.0, 39.0, 40.0, 41.0, 39.0, 39.0, 36.0, 35.0, 31.0, 30.0, 34.0, 34.0, 21.0, 25.0, 37.0, 43.0, 35.0, 33.0, 32.0, 43.0, 47.0, 49.0, 51.0, 51.0, 53.0, 43.0, 42.0, 38.0, 48.0, 50.0, 49.0, 50.0, 50.0, 52.0, 44.0, 41.0, 38.0, 47.0, 48.0, 47.0, 47.0, 47.0, 48.0, 48.0, 48.0, 48.0, 47.0, 46.0, 44.0, 40.0, 37.0, 37.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 29.0, 15.0, 17.0, 19.0, 21.0, 21.0, 21.0, 21.0, 21.0, 18.0, 18.0, 16.0, 17.0, 18.0, 17.0, 17.0, 16.0, 16.0, 15.0, 11.0, 11.0, 13.0, 13.0, 13.0, 12.0, 12.0, 12.0, 11.0, 9.0, 9.0, 7.0, 6.0, 7.0, 6.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+        
+# TaFCN_error=[233.73621826171876, 232.76614990234376, 232.04132690429688, 231.273193359375, 229.15783081054687, 221.4592041015625, 215.25283813476562, 210.63145751953124, 209.96209106445312, 211.78107299804688, 213.3647216796875, 215.6461181640625, 215.2598083496094, 214.700634765625, 214.26798095703126, 213.36292114257813, 213.4515625, 215.05569458007812, 217.331298828125, 217.8596435546875, 216.78660888671874, 212.75181274414064, 213.2952392578125, 222.71567993164064, 234.3091247558594, 232.69078979492187, 224.5970886230469, 217.66658935546874, 212.45778198242186, 213.05860595703126, 220.47390747070312, 221.4851867675781, 222.330078125, 218.81192626953126, 211.65349731445312, 211.9800598144531, 212.4668762207031, 216.3446838378906, 218.7637145996094, 221.88330688476563, 219.53104248046876, 210.12131958007814, 205.59589233398438, 207.935009765625, 222.83987426757812, 226.16697998046874, 229.0124267578125, 228.08814086914063, 223.25645751953124, 216.60152587890624, 217.27721557617187, 221.80505981445313, 207.17865600585938, 197.1079833984375, 194.3415100097656, 195.0280334472656, 179.996826171875, 171.78228759765625, 184.22271728515625, 184.91537475585938, 189.92159423828124, 193.85167846679687, 194.59673461914062, 185.61871337890625, 186.15867919921874, 197.231494140625, 197.429345703125, 198.5843994140625, 203.1926025390625, 215.23788452148438, 209.32833862304688, 205.5349548339844, 199.4323303222656, 203.71276245117187, 212.7255126953125, 215.09335327148438, 215.57877197265626, 213.62763671875, 212.81029052734374, 214.99459838867188, 221.76080932617188, 227.79724731445313, 231.9120361328125, 235.12252807617188, 236.53118896484375, 234.948486328125, 234.80711059570314, 241.49258422851562, 246.1890441894531, 245.33095092773436, 244.63858642578126, 243.39429931640626, 238.80361938476562, 237.62827758789064, 233.25220947265626, 228.119921875, 223.1624267578125, 225.0426452636719, 223.72459716796874, 227.86874389648438, 228.27218017578124, 234.23276977539064, 237.84694213867186, 241.2215576171875, 246.16244506835938, 251.892041015625, 252.239013671875, 251.4005859375, 257.07057495117186, 266.0577331542969, 261.8092529296875, 255.71029663085938, 245.6339538574219, 236.8604736328125, 241.89623413085937, 234.8349853515625, 231.0574157714844, 229.8353759765625, 241.38034057617188, 246.75062866210936, 245.16345825195313, 248.460791015625, 246.16755065917968, 242.96047973632812, 248.20276794433593, 257.44920959472654, 262.72228088378904, 266.2411743164063, 271.84559631347656, 268.2837615966797, 265.2854705810547, 268.4452819824219, 275.1179260253906, 273.9887237548828, 267.0797119140625, 262.48084411621096, 258.7828338623047, 251.46455993652344, 241.17965393066407, 232.41156005859375, 226.20226135253907, 215.94066162109374, 217.9768798828125, 212.27077026367186, 205.8636962890625, 198.87208251953126, 190.6219970703125, 185.645263671875, 191.79407958984376, 191.3667419433594, 190.54924926757812, 185.63843383789063, 185.94244995117188, 200.1991180419922, 213.9168731689453, 215.88089294433593, 222.49326782226564, 223.22942504882812, 212.67758178710938, 203.14270935058593, 219.10111389160156, 227.6530303955078, 229.3610046386719, 236.17393798828124, 226.52357482910156, 217.2952453613281, 215.27081298828125, 223.78135986328124, 219.50808410644532, 210.87273254394532, 216.84405517578125, 215.55770874023438, 214.44686889648438, 215.44634094238282, 223.4820129394531, 227.50786437988282, 231.4711151123047, 239.50312194824218, 247.1863586425781, 244.88663024902343, 241.94760437011718, 250.290673828125, 255.40757751464844, 249.79248657226563, 246.96652526855468, 246.89203186035155, 241.28521118164062, 232.0681365966797, 225.88723754882812, 224.53907775878906, 223.1708190917969, 218.8315399169922, 220.51925659179688, 218.53673706054687, 212.88270568847656, 208.50631103515624, 196.84372253417968, 197.86355590820312, 208.2471466064453, 205.16092529296876, 203.83441162109375, 193.1256866455078, 193.9606903076172, 197.89990234375, 204.6542755126953, 207.60321350097655, 207.74706420898437, 208.9096466064453, 201.6711639404297, 186.92813720703126, 194.31505432128907, 191.9890625, 201.93884887695313, 205.2461151123047, 197.86788940429688, 196.78824157714843, 192.51210327148436, 190.39032897949218, 185.5568054199219, 193.18136291503907, 194.79654235839843, 191.76464233398437, 190.26378784179687, 195.44147033691405, 204.4558532714844, 201.2634704589844, 204.83587036132812, 213.10671997070312, 212.9345458984375, 199.34640197753907, 202.27456665039062, 206.24028625488282, 208.20528869628907, 206.5513153076172, 201.33132934570312, 196.26217346191407, 192.4618408203125, 189.14791870117188, 184.34476623535156, 186.2701385498047, 186.78043518066406, 184.6370361328125, 183.78864135742188, 184.10240173339844, 179.90760498046876, 178.21352844238282, 173.25917358398436, 174.4719665527344, 176.7918273925781, 181.72181701660156, 181.85034790039063, 174.9049285888672, 174.00490112304686, 167.60903625488282, 174.0874053955078, 176.48806457519532, 176.81559143066406, 184.5586151123047, 181.32671508789062, 175.43594665527343, 170.5407287597656, 177.01039123535156, 178.24416809082032, 173.92434387207032, 180.57322998046874, 180.79768981933594, 174.24561462402343, 168.8147766113281, 163.38417358398436, 169.5365234375, 178.01719360351564, 182.66878356933594, 182.48989868164062, 180.5145294189453, 180.764208984375, 178.65867919921874, 182.91073608398438, 180.33968505859374, 187.1057373046875, 200.9744903564453, 201.9693115234375, 193.01897277832032, 194.0814697265625, 200.97994689941407, 195.69361267089843, 191.03187255859376, 186.92322387695313, 189.60736389160155, 193.40784606933593, 191.84534912109376, 191.17209167480468, 189.28182678222657, 185.15109558105468, 176.9284240722656, 174.05978088378907, 174.06060791015625, 175.0039093017578, 178.68993225097657, 183.21029968261718, 182.66588439941407, 182.5064483642578, 185.6112274169922, 192.3707244873047, 197.41182861328124, 200.9964172363281, 202.85869750976562, 198.55019836425782, 194.34200134277344, 191.38255310058594, 193.10970153808594, 187.3966491699219, 183.93302001953126, 182.53139038085936, 176.80912170410156, 170.548828125, 164.7770751953125, 165.0722198486328, 165.1385040283203, 164.77529602050782, 161.9061309814453, 166.07931213378907, 163.79566345214843, 158.81371154785157, 155.41865234375, 161.50340270996094, 176.5304718017578, 182.17355651855468, 177.3363250732422, 170.7026123046875, 166.61871643066405, 169.40050048828124, 174.3066650390625, 179.65306091308594, 187.9072265625, 192.05129699707032, 194.43101806640624, 195.97434997558594, 187.0048858642578, 179.8050079345703, 174.69986267089843, 173.5043182373047, 171.0877685546875, 168.07650451660157, 167.8123352050781, 172.30667419433593, 176.932080078125, 182.08460998535156, 185.27884521484376, 182.38133239746094, 177.95763244628907, 175.92099304199218, 178.78312072753906, 174.3278839111328, 171.9819793701172, 172.83084411621093, 168.51559448242188, 163.4302490234375, 158.32210388183594, 164.42383422851563, 163.76436462402344, 162.82095642089843, 159.17884216308593, 160.86205444335937, 157.32877197265626, 152.5048400878906, 147.91437683105468, 152.4788391113281, 148.64852600097657, 147.52467041015626, 153.27680053710938, 152.26719970703124, 139.43399047851562, 134.12795715332032, 136.49643249511718, 137.9797607421875, 142.45810241699218, 143.8839141845703, 151.4748748779297, 153.24180603027344, 146.67862854003906, 143.96385192871094, 141.93671569824218, 144.51954956054686, 157.751806640625, 158.2719299316406, 156.02521057128905, 148.73178100585938, 144.617431640625, 156.35982971191407, 158.80450439453125, 158.39053955078126, 158.0891571044922, 161.49912719726564, 159.7821533203125, 155.81775817871093, 155.7190734863281, 156.77895812988282, 160.3724334716797, 160.91116027832032, 158.63127746582032, 155.0868927001953, 154.2619171142578, 155.50168151855468, 155.68963928222655, 153.16039428710937, 154.50732421875, 154.35923461914064, 152.77266540527344, 151.35987548828126, 156.7992950439453, 161.06217346191406, 161.36721496582032, 161.94993896484374, 163.9277130126953, 161.93343505859374, 157.8072265625, 154.56112976074218, 155.04473876953125, 152.20628967285157, 150.79471130371093, 145.0475616455078, 140.6997039794922, 132.11312866210938, 125.624462890625, 126.35753479003907, 134.25670776367187, 131.01214904785155, 136.78537139892578, 136.81344299316407, 133.3283905029297, 132.26623992919923, 130.65589599609376, 136.9246368408203, 139.54189453125, 144.9328399658203, 153.26893615722656, 153.50267639160157, 147.85724639892578, 142.19910430908203, 138.47337951660157, 139.64275054931642, 135.33780822753906, 132.4083984375, 133.09209899902345, 134.61085815429686, 132.30168762207032, 125.67577209472657, 120.37588806152344, 125.01250915527343, 128.0292495727539, 127.76764526367188, 122.94951782226562, 116.61193542480468, 113.28773803710938, 113.93735961914062, 114.61090393066407, 111.75779724121094, 111.38736877441406, 108.08814392089843, 108.19627380371094, 109.85728149414062, 107.31764526367188, 105.5768310546875, 109.69436798095703, 111.51382293701172, 112.5979736328125, 110.74620056152344, 109.8630584716797, 116.91919555664063, 117.60198059082032, 116.52388000488281, 118.29540252685547, 116.31922302246093, 114.3239974975586, 112.67129821777344, 110.73300170898438, 109.02839813232421, 108.61811065673828, 104.9126968383789, 97.7381820678711, 94.55225372314453, 93.02432250976562, 92.59966430664062, 92.38526916503906, 90.25654602050781, 88.98030090332031, 88.92020568847656, 87.37965393066406, 88.55746765136719, 89.07707214355469, 90.63760375976562, 88.98282470703126, 87.84288940429687, 87.88786010742187, 88.68302001953126, 87.36397399902344, 84.32302551269531, 80.9555450439453, 86.64249267578126, 89.95148315429688, 87.45168762207031, 82.36541137695312, 84.05504150390625, 83.30889892578125, 85.019677734375, 84.26567077636719, 86.41275634765626, 85.39352569580078, 82.98197021484376, 80.72621154785156, 82.84837799072265, 82.62501068115235, 81.88328399658204, 81.8044448852539, 80.72386169433594, 79.49248809814453, 76.53971862792969, 79.46505889892578, 78.3675323486328, 78.34935302734375, 76.96156311035156, 77.34117736816407, 75.96818542480469, 73.335205078125, 71.61273498535157, 74.01110382080078, 73.11777191162109, 74.51119995117188, 71.98056335449219, 65.82232971191407, 66.2762451171875, 65.81289367675781, 68.19395141601562, 65.83837585449218, 63.117140197753905, 61.89197998046875, 62.73060760498047, 64.42098999023438, 64.37947998046874, 65.60719299316406, 67.49692077636719, 65.78528289794922, 63.58561553955078, 62.13142700195313, 60.60595703125, 59.766787719726565, 58.883984375, 57.30829620361328, 59.08920440673828, 59.0684326171875, 57.679693603515624, 57.20458831787109, 57.53780975341797, 56.023947143554686, 55.80469665527344, 56.35388031005859, 57.18438568115234, 57.9048568725586, 54.87735137939453, 51.91768035888672, 52.530068969726564, 51.348812866210935, 52.17709503173828, 52.36663818359375, 55.50587463378906, 56.02353057861328, 53.571249389648436, 54.148179626464845, 56.76649780273438, 56.189141845703126, 56.47757263183594, 61.428280639648435, 63.019264221191406, 59.6216796875, 54.441157531738284, 55.342864990234375, 56.342218017578126, 61.31925048828125, 61.822427368164064, 59.09760284423828, 58.407554626464844, 58.50985107421875, 56.70051879882813, 56.321722412109374, 56.04762420654297, 59.20051879882813, 61.881831359863284, 64.89915313720704, 64.51047058105469, 64.26976470947265, 68.74510955810547, 66.58922729492187, 63.839630126953125, 61.76357879638672, 59.48664245605469, 52.88997192382813, 46.10159912109375, 46.23700103759766, 49.479364013671876, 47.371514892578126, 47.381744384765625, 46.69851226806641, 46.89303436279297, 47.938482666015624, 50.702326965332034, 47.73002166748047, 47.77621307373047, 51.28431243896485, 57.413873291015626, 54.15601654052735, 50.25373840332031, 49.57619171142578, 57.22374725341797, 60.86728210449219, 59.98575286865234, 55.82243957519531, 58.50211944580078, 58.55494079589844, 55.330178833007814, 48.20941162109375, 51.80338897705078, 58.43488311767578, 62.527601623535155, 61.19282989501953, 61.321380615234375, 61.21528778076172, 58.71910095214844, 51.55500030517578, 51.87752990722656, 52.85875701904297, 57.601783752441406, 62.62952728271485, 60.65586395263672, 56.50494232177734, 55.70552520751953, 54.97120056152344, 57.27282867431641, 58.620828247070314, 62.81276397705078, 63.73394775390625, 59.90816955566406, 56.421939086914065, 57.23741607666015, 56.72869720458984, 58.483587646484374, 60.15382080078125, 63.836961364746095, 60.4486328125, 54.37204284667969, 56.57389526367187, 57.1586181640625, 54.02117004394531, 50.57855377197266, 51.807168579101564, 55.31989288330078, 53.888807678222655, 49.20870666503906, 49.310951232910156, 50.26485900878906, 51.3700927734375, 51.216427612304685, 47.57576446533203, 42.84201202392578, 41.46237030029297, 46.78870391845703, 47.46685028076172, 43.332177734375, 38.19630126953125, 37.24781494140625, 38.615866088867186, 38.982643127441406, 39.59981689453125, 42.02662811279297, 40.78079833984375, 39.22053527832031, 36.79718780517578, 35.97910614013672, 39.413998413085935, 39.20321197509766, 40.79789733886719, 42.06519470214844, 44.94947052001953, 40.00185089111328, 39.87420654296875, 39.187303161621095, 40.427433776855466, 40.27087249755859, 41.85726470947266, 38.793801879882814, 38.991084289550784, 38.478085327148435, 38.97046661376953, 35.40807189941406, 35.55740509033203, 34.08680877685547, 31.872640991210936, 33.4380615234375, 34.355455017089845, 34.17203216552734, 33.275091552734374, 33.06853332519531, 32.15201263427734, 32.58553924560547, 32.33357238769531, 30.16300735473633, 32.76860733032227, 32.82425994873047, 31.507886505126955, 32.14586410522461, 34.305989837646486, 33.7050666809082, 32.89646759033203, 30.69223098754883, 27.38671417236328, 26.277545928955078, 28.165279388427734, 27.590791320800783, 26.548894500732423, 28.413249969482422, 29.671510314941408, 26.79346160888672, 24.551146697998046, 24.698855590820312, 25.350038909912108, 28.672476959228515, 30.033524322509766, 28.730714416503908, 27.22966079711914, 26.4642692565918, 24.586326599121094, 25.745311737060547, 26.704805755615233, 29.196155548095703, 26.380724334716795, 27.285682678222656, 28.821355438232423, 27.174435424804688, 25.629964447021486, 23.701654815673827, 25.37286376953125, 26.342017364501952, 26.061138153076172, 24.75322265625, 25.40284957885742, 24.67076416015625, 24.642374420166014, 24.73909912109375, 27.868128967285156, 28.38462677001953, 26.244815063476562, 27.01693344116211, 28.30866241455078, 27.28201675415039, 26.62985153198242, 25.89431381225586, 25.431398010253908, 27.586854553222658, 28.043850708007813, 29.438983917236328, 32.64683227539062, 32.34921035766602, 31.609438323974608, 33.73126907348633, 34.89558715820313, 35.83146667480469, 34.6866455078125, 35.574781036376955, 36.790505981445314, 37.74914321899414, 36.21039657592773, 38.10196228027344, 39.14063720703125, 42.482018280029294, 40.816192626953125, 40.5820930480957, 41.84001159667969, 44.464175415039065, 44.00750198364258, 40.841741943359374, 39.0741943359375, 42.09846878051758, 45.46841201782227, 43.88251647949219, 44.33492736816406, 49.29881973266602, 47.06992034912109, 42.850965881347655, 45.71634750366211, 45.124392700195315, 47.376913452148436, 50.91760940551758, 34.810140228271486, 23.185843658447265, 23.365017700195313, 22.318406677246095, 22.324717712402343, 22.330617141723632, 25.753804397583007, 24.293913269042967, 27.878487014770506, 27.801503372192382, 29.51559829711914, 28.609791946411132, 24.92127571105957, 21.921529388427736, 22.533114624023437, 25.3783935546875, 24.87556610107422, 24.141765213012697, 22.750718688964845, 21.66647415161133, 24.487873840332032, 24.498975944519042, 24.345964241027833, 24.353329849243163, 25.44840030670166, 25.865285301208495, 24.979733085632326, 26.80478439331055, 28.878277015686034, 27.78145866394043, 25.218845939636232, 23.394370079040527, 22.409227752685545, 21.510893058776855, 18.86921329498291, 16.777799797058105, 15.260125732421875, 14.627598762512207, 13.9902738571167, 13.53182315826416, 13.282184982299805, 12.56845645904541, 12.30984287261963, 9.55405387878418, 9.017498016357422, 9.4084867477417, 8.900893020629884, 8.063765716552734, 8.49095802307129, 8.542118072509766, 9.10329990386963, 8.925061988830567, 8.43088207244873, 9.499856948852539, 9.32634391784668, 10.542783546447755, 9.428791809082032, 9.840045928955078, 9.504936408996581, 8.385100173950196, 9.093200874328613, 11.566422653198241, 11.886865997314453, 10.639815998077392, 8.504233264923096, 7.435840892791748, 7.13827543258667, 7.227834033966064, 7.4304811477661135, 7.944094562530518, 8.252573490142822, 8.868411827087403, 9.426864433288575, 9.794993209838868, 10.035904884338379, 9.987264156341553, 9.872835445404053, 10.116108655929565, 10.191609001159668, 10.988156032562255, 11.688128566741943, 11.951312589645386, 12.651931190490723, 12.974178457260132]
+# TaFCN_error=[233.73620000000005, 232.76620000000003, 232.04139999999995, 231.27319999999997, 229.15783999999996, 221.45916999999997, 215.25279999999998, 210.63149999999996, 209.96209999999996, 211.78110000000004, 213.36474999999996, 215.64606000000003, 215.25977, 214.7006, 214.03300000000002, 212.22410000000002, 211.09929999999997, 212.19449999999995, 214.66660000000002, 213.76390000000004, 213.14710000000002, 212.75183000000004, 213.2953, 222.71569999999997, 234.30909999999994, 232.69079999999997, 224.59704999999997, 216.78186000000005, 208.02405, 205.31679999999994, 209.19629999999995, 209.0412, 208.0729, 200.34490000000005, 193.1947, 186.64624000000003, 184.27949999999998, 185.31470000000002, 187.01229999999998, 187.00490000000002, 182.85540000000003, 177.1884, 168.7536, 170.21185000000003, 191.15215999999998, 198.25980000000004, 198.40380000000005, 198.13390000000004, 197.27210000000002, 181.88189999999997, 181.5942, 186.69640000000004, 175.23159999999996, 171.49207, 175.96349999999995, 184.85924999999997, 160.86725, 158.04459999999995, 177.39386000000002, 184.37879999999996, 189.9216, 193.85170000000005, 194.21000000000004, 176.81415000000004, 171.8474, 185.30589999999995, 182.15179999999998, 174.30676000000005, 175.23157000000003, 180.63639999999998, 165.49239999999998, 163.80909999999994, 154.92639999999994, 157.86316, 172.2745, 175.85974, 177.6554, 178.55334000000005, 177.85350000000005, 179.64513999999997, 188.7663, 198.28156, 204.4914, 208.2767, 210.64293999999995, 210.45759999999996, 209.3723, 217.56650000000002, 225.93024000000003, 224.50710000000004, 220.30597, 216.8908, 209.08704, 209.72473000000002, 204.35360000000003, 197.93960000000004, 191.23015999999996, 191.2156, 195.0054, 192.8967, 191.89464999999996, 199.79870000000005, 205.36395000000005, 212.52409999999998, 217.9918, 224.10474, 224.55039999999997, 221.13649999999996, 231.53089999999997, 246.24694999999997, 241.77124000000003, 231.37869999999998, 220.33900000000006, 207.91143999999997, 206.35379999999998, 205.36270000000002, 199.76779999999997, 199.62694999999997, 214.21014000000002, 221.81219999999996, 221.6205, 223.67834000000005, 225.74170000000004, 221.3632, 224.7654, 237.84048, 246.29395, 248.404, 252.04424999999998, 252.79149999999998, 247.86172, 249.18903, 255.72771999999998, 254.93110000000001, 248.83008, 240.93194999999997, 232.63177000000002, 223.54139999999995, 212.74365, 204.86803999999995, 195.46259999999995, 186.1028, 183.00183000000004, 172.5824, 167.8021, 161.28216999999995, 155.03625, 149.39306999999997, 155.61834999999996, 147.19556, 150.52655000000004, 148.5634, 152.18910000000005, 168.13559999999995, 190.36239999999998, 190.24080000000004, 190.99963000000002, 193.01300000000003, 183.43939999999998, 176.21155, 191.63660000000004, 204.19585999999998, 206.75308, 207.87946, 200.86932000000002, 192.48974999999996, 194.37739999999997, 208.6735, 201.65480000000002, 194.1504, 194.78638, 195.24790000000002, 190.9269, 187.02155000000005, 195.97827, 199.30762, 201.93427000000003, 207.58368000000002, 215.51343000000003, 213.09723000000002, 210.38592999999997, 218.9669, 228.59503, 226.7661, 227.38727, 226.97480000000002, 219.29697, 211.13623, 205.4334, 205.33319999999998, 200.44940000000003, 195.71344, 195.58627, 187.78019999999998, 183.16341999999997, 175.50664999999998, 165.1839, 167.59980000000002, 173.09552000000002, 168.22345, 167.41455000000002, 161.09726, 160.72930000000002, 166.75869999999998, 178.5643, 179.66201999999998, 177.29266, 177.65714000000003, 173.66073999999998, 159.44946, 164.68292000000002, 165.91649999999998, 174.75668000000002, 176.60278, 168.21548, 163.7267, 161.57837, 160.87085000000002, 155.6325, 160.61059999999998, 159.34951999999998, 154.97809999999998, 150.56607000000002, 154.66840000000002, 166.78647, 164.24872, 168.5355, 175.8986, 178.54208, 163.25497000000001, 166.02026, 175.05901999999998, 178.82135, 179.0716, 175.53915, 168.37743999999998, 161.76556, 157.26425, 153.32080000000002, 153.51456000000002, 150.94376, 148.82891999999998, 147.46710000000002, 143.98932000000002, 140.03458, 137.86554, 137.95135, 135.97003, 140.77524, 147.15215999999998, 142.77515, 141.26952999999997, 140.0227, 139.01553, 149.53019999999998, 151.38754, 154.5328, 158.77182, 153.39600000000002, 147.1538, 142.22836, 152.38614, 153.04595999999998, 151.08096, 154.25894, 149.42039999999997, 143.28485, 138.61612000000002, 136.36142, 144.85748, 154.83706999999998, 158.72174, 158.23764, 153.6834, 147.15233999999998, 146.2624, 152.78345000000002, 154.41609999999997, 164.21808, 179.65976, 181.27382999999998, 173.35244999999998, 170.61646000000002, 172.11896000000002, 166.85474, 164.13279999999997, 165.5082, 169.72964000000002, 171.19696, 171.77066000000002, 168.45157, 160.88608, 160.29077, 154.14877, 156.3043, 156.25293, 160.18018, 164.89780000000002, 167.23462, 163.73306000000002, 158.84267999999997, 158.26602000000003, 162.79680000000002, 165.94299999999998, 167.67957, 166.85617000000002, 160.56293, 156.47266000000002, 153.04868, 155.20531999999997, 150.82465000000002, 150.58465999999999, 151.13287000000003, 149.30234000000002, 148.39780000000002, 146.59089999999998, 154.02337999999997, 153.61682000000002, 154.14452999999997, 152.48212, 150.21954, 143.94846, 140.71337999999997, 140.41962, 150.82474000000002, 168.19537000000003, 171.83715999999998, 167.26178, 160.13240000000002, 153.53607, 154.47516000000002, 155.45882999999998, 154.07144, 156.12401999999997, 159.41082999999998, 164.01960000000003, 168.56635, 156.80927000000003, 149.07822, 144.92206, 139.75345, 137.25372, 135.80142, 135.09875, 139.57909999999998, 145.75687, 150.1778, 151.56484999999998, 149.17489999999998, 146.051, 145.25150000000002, 150.78386999999998, 148.03521999999998, 149.83440000000002, 153.98325, 156.03692999999998, 157.08141999999998, 156.8086, 164.42383, 163.76436999999999, 162.82098000000002, 159.17886, 160.12619999999998, 153.10464000000002, 150.63139999999999, 146.91109999999998, 152.47881999999998, 148.6485, 147.5247, 148.22858000000002, 138.2425, 128.41879999999998, 127.91692999999998, 135.04534999999998, 137.97976999999997, 142.4581, 143.88389999999998, 148.56099999999998, 144.5876, 136.15964000000002, 131.44335999999998, 131.96808, 137.3161, 147.07920000000001, 145.21820000000002, 145.27053999999998, 141.02859999999998, 139.62048, 154.02652, 158.80450000000002, 158.39056, 158.08917000000002, 161.49915, 159.78217, 149.78165, 144.91091999999998, 144.23788000000002, 143.69826999999998, 139.25439999999998, 133.24567000000002, 127.73723999999999, 126.08321999999998, 127.99761999999998, 128.87418000000002, 129.45675999999997, 135.62488000000002, 141.20502, 146.8515, 151.24045, 156.79930000000002, 161.06220000000002, 161.36721999999997, 161.94995, 163.92773, 161.9334, 157.80721999999997, 154.5611, 155.04474, 152.2063, 150.79469999999998, 145.04757999999998, 140.6997, 129.39812999999998, 123.09960000000001, 126.35753999999997, 134.25670000000002, 131.01215000000002, 136.78534000000002, 136.81345, 133.32837, 132.26623999999998, 130.65589999999997, 136.92462, 139.5419, 144.93286, 153.26891999999998, 153.50266, 147.85727000000003, 142.19913000000003, 138.47336, 139.64272999999997, 133.82706000000002, 128.2636, 128.70031999999998, 131.49077999999997, 128.35883, 122.00815, 115.601, 118.45764000000003, 118.85969999999998, 118.47635000000002, 116.36556999999999, 114.44585999999998, 113.08120000000002, 113.93734999999998, 114.61090000000002, 111.75779999999997, 111.38736, 106.22232000000002, 105.75677000000002, 108.45800000000003, 106.89758, 105.57684, 109.69439999999997, 109.09293000000002, 109.51508000000001, 109.0607, 109.86309999999997, 116.91919999999999, 117.60199999999998, 116.52386000000001, 118.29538000000002, 116.31923999999998, 114.32400000000001, 112.67129999999997, 110.733, 109.02838000000003, 108.61810000000003, 104.91271999999998, 97.7382, 91.40093999999999, 85.87518, 82.19765999999998, 80.07663000000002, 76.46582000000001, 75.04775999999998, 72.53179999999998, 68.73903999999999, 67.33535999999998, 66.70508000000001, 69.74423000000002, 71.50693000000001, 73.19794000000002, 76.61792000000003, 81.54282, 82.51006999999998, 80.25655999999998, 77.75653, 83.37527, 85.66973999999999, 84.76280000000003, 82.06670000000003, 84.05502000000001, 83.3089, 85.01968, 84.26566000000003, 85.5378, 84.37545999999998, 82.17779999999999, 80.72622999999999, 82.84840000000003, 82.625, 81.88330000000002, 81.80446999999998, 79.49471999999997, 77.95030000000003, 76.53969999999998, 79.46506, 78.36752000000001, 78.34937000000002, 76.96158000000003, 77.34116, 74.76763999999997, 71.59926999999999, 71.61273, 74.01114000000001, 73.11777000000001, 74.51119999999997, 71.98056000000003, 65.82233000000002, 62.23149999999998, 58.44745, 58.70618000000002, 55.929959999999994, 53.921749999999975, 54.08181999999999, 52.70373999999998, 49.47606999999999, 46.80977999999999, 45.23795000000001, 47.92380000000003, 48.08746000000002, 48.851200000000006, 50.56796000000003, 53.50905999999998, 53.352599999999995, 50.73358000000002, 48.452639999999974, 54.12099999999998, 55.82727, 54.66144000000003, 53.97300000000001, 55.008500000000026, 53.219100000000026, 54.914120000000025, 56.35390000000001, 55.46588000000003, 51.53951999999998, 49.33699999999999, 49.26218, 51.624239999999986, 51.34881999999999, 52.147519999999986, 51.909940000000006, 49.529240000000016, 43.47028, 41.97476, 50.55576000000002, 55.53739999999999, 56.18915999999999, 56.47757999999999, 61.42827, 60.64688000000001, 54.36493999999999, 52.78654, 54.300129999999996, 53.19514000000001, 57.84512000000001, 58.60346999999999, 51.53353999999999, 46.20773, 42.238699999999994, 38.969330000000014, 36.79964000000001, 37.20882, 41.58357000000001, 41.09026, 40.59943999999999, 36.381519999999995, 34.770070000000004, 36.82414, 35.295620000000014, 34.72785999999999, 39.65628000000001, 41.356120000000004, 39.75627, 35.98657, 32.453689999999995, 43.89282, 41.51366999999999, 35.73224999999999, 31.109790000000004, 33.22476, 35.388029999999986, 41.24284, 39.74055000000001, 41.262939999999986, 34.931520000000006, 30.69193999999999, 25.591550000000012, 27.31438, 38.26514, 50.11503999999999, 51.89626999999999, 47.90201999999999, 45.69875999999999, 45.989779999999996, 47.93912, 46.21489, 44.55229, 50.66954000000001, 58.066509999999994, 56.99807999999999, 58.477329999999995, 60.62291999999999, 59.03945999999999, 54.56143, 50.40768, 50.725779999999986, 52.01626999999999, 57.60177999999999, 62.54486, 56.31267, 53.327380000000005, 55.70554000000001, 54.97119000000001, 57.27284, 58.62082000000001, 62.81277, 63.73394999999999, 58.07015999999999, 54.464049999999986, 57.23741000000001, 56.7287, 58.48357999999999, 60.153819999999996, 63.83694, 60.44864000000001, 54.10767999999999, 55.269000000000005, 54.79172, 51.32227, 49.131069999999994, 51.80717000000001, 55.319919999999996, 53.8888, 49.208709999999996, 48.67361, 47.93361999999999, 48.52596, 47.54193000000001, 44.55188000000001, 41.55090000000001, 41.46236999999999, 46.78871000000001, 45.57885999999999, 40.98276000000001, 38.19630000000001, 37.01543000000001, 37.36591999999999, 38.80563000000001, 39.599819999999994, 42.026610000000005, 40.780789999999996, 36.27074999999999, 33.98349999999999, 33.60727, 38.28966, 39.20320000000001, 40.76785000000001, 37.78307000000001, 36.06818999999999, 34.87843000000001, 37.74399, 38.06460000000001, 40.196979999999996, 40.27087, 41.85727, 37.95206999999999, 33.39114000000001, 36.88869, 34.138000000000005, 32.479870000000005, 31.14690999999999, 31.45908, 31.403400000000005, 32.94290000000001, 29.27691999999999, 27.531710000000004, 26.87379999999999, 26.255619999999993, 24.58664999999999, 24.4889, 25.52042, 26.99161000000001, 28.92989, 25.01034999999999, 23.857640000000004, 25.61009999999999, 29.539029999999997, 27.589200000000005, 26.11044000000001, 22.11099999999999, 20.863619999999997, 21.01857000000001, 25.52829, 23.171189999999996, 22.087369999999993, 19.06638000000001, 19.69649000000001, 17.149190000000004, 16.46786, 15.287299999999988, 16.534789999999987, 12.594789999999989, 9.47480999999999, 8.943630000000013, 14.33623, 14.613709999999998, 15.14455000000001, 14.207240000000013, 13.408719999999988, 10.77843999999999, 12.580180000000013, 11.248979999999989, 12.087524000000002, 12.806984, 11.40773, 11.288669999999996, 7.812330000000003, 6.933700000000002, 7.530779999999993, 8.103210000000004, 7.432959999999994, 9.605909999999994, 7.221599999999995, 6.157004999999998, 1.1633700000000005, -0.5873399999999975, 1.320210000000003, 0.6608399999999932, -1.3823699999999945, -1.027159999999995, -0.6426850000000002, -0.045394999999999186, -0.3803699999999992, -2.918379999999999, -3.722759999999994, -5.451740000000001, -9.646834999999996, -9.549210000000002, -9.009445, -11.33126, -12.695594999999997, -13.831469999999996, -12.88664, -13.974779999999996, -15.390500000000003, -16.549149999999997, -15.210390000000004, -17.301956000000004, -18.540633999999997, -22.082015999999996, -20.616196000000002, -20.582089999999994, -22.040015999999994, -24.864180000000005, -24.6075, -21.641750000000002, -20.074196, -23.298479999999998, -26.868409999999997, -25.482506, -26.134925999999993, -31.298829999999995, -29.269936, -25.250960000000006, -28.316329999999994, -27.924390000000002, -30.376914999999997, -34.117599999999996, -16.968490000000003, -5.054500000000004, -5.290670000000006, -3.6618799999999965, -1.1526000000000067, 4.598785000000007, -0.1346900000000062, -1.5079199999999986, -7.386989999999997, -5.671229999999994, -4.248795000000001, -2.504810000000006, 1.5311300000000045, 11.156950000000002, 14.176400000000001, 9.808937, 8.90542, 6.3387949999999975, 13.817860000000003, 18.371634999999998, 24.487876999999997, 24.498973999999997, 24.345962999999998, 24.353324999999998, 22.113590000000002, 22.066536, 22.88192, 26.804789, 28.878277, 27.781458, 25.218847, 23.39437, 21.22673, 17.202216999999997, 14.925823000000001, 14.140279999999997, 15.260121999999999, 14.627600000000001, 13.990274, 13.531824, 13.282184999999998, 12.568460000000002, 12.024384000000001, 7.621741999999998, 5.196064, 1.792904, 2.7766499999999965, 2.284930000000003, 2.237408000000002, 1.6046699999999987, 1.4186300000000003, 1.100168, 0.6147770000000001, -1.0157659999999993, -0.9889679999999998, -2.316645000000001, -1.3069300000000013, -2.1347799999999992, -2.2125549999999983, -1.4071670000000012, -2.3866709999999998, -6.091946, -6.897580000000001, -5.645409999999998, -3.687764999999999, -2.892389999999999, -2.8157560000000004, -2.983146999999999, -3.3722700000000003, -4.187892999999999, -4.870899999999999, -5.846947, -6.807359999999999, -7.591716999999999, -8.167192, -8.387263999999998, -8.4728365, -8.916109, -9.191609, -10.188156, -11.088129, -11.551312, -12.45193, -12.974179]
+DCNN_error=[63.986474609375, 63.261041259765626, 62.710113525390625, 62.344921875, 62.6014404296875, 63.876495361328125, 66.05001831054688, 69.06141967773438, 72.85137939453125, 76.59771118164062, 80.24156494140625, 83.63798217773437, 86.842333984375, 89.91854858398438, 92.93871459960937, 95.98670654296875, 99.10057373046875, 102.29727172851562, 105.54703369140626, 108.73153686523438, 111.77089233398438, 114.46926879882812, 116.6169677734375, 118.08538208007812, 118.64241333007813, 119.28196411132812, 120.39711303710938, 122.30116577148438, 124.92074584960938, 128.61160278320312, 131.94698486328124, 134.92783203125, 138.28222045898437, 142.03148193359374, 146.11878051757813, 150.55413818359375, 155.21461181640626, 159.26392211914063, 162.87332763671876, 166.8422424316406, 170.99511108398437, 175.2596862792969, 179.39652099609376, 183.31195068359375, 186.01788940429688, 187.2792541503906, 188.42349243164062, 189.68535766601562, 191.33914184570312, 193.26758422851563, 195.856787109375, 195.26227416992188, 192.611865234375, 188.09407958984374, 182.908251953125, 178.04356079101564, 173.68563842773438, 170.23937377929687, 167.15555419921876, 163.75963134765624, 160.66275634765626, 158.1666748046875, 156.39548950195314, 154.99386596679688, 154.39220581054687, 153.8820007324219, 152.93331298828124, 152.167529296875, 152.20449829101562, 154.0250244140625, 157.2806884765625, 163.45000610351562, 169.76567993164062, 175.60061645507812, 180.82854614257812, 185.030908203125, 188.78998413085938, 192.201171875, 195.1787536621094, 197.88427734375, 200.2893798828125, 202.28466186523437, 203.94334716796874, 205.51215209960938, 206.92243041992188, 208.18609008789062, 209.36596069335937, 210.4841064453125, 211.29969482421876, 211.79991455078124, 213.28472290039062, 215.4092224121094, 218.11890258789063, 221.513916015625, 223.96641845703124, 225.70584106445312, 226.89114379882812, 227.12948608398438, 226.8241760253906, 226.52833251953126, 226.39217529296874, 226.52186279296876, 226.9025451660156, 227.6240234375, 227.79611206054688, 227.55938720703125, 227.35023803710936, 227.02678833007812, 226.66912841796875, 226.15798950195312, 225.43805541992188, 224.29053955078126, 223.31961059570312, 223.1979187011719, 224.490771484375, 226.30833129882814, 228.21839599609376, 230.66373291015626, 232.95298767089844, 234.0076110839844, 234.5484405517578, 234.4713134765625, 234.06197509765624, 233.2391143798828, 232.39437561035157, 230.932470703125, 228.59203186035157, 225.96481018066407, 223.23448181152344, 220.705859375, 218.15288391113282, 216.02045288085938, 213.44993286132814, 210.06654663085936, 206.385107421875, 203.95938720703126, 202.35320434570312, 201.74828491210937, 202.08126831054688, 203.08798217773438, 203.7758544921875, 204.25728759765624, 205.65005493164062, 207.17822265625, 208.96695251464843, 210.68429565429688, 211.91036376953124, 212.81307678222657, 214.4770721435547, 216.5602264404297, 218.88884582519532, 221.5291748046875, 224.17421264648436, 225.50787048339845, 225.81162109375, 225.37249145507812, 225.309423828125, 225.52911071777345, 226.2321319580078, 227.93873901367186, 229.0366943359375, 229.4759063720703, 228.93273010253907, 229.67672424316407, 231.06082153320312, 232.98782653808593, 235.09626159667968, 236.58545837402343, 236.47257385253906, 235.81365356445312, 234.8102294921875, 233.4429718017578, 231.9427734375, 230.7619140625, 229.0893096923828, 226.54671936035157, 223.87764892578124, 221.3579315185547, 218.92165832519532, 216.36058959960937, 214.3211669921875, 211.86504821777345, 208.7010467529297, 204.56793212890625, 200.54613647460937, 197.8703155517578, 196.22243957519532, 195.8283203125, 196.44764404296876, 197.73173828125, 198.4547546386719, 198.8470703125, 199.51833801269532, 200.60390014648436, 201.64959716796875, 202.0199951171875, 201.8903015136719, 201.99256896972656, 202.54239501953126, 203.3685089111328, 204.40057983398438, 205.61896667480468, 206.2393371582031, 205.9250061035156, 204.53597106933594, 202.70985107421876, 200.99358825683595, 199.67129211425782, 198.79483642578126, 198.18873291015626, 197.4477569580078, 196.04497680664062, 194.31473083496093, 193.09734191894532, 192.46127624511718, 191.84422607421874, 190.45943908691407, 188.0728546142578, 185.22457580566407, 182.4428741455078, 179.78038330078124, 177.14151916503906, 174.8057830810547, 172.23340454101563, 169.2977722167969, 165.48037414550782, 161.77965393066407, 158.22366333007812, 154.89150390625, 151.765283203125, 149.1477844238281, 146.39511413574218, 143.22417907714845, 140.11176452636718, 137.62965698242186, 136.698486328125, 136.42244873046874, 136.7546142578125, 137.72368469238282, 139.28825988769532, 140.3848846435547, 141.18365783691405, 142.12560119628907, 143.55589599609374, 144.9484893798828, 146.51160278320313, 148.58814086914063, 150.10934448242188, 151.38904418945313, 152.8381774902344, 154.75453186035156, 156.70875549316406, 158.86811218261718, 160.69530334472657, 161.8854553222656, 161.71693115234376, 161.10973510742187, 160.98052368164062, 161.0694549560547, 161.4449890136719, 162.6632110595703, 163.77662658691406, 163.70263061523437, 162.90831298828124, 162.7207458496094, 162.93041381835937, 163.32034912109376, 164.37431945800782, 164.64051818847656, 164.46970825195314, 163.83800354003907, 162.69804992675782, 161.1908752441406, 160.47821350097655, 160.21116943359374, 160.4199249267578, 160.642236328125, 160.2338073730469, 160.0448211669922, 160.1305358886719, 159.56600952148438, 158.64763793945312, 157.9775421142578, 157.53833618164063, 156.51070251464844, 155.91192932128905, 155.7796875, 155.892333984375, 156.15835571289062, 155.92057189941406, 156.3307678222656, 157.24461669921874, 158.04006652832032, 158.22567749023438, 158.70982360839844, 158.60275573730468, 158.3231689453125, 157.9083465576172, 157.39413757324218, 156.80856018066407, 156.4133728027344, 155.52662048339843, 153.95748291015624, 151.79100341796874, 149.82374877929686, 147.86636047363282, 146.0323455810547, 144.33128356933594, 142.4884246826172, 140.18261108398437, 136.98105163574218, 133.66514892578124, 131.3188690185547, 130.09854736328126, 129.55853271484375, 130.38925170898438, 131.86368408203126, 133.0427734375, 133.8685333251953, 134.31044921875, 135.60324401855468, 137.17583923339845, 139.25872802734375, 141.6639434814453, 144.6197937011719, 147.85350952148437, 150.4334747314453, 152.369970703125, 153.76861267089845, 154.10982360839844, 153.3266571044922, 151.47168884277343, 149.38468322753906, 147.3278045654297, 146.13119506835938, 145.38741149902344, 145.30272827148437, 145.17725219726563, 144.50910339355468, 142.91021118164062, 139.94297485351564, 136.68816833496095, 133.17363891601562, 129.68767395019532, 126.48709411621094, 123.95556030273437, 122.54751892089844, 121.6169189453125, 120.77501220703125, 120.23404541015626, 119.40755310058594, 118.09361877441407, 115.80870666503907, 113.4946075439453, 112.03236083984375, 111.61022033691407, 112.01869506835938, 113.84251098632812, 116.32984313964843, 118.46385803222657, 120.24189147949218, 121.56885070800782, 123.5921844482422, 125.90715026855469, 128.68753051757812, 131.73411254882814, 135.09234924316405, 137.83799743652344, 140.15784912109376, 142.66195068359374, 145.3863067626953, 147.9771697998047, 150.55567932128906, 152.64772338867186, 153.46484985351563, 153.45103149414064, 153.04016723632813, 152.81185302734374, 152.72322998046874, 153.261865234375, 153.1896484375, 152.33708801269532, 150.57884826660157, 149.58265075683593, 149.02634887695314, 149.18467712402344, 149.76585388183594, 150.72159118652343, 151.3211669921875, 150.51780700683594, 149.04449462890625, 147.34911499023437, 145.41897277832032, 143.59719848632812, 142.0892791748047, 140.60691528320314, 139.01387634277344, 137.09398498535157, 135.25672302246093, 133.48228149414064, 131.79323120117186, 129.70964965820312, 126.88612365722656, 123.32056579589843, 119.78447570800782, 116.8263427734375, 114.99644165039062, 114.36219787597656, 114.70449523925781, 116.08929138183593, 117.67637939453125, 118.88347778320312, 119.74305114746093, 121.12229614257812, 122.88117370605468, 124.8244384765625, 127.09537963867187, 129.70262451171874, 131.97315673828126, 133.91116027832032, 135.3873718261719, 137.27646179199218, 139.07782287597655, 140.7108154296875, 141.88265991210938, 142.3884704589844, 141.5693145751953, 140.6350128173828, 139.55734558105468, 138.71748962402344, 138.33228454589843, 138.3629608154297, 138.29692993164062, 137.67454223632814, 137.12897033691405, 137.05160217285157, 136.6555389404297, 135.99677124023438, 135.32765502929686, 134.1475372314453, 132.4213439941406, 129.79560241699218, 127.15290222167968, 125.10145263671875, 123.53419799804688, 122.0705352783203, 120.95647277832032, 119.29681396484375, 117.7365936279297, 115.85001220703126, 113.76544494628907, 111.41891174316406, 109.12680358886719, 106.48003845214843, 103.78321228027343, 101.43153381347656, 99.76643676757813, 98.80895690917968, 98.11441040039062, 97.5970703125, 97.1845947265625, 96.28500671386719, 95.03063659667968, 93.709912109375, 92.87376708984375, 92.49293212890625, 92.31400451660156, 92.17040405273437, 91.9845947265625, 91.55389709472657, 91.05393676757812, 91.0922637939453, 91.40501403808594, 92.05472717285156, 92.68914489746093, 92.86842041015625, 92.68636779785156, 91.94752807617188, 91.05360107421875, 90.04817199707031, 88.86281127929688, 87.68294677734374, 86.09690551757812, 83.88584899902344, 81.40816040039063, 78.97366333007812, 76.53296203613282, 74.20553588867188, 71.8506591796875, 69.7895751953125, 67.48192749023437, 64.6898193359375, 61.88728637695313, 59.443295288085935, 57.405172729492186, 55.495547485351565, 53.771759033203125, 52.07791748046875, 51.062890625, 50.35270690917969, 49.999325561523435, 49.92963562011719, 50.284228515625, 50.35675048828125, 50.26862487792969, 50.166558837890626, 50.488067626953125, 50.91524963378906, 51.53872375488281, 52.21171569824219, 53.067929077148435, 53.530697631835935, 53.747344970703125, 54.14524536132812, 54.82962341308594, 55.85318298339844, 56.903439331054685, 57.800070190429686, 57.9649169921875, 57.863851928710936, 57.51804809570312, 57.62823486328125, 57.95213012695312, 58.7957275390625, 59.547482299804685, 60.148861694335935, 60.070892333984375, 59.90297241210938, 59.45894775390625, 58.889337158203126, 58.101968383789064, 57.35878295898438, 56.26632385253906, 54.63943786621094, 52.84493408203125, 51.20185546875, 49.660073852539064, 48.2677734375, 47.05582885742187, 45.77668762207031, 44.1984619140625, 42.048919677734375, 39.844418334960935, 37.8294921875, 36.22688598632813, 34.592919921875, 33.31679992675781, 32.051168823242186, 31.431048583984374, 31.261764526367188, 31.676263427734376, 32.43939208984375, 33.5571533203125, 34.262576293945315, 34.655514526367185, 34.8580810546875, 35.43194580078125, 36.155609130859375, 37.16904296875, 38.40062255859375, 39.90823974609375, 40.94580230712891, 41.70945281982422, 42.61004791259766, 43.9222900390625, 45.509893798828124, 47.04214019775391, 48.81091156005859, 50.11641387939453, 50.86148376464844, 51.275254821777345, 52.32830657958984, 54.05823974609375, 56.500146484375, 59.06532745361328, 61.403150939941405, 62.968267822265624, 63.992984008789065, 64.48092041015624, 64.70303497314453, 64.72323608398438, 64.92035217285157, 64.61486206054687, 63.73883819580078, 62.41966247558594, 61.0567138671875, 59.61001739501953, 57.993212890625, 56.578125, 55.424383544921874, 53.9150634765625, 51.57081298828125, 48.84957733154297, 46.848245239257814, 45.34478759765625, 43.8569839477539, 42.77002105712891, 41.78936920166016, 41.912411499023435, 42.516535949707034, 43.85584716796875, 45.42020263671875, 47.352822875976564, 48.89677276611328, 50.030270385742185, 50.63644866943359, 51.90536651611328, 53.639897155761716, 55.77792053222656, 57.6836654663086, 59.595892333984374, 61.219705200195314, 62.77688140869141, 63.24305267333985, 63.39914245605469, 64.0923858642578, 64.97527313232422, 65.90161437988282, 66.18008422851562, 66.41635284423828, 66.5580825805664, 66.19294891357421, 65.68232727050781, 65.83219146728516, 66.37875823974609, 66.93888092041016, 66.96722869873047, 66.91474609375, 66.90418548583985, 66.55329284667968, 65.69924926757812, 65.40727386474609, 65.63068237304688, 66.50208129882813, 67.2724838256836, 67.41276092529297, 67.2317138671875, 66.55211639404297, 65.47676239013671, 64.80093688964844, 64.62399444580078, 65.09813690185547, 65.48589782714843, 65.31852416992187, 64.78955993652343, 63.79671173095703, 62.4110107421875, 60.9630126953125, 59.73988342285156, 58.81735687255859, 57.74023284912109, 56.151573181152344, 54.3779052734375, 52.67549438476563, 50.8534423828125, 49.20814208984375, 47.81138153076172, 46.679324340820315, 45.41584930419922, 44.01457824707031, 42.70628356933594, 41.39949340820313, 39.80360565185547, 38.1081298828125, 37.08639221191406, 36.85070037841797, 37.013720703125, 37.243228149414065, 37.474327087402344, 37.52689819335937, 37.33811492919922, 36.985391235351564, 37.12637023925781, 37.67292938232422, 38.314753723144534, 39.010920715332034, 39.59454193115234, 40.146366882324216, 40.18206787109375, 39.800454711914064, 39.45063781738281, 39.84676055908203, 40.51656799316406, 41.00297393798828, 41.07578735351562, 41.03855590820312, 40.58052978515625, 39.86266174316406, 39.01969757080078, 38.47286529541016, 38.71251220703125, 38.905531311035155, 38.78459930419922, 38.48997344970703, 38.054420471191406, 37.2605224609375, 36.406060791015626, 35.590634155273435, 35.11019287109375, 34.45256042480469, 33.55960388183594, 32.34215240478515, 31.03704071044922, 29.667152404785156, 28.228343200683593, 26.83016357421875, 25.91581573486328, 25.163665771484375, 24.213743591308592, 23.56950988769531, 22.691378784179687, 21.97735595703125, 21.01795349121094, 19.8415771484375, 19.010487365722657, 18.815469360351564, 18.829862976074217, 18.973274993896485, 19.087748718261718, 19.211827087402344, 19.02359085083008, 18.663509368896484, 18.183757019042968, 18.239437103271484, 18.504596710205078, 18.944967651367186, 19.399497985839844, 19.814521026611327, 19.77559127807617, 19.125115203857423, 18.245415496826173, 18.041680145263673, 18.30026626586914, 18.65371170043945, 18.682041931152344, 18.360194396972656, 17.889459228515626, 17.059345245361328, 16.089522552490234, 15.438436126708984, 15.374158477783203, 15.334114074707031, 15.144740295410156, 14.478789520263671, 13.836579895019531, 12.8108642578125, 11.698703002929687, 10.42715072631836, 9.713011169433594, 8.888970184326173, 7.930443572998047, 6.986886596679687, 6.292155456542969, 5.600279235839844, 5.231379699707031, 4.915562438964844, 5.141961669921875, 5.388238525390625, 5.863034057617187, 6.89534912109375, 8.240090942382812, 9.513729858398438, 11.239297485351562, 13.006246185302734, 14.62765121459961, 15.51556854248047, 16.08823165893555, 16.560450744628906, 16.947061157226564, 17.241258239746095, 17.862033081054687, 18.643634796142578, 19.58534164428711, 20.12973556518555, 20.291075134277342, 20.1887565612793, 20.086100769042968, 19.8838249206543, 19.775181579589844, 19.522442626953126, 19.63373718261719, 19.865105438232423, 19.55435256958008, 19.14752960205078, 19.819300842285156, 20.647807312011718, 20.8030143737793, 20.068544769287108, 19.26821365356445, 18.603218841552735, 17.700846099853514, 15.955748748779296, 13.634007263183594, 11.20521240234375, 8.525308227539062, 5.7429756164550785, 3.6272682189941405, 2.6080322265625, 2.6500640869140626, 3.987841033935547, 6.365500640869141, 8.888564300537109, 11.534742736816407, 14.526385498046874, 17.980789566040038, 20.654208374023437, 21.886970901489256, 22.673699188232423, 23.811353302001955, 25.19037437438965, 26.179095458984374, 26.503711128234862, 26.54196376800537, 26.346037101745605, 25.87341594696045, 25.470005607604982, 25.13487148284912, 25.50108585357666, 25.792264556884767, 25.922682189941405, 25.410983276367187, 24.570085525512695, 23.562225532531738, 22.345253562927248, 21.153051948547365, 20.143142700195312, 19.768018913269042, 19.491908836364747, 19.07895431518555, 18.32108917236328, 17.50838565826416, 16.736599349975585, 15.912868881225586, 15.070473098754883, 13.477946472167968, 12.018620681762695, 10.692218780517578, 9.644806098937988, 8.750639152526855, 7.9875383377075195, 7.340615081787109, 6.806809806823731, 6.390196800231934, 6.06643123626709, 5.868871116638184, 5.695167922973633, 5.566484832763672, 5.565377426147461, 5.572785949707031, 5.647459411621094, 5.958567047119141, 6.327593612670898, 6.815808486938477, 7.196856689453125, 7.425352478027344, 7.642261123657226, 7.942873764038086, 8.333687114715577, 8.852534770965576, 9.540770530700684, 10.253349781036377, 10.996751308441162, 11.678934574127197, 12.394623947143554, 13.089692306518554, 13.886173534393311, 14.717467498779296, 15.478913116455079, 16.24400281906128, 17.026762866973876, 17.79255576133728, 18.547845077514648, 19.22737874984741, 19.990839529037476, 20.80171141624451]    
+
+TaFCN_error=[233.73620000000005, 232.76620000000003, 232.04139999999995, 231.27319999999997, 229.15783999999996, 221.45916999999997, 215.25279999999998, 210.63149999999996, 209.96209999999996, 211.78110000000004, 213.36474999999996, 215.64606000000003, 215.25977, 214.7006, 214.03300000000002, 212.22410000000002, 211.09929999999997, 212.19449999999995, 214.66660000000002, 213.76390000000004, 213.14710000000002, 212.75183000000004, 213.2953, 222.71569999999997, 234.30909999999994, 232.69079999999997, 224.59704999999997, 216.78186000000005, 208.02405, 205.31679999999994, 209.19629999999995, 209.0412, 208.0729, 200.34490000000005, 193.1947, 186.64624000000003, 184.27949999999998, 185.31470000000002, 187.01229999999998, 187.00490000000002, 182.85540000000003, 177.1884, 168.7536, 170.21185000000003, 191.15215999999998, 198.25980000000004, 198.40380000000005, 198.13390000000004, 197.27210000000002, 181.88189999999997, 181.5942, 186.69640000000004, 175.23159999999996, 171.49207, 175.96349999999995, 184.85924999999997, 160.86725, 158.04459999999995, 177.39386000000002, 184.37879999999996, 189.9216, 193.85170000000005, 194.21000000000004, 176.81415000000004, 171.8474, 185.30589999999995, 182.15179999999998, 174.30676000000005, 175.23157000000003, 180.63639999999998, 165.49239999999998, 163.80909999999994, 154.92639999999994, 157.86316, 172.2745, 175.85974, 177.6554, 178.55334000000005, 177.85350000000005, 179.64513999999997, 188.7663, 198.28156, 204.4914, 208.2767, 210.64293999999995, 210.45759999999996, 209.3723, 217.56650000000002, 225.93024000000003, 224.50710000000004, 220.30597, 216.8908, 209.08704, 209.72473000000002, 204.35360000000003, 197.93960000000004, 191.23015999999996, 191.2156, 195.0054, 192.8967, 191.89464999999996, 199.79870000000005, 205.36395000000005, 212.52409999999998, 217.9918, 224.10474, 224.55039999999997, 221.13649999999996, 231.53089999999997, 246.24694999999997, 241.77124000000003, 231.37869999999998, 220.33900000000006, 207.91143999999997, 206.35379999999998, 205.36270000000002, 199.76779999999997, 199.62694999999997, 214.21014000000002, 221.81219999999996, 221.6205, 223.67834000000005, 225.74170000000004, 221.3632, 224.7654, 237.84048, 246.29395, 248.404, 252.04424999999998, 252.79149999999998, 247.86172, 249.18903, 255.72771999999998, 254.93110000000001, 248.83008, 240.93194999999997, 232.63177000000002, 223.54139999999995, 212.74365, 204.86803999999995, 195.46259999999995, 186.1028, 183.00183000000004, 172.5824, 167.8021, 161.28216999999995, 155.03625, 149.39306999999997, 155.61834999999996, 147.19556, 150.52655000000004, 148.5634, 152.18910000000005, 168.13559999999995, 190.36239999999998, 190.24080000000004, 190.99963000000002, 193.01300000000003, 183.43939999999998, 176.21155, 191.63660000000004, 204.19585999999998, 206.75308, 207.87946, 200.86932000000002, 192.48974999999996, 194.37739999999997, 208.6735, 201.65480000000002, 194.1504, 194.78638, 195.24790000000002, 190.9269, 187.02155000000005, 195.97827, 199.30762, 201.93427000000003, 207.58368000000002, 215.51343000000003, 213.09723000000002, 210.38592999999997, 218.9669, 228.59503, 226.7661, 227.38727, 226.97480000000002, 219.29697, 211.13623, 205.4334, 205.33319999999998, 200.44940000000003, 195.71344, 195.58627, 187.78019999999998, 183.16341999999997, 175.50664999999998, 165.1839, 167.59980000000002, 173.09552000000002, 168.22345, 167.41455000000002, 161.09726, 160.72930000000002, 166.75869999999998, 178.5643, 179.66201999999998, 177.29266, 177.65714000000003, 173.66073999999998, 159.44946, 164.68292000000002, 165.91649999999998, 174.75668000000002, 176.60278, 168.21548, 163.7267, 161.57837, 160.87085000000002, 155.6325, 160.61059999999998, 159.34951999999998, 154.97809999999998, 150.56607000000002, 154.66840000000002, 166.78647, 164.24872, 168.5355, 175.8986, 178.54208, 163.25497000000001, 166.02026, 175.05901999999998, 178.82135, 179.0716, 175.53915, 168.37743999999998, 161.76556, 157.26425, 153.32080000000002, 153.51456000000002, 150.94376, 148.82891999999998, 147.46710000000002, 143.98932000000002, 140.03458, 137.86554, 137.95135, 135.97003, 140.77524, 147.15215999999998, 142.77515, 141.26952999999997, 140.0227, 139.01553, 149.53019999999998, 151.38754, 154.5328, 158.77182, 153.39600000000002, 147.1538, 142.22836, 152.38614, 153.04595999999998, 151.08096, 154.25894, 149.42039999999997, 143.28485, 138.61612000000002, 136.36142, 144.85748, 154.83706999999998, 158.72174, 158.23764, 153.6834, 147.15233999999998, 146.2624, 152.78345000000002, 154.41609999999997, 164.21808, 179.65976, 181.27382999999998, 173.35244999999998, 170.61646000000002, 172.11896000000002, 166.85474, 164.13279999999997, 165.5082, 169.72964000000002, 171.19696, 171.77066000000002, 168.45157, 160.88608, 160.29077, 154.14877, 156.3043, 156.25293, 160.18018, 164.89780000000002, 167.23462, 163.73306000000002, 158.84267999999997, 158.26602000000003, 162.79680000000002, 165.94299999999998, 167.67957, 166.85617000000002, 160.56293, 156.47266000000002, 153.04868, 155.20531999999997, 150.82465000000002, 150.58465999999999, 151.13287000000003, 149.30234000000002, 148.39780000000002, 146.59089999999998, 154.02337999999997, 153.61682000000002, 154.14452999999997, 152.48212, 150.21954, 143.94846, 140.71337999999997, 140.41962, 150.82474000000002, 168.19537000000003, 171.83715999999998, 167.26178, 160.13240000000002, 153.53607, 154.47516000000002, 155.45882999999998, 154.07144, 156.12401999999997, 159.41082999999998, 164.01960000000003, 168.56635, 156.80927000000003, 149.07822, 144.92206, 139.75345, 137.25372, 135.80142, 135.09875, 139.57909999999998, 145.75687, 150.1778, 151.56484999999998, 149.17489999999998, 146.051, 145.25150000000002, 150.78386999999998, 148.03521999999998, 149.83440000000002, 153.98325, 156.03692999999998, 157.08141999999998, 156.8086, 164.42383, 163.76436999999999, 162.82098000000002, 159.17886, 160.12619999999998, 153.10464000000002, 150.63139999999999, 146.91109999999998, 152.47881999999998, 148.6485, 147.5247, 148.22858000000002, 138.2425, 128.41879999999998, 127.91692999999998, 135.04534999999998, 137.97976999999997, 142.4581, 143.88389999999998, 148.56099999999998, 144.5876, 136.15964000000002, 131.44335999999998, 131.96808, 137.3161, 147.07920000000001, 145.21820000000002, 145.27053999999998, 141.02859999999998, 139.62048, 154.02652, 158.80450000000002, 158.39056, 158.08917000000002, 161.49915, 159.78217, 149.78165, 144.91091999999998, 144.23788000000002, 143.69826999999998, 139.25439999999998, 133.24567000000002, 127.73723999999999, 126.08321999999998, 127.99761999999998, 128.87418000000002, 129.45675999999997, 135.62488000000002, 141.20502, 146.8515, 151.24045, 156.79930000000002, 161.06220000000002, 161.36721999999997, 161.94995, 163.92773, 161.9334, 157.80721999999997, 154.5611, 155.04474, 152.2063, 150.79469999999998, 145.04757999999998, 140.6997, 129.39812999999998, 123.09960000000001, 126.35753999999997, 134.25670000000002, 131.01215000000002, 136.78534000000002, 136.81345, 133.32837, 132.26623999999998, 130.65589999999997, 136.92462, 139.5419, 144.93286, 153.26891999999998, 153.50266, 147.85727000000003, 142.19913000000003, 138.47336, 139.64272999999997, 133.82706000000002, 128.2636, 128.70031999999998, 131.49077999999997, 128.35883, 122.00815, 115.601, 118.45764000000003, 118.85969999999998, 118.47635000000002, 116.36556999999999, 114.44585999999998, 113.08120000000002, 113.93734999999998, 114.61090000000002, 111.75779999999997, 111.38736, 106.22232000000002, 105.75677000000002, 108.45800000000003, 106.89758, 105.57684, 109.69439999999997, 109.09293000000002, 109.51508000000001, 109.0607, 109.86309999999997, 116.91919999999999, 117.60199999999998, 116.52386000000001, 118.29538000000002, 116.31923999999998, 114.32400000000001, 112.67129999999997, 110.733, 109.02838000000003, 108.61810000000003, 104.91271999999998, 97.7382, 91.40093999999999, 85.87518, 82.19765999999998, 80.07663000000002, 76.46582000000001, 75.04775999999998, 72.53179999999998, 68.73903999999999, 67.33535999999998, 66.70508000000001, 69.74423000000002, 71.50693000000001, 73.19794000000002, 76.61792000000003, 81.54282, 82.51006999999998, 80.25655999999998, 77.75653, 83.37527, 85.66973999999999, 84.76280000000003, 82.06670000000003, 84.05502000000001, 83.3089, 85.01968, 84.26566000000003, 85.5378, 84.37545999999998, 82.17779999999999, 80.72622999999999, 82.84840000000003, 82.625, 81.88330000000002, 81.80446999999998, 79.49471999999997, 77.95030000000003, 76.53969999999998, 79.46506, 78.36752000000001, 78.34937000000002, 76.96158000000003, 77.34116, 74.76763999999997, 71.59926999999999, 71.61273, 74.01114000000001, 73.11777000000001, 74.51119999999997, 71.98056000000003, 65.82233000000002, 62.23149999999998, 58.44745, 58.70618000000002, 55.929959999999994, 53.921749999999975, 54.08181999999999, 52.70373999999998, 49.47606999999999, 46.80977999999999, 45.23795000000001, 47.92380000000003, 48.08746000000002, 48.851200000000006, 50.56796000000003, 53.50905999999998, 53.352599999999995, 50.73358000000002, 48.452639999999974, 54.12099999999998, 55.82727, 54.66144000000003, 53.97300000000001, 55.008500000000026, 53.219100000000026, 54.914120000000025, 56.35390000000001, 55.46588000000003, 51.53951999999998, 49.33699999999999, 49.26218, 51.624239999999986, 51.34881999999999, 52.147519999999986, 51.909940000000006, 49.529240000000016, 43.47028, 41.97476, 50.55576000000002, 55.53739999999999, 56.18915999999999, 56.47757999999999, 61.42827, 60.64688000000001, 54.36493999999999, 52.78654, 54.300129999999996, 53.19514000000001, 57.84512000000001, 58.60346999999999, 51.53353999999999, 46.20773, 42.238699999999994, 38.969330000000014, 36.79964000000001, 37.20882, 41.58357000000001, 41.09026, 40.59943999999999, 36.381519999999995, 34.770070000000004, 36.82414, 35.295620000000014, 34.72785999999999, 39.65628000000001, 41.356120000000004, 39.75627, 35.98657, 32.453689999999995, 43.89282, 41.51366999999999, 35.73224999999999, 31.109790000000004, 33.22476, 35.388029999999986, 41.24284, 39.74055000000001, 41.262939999999986, 34.931520000000006, 30.69193999999999, 25.591550000000012, 27.31438, 38.26514, 50.11503999999999, 51.89626999999999, 47.90201999999999, 45.69875999999999, 45.989779999999996, 47.93912, 46.21489, 44.55229, 50.66954000000001, 58.066509999999994, 56.99807999999999, 58.477329999999995, 60.62291999999999, 59.03945999999999, 54.56143, 50.40768, 50.725779999999986, 52.01626999999999, 57.60177999999999, 62.54486, 56.31267, 53.327380000000005, 55.70554000000001, 54.97119000000001, 57.27284, 58.62082000000001, 62.81277, 63.73394999999999, 58.07015999999999, 54.464049999999986, 57.23741000000001, 56.7287, 58.48357999999999, 60.153819999999996, 63.83694, 60.44864000000001, 54.10767999999999, 55.269000000000005, 54.79172, 51.32227, 49.131069999999994, 51.80717000000001, 55.319919999999996, 53.8888, 49.208709999999996, 48.67361, 47.93361999999999, 48.52596, 47.54193000000001, 44.55188000000001, 41.55090000000001, 41.46236999999999, 46.78871000000001, 45.57885999999999, 40.98276000000001, 38.19630000000001, 37.01543000000001, 37.36591999999999, 38.80563000000001, 39.599819999999994, 42.026610000000005, 40.780789999999996, 36.27074999999999, 33.98349999999999, 33.60727, 38.28966, 39.20320000000001, 40.76785000000001, 37.78307000000001, 36.06818999999999, 34.87843000000001, 37.74399, 38.06460000000001, 40.196979999999996, 40.27087, 41.85727, 37.95206999999999, 33.39114000000001, 36.88869, 34.138000000000005, 32.479870000000005, 31.14690999999999, 31.45908, 31.403400000000005, 32.94290000000001, 29.27691999999999, 27.531710000000004, 26.87379999999999, 26.255619999999993, 24.58664999999999, 24.4889, 25.52042, 26.99161000000001, 28.92989, 25.01034999999999, 23.857640000000004, 25.61009999999999, 29.539029999999997, 27.589200000000005, 26.11044000000001, 22.11099999999999, 20.863619999999997, 21.01857000000001, 25.52829, 23.171189999999996, 22.087369999999993, 19.06638000000001, 19.69649000000001, 17.149190000000004, 16.46786, 15.287299999999988, 16.534789999999987, 12.594789999999989, 9.47480999999999, 8.943630000000013, 14.33623, 14.613709999999998, 15.14455000000001, 14.207240000000013, 13.408719999999988, 10.77843999999999, 12.580180000000013, 11.248979999999989, 12.087524000000002, 12.806984, 11.40773, 11.288669999999996, 7.812330000000003, 6.933700000000002, 7.530779999999993, 8.103210000000004, 7.432959999999994, 9.605909999999994, 7.221599999999995, 6.157004999999998, 1.1633700000000005, 0.5873399999999975, 1.320210000000003, 0.6608399999999932, 1.3823699999999945, 1.027159999999995, 0.6426850000000002, 0.045394999999999186, 0.3803699999999992, 2.918379999999999, 3.722759999999994, 5.451740000000001, 9.646834999999996, 9.549210000000002, 9.009445, 11.33126, 12.695594999999997, 13.831469999999996, 12.88664, 13.974779999999996, 15.390500000000003, 16.549149999999997, 15.210390000000004, 17.301956000000004, 18.540633999999997, 22.082015999999996, 20.616196000000002, 20.582089999999994, 22.040015999999994, 24.864180000000005, 24.6075, 21.641750000000002, 20.074196, 23.298479999999998, 26.868409999999997, 25.482506, 26.134925999999993, 31.298829999999995, 29.269936, 25.250960000000006, 28.316329999999994, 27.924390000000002, 30.376914999999997, 34.117599999999996, 16.968490000000003, 5.054500000000004, 5.290670000000006, 3.6618799999999965, 1.1526000000000067, 4.598785000000007, 0.1346900000000062, 1.5079199999999986, 7.386989999999997, 5.671229999999994, 4.248795000000001, 2.504810000000006, 1.5311300000000045, 11.156950000000002, 14.176400000000001, 9.808937, 8.90542, 6.3387949999999975, 13.817860000000003, 18.371634999999998, 24.487876999999997, 24.498973999999997, 24.345962999999998, 24.353324999999998, 22.113590000000002, 22.066536, 22.88192, 26.804789, 28.878277, 27.781458, 25.218847, 23.39437, 21.22673, 17.202216999999997, 14.925823000000001, 14.140279999999997, 15.260121999999999, 14.627600000000001, 13.990274, 13.531824, 13.282184999999998, 12.568460000000002, 12.024384000000001, 7.621741999999998, 5.196064, 1.792904, 2.7766499999999965, 2.284930000000003, 2.237408000000002, 1.6046699999999987, 1.4186300000000003, 1.100168, 0.6147770000000001, 1.0157659999999993, 0.9889679999999998, 2.316645000000001, 1.3069300000000013, 2.1347799999999992, 2.2125549999999983, 1.4071670000000012, 2.3866709999999998, 6.091946, 6.897580000000001, 5.645409999999998, 3.687764999999999, 2.892389999999999, 2.8157560000000004, 2.983146999999999, 3.3722700000000003, 4.187892999999999, 4.870899999999999, 5.846947, 6.807359999999999, 7.591716999999999, 8.167192, 8.387263999999998, 8.4728365, 8.916109, 9.191609, 10.188156, 11.088129, 11.551312, 12.45193, 12.974179]
+
+
+# Our_error=[29, 28, 28, 28, 28, 63, 87, 89, 89, 89, 89, 89, 87, 87, 87, 86, 86, 82, 82, 82, 82, 82, 82, 81, 81, 81, 77, 77, 79, 81, 81, 81, 82, 79, 79, 81, 81, 81, 82, 82, 80, 80, 81, 81, 81, 82, 82, 81, 81, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 86, 86, 86, 86, 87, 87, 87, 87, 87, 87, 87, 89, 89, 89, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 86, 86, 82, 82, 82, 82, 87, 87, 87, 87, 87, 86, 82, 87, 87, 97, 101, 101, 101, 101, 101, 101, 101, 101, 107, 107, 101, 101, 101, 101, 101, 106, 107, 107, 107, 107, 107, 107, 107, 107, 108, 108, 108, 108, 107, 107, 108, 107, 102, 101, 101, 101, 101, 102, 102, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 100, 100, 100, 100, 100, 100, 100, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 96, 97, 97, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 91, 91, 91, 91, 91, 91, 90, 90, 89, 89, 89, 89, 87, 87, 82, 81, 81, 81, 81, 81, 82, 82, 81, 82, 86, 87, 87, 87, 87, 87, 87, 87, 89, 89, 89, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 90, 90, 90, 90, 90, 89, 89, 89, 87, 87, 87, 86, 82, 81, 81, 81, 81, 81, 81, 81, 81, 82, 81, 82, 82, 86, 86, 87, 87, 87, 87, 87, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 87, 87, 87, 87, 87, 87, 86, 82, 81, 81, 81, 81, 81, 80, 79, 79, 79, 78, 78, 78, 77, 77, 71, 71, 69, 67, 67, 67, 67, 67, 66, 66, 66, 66, 66, 66, 66, 66, 65, 65, 65, 65, 66, 66, 65, 65, 65, 65, 64, 63, 63, 63, 62, 61, 61, 45, 44, 44, 43, 43, 41, 41, 37, 36, 36, 25, 25, 25, 25, 25, 25, 25, 25, 25, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 23, 23, 22, 22, 21, 21, 20, 20, 19, 18, 13, 13, 13, 12, 12, 12, 12, 12, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 15, 18, 18, 18, 19, 19, 19, 18, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 23, 23, 23, 23, 23, 23, 23, 24, 24, 23, 23, 23, 22, 21, 21, 20, 19, 15, 13, 12, 12, 11, 10, 2, 2, 1, 0, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 3, 3, 0, 1, 1, 1, 1, 2, 7, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 8, 7, 3, 2, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6, 7, 8, 9, 11, 11, 12, 12, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 16, 17, 18, 18, 18, 18, 19, 19, 20, 22, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 22, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 21, 20, 20, 20, 19, 19, 18, 18, 18, 18, 16, 15, 15, 15, 15, 15, 14, 14, 14, 14, 14, 14]
+
+Our_error=[26, 26, 26, 26, 26, 99, 141, 143, 143, 144, 141, 136, 135, 135, 134, 133, 131, 131, 125, 125, 125, 125, 125, 123, 123, 122, 131, 134, 135, 136, 140, 136, 136, 141, 141, 143, 143, 143, 143, 141, 143, 143, 143, 143, 143, 143, 143, 143, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 145, 145, 145, 145, 145, 145, 145, 145, 145, 144, 144, 144, 143, 143, 143, 143, 141, 141, 141, 141, 141, 140, 140, 136, 136, 140, 141, 141, 141, 136, 136, 136, 135, 135, 135, 135, 135, 136, 161, 151, 151, 154, 154, 155, 155, 155, 155, 154, 154, 155, 155, 155, 155, 155, 155, 154, 154, 154, 154, 154, 154, 154, 154, 151, 151, 151, 151, 151, 151, 151, 151, 154, 154, 154, 154, 151, 151, 154, 154, 151, 154, 154, 154, 154, 154, 154, 154, 155, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 155, 155, 155, 155, 155, 155, 155, 155, 154, 155, 155, 154, 154, 154, 154, 154, 155, 155, 154, 154, 154, 154, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 154, 154, 155, 154, 154, 154, 154, 154, 151, 154, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 154, 154, 154, 154, 154, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 150, 150, 150, 150, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 150, 150, 150, 150, 150, 146, 146, 146, 146, 146, 146, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 150, 146, 146, 146, 146, 146, 146, 146, 146, 145, 145, 145, 145, 145, 145, 145, 144, 144, 144, 143, 143, 144, 144, 144, 144, 144, 144, 144, 144, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 144, 144, 144, 144, 144, 143, 143, 143, 141, 141, 141, 140, 136, 136, 135, 135, 135, 135, 135, 133, 132, 131, 131, 125, 125, 125, 125, 125, 125, 123, 123, 122, 121, 121, 120, 120, 120, 120, 120, 120, 120, 120, 120, 119, 119, 119, 119, 118, 117, 117, 116, 115, 115, 99, 99, 99, 98, 97, 96, 95, 91, 90, 90, 79, 79, 79, 79, 78, 78, 78, 78, 78, 78, 78, 78, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 78, 77, 77, 77, 77, 77, 77, 77, 76, 76, 75, 75, 74, 73, 72, 72, 69, 67, 66, 66, 66, 65, 64, 64, 56, 56, 56, 55, 55, 55, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 55, 55, 55, 54, 54, 52, 52, 52, 52, 52, 52, 51, 51, 50, 50, 49, 48, 48, 48, 48, 48, 48, 49, 49, 50, 50, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 54, 54, 54, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 64, 64, 64, 64, 65, 64, 64, 64, 65, 65, 65, 66, 66, 65, 64, 64, 56, 56, 55, 52, 52, 51, 49, 47, 46, 43, 43, 42, 40, 40, 40, 39, 38, 38, 38, 37, 36, 36, 36, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 34, 34, 34, 34, 34, 34, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 36, 35, 35, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 25, 24, 24, 24, 24, 24, 24, 23, 21, 21, 18, 11, 10, 10, 9, 9, 7, 5, 3, 3, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 3, 3, 6, 7, 8, 8, 9, 9, 9, 10, 11, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 20, 18, 18, 10, 9, 7, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
+
+Hu_error=[]
+for i in range(len(Hu)):    
+    Hu_error.append(abs(len(Hu)-i-Hu[i]))
+    
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+
+ax.plot(np.arange(min_len,0,-1),Si_error[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Zhang_error[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Hu_error[-min_len:], linewidth=2,c='#A67D3D',label="Hu' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),DCNN_error[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),TaFCN_error[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Our_error[-min_len:], linewidth=2,c='r',label="Our",linestyle='--')
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+# error_rate=0.2
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.array(np.arange(min_len,0,-1))*(error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+
+
+def rmse(x):
+    s=0
+    for i in x:
+        s+=i*i
+    r=sqrt(s/len(x))
+    return r
+
+def aae(x):
+    
+    return np.mean(np.abs(x))
+
+
+def score(Y_test,Y_pred) :
+      
+    s=0    
+    for i in range(len(Y_pred)):
+        if Y_pred[i]>Y_test[i]:
+            s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+        else:
+            s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+    # print('unbalanced_penalty_score{}'.format(s))
+    return s  
+  
+def error_range(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+     
+    error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return error_range
+
+def error_list(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+
+    error_list=Y_test-Y_pred
+    # Y_test =np.array(Y_test)  
+    # Y_pred =np.array(Y_pred) 
+     
+    # error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return list(error_list)
+
+print(list((error_list(groud_truth[-min_len:],Si[-min_len:]),error_list(groud_truth[-min_len:],Zhang[-min_len:]),error_list(groud_truth[-min_len:],Hu[-min_len:]),error_list(groud_truth[-min_len:],DCNN[-min_len:]),error_list(groud_truth[-min_len:],TaFCN[-min_len:]),error_list(groud_truth[-min_len:],Our[-min_len:]))))
+        
+print(list((rmse(Si_error[-min_len:]),rmse(Zhang_error[-min_len:]),rmse(Hu_error[-min_len:]),rmse(DCNN_error[-min_len:]),rmse(TaFCN_error[-min_len:]),rmse(Our_error[-min_len:]))))
+print(list((aae(Si_error[-min_len:]),aae(Zhang_error[-min_len:]),aae(Hu_error[-min_len:]),aae(DCNN_error[-min_len:]),aae(TaFCN_error[-min_len:]),aae(Our_error[-min_len:]))))
+print(list((score(groud_truth[-min_len:],Si[-min_len:]),score(groud_truth[-min_len:],Zhang[-min_len:]),score(groud_truth[-min_len:],Hu[-min_len:]),score(groud_truth[-min_len:],DCNN[-min_len:]),score(groud_truth[-min_len:],TaFCN[-min_len:]),score(groud_truth[-min_len:],Our[-min_len:]))))
+print(list((error_range(groud_truth[-min_len:],Si[-min_len:]),error_range(groud_truth[-min_len:],Zhang[-min_len:]),error_range(groud_truth[-min_len:],Hu[-min_len:]),error_range(groud_truth[-min_len:],DCNN[-min_len:]),error_range(groud_truth[-min_len:],TaFCN[-min_len:]),error_range(groud_truth[-min_len:],Our[-min_len:]))))
+def predcition(targets,predictions):
+    targets=np.array(targets)
+    predictions=np.array(predictions)
+    
+    
+    # targets=np.array(targets)
+    # predictions=np.array(predictions)
+
+    
+
+    smape = np.sum( np.abs(predictions - targets)) /np.sum( np.abs( targets)) * 100
+    print(np.sum( np.abs(predictions - targets)))
+    
+    print(np.sum( np.abs( targets)))
+    
+    print(smape)
+    y=100-smape
+    
+    # print(y)
+    return y 
+
+print(list((predcition(groud_truth[-min_len:],Si[-min_len:]),predcition(groud_truth[-min_len:],Zhang[-min_len:]),predcition(groud_truth[-min_len:],Hu[-min_len:]),predcition(groud_truth[-min_len:],DCNN[-min_len:]),predcition(groud_truth[-min_len:],TaFCN[-min_len:]),predcition(groud_truth[-min_len:],Our[-min_len:]))))
+
+# print(groud_truth[-min_len:],Si[-min_len:]),aae(Zhang_error[-min_len:]),aae(TaFCN_error[-min_len:]),aae(Our_error[-min_len:]))
+
+
+
+# print(rmse(Si_error),rmse(Zhang_error),rmse(TaFCN_error),rmse(Our_error))
+
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+    
+plt.xlabel('Actual RUL (cycle)',font1) #X轴标签
+plt.ylabel("Absolute prediction error (cycle)",font1) #Y轴标签
+plt.gca().invert_xaxis()
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_cacle_3536_38.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_cacle_3536_38.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
+
+
+
+xlabel='Actual RUL'
+ylabel='Prediction error'
+
diff --git a/code/comparison experiments/cacle/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt b/code/comparison experiments/cacle/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt
new file mode 100644
index 0000000..c84ed61
--- /dev/null
+++ b/code/comparison experiments/cacle/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt	
@@ -0,0 +1,806 @@
+2023-03-22 19:18:03,735 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,736 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,825 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,826 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,460 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,461 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:20:00,213 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+2023-03-22 19:20:00,235 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+2023-03-22 19:20:00,241 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+2023-03-22 19:20:00,258 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+2023-03-22 19:20:00,312 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+2023-03-22 19:20:00,313 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+2023-03-22 19:20:00,314 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+2023-03-22 19:20:02,670 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+2023-03-22 19:20:02,671 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+2023-03-22 19:20:02,901 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+2023-03-22 19:20:02,940 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+2023-03-22 19:20:02,990 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+2023-03-22 19:20:03,741 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+2023-03-22 19:20:03,983 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+2023-03-22 19:20:04,011 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+2023-03-22 19:20:04,216 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
+
+2023-03-22 19:22:47,451 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,452 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,545 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,546 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:52,870 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,872 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
+2023-03-22 19:22:52,873 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,879 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,888 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'YouYuan' (SIMYOU.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal heavy normal>) = 10.43
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrak.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibril.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Deng.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STFangsong' (STFANGSO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal black normal>) = 11.525
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal demi condensed>) = 10.44
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'icomoon' (iqiyi_logov5.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXihei' (STXIHEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrakb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextrab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal medium normal>) = 11.145
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,929 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXinwei' (STXINWEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal demi normal>) = 11.24
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra' (mtextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,938 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariblk.ttf) normal normal black normal>) = 6.888636363636364
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,949 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STHupo' (STHUPO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal semibold normal>) = 11.24
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger' (Symbol Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclid.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZYaoTi' (FZYTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal bold condensed>) = 7.8986363636363635
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger' (Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger Expert' (Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'KaiTi' (simkai.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZShuTi' (FZSTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal heavy normal>) = 11.43
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'LiSu' (SIMLI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,978 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Unicode MS' (ARIALUNI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STLiti' (STLITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,994 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STKaiti' (STKAITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger Expert' (Symbol Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal semibold normal>) = 10.24
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal medium condensed>) = 10.344999999999999
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal black normal>) = 11.525
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXingkai' (STXINGKA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STZhongsong' (STZHONGS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Fences' (fences.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 7.613636363636363
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal bold condensed>) = 6.8986363636363635
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FangSong' (simfang.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 6.613636363636363
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STSong' (STSONG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Mincho' (MSMINCHO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra Tiger' (Mt Extra Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,040 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STCaiyun' (STCAIYUN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,042 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,057 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal medium normal>) = 10.145
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWAD.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,063 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal regular normal>) = 10.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal bold condensed>) = 11.535
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWDB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimHei' (simhei.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,071 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('C:\\Users\\Administrator\\anaconda3\\envs\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000.
+2023-03-22 19:23:05,774 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_428:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,776 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_427:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,778 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_426:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,779 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_425:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,781 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_424:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,782 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_423:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,783 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_422:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,784 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_421:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,785 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_420:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,786 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_419:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,787 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_418:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,788 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_417:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,789 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_416:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,791 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_415:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,792 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_414:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,793 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_413:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,794 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_412:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,795 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_411:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,796 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_410:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,798 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_409:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,799 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_408:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:24:47,221 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,222 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,315 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,316 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,162 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,163 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,082 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,176 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,177 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,332 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,333 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,407 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,505 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,506 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,215 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,216 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,143 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,243 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,244 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,337 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,338 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,432 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,433 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,264 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,265 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,578 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,579 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,674 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,675 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,053 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,054 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,145 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,912 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,913 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,422 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,423 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,515 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,517 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,455 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,456 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,550 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,551 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,409 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,496 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,497 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,883 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,884 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,970 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,971 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,988 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,989 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,080 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,943 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,944 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,041 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,042 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,890 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,891 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,985 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,984 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,071 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,072 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,916 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,917 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,073 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,074 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,166 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,168 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,894 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,897 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,005 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
diff --git a/code/comparison experiments/cacle/tafcn2022/TaFCN.py b/code/comparison experiments/cacle/tafcn2022/TaFCN.py
new file mode 100644
index 0000000..3a501c0
--- /dev/null
+++ b/code/comparison experiments/cacle/tafcn2022/TaFCN.py	
@@ -0,0 +1,722 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 22 16:37:59 2023
+
+@author: Administrator
+"""
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-0.5
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+# print(os.path.abspath(os.path.join(os.getcwd(), "../../../")))
+# last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+# def get_data_list(CS2_35_cap_dropOutlier):
+#     worksheet = xlrd.open_workbook('F:\桌面11.17\project\RUL_guassion\handled_dataset\CACLE\{}.xlsx'.format(CS2_35_cap_dropOutlier))
+#     sheet_names= worksheet.sheet_names()
+#     print(sheet_names)
+#     CS2_35=[]
+#     for sheet_name in sheet_names:
+#         sheet = worksheet.sheet_by_name(sheet_name)
+#         rows = sheet.nrows # 获取行数
+#         cols = sheet.ncols # 获取列数，尽管没用到
+#         all_content = []
+    
+    
+#         CS2_35 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+
+
+
+#     fig, ax = plt.subplots()
+#     # 在生成的坐标系下画折线图
+#     ax.plot(CS2_35, linewidth=1)
+#     # 显示图形
+#     plt.show()
+    
+#     return CS2_35
+
+# CS2_35=get_data_list("CS2_35_cap_dropOutlier")
+# CS2_36=get_data_list("CS2_36_cap_dropOutlier")
+# CS2_37=get_data_list("CS2_37_cap_dropOutlier")
+# CS2_38=get_data_list("CS2_38_cap_dropOutlier")
+
+
+def get_data_list():
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_35_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_35=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_35 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+
+
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_36_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_36=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_36 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_37_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_37=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_37 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+        
+    worksheet = xlrd.open_workbook(last_last_last_last_path+r'\dataset\CACLE\CS2_38_cap_dropOutlier.xlsx')
+    sheet_names= worksheet.sheet_names()
+    print(sheet_names)
+    CS2_38=[]
+    for sheet_name in sheet_names:
+        sheet = worksheet.sheet_by_name(sheet_name)
+        rows = sheet.nrows # 获取行数
+        cols = sheet.ncols # 获取列数，尽管没用到
+        all_content = []
+    
+    
+        CS2_38 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="CS2_35")
+    ax.plot(CS2_36, linewidth=1,c='g',label="CS2_36")
+    ax.plot(CS2_37, linewidth=1,c='y',label="CS2_37")
+    ax.plot(CS2_38, linewidth=1,c='r',label="CS2_38")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_cycle_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_cycle_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35,CS2_36,CS2_37,CS2_38
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+# print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+# print(CS2_35)
+
+
+# print(CS2_35[0])
+# print(CS2_36[0])
+# print(CS2_37[0])
+# print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return list(CS235)
+
+# CS235=get_health_list(CS2_35,shed) 
+# CS236=get_health_list(CS2_36,shed) 
+# CS237=get_health_list(CS2_37,shed) 
+# CS238=get_health_list(CS2_38,shed)   
+
+# fig, ax = plt.subplots()
+# # 在生成的坐标系下画折线图
+# ax.plot(CS235, linewidth=1)
+# ax.plot(CS236, linewidth=1)
+# ax.plot(CS237, linewidth=1)
+# ax.plot(CS238, linewidth=1)
+
+# # 显示图形
+# plt.show()
+
+def get_input_out_2(CS236,CS237,time_windows):
+
+    
+    # CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    # CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    # for i in range(time_windows-1-2):
+    #     CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    # CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    # for i in range(len(CS235_health)-time_windows+1):        
+    #     x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+    #     y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+
+
+
+
+
+
+
+
+
+
+
+
+def get_input_out_3(CS235,CS236,CS237,time_windows):
+    # min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    # input_list=[]
+    # output_list=[]
+    
+    # true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    CS237_health=get_health_list(CS237,shed)     
+    
+    print(len(CS237_health))
+    print("hhhhhhhhhhhh")
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    for i in range(time_windows-1-2):
+        CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    for i in range(len(CS235_health)-time_windows+1):        
+        x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+        y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    print(y_test_array.shape)
+    print("jjjjjjjjjj")
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+    
+    
+    
+    
+    
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_3(CS2_35,CS2_36,CS2_37,20)      
+
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+
+
+
+
+
+
+
+#import tensorflow as tf
+import os
+import logging
+import numpy as np
+#from numpy import trans
+import matplotlib.pyplot as plt
+#import tensorflow as tf
+# import CMAPSSDataset
+import pandas as pd
+import datetime
+import keras
+from keras.layers import Lambda
+import math
+import keras.backend as K
+import tensorflow as tf
+from tfdeterminism import patch
+from sklearn.model_selection import train_test_split
+from keras.utils.vis_utils import plot_model
+patch()
+# tf.random.set_seed(0)
+#import keras
+#flags = tf.flags
+#flags.DEFINE_string("weights", None, 'weights of the network')################# the file path of weights
+#flags.DEFINE_integer("epochs", 100, 'train epochs')
+#flags.DEFINE_integer("batch_size", 32, 'batch size for train/test')
+#flags.DEFINE_integer("sequence_length", 32, 'sequence length')
+#flags.DEFINE_boolean('debug', False, 'debugging mode or not')
+#FLAGS = flags.FLAGS
+
+def root_mean_squared_error(y_true, y_pred):
+        return K.sqrt(K.mean(K.square(y_pred - y_true),axis=0))##################  axis=0
+
+def rmse(predictions, targets):
+    return np.sqrt(((predictions - targets) ** 2).mean())
+
+
+
+
+    
+segment=3
+
+
+
+run_times=10
+
+
+
+nb_epochs=2000           #200
+batch_size=64    ## 64        #####300
+# sequence_length=31    ############# min31  max303
+
+patience=50
+patience_reduce_lr=20
+
+
+
+
+
+seed=2
+
+
+
+num_filter1=64
+num_filter2=128
+num_filter3=64
+
+
+
+kernel1_size=8
+kernel2_size=5
+kernel3_size=3
+
+
+
+sequence_length=20
+
+
+
+
+
+
+
+             
+# X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_37,sequence_length)  
+
+# rul_pred_array
+# [640.24347, 640.20013, 640.3949, 639.9918, 640.571, 646.75604, 651.7483, 655.2514, 654.5609, 652.39636, 649.5043, 646.4637, 645.68195, 645.19366, 644.9176, 645.39813, 645.7279, 644.50037, 641.3435, 641.41376, 640.60297, 638.2412, 637.4022, 628.928, 615.764, 612.9235, 616.69885, 624.5115, 634.5726, 637.0209, 633.8705, 630.7835, 630.06934, 633.02484, 639.5514, 646.5034, 650.5528, 650.7099, 647.9696, 645.11127, 646.1539, 651.57587, 659.193, 664.71124, 653.04913, 635.4428, 632.5107, 630.7191, 630.21234, 640.8147, 647.58624, 652.1958, 652.71497, 655.0368, 651.8627, 637.77264, 653.50867, 659.53284, 651.7022, 626.96924, 618.814, 616.1976, 610.02576, 620.1486, 634.3268, 627.6339, 619.2191, 626.0749, 624.98254, 620.33704, 636.3168, 638.2206, 642.8459, 642.9759, 632.639, 623.9098, 622.5685, 619.7476, 613.49384, 615.65686, 608.86804, 596.9789, 590.1312, 584.25037, 579.9601, 575.725, 575.6419, 571.31537, 561.8876, 558.6213, 557.2253, 555.5353, 555.69836, 564.5367, 564.0225, 560.89685, 566.91785, 572.56433, 578.5969, 575.66003, 570.72595, 572.0209, 571.9004, 563.69946, 555.95306, 548.0949, 541.42786, 532.4127, 530.92993, 532.2799, 524.29456, 509.51538, 510.7059, 515.5668, 524.14026, 534.98773, 532.37683, 533.63245, 538.4323, 537.9903, 530.97424, 525.3932, 525.0902, 523.8796, 521.9207, 522.4716, 522.3078, 519.0744, 503.16446, 497.73944, 494.4536, 490.22064, 486.98526, 488.5766, 490.22052, 480.5122, 478.7509, 481.09082, 482.14496, 487.3956, 501.07788, 507.83096, 510.82855, 514.1886, 520.529, 528.2003, 531.2668, 533.62244, 535.77686, 536.05444, 534.992, 536.0674, 526.97845, 520.444, 514.7549, 514.60266, 510.49698, 492.87842, 486.68042, 489.18024, 487.86407, 493.12344, 502.24814, 507.2752, 505.0594, 502.6546, 508.49692, 515.8886, 512.3586, 495.93857, 497.85687, 503.31705, 501.9515, 500.38654, 503.93488, 506.4012, 498.1017, 493.38165, 491.3588, 484.64496, 479.03833, 480.34796, 485.23322, 481.00708, 469.2503, 473.8952, 475.01407, 473.83368, 477.5022, 490.69565, 496.0235, 500.13678, 505.7071, 510.3401, 514.5818, 516.79047, 521.4042, 525.77136, 531.6742, 526.17505, 523.2844, 522.57513, 521.20154, 517.2708, 512.82245, 506.66318, 490.378, 472.54507, 475.70197, 472.7846, 476.53912, 490.65152, 491.063, 483.98737, 477.07404, 479.06137, 487.07657, 488.23895, 486.39795, 480.12592, 481.69897, 479.53598, 481.5044, 482.85034, 488.53644, 486.80307, 477.2146, 478.35464, 477.64566, 473.77832, 473.23456, 486.04517, 485.87183, 478.88922, 475.69904, 477.57495, 481.66876, 487.91022, 491.8163, 493.77466, 494.78394, 494.67975, 494.65204, 492.91022, 493.60287, 497.2452, 498.91803, 500.8898, 500.73114, 499.069, 491.13852, 483.8565, 486.63998, 483.98712, 485.1898, 485.20322, 473.09967, 468.85028, 463.25342, 460.81635, 465.31866, 470.7635, 472.9676, 462.48248, 459.13403, 457.73492, 454.45728, 457.55475, 463.3807, 467.6935, 470.2746, 459.94135, 458.675, 454.15094, 455.8497, 460.2911, 469.78387, 470.7398, 463.00458, 460.8978, 451.22247, 441.43497, 439.33163, 448.28564, 450.05722, 444.73315, 448.5494, 449.27667, 447.98676, 441.75128, 438.5031, 436.014, 438.68512, 438.96152, 447.70865, 450.99683, 454.56583, 454.7442, 455.3631, 448.5335, 444.71112, 447.95987, 451.93878, 450.49203, 443.31094, 437.7587, 433.5808, 428.19214, 424.17245, 426.73608, 427.09732, 423.4256, 417.48486, 417.67926, 417.13104, 413.35287, 415.41708, 417.77222, 417.74066, 409.31415, 409.08896, 406.56772, 403.19257, 407.55902, 407.32608, 406.52203, 402.5905, 396.56866, 389.7909, 388.86777, 390.1569, 396.4772, 398.55396, 400.65598, 395.29965, 394.9238, 392.0855, 389.66895, 383.55658, 385.97455, 384.8082, 383.8585, 384.91153, 388.11737, 390.483, 392.6076, 389.6229, 384.8108, 379.69302, 378.148, 380.1823, 382.2311, 383.6507, 379.7574, 381.7326, 381.01678, 377.45853, 372.5294, 372.2347, 371.7366, 367.67215, 361.45663, 351.82034, 349.89777, 344.29578, 343.5362, 340.2873, 336.19623, 333.84653, 335.27438, 333.67557, 334.24432, 332.60538, 343.7238, 346.82083, 346.0822, 338.72803, 343.62967, 341.94714, 339.06668, 343.38385, 347.50778, 355.56696, 358.80896, 353.55145, 343.01862, 337.47784, 335.79044, 338.89288, 342.90417, 341.72226, 334.47052, 333.8459, 332.93073, 332.11237, 330.59412, 336.3993, 338.14456, 335.89752, 337.55478, 341.63608, 344.81378, 347.62674, 344.73932, 341.97025, 340.934, 336.99316, 328.0442, 322.98364, 319.25464, 316.7835, 308.04007, 308.29486, 306.0722, 307.45197, 305.7146, 307.9315, 310.87018, 312.6007, 306.68314, 308.7287, 310.1643, 311.17102, 316.44843, 324.15204, 325.726, 320.33633, 319.40482, 316.39874, 312.94183, 313.3718, 316.24673, 318.24664, 315.59894, 305.9112, 302.1676, 295.9246, 288.29102, 288.19223, 289.39233, 292.59583, 290.32056, 290.36453, 293.25702, 297.73212, 294.4601, 291.6413, 296.96906, 304.10895, 306.18134, 301.86758, 299.82748, 301.68082, 305.42282, 307.62396, 307.46042, 306.98132, 307.37753, 308.87552, 310.73245, 314.38422, 312.92465, 312.04907, 309.44843, 309.6513, 305.00504, 303.96277, 302.33353, 299.69058, 294.6346, 292.75098, 288.97595, 287.17535, 287.40656, 287.86542, 288.61063, 288.23895, 286.3177, 286.4428, 285.01825, 288.2702, 294.51172, 297.676, 301.3759, 299.75543, 303.40646, 304.3763, 304.1898, 305.21912, 307.51996, 308.66104, 307.83197, 304.33896, 301.91077, 299.1038, 292.10333, 286.66003, 284.12866, 286.8969, 287.22003, 277.47342, 273.80035, 275.33646, 276.80466, 275.2891, 274.13705, 270.9247, 271.55927, 269.88998, 272.45746, 275.52594, 277.23923, 274.1186, 272.7917, 270.1638, 272.06143, 272.2818, 275.8177, 275.0721, 270.05328, 268.71704, 266.77106, 263.67163, 264.67352, 265.04727, 269.49652, 269.3902, 266.01746, 264.7699, 262.10925, 258.40765, 259.8572, 264.89197, 267.45807, 269.16187, 266.52985, 268.9898, 267.648, 265.61414, 265.87796, 267.14444, 268.0799, 267.7338, 263.80682, 264.78885, 261.0429, 256.83954, 257.59808, 257.4457, 258.1487, 250.58025, 250.26846, 252.64047, 253.54524, 253.44617, 252.35893, 247.77356, 247.77739, 251.13217, 250.5302, 255.94434, 256.38907, 254.66939, 254.44888, 251.11235, 247.5216, 247.16602, 252.81174, 254.92041, 249.46724, 243.55757, 242.75476, 237.1565, 235.61829, 226.78934, 233.36252, 239.36612, 240.17563, 232.0682, 230.65898, 223.47763, 220.96233, 226.65498, 230.10245, 232.32849, 230.64526, 232.43745, 228.08435, 221.94405, 216.0332, 218.75134, 221.70491, 220.34445, 216.00188, 218.15518, 212.2543, 214.5704, 217.60672, 217.36809, 219.17513, 206.65019, 207.58281, 213.10092, 215.1283, 211.45566, 209.8176, 203.66362, 201.99156, 197.7405, 202.53758, 207.58304, 214.78964, 208.06274, 197.82027, 187.9039, 184.13626, 188.41776, 191.54893, 184.91408, 179.74545, 180.67361, 182.50247, 175.06107, 169.12024, 172.31123, 169.13211, 161.719, 160.58282, 163.13376, 168.6181, 166.62485, 164.98686, 161.12735, 159.90038, 160.58273, 163.12546, 160.36426, 158.47395, 156.54025, 156.10408, 152.5765, 150.96582, 151.27786, 154.69696, 152.80173, 150.36841, 148.40848, 147.55351, 144.45813, 141.62021, 145.37526, 146.10939, 143.76115, 143.05348, 144.45964, 142.82172, 140.7695, 137.71933, 139.36415, 140.37056, 139.1219, 137.8555, 136.03976, 135.1801, 136.64798, 136.81514, 133.22604, 131.54697, 133.0472, 133.43733, 134.54768, 131.07271, 132.04716, 131.725, 132.71071, 129.41519, 129.87436, 131.85294, 132.3902, 130.1565, 126.74434, 127.134384, 128.89755, 128.92612, 127.67478, 125.52577, 125.291504, 123.774124, 117.77948, 119.34169, 117.84503, 122.25, 119.98496, 119.017044, 116.07314, 117.103065, 112.97141, 112.636856, 109.634705, 112.90122, 114.5151, 113.1733, 111.953354, 113.10333, 114.24843, 112.60173, 112.49158, 111.11163, 112.7089, 112.16124, 110.63666, 107.96855, 106.945724, 105.93483, 107.34281, 106.78833, 105.33592, 100.15238, 100.2741, 99.22767, 100.28179, 97.13292, 97.71443, 97.28721, 98.23787, 94.59469, 96.60347, 96.17084, 94.78067, 89.79748, 84.555855, 85.70781, 86.21472, 89.18805, 83.432465, 81.637634, 79.31327, 78.641464, 74.52164, 74.07057, 75.617386, 78.34579, 76.822914, 74.24259, 73.6123, 73.937744, 73.45061, 71.64734, 73.46444, 74.63379, 76.433464, 75.608925, 73.41379, 72.85418, 73.895, 73.950134, 73.42967, 73.5643, 73.90843, 74.10304, 72.74945, 75.31871, 73.224495, 73.21852, 74.96828, 73.53278, 75.26824, 72.69883, 71.13614, 69.060715, 68.755455, 65.76591, 63.080578, 65.2794, 66.82943, 68.65901, 65.324036, 65.76944, 65.19064, 65.49709, 62.532, 60.555847, 63.526623, 64.705666, 65.00896, 60.208366, 58.039917, 56.672325, 66.49434, 73.38521, 71.766106, 79.48253, 77.37901, 77.0892, 80.381935, 68.261, 54.407707, 49.621906, 47.18746, 46.105682, 44.745647, 49.71516, 51.08168, 50.82464, 49.59134, 41.067627, 41.085026, 39.178093, 31.74112, 29.012356, 32.03701, 33.80715, 33.95674, 30.541077, 25.240862, 20.215975, 18.524124, 18.284771, 18.85009, 20.536297, 20.707644]
+# true_out_array
+# [820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+# error_pred_array
+# [179.7565185546875, 178.79989624023438, 177.6050537109375, 177.008203125, 175.4289978027344, 168.24390258789063, 162.25174560546876, 161.15410766601562, 163.50623168945313, 165.84512939453126, 170.06134643554688, 171.7759582519531, 172.05897827148436, 172.41018676757812, 172.23230590820313, 171.48643798828124, 171.5051696777344, 172.818701171875, 174.41045532226562, 174.56839599609376, 174.45684814453125, 170.90479736328126, 168.86195068359376, 172.88038940429686, 180.23604736328124, 182.446142578125, 182.12069702148438, 177.3651916503906, 171.44429931640624, 170.8995788574219, 175.910107421875, 180.3989685058594, 179.3541015625, 178.80352172851562, 172.3091064453125, 169.62869262695312, 168.15960693359375, 170.5484375, 174.09120483398436, 175.32583618164062, 175.476171875, 168.7642333984375, 164.18056640625, 162.38619384765624, 167.02123413085937, 177.32906494140624, 178.11329345703126, 179.7748229980469, 179.30670776367188, 170.5741943359375, 166.9589111328125, 169.7216369628906, 166.0891906738281, 155.1750915527344, 149.71173095703125, 150.37482299804688, 144.0145690917969, 140.3317138671875, 147.02365112304688, 150.8654541015625, 151.7326232910156, 151.75352783203124, 159.52977294921874, 154.78228759765625, 145.2505676269531, 153.72041015625, 160.1048828125, 161.26539306640626, 165.32398681640626, 175.60254516601563, 166.88983154296875, 162.37476806640626, 158.15640869140626, 159.88076171875, 167.18407592773437, 171.09931640625, 171.11254272460937, 172.9453369140625, 172.52877197265624, 171.33228759765626, 175.92427978515624, 182.7908447265625, 186.44043579101563, 190.3085693359375, 193.01817626953124, 192.909228515625, 192.808935546875, 196.55776977539062, 202.4654968261719, 202.954443359375, 203.91345825195313, 205.1114990234375, 203.72363891601563, 199.26586303710937, 200.18734741210938, 200.48584594726563, 194.55921630859376, 188.70052490234374, 183.81255493164062, 187.2754150390625, 186.6035949707031, 190.1190979003906, 190.40831909179687, 196.6985595703125, 202.06630249023436, 204.47492065429688, 209.83651428222657, 218.24452514648436, 216.37741088867188, 215.08435668945313, 221.67654418945312, 228.81846313476564, 225.42987365722655, 223.43944702148437, 214.81390075683595, 206.06745300292968, 209.17894897460937, 209.08324279785157, 200.9418151855469, 201.56575927734374, 204.03441772460937, 207.148486328125, 207.52568664550782, 205.57842407226562, 206.91216430664062, 202.0834930419922, 203.49945678710938, 206.77870483398436, 217.2460906982422, 221.0577880859375, 224.63600158691406, 230.13040466308593, 229.98882751464845, 229.353125, 229.47936096191407, 236.73439025878906, 235.83736572265624, 231.4301971435547, 231.51639099121093, 227.06044921875, 216.80284729003907, 209.74478759765626, 204.236962890625, 200.72432861328124, 196.03775329589843, 186.21648864746095, 184.21380004882812, 182.51966247558593, 181.1163757324219, 180.33003845214844, 183.28564453125, 186.28760375976563, 191.70939025878906, 195.15338745117188, 196.1662628173828, 194.755419921875, 197.0860809326172, 210.77368774414063, 215.13067932128905, 208.06777648925782, 208.67402038574218, 199.48131713867187, 190.03124389648437, 186.4876220703125, 186.69161376953124, 191.2678649902344, 184.67794799804688, 175.84373168945314, 177.2785675048828, 186.4111358642578, 184.18742980957032, 177.91262512207032, 180.7034912109375, 178.7471710205078, 175.93558044433593, 177.69955749511718, 183.43543701171876, 185.91075744628907, 187.7384826660156, 194.80844116210938, 200.54564819335937, 197.91241455078125, 193.68587341308594, 198.02142639160155, 205.8739501953125, 198.7673797607422, 194.74513244628906, 195.8885528564453, 192.78096923828124, 183.442724609375, 178.01744079589844, 171.57097473144532, 165.43389282226562, 162.51463623046874, 155.66351318359375, 157.25739440917968, 152.93008728027343, 148.638330078125, 142.9440490722656, 143.9458984375, 147.62482604980468, 147.71921081542968, 148.15150756835936, 151.04509887695312, 149.54561462402344, 153.99246520996093, 164.50848388671875, 175.6171081542969, 173.90514526367187, 176.4310729980469, 170.2128448486328, 153.85843811035156, 155.2169982910156, 159.40526733398437, 164.51043701171875, 162.82093505859376, 155.44149169921874, 155.6750274658203, 153.80923461914062, 157.27037353515624, 152.96240844726563, 155.82096252441406, 156.08025817871095, 154.05110778808594, 151.2430908203125, 154.05311584472656, 159.84225158691407, 156.6392364501953, 157.0367004394531, 161.74502563476562, 160.98980102539062, 148.9339569091797, 149.40882568359376, 152.21820678710938, 152.5699676513672, 149.366064453125, 143.59757385253906, 138.7801544189453, 136.56786193847657, 135.14898681640625, 132.03964233398438, 133.7353302001953, 136.9209442138672, 137.568310546875, 136.28704833984375, 136.6195495605469, 135.17141723632812, 133.59551391601562, 131.78803405761718, 136.5363006591797, 138.7307891845703, 141.78267517089844, 142.51752014160155, 140.19783935546874, 138.4231201171875, 133.60730895996093, 134.54212036132813, 135.48690795898438, 137.3046447753906, 140.9838653564453, 140.92599792480468, 137.45010681152343, 133.20181579589843, 133.17230224609375, 134.99386596679688, 134.43988037109375, 138.07477111816405, 139.1183654785156, 134.92301330566406, 130.55822143554687, 120.61651306152343, 125.59208068847656, 126.3481689453125, 128.58593139648437, 128.94729309082032, 127.81248779296875, 122.52344665527343, 120.16149291992187, 122.83105163574218, 121.92239379882812, 128.5944854736328, 136.66152954101562, 137.9257385253906, 128.35240783691407, 129.64300842285155, 136.493115234375, 134.06129760742186, 130.38226623535155, 128.14361877441405, 132.49593505859374, 134.8359802246094, 134.32315368652343, 129.7771789550781, 133.43130798339843, 127.51793518066407, 119.56022644042969, 110.48741455078125, 106.70439147949219, 107.67403259277344, 111.2309326171875, 117.51367797851563, 114.67778625488282, 110.32808227539063, 110.85125122070312, 118.47029113769531, 125.72589721679688, 129.32477722167968, 134.5232666015625, 138.78748168945313, 135.73402099609376, 132.2039367675781, 135.3686767578125, 136.96416931152345, 132.2856201171875, 129.3519287109375, 128.85712890625, 123.44877624511719, 118.08884887695312, 113.6595458984375, 115.55279541015625, 119.23590087890625, 120.94646606445312, 122.24330444335938, 124.397705078125, 123.07764587402343, 121.78179321289062, 117.96878967285156, 121.67881469726562, 127.35490417480469, 127.12509155273438, 126.00777587890624, 121.90390014648438, 121.19705505371094, 122.0992431640625, 131.2197021484375, 138.04676208496093, 139.99996337890624, 139.40809020996093, 141.52544250488282, 140.38384094238282, 140.10520935058594, 140.7014373779297, 138.91506958007812, 133.72095642089843, 129.38636169433593, 125.2712646484375, 126.77495422363282, 129.41913452148438, 133.13768005371094, 135.23617553710938, 132.19354553222655, 128.60519409179688, 125.63163146972656, 126.7411376953125, 122.4163330078125, 119.42933959960938, 118.5643798828125, 117.73031005859374, 113.07314758300781, 108.89171752929687, 107.0739013671875, 108.46561279296876, 115.62030639648438, 117.52095031738281, 120.13028869628906, 122.08038330078125, 126.49396667480468, 123.49295959472656, 122.57789611816406, 118.98948364257812, 119.52918090820313, 120.13092041015625, 126.47816772460938, 119.03826293945312, 111.88948974609374, 109.35274963378906, 111.82169799804687, 107.62633972167968, 105.20714721679687, 108.70227355957032, 110.76703186035157, 107.93831787109374, 104.1233154296875, 101.27919311523438, 103.490087890625, 116.78083190917968, 120.98506774902344, 119.46051940917968, 113.39412536621094, 107.68720397949218, 107.66846923828125, 112.59235229492188, 113.13468322753906, 112.96307373046875, 112.4247039794922, 111.80899047851562, 108.92200012207032, 109.1166259765625, 111.35332336425782, 112.96802673339843, 110.70341186523437, 108.6189453125, 105.91245727539062, 107.75846862792969, 107.75487670898437, 105.59036865234376, 105.01410827636718, 107.29686279296875, 105.18055419921875, 105.09582824707032, 102.316650390625, 104.85552062988282, 102.76712646484376, 102.43878784179688, 100.94852600097656, 105.38121032714844, 103.33713073730469, 98.4600830078125, 93.14234008789063, 94.68620910644532, 92.2329315185547, 91.25899047851563, 92.632861328125, 94.46076049804688, 85.0501220703125, 80.76817016601562, 80.39096374511719, 78.67698669433594, 78.43017883300782, 79.96627807617188, 84.11742095947265, 84.41588134765625, 81.99895324707032, 82.23396606445313, 87.90995483398437, 86.957568359375, 88.07540588378906, 94.7089630126953, 93.80777130126953, 92.87327270507812, 89.83680725097656, 93.73943634033203, 93.94716796875, 91.72270202636719, 88.7950653076172, 90.03356018066407, 90.80411682128906, 86.38059692382812, 80.75492858886719, 80.10723571777343, 87.0032745361328, 86.61790924072265, 83.95692138671875, 77.39305725097657, 71.60750427246094, 72.748388671875, 72.69745483398438, 73.19031677246093, 73.93069152832031, 73.31939392089843, 71.1854461669922, 70.25902099609375, 69.59869384765625, 70.20079956054687, 73.2107940673828, 73.5567138671875, 73.01997680664063, 70.14876708984374, 67.31105041503906, 64.23593444824219, 62.96726989746094, 61.98131256103515, 62.085662841796875, 65.25503082275391, 68.47254486083985, 64.2993881225586, 62.194140625, 60.02036590576172, 59.48621673583985, 58.48274536132813, 60.94374542236328, 63.39827117919922, 64.13004608154297, 64.4966812133789, 65.10137939453125, 65.69117736816406, 65.4892578125, 67.91686248779297, 71.05202789306641, 70.73869171142579, 71.95375061035156, 72.41808471679687, 70.75357055664062, 67.78683776855469, 64.69425964355469, 61.178506469726564, 58.04363250732422, 54.7202880859375, 54.10384826660156, 54.927879333496094, 53.988652038574216, 53.23516998291016, 50.18997039794922, 48.22997131347656, 47.5810043334961, 47.40792999267578, 47.285160827636716, 46.4001220703125, 48.70554809570312, 51.63339691162109, 49.8622329711914, 48.073542785644534, 46.928375244140625, 49.3717529296875, 48.87038726806641, 51.97164764404297, 53.838479614257814, 55.498051452636716, 52.601678466796876, 50.47318878173828, 48.139888000488284, 47.948019409179686, 47.26488037109375, 51.94290008544922, 53.487098693847656, 55.86692657470703, 53.28361053466797, 50.93399200439453, 49.534219360351564, 48.77308807373047, 47.57017974853515, 52.630366516113284, 57.59593048095703, 58.215327453613284, 58.01747283935547, 55.8380615234375, 57.42401275634766, 57.59318542480469, 57.371681213378906, 59.7119873046875, 60.88514709472656, 61.72842254638672, 62.40176849365234, 62.4031494140625, 60.939143371582034, 57.106964111328125, 54.18020782470703, 52.52557525634766, 51.90726776123047, 52.868263244628906, 49.33946838378906, 48.48013153076172, 49.33152618408203, 50.68386535644531, 52.16461181640625, 52.37880096435547, 48.46585693359375, 48.92523193359375, 52.74629669189453, 53.56917266845703, 59.452142333984376, 59.281533813476564, 56.54429626464844, 58.055274963378906, 57.98653564453125, 57.875047302246095, 58.50056457519531, 63.57459411621094, 64.95002593994141, 60.59887847900391, 54.64120330810547, 53.199510192871095, 49.23833312988281, 50.48830871582031, 48.01563262939453, 52.80784149169922, 57.09694061279297, 57.10055389404297, 50.128912353515624, 50.286198425292966, 48.51468505859375, 47.236933898925784, 52.9168212890625, 57.787252807617186, 59.34640808105469, 58.30819549560547, 57.75082244873047, 54.10471954345703, 53.562522888183594, 52.7595932006836, 56.898939514160155, 57.916998291015624, 57.485939025878906, 56.198309326171874, 54.905076599121095, 47.82501983642578, 49.64864044189453, 52.12993621826172, 50.560838317871095, 52.601766967773436, 43.481825256347655, 43.924169921875, 48.49085388183594, 51.315617370605466, 49.392050170898436, 49.398617553710935, 44.88942260742188, 41.999105834960936, 40.573158264160156, 47.47792053222656, 55.183509826660156, 60.735678100585936, 53.21940612792969, 45.724046325683595, 40.88388671875, 40.93474426269531, 46.717282104492185, 47.380958557128906, 45.88717803955078, 39.91154327392578, 40.06141662597656, 41.208306884765626, 39.99677734375, 38.09983367919922, 40.55144348144531, 34.38941497802735, 38.89256134033203, 35.228318786621095, 34.413038635253905, 36.64636383056641, 36.68757781982422, 35.981764221191405, 33.027078247070314, 34.83157043457031, 40.65508270263672, 40.333134460449216, 37.142852783203125, 34.25389404296875, 34.85689849853516, 34.160316467285156, 30.346939086914062, 32.45440826416016, 38.6160171508789, 37.91220245361328, 33.64551620483398, 32.92597961425781, 34.23843841552734, 33.29162063598633, 28.212077331542968, 29.41297607421875, 32.192335510253905, 31.588609313964845, 31.202882385253908, 32.07442398071289, 30.756808471679687, 30.255961608886718, 28.124858856201172, 27.459444427490233, 30.165607452392578, 29.22720947265625, 27.715966796875, 29.802952575683594, 30.283824920654297, 30.300350952148438, 29.800392150878906, 27.15015411376953, 25.7131591796875, 26.588068389892577, 25.450454711914062, 25.267532348632812, 27.122042083740233, 27.96265106201172, 25.459687805175783, 22.429878997802735, 23.073983001708985, 22.9243408203125, 23.9929931640625, 25.20982666015625, 25.586458587646483, 26.652890014648438, 25.619548034667968, 24.71044464111328, 26.41111831665039, 26.795886993408203, 28.591026306152344, 26.659796905517577, 26.747905731201172, 27.18464584350586, 27.374628448486327, 26.866575622558592, 22.742890930175783, 25.55388641357422, 24.98643264770508, 25.63222885131836, 23.59993896484375, 24.93462371826172, 24.53389663696289, 25.116465759277343, 21.93481903076172, 24.157333374023438, 24.482035064697264, 23.42351531982422, 22.804337310791016, 24.220940399169923, 24.913623046875, 23.706122589111327, 24.765341186523436, 24.217237091064455, 25.332565307617188, 25.03148956298828, 24.361714935302736, 23.366253662109376, 23.504620361328126, 23.172919464111327, 24.839344024658203, 24.523575592041016, 24.319757080078126, 21.58119888305664, 22.012639617919923, 21.869361877441406, 23.0734001159668, 22.175414276123046, 23.073333740234375, 22.03469314575195, 23.407337188720703, 20.993476104736327, 23.74037094116211, 24.037442779541017, 24.41908493041992, 20.86664276123047, 18.33052978515625, 20.336353302001953, 20.086328125, 22.30056686401367, 18.622786712646484, 17.767452239990234, 17.376512908935545, 18.63839645385742, 16.525440216064453, 16.882891845703124, 17.228534698486328, 18.302884674072267, 17.495103454589845, 15.387251281738282, 15.831942749023437, 17.702149200439454, 17.083902740478514, 16.220211029052734, 17.955626678466796, 18.995665740966796, 20.77346420288086, 20.446353149414062, 19.086974334716796, 19.559869384765626, 21.103762817382812, 21.950128936767577, 22.229671478271484, 23.16430206298828, 24.30842742919922, 25.303042602539062, 24.74945068359375, 28.118701934814453, 26.824491119384767, 27.618517303466795, 30.168273162841796, 29.532778167724608, 32.06824493408203, 30.298825836181642, 29.536144256591797, 28.260711669921875, 28.75545654296875, 26.565907669067382, 24.68057746887207, 27.679396438598634, 30.029430770874022, 32.6590087890625, 30.124036407470705, 31.369441986083984, 31.590645599365235, 32.697103881835936, 30.53200035095215, 29.35584716796875, 33.12661476135254, 35.10567016601563, 36.2089599609375, 32.20836868286133, 30.839923095703124, 30.27232437133789, 40.894338607788086, 48.58521194458008, 47.76611099243164, 56.28252601623535, 54.97902336120605, 55.489206314086914, 59.58192939758301, 48.26099052429199, 35.20770416259766, 31.22190418243408, 29.587460899353026, 29.3056791305542, 28.756065368652344, 34.51516246795654, 36.68167839050293, 37.224635696411134, 36.79134254455566, 29.06762752532959, 29.885022354125976, 28.778093338012695, 22.14111852645874, 20.212356090545654, 24.037010669708252, 26.607150650024415, 27.556742095947264, 24.94107437133789, 20.440859413146974, 16.21597385406494, 15.324123764038086, 15.884771537780761, 17.25009078979492, 19.73629641532898, 20.707643270492554]
+
+
+X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_38,sequence_length) 
+
+# rul_pred_array
+# [640.2638, 640.2338, 639.9586, 639.7268, 640.84216, 647.54083, 652.7472, 656.3685, 656.0379, 653.2189, 650.63525, 647.35394, 646.74023, 646.2994, 645.967, 646.7759, 646.9007, 644.8055, 641.3334, 641.2361, 640.8529, 640.24817, 638.7047, 628.2843, 615.6909, 616.3092, 623.40295, 630.21814, 637.97595, 639.6832, 634.8037, 633.9588, 633.9271, 640.6551, 646.8053, 652.35376, 653.7205, 651.6853, 648.9877, 647.9951, 651.1446, 655.8116, 663.2464, 660.78815, 638.84784, 630.7402, 629.5962, 628.8661, 628.7279, 643.1181, 642.4058, 636.3036, 646.7684, 649.50793, 644.0365, 634.14075, 657.13275, 658.9554, 638.60614, 630.6212, 624.0784, 619.1483, 617.79, 634.18585, 638.1526, 623.6941, 625.8482, 632.69324, 630.76843, 624.3636, 638.5076, 639.1909, 647.0736, 643.13684, 627.7255, 623.14026, 620.3446, 618.44666, 618.1465, 615.35486, 605.2337, 594.71844, 587.5086, 582.7233, 579.35706, 578.5424, 578.6277, 569.4335, 560.06976, 560.4929, 563.69403, 566.1092, 572.91296, 571.27527, 575.6464, 581.0604, 586.76984, 585.7844, 580.9946, 582.1033, 582.10535, 573.2013, 566.63605, 558.4759, 552.0082, 544.89526, 543.4496, 545.8635, 534.4691, 518.75305, 522.22876, 531.6213, 541.661, 553.08856, 553.6462, 553.6373, 558.2322, 557.37305, 541.78986, 533.1878, 532.3795, 529.32166, 526.2583, 529.6368, 525.2346, 511.15952, 501.70605, 498.596, 493.95575, 492.2085, 496.13828, 493.81097, 486.27228, 486.0689, 491.16992, 498.06805, 505.36823, 513.4586, 523.25635, 530.13196, 538.5374, 546.8972, 548.99817, 558.4176, 562.1979, 567.71783, 572.96375, 577.60693, 570.38165, 577.80444, 573.47345, 574.4366, 569.8109, 552.8644, 529.6376, 528.7592, 527.00037, 523.987, 532.5606, 538.78845, 522.3634, 508.80414, 505.24692, 503.12054, 509.13068, 516.51025, 513.6226, 498.3265, 504.3452, 510.8496, 509.21362, 507.7521, 511.0731, 513.97845, 504.02173, 499.69238, 496.06573, 489.41632, 480.48657, 481.90277, 483.61407, 474.0331, 463.40497, 464.2339, 462.61273, 462.0252, 468.70303, 475.86377, 480.5666, 479.6668, 483.5506, 487.28656, 486.41373, 493.2198, 496.83658, 503.49335, 512.8161, 509.4002, 502.90448, 506.77655, 506.58545, 511.90274, 511.2707, 504.2413, 491.4357, 489.33798, 490.70734, 489.34286, 492.33926, 505.55054, 499.31708, 497.0835, 487.24332, 484.39722, 491.78452, 495.2733, 496.42163, 496.12915, 500.3675, 494.3894, 494.65048, 498.0219, 501.43393, 496.3316, 483.21353, 484.75128, 479.4645, 471.1014, 467.45792, 481.74503, 477.97974, 467.94098, 463.17865, 461.9284, 464.46085, 470.62256, 476.23444, 479.73575, 482.6792, 481.48544, 483.05624, 484.17108, 484.5329, 487.01068, 489.96542, 491.13446, 490.04865, 491.02997, 485.22476, 477.84784, 481.22485, 481.73047, 481.9773, 481.98447, 470.4698, 467.61246, 463.4672, 458.22818, 462.604, 467.8462, 471.77164, 460.61386, 458.95404, 459.91904, 455.74106, 459.5796, 464.71515, 468.38388, 469.63858, 460.14252, 449.16293, 444.27826, 443.76236, 447.3166, 452.84766, 452.7376, 445.21655, 442.5839, 431.78192, 415.34024, 412.72617, 419.64755, 421.38354, 418.88104, 423.14526, 424.8672, 422.4918, 417.27036, 414.80304, 413.22934, 415.54843, 422.11392, 421.70923, 426.85123, 423.6957, 422.74707, 417.81982, 412.1022, 408.76538, 411.26694, 415.15732, 414.73398, 409.2032, 405.057, 402.32043, 402.14383, 407.43707, 410.52734, 412.95132, 409.79468, 413.17535, 412.41534, 410.86713, 411.69766, 411.6022, 412.4091, 403.97662, 403.38318, 401.85547, 402.51788, 403.78046, 409.05154, 411.28662, 410.58038, 399.17526, 380.80463, 376.16284, 379.73822, 385.8676, 391.46393, 389.52484, 387.54117, 387.92856, 384.87598, 380.58917, 374.9804, 369.43365, 380.19073, 386.92178, 390.07794, 394.24655, 395.74628, 396.19858, 395.90125, 390.4209, 383.24313, 377.8222, 375.43515, 376.8251, 378.949, 378.7485, 372.21613, 373.96478, 371.1656, 366.01675, 362.96307, 360.91858, 360.1914, 351.57617, 351.23563, 351.17902, 353.82114, 351.8738, 357.89536, 359.3686, 362.0889, 355.52118, 358.3515, 358.4753, 356.77142, 365.7575, 374.5812, 374.08307, 365.95465, 362.02023, 356.5419, 354.1161, 348.439, 351.4124, 358.84036, 362.55664, 361.03192, 354.6839, 343.9208, 344.7818, 343.72946, 346.9714, 347.37952, 331.97348, 326.1955, 325.60944, 324.91083, 320.50085, 321.21783, 330.21835, 334.08908, 333.76212, 333.30173, 336.7456, 341.75433, 346.26276, 346.91678, 344.00238, 342.12582, 340.54324, 333.37512, 326.79498, 320.1485, 314.75955, 308.2007, 302.9378, 301.63278, 300.05005, 297.07227, 298.0666, 301.19278, 303.4389, 301.95526, 303.7937, 304.2053, 308.95242, 312.3003, 322.60187, 327.9004, 323.64246, 314.7433, 316.98785, 310.21466, 309.18655, 311.67163, 311.73376, 312.3441, 305.07538, 301.4581, 295.06714, 285.73108, 284.49734, 289.14273, 293.80087, 296.52664, 294.35727, 299.17294, 303.7364, 302.29968, 298.50922, 300.64117, 305.99185, 311.399, 307.54236, 306.1403, 305.52365, 306.63443, 307.55414, 307.9188, 306.06265, 304.3891, 306.2422, 305.61264, 309.77768, 309.24323, 305.542, 306.10242, 306.42316, 301.3056, 300.90707, 299.48492, 298.9393, 297.1369, 289.0808, 287.398, 287.47614, 284.70462, 285.68076, 286.676, 287.3287, 288.267, 288.97162, 288.3819, 291.08728, 297.2618, 302.59906, 307.12482, 309.80234, 310.92337, 313.53418, 313.95224, 315.4682, 318.26096, 318.66464, 318.29492, 314.25577, 311.49307, 308.80206, 304.38208, 298.45718, 296.48993, 297.74344, 299.24347, 292.62473, 289.33026, 289.2372, 290.9333, 287.94498, 287.6911, 284.98032, 284.73434, 282.4622, 282.62454, 283.8222, 284.27377, 281.1516, 280.375, 280.1167, 279.19553, 280.50528, 281.0497, 281.4603, 277.53494, 277.63248, 276.65063, 277.03842, 275.65884, 277.23236, 279.40073, 278.38727, 274.98886, 274.88223, 272.4888, 274.01944, 279.17767, 281.7685, 284.55255, 283.29382, 285.07004, 286.07825, 284.91818, 285.29626, 287.52393, 289.19022, 289.76205, 286.0762, 284.91254, 283.1488, 280.43204, 276.49094, 275.6474, 277.26642, 278.54736, 271.879, 269.17273, 269.33856, 269.027, 266.9915, 267.7809, 265.08588, 262.6461, 262.53412, 265.46048, 266.663, 265.73782, 262.37576, 261.65118, 259.85248, 259.09006, 260.47076, 265.52972, 266.02524, 256.44424, 250.4626, 248.81084, 247.52242, 241.57173, 241.35312, 246.63506, 247.21346, 244.69987, 244.80486, 239.15488, 237.39653, 243.46646, 247.79227, 250.7613, 253.03067, 254.20036, 252.79118, 247.41643, 246.90974, 246.40056, 249.61848, 250.22993, 247.17586, 247.70438, 247.27214, 241.34372, 238.64388, 239.24373, 242.01343, 244.54631, 232.10718, 233.48633, 238.26775, 241.89021, 238.77524, 235.61197, 228.75716, 229.25945, 226.73706, 232.06848, 235.30806, 239.40845, 236.68562, 224.73486, 211.88496, 209.10373, 212.09798, 213.30124, 212.01022, 209.06088, 209.78511, 210.44771, 203.33046, 194.93349, 195.00192, 192.52267, 189.37708, 189.96054, 193.43857, 196.59232, 195.27422, 192.98373, 186.39822, 180.45514, 185.68733, 187.67262, 184.29446, 184.02881, 180.72716, 178.37918, 173.18723, 171.26605, 175.92984, 178.53595, 174.76259, 174.2713, 171.51642, 168.84618, 164.16306, 166.55136, 171.89232, 169.731, 169.20828, 171.67773, 172.86893, 169.19283, 164.68008, 165.1112, 168.79129, 168.32639, 168.06638, 166.47404, 166.45807, 168.44812, 170.4491, 169.53763, 163.21129, 163.42114, 167.01724, 168.8037, 168.98457, 167.63408, 165.19437, 163.40018, 159.97339, 160.21921, 163.72925, 165.0165, 164.39273, 158.71034, 156.7968, 154.23215, 156.21693, 156.93181, 157.12157, 153.25601, 151.9354, 148.80302, 147.72913, 145.14273, 148.04793, 151.60886, 147.11131, 148.862, 149.52013, 149.85309, 148.54092, 147.5966, 145.0571, 147.72308, 148.46829, 148.1262, 147.74438, 148.41335, 147.5111, 145.47958, 143.00839, 140.07011, 142.98965, 143.14236, 140.3899, 135.46097, 136.4108, 136.88956, 139.889, 140.13638, 138.98143, 133.47171, 134.82881, 134.91263, 136.93362, 135.30351, 136.85081, 136.53214, 136.7127, 134.46521, 137.40521, 139.52519, 139.05637, 132.66377, 131.38629, 129.85545, 129.79276, 129.59128, 131.22156, 128.41982, 128.75102, 126.912476, 125.193016, 125.59227, 124.71133, 127.18767, 127.0663, 125.46922, 123.89679, 123.56704, 120.39409, 121.7784, 121.842995, 125.83663, 126.58734, 123.67979, 123.33916, 124.38237, 123.02716, 121.642685, 120.045395, 119.38037, 120.91838, 120.72276, 121.45174, 124.646835, 123.54921, 122.009445, 123.33126, 123.695595, 123.83147, 121.88664, 121.97478, 122.3905, 122.54915, 120.21039, 121.301956, 121.540634, 124.082016, 121.616196, 120.58209, 121.040016, 122.86418, 121.6075, 117.64175, 115.074196, 117.29848, 119.86841, 117.482506, 117.134926, 121.29883, 118.269936, 113.25096, 115.31633, 113.92439, 115.376915, 118.1176, 99.96849, 87.0545, 86.29067, 83.66188, 80.1526, 73.401215, 77.13469, 77.50792, 82.38699, 79.67123, 77.248795, 74.50481, 69.46887, 58.84305, 54.8236, 58.191063, 58.09458, 59.661205, 51.18214, 45.628365, 38.512123, 37.501026, 36.654037, 35.646675, 36.88641, 35.933464, 34.11808, 29.195211, 26.121723, 26.218542, 27.781153, 28.60563, 29.77327, 32.797783, 34.074177, 33.85972, 31.739878, 31.3724, 31.009726, 30.468176, 29.717815, 29.43154, 28.975616, 32.378258, 33.803936, 36.207096, 34.22335, 33.71507, 32.762592, 32.39533, 31.58137, 30.899832, 30.385223, 31.015766, 29.988968, 30.316645, 28.30693, 28.13478, 27.212555, 25.407167, 25.386671, 28.091946, 27.89758, 25.64541, 22.687765, 20.89239, 19.815756, 18.983147, 18.37227, 18.187893, 17.8709, 17.846947, 17.80736, 17.591717, 17.167192, 16.387264, 15.4728365, 14.916109, 14.191609, 14.188156, 14.088129, 13.551312, 13.45193, 12.974179]
+# true_out_array
+# [874.0, 873.0, 872.0, 871.0, 870.0, 869.0, 868.0, 867.0, 866.0, 865.0, 864.0, 863.0, 862.0, 861.0, 860.0, 859.0, 858.0, 857.0, 856.0, 855.0, 854.0, 853.0, 852.0, 851.0, 850.0, 849.0, 848.0, 847.0, 846.0, 845.0, 844.0, 843.0, 842.0, 841.0, 840.0, 839.0, 838.0, 837.0, 836.0, 835.0, 834.0, 833.0, 832.0, 831.0, 830.0, 829.0, 828.0, 827.0, 826.0, 825.0, 824.0, 823.0, 822.0, 821.0, 820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+# error_pred_array
+# [233.73621826171876, 232.76614990234376, 232.04132690429688, 231.273193359375, 229.15783081054687, 221.4592041015625, 215.25283813476562, 210.63145751953124, 209.96209106445312, 211.78107299804688, 213.3647216796875, 215.6461181640625, 215.2598083496094, 214.700634765625, 214.26798095703126, 213.36292114257813, 213.4515625, 215.05569458007812, 217.331298828125, 217.8596435546875, 216.78660888671874, 212.75181274414064, 213.2952392578125, 222.71567993164064, 234.3091247558594, 232.69078979492187, 224.5970886230469, 217.66658935546874, 212.45778198242186, 213.05860595703126, 220.47390747070312, 221.4851867675781, 222.330078125, 218.81192626953126, 211.65349731445312, 211.9800598144531, 212.4668762207031, 216.3446838378906, 218.7637145996094, 221.88330688476563, 219.53104248046876, 210.12131958007814, 205.59589233398438, 207.935009765625, 222.83987426757812, 226.16697998046874, 229.0124267578125, 228.08814086914063, 223.25645751953124, 216.60152587890624, 217.27721557617187, 221.80505981445313, 207.17865600585938, 197.1079833984375, 194.3415100097656, 195.0280334472656, 179.996826171875, 171.78228759765625, 184.22271728515625, 184.91537475585938, 189.92159423828124, 193.85167846679687, 194.59673461914062, 185.61871337890625, 186.15867919921874, 197.231494140625, 197.429345703125, 198.5843994140625, 203.1926025390625, 215.23788452148438, 209.32833862304688, 205.5349548339844, 199.4323303222656, 203.71276245117187, 212.7255126953125, 215.09335327148438, 215.57877197265626, 213.62763671875, 212.81029052734374, 214.99459838867188, 221.76080932617188, 227.79724731445313, 231.9120361328125, 235.12252807617188, 236.53118896484375, 234.948486328125, 234.80711059570314, 241.49258422851562, 246.1890441894531, 245.33095092773436, 244.63858642578126, 243.39429931640626, 238.80361938476562, 237.62827758789064, 233.25220947265626, 228.119921875, 223.1624267578125, 225.0426452636719, 223.72459716796874, 227.86874389648438, 228.27218017578124, 234.23276977539064, 237.84694213867186, 241.2215576171875, 246.16244506835938, 251.892041015625, 252.239013671875, 251.4005859375, 257.07057495117186, 266.0577331542969, 261.8092529296875, 255.71029663085938, 245.6339538574219, 236.8604736328125, 241.89623413085937, 234.8349853515625, 231.0574157714844, 229.8353759765625, 241.38034057617188, 246.75062866210936, 245.16345825195313, 248.460791015625, 246.16755065917968, 242.96047973632812, 248.20276794433593, 257.44920959472654, 262.72228088378904, 266.2411743164063, 271.84559631347656, 268.2837615966797, 265.2854705810547, 268.4452819824219, 275.1179260253906, 273.9887237548828, 267.0797119140625, 262.48084411621096, 258.7828338623047, 251.46455993652344, 241.17965393066407, 232.41156005859375, 226.20226135253907, 215.94066162109374, 217.9768798828125, 212.27077026367186, 205.8636962890625, 198.87208251953126, 190.6219970703125, 185.645263671875, 191.79407958984376, 191.3667419433594, 190.54924926757812, 185.63843383789063, 185.94244995117188, 200.1991180419922, 213.9168731689453, 215.88089294433593, 222.49326782226564, 223.22942504882812, 212.67758178710938, 203.14270935058593, 219.10111389160156, 227.6530303955078, 229.3610046386719, 236.17393798828124, 226.52357482910156, 217.2952453613281, 215.27081298828125, 223.78135986328124, 219.50808410644532, 210.87273254394532, 216.84405517578125, 215.55770874023438, 214.44686889648438, 215.44634094238282, 223.4820129394531, 227.50786437988282, 231.4711151123047, 239.50312194824218, 247.1863586425781, 244.88663024902343, 241.94760437011718, 250.290673828125, 255.40757751464844, 249.79248657226563, 246.96652526855468, 246.89203186035155, 241.28521118164062, 232.0681365966797, 225.88723754882812, 224.53907775878906, 223.1708190917969, 218.8315399169922, 220.51925659179688, 218.53673706054687, 212.88270568847656, 208.50631103515624, 196.84372253417968, 197.86355590820312, 208.2471466064453, 205.16092529296876, 203.83441162109375, 193.1256866455078, 193.9606903076172, 197.89990234375, 204.6542755126953, 207.60321350097655, 207.74706420898437, 208.9096466064453, 201.6711639404297, 186.92813720703126, 194.31505432128907, 191.9890625, 201.93884887695313, 205.2461151123047, 197.86788940429688, 196.78824157714843, 192.51210327148436, 190.39032897949218, 185.5568054199219, 193.18136291503907, 194.79654235839843, 191.76464233398437, 190.26378784179687, 195.44147033691405, 204.4558532714844, 201.2634704589844, 204.83587036132812, 213.10671997070312, 212.9345458984375, 199.34640197753907, 202.27456665039062, 206.24028625488282, 208.20528869628907, 206.5513153076172, 201.33132934570312, 196.26217346191407, 192.4618408203125, 189.14791870117188, 184.34476623535156, 186.2701385498047, 186.78043518066406, 184.6370361328125, 183.78864135742188, 184.10240173339844, 179.90760498046876, 178.21352844238282, 173.25917358398436, 174.4719665527344, 176.7918273925781, 181.72181701660156, 181.85034790039063, 174.9049285888672, 174.00490112304686, 167.60903625488282, 174.0874053955078, 176.48806457519532, 176.81559143066406, 184.5586151123047, 181.32671508789062, 175.43594665527343, 170.5407287597656, 177.01039123535156, 178.24416809082032, 173.92434387207032, 180.57322998046874, 180.79768981933594, 174.24561462402343, 168.8147766113281, 163.38417358398436, 169.5365234375, 178.01719360351564, 182.66878356933594, 182.48989868164062, 180.5145294189453, 180.764208984375, 178.65867919921874, 182.91073608398438, 180.33968505859374, 187.1057373046875, 200.9744903564453, 201.9693115234375, 193.01897277832032, 194.0814697265625, 200.97994689941407, 195.69361267089843, 191.03187255859376, 186.92322387695313, 189.60736389160155, 193.40784606933593, 191.84534912109376, 191.17209167480468, 189.28182678222657, 185.15109558105468, 176.9284240722656, 174.05978088378907, 174.06060791015625, 175.0039093017578, 178.68993225097657, 183.21029968261718, 182.66588439941407, 182.5064483642578, 185.6112274169922, 192.3707244873047, 197.41182861328124, 200.9964172363281, 202.85869750976562, 198.55019836425782, 194.34200134277344, 191.38255310058594, 193.10970153808594, 187.3966491699219, 183.93302001953126, 182.53139038085936, 176.80912170410156, 170.548828125, 164.7770751953125, 165.0722198486328, 165.1385040283203, 164.77529602050782, 161.9061309814453, 166.07931213378907, 163.79566345214843, 158.81371154785157, 155.41865234375, 161.50340270996094, 176.5304718017578, 182.17355651855468, 177.3363250732422, 170.7026123046875, 166.61871643066405, 169.40050048828124, 174.3066650390625, 179.65306091308594, 187.9072265625, 192.05129699707032, 194.43101806640624, 195.97434997558594, 187.0048858642578, 179.8050079345703, 174.69986267089843, 173.5043182373047, 171.0877685546875, 168.07650451660157, 167.8123352050781, 172.30667419433593, 176.932080078125, 182.08460998535156, 185.27884521484376, 182.38133239746094, 177.95763244628907, 175.92099304199218, 178.78312072753906, 174.3278839111328, 171.9819793701172, 172.83084411621093, 168.51559448242188, 163.4302490234375, 158.32210388183594, 164.42383422851563, 163.76436462402344, 162.82095642089843, 159.17884216308593, 160.86205444335937, 157.32877197265626, 152.5048400878906, 147.91437683105468, 152.4788391113281, 148.64852600097657, 147.52467041015626, 153.27680053710938, 152.26719970703124, 139.43399047851562, 134.12795715332032, 136.49643249511718, 137.9797607421875, 142.45810241699218, 143.8839141845703, 151.4748748779297, 153.24180603027344, 146.67862854003906, 143.96385192871094, 141.93671569824218, 144.51954956054686, 157.751806640625, 158.2719299316406, 156.02521057128905, 148.73178100585938, 144.617431640625, 156.35982971191407, 158.80450439453125, 158.39053955078126, 158.0891571044922, 161.49912719726564, 159.7821533203125, 155.81775817871093, 155.7190734863281, 156.77895812988282, 160.3724334716797, 160.91116027832032, 158.63127746582032, 155.0868927001953, 154.2619171142578, 155.50168151855468, 155.68963928222655, 153.16039428710937, 154.50732421875, 154.35923461914064, 152.77266540527344, 151.35987548828126, 156.7992950439453, 161.06217346191406, 161.36721496582032, 161.94993896484374, 163.9277130126953, 161.93343505859374, 157.8072265625, 154.56112976074218, 155.04473876953125, 152.20628967285157, 150.79471130371093, 145.0475616455078, 140.6997039794922, 132.11312866210938, 125.624462890625, 126.35753479003907, 134.25670776367187, 131.01214904785155, 136.78537139892578, 136.81344299316407, 133.3283905029297, 132.26623992919923, 130.65589599609376, 136.9246368408203, 139.54189453125, 144.9328399658203, 153.26893615722656, 153.50267639160157, 147.85724639892578, 142.19910430908203, 138.47337951660157, 139.64275054931642, 135.33780822753906, 132.4083984375, 133.09209899902345, 134.61085815429686, 132.30168762207032, 125.67577209472657, 120.37588806152344, 125.01250915527343, 128.0292495727539, 127.76764526367188, 122.94951782226562, 116.61193542480468, 113.28773803710938, 113.93735961914062, 114.61090393066407, 111.75779724121094, 111.38736877441406, 108.08814392089843, 108.19627380371094, 109.85728149414062, 107.31764526367188, 105.5768310546875, 109.69436798095703, 111.51382293701172, 112.5979736328125, 110.74620056152344, 109.8630584716797, 116.91919555664063, 117.60198059082032, 116.52388000488281, 118.29540252685547, 116.31922302246093, 114.3239974975586, 112.67129821777344, 110.73300170898438, 109.02839813232421, 108.61811065673828, 104.9126968383789, 97.7381820678711, 94.55225372314453, 93.02432250976562, 92.59966430664062, 92.38526916503906, 90.25654602050781, 88.98030090332031, 88.92020568847656, 87.37965393066406, 88.55746765136719, 89.07707214355469, 90.63760375976562, 88.98282470703126, 87.84288940429687, 87.88786010742187, 88.68302001953126, 87.36397399902344, 84.32302551269531, 80.9555450439453, 86.64249267578126, 89.95148315429688, 87.45168762207031, 82.36541137695312, 84.05504150390625, 83.30889892578125, 85.019677734375, 84.26567077636719, 86.41275634765626, 85.39352569580078, 82.98197021484376, 80.72621154785156, 82.84837799072265, 82.62501068115235, 81.88328399658204, 81.8044448852539, 80.72386169433594, 79.49248809814453, 76.53971862792969, 79.46505889892578, 78.3675323486328, 78.34935302734375, 76.96156311035156, 77.34117736816407, 75.96818542480469, 73.335205078125, 71.61273498535157, 74.01110382080078, 73.11777191162109, 74.51119995117188, 71.98056335449219, 65.82232971191407, 66.2762451171875, 65.81289367675781, 68.19395141601562, 65.83837585449218, 63.117140197753905, 61.89197998046875, 62.73060760498047, 64.42098999023438, 64.37947998046874, 65.60719299316406, 67.49692077636719, 65.78528289794922, 63.58561553955078, 62.13142700195313, 60.60595703125, 59.766787719726565, 58.883984375, 57.30829620361328, 59.08920440673828, 59.0684326171875, 57.679693603515624, 57.20458831787109, 57.53780975341797, 56.023947143554686, 55.80469665527344, 56.35388031005859, 57.18438568115234, 57.9048568725586, 54.87735137939453, 51.91768035888672, 52.530068969726564, 51.348812866210935, 52.17709503173828, 52.36663818359375, 55.50587463378906, 56.02353057861328, 53.571249389648436, 54.148179626464845, 56.76649780273438, 56.189141845703126, 56.47757263183594, 61.428280639648435, 63.019264221191406, 59.6216796875, 54.441157531738284, 55.342864990234375, 56.342218017578126, 61.31925048828125, 61.822427368164064, 59.09760284423828, 58.407554626464844, 58.50985107421875, 56.70051879882813, 56.321722412109374, 56.04762420654297, 59.20051879882813, 61.881831359863284, 64.89915313720704, 64.51047058105469, 64.26976470947265, 68.74510955810547, 66.58922729492187, 63.839630126953125, 61.76357879638672, 59.48664245605469, 52.88997192382813, 46.10159912109375, 46.23700103759766, 49.479364013671876, 47.371514892578126, 47.381744384765625, 46.69851226806641, 46.89303436279297, 47.938482666015624, 50.702326965332034, 47.73002166748047, 47.77621307373047, 51.28431243896485, 57.413873291015626, 54.15601654052735, 50.25373840332031, 49.57619171142578, 57.22374725341797, 60.86728210449219, 59.98575286865234, 55.82243957519531, 58.50211944580078, 58.55494079589844, 55.330178833007814, 48.20941162109375, 51.80338897705078, 58.43488311767578, 62.527601623535155, 61.19282989501953, 61.321380615234375, 61.21528778076172, 58.71910095214844, 51.55500030517578, 51.87752990722656, 52.85875701904297, 57.601783752441406, 62.62952728271485, 60.65586395263672, 56.50494232177734, 55.70552520751953, 54.97120056152344, 57.27282867431641, 58.620828247070314, 62.81276397705078, 63.73394775390625, 59.90816955566406, 56.421939086914065, 57.23741607666015, 56.72869720458984, 58.483587646484374, 60.15382080078125, 63.836961364746095, 60.4486328125, 54.37204284667969, 56.57389526367187, 57.1586181640625, 54.02117004394531, 50.57855377197266, 51.807168579101564, 55.31989288330078, 53.888807678222655, 49.20870666503906, 49.310951232910156, 50.26485900878906, 51.3700927734375, 51.216427612304685, 47.57576446533203, 42.84201202392578, 41.46237030029297, 46.78870391845703, 47.46685028076172, 43.332177734375, 38.19630126953125, 37.24781494140625, 38.615866088867186, 38.982643127441406, 39.59981689453125, 42.02662811279297, 40.78079833984375, 39.22053527832031, 36.79718780517578, 35.97910614013672, 39.413998413085935, 39.20321197509766, 40.79789733886719, 42.06519470214844, 44.94947052001953, 40.00185089111328, 39.87420654296875, 39.187303161621095, 40.427433776855466, 40.27087249755859, 41.85726470947266, 38.793801879882814, 38.991084289550784, 38.478085327148435, 38.97046661376953, 35.40807189941406, 35.55740509033203, 34.08680877685547, 31.872640991210936, 33.4380615234375, 34.355455017089845, 34.17203216552734, 33.275091552734374, 33.06853332519531, 32.15201263427734, 32.58553924560547, 32.33357238769531, 30.16300735473633, 32.76860733032227, 32.82425994873047, 31.507886505126955, 32.14586410522461, 34.305989837646486, 33.7050666809082, 32.89646759033203, 30.69223098754883, 27.38671417236328, 26.277545928955078, 28.165279388427734, 27.590791320800783, 26.548894500732423, 28.413249969482422, 29.671510314941408, 26.79346160888672, 24.551146697998046, 24.698855590820312, 25.350038909912108, 28.672476959228515, 30.033524322509766, 28.730714416503908, 27.22966079711914, 26.4642692565918, 24.586326599121094, 25.745311737060547, 26.704805755615233, 29.196155548095703, 26.380724334716795, 27.285682678222656, 28.821355438232423, 27.174435424804688, 25.629964447021486, 23.701654815673827, 25.37286376953125, 26.342017364501952, 26.061138153076172, 24.75322265625, 25.40284957885742, 24.67076416015625, 24.642374420166014, 24.73909912109375, 27.868128967285156, 28.38462677001953, 26.244815063476562, 27.01693344116211, 28.30866241455078, 27.28201675415039, 26.62985153198242, 25.89431381225586, 25.431398010253908, 27.586854553222658, 28.043850708007813, 29.438983917236328, 32.64683227539062, 32.34921035766602, 31.609438323974608, 33.73126907348633, 34.89558715820313, 35.83146667480469, 34.6866455078125, 35.574781036376955, 36.790505981445314, 37.74914321899414, 36.21039657592773, 38.10196228027344, 39.14063720703125, 42.482018280029294, 40.816192626953125, 40.5820930480957, 41.84001159667969, 44.464175415039065, 44.00750198364258, 40.841741943359374, 39.0741943359375, 42.09846878051758, 45.46841201782227, 43.88251647949219, 44.33492736816406, 49.29881973266602, 47.06992034912109, 42.850965881347655, 45.71634750366211, 45.124392700195315, 47.376913452148436, 50.91760940551758, 34.810140228271486, 23.185843658447265, 23.365017700195313, 22.318406677246095, 22.324717712402343, 22.330617141723632, 25.753804397583007, 24.293913269042967, 27.878487014770506, 27.801503372192382, 29.51559829711914, 28.609791946411132, 24.92127571105957, 21.921529388427736, 22.533114624023437, 25.3783935546875, 24.87556610107422, 24.141765213012697, 22.750718688964845, 21.66647415161133, 24.487873840332032, 24.498975944519042, 24.345964241027833, 24.353329849243163, 25.44840030670166, 25.865285301208495, 24.979733085632326, 26.80478439331055, 28.878277015686034, 27.78145866394043, 25.218845939636232, 23.394370079040527, 22.409227752685545, 21.510893058776855, 18.86921329498291, 16.777799797058105, 15.260125732421875, 14.627598762512207, 13.9902738571167, 13.53182315826416, 13.282184982299805, 12.56845645904541, 12.30984287261963, 9.55405387878418, 9.017498016357422, 9.4084867477417, 8.900893020629884, 8.063765716552734, 8.49095802307129, 8.542118072509766, 9.10329990386963, 8.925061988830567, 8.43088207244873, 9.499856948852539, 9.32634391784668, 10.542783546447755, 9.428791809082032, 9.840045928955078, 9.504936408996581, 8.385100173950196, 9.093200874328613, 11.566422653198241, 11.886865997314453, 10.639815998077392, 8.504233264923096, 7.435840892791748, 7.13827543258667, 7.227834033966064, 7.4304811477661135, 7.944094562530518, 8.252573490142822, 8.868411827087403, 9.426864433288575, 9.794993209838868, 10.035904884338379, 9.987264156341553, 9.872835445404053, 10.116108655929565, 10.191609001159668, 10.988156032562255, 11.688128566741943, 11.951312589645386, 12.651931190490723, 12.974178457260132]
+
+
+for FD in [1]: ######['1','2','3','4']
+    # if max_life==110 and FD=='1':
+    #     continue
+    # if max_life==110 and FD=='2':
+        # continue
+    
+    FD_feature_columns=[]
+ 
+
+
+
+
+
+
+
+
+
+
+
+    method_name='grid_FD{}_TaFCN_npseed{}_segment_{}'.format(FD,seed,segment)
+    # method_name='FCN_RUL_1out_train_split_test'
+    dataset='cmapssd'
+    
+    
+    def unbalanced_penalty_score_1out(Y_test,Y_pred) :
+          
+        s=0    
+        for i in range(len(Y_pred)):
+            if Y_pred[i]>Y_test[i]:
+                s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+            else:
+                s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+        print('unbalanced_penalty_score{}'.format(s))
+        return s  
+      
+    def error_range_1out(Y_test,Y_pred) :           
+        error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+        print('error range{}'.format(error_range))
+        return error_range
+    
+ 
+    
+    X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1,1)
+    
+    X_test=X_test.reshape(X_test.shape[0],X_train.shape[1],1,1)
+    
+    # x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+    
+    
+    import six
+    
+    import keras.backend as K
+    from keras.utils.generic_utils import deserialize_keras_object
+    from keras.utils.generic_utils import serialize_keras_object
+    from tensorflow.python.ops import math_ops
+    from tensorflow.python.util.tf_export import tf_export
+    
+    
+    
+    
+    
+    from tensorflow.python.ops import math_ops
+    
+
+    
+
+    
+    
+    #########np.greater_equal([4, 2, 1], [2, 2, 2])array([ True, True, False])
+    #############tf.cast( ) 或者K.cast( ) 是执行 tensorflow 中的张量数据类型转换，比如读入的图片是int8类型的，一定要在训练的时候把图片的数据格式转换为float32.
+    
+    ################reduce_sum reduce dimensinality and get sum
+
+
+    
+
+            #return inputs*x   
+    
+
+    
+    
+
+
+    # reshape_size=len(FD_feature_columns)*int((sequence_length/3))
+    def FCN_model():
+    #    in0 = keras.Input(shape=(sequence_length,train_feature_slice.shape[1]))  # shape: (batch_size, 3, 2048)
+    #    in0_shaped= keras.layers.Reshape((train_feature_slice.shape[1],sequence_length,1))(in0)   
+    
+        in0 = keras.Input(shape=(X_train.shape[1],X_train.shape[2],X_train.shape[3]),name='layer_13')  # shape: (batch_size, 3, 2048)
+    #    begin_senet=SeBlock()(in0)
+        x = keras.layers.AveragePooling2D(pool_size=(int(sequence_length/segment), 1), strides=int(sequence_length/segment),name='layer_12')(in0)
+        # x = keras.layers.Reshape((-1,1))(x) 
+        
+        # x = keras.layers.Reshape((len(FD_feature_columns)*int((sequence_length/3)),))(x)             
+        x = keras.layers.Reshape((-1,))(x)               
+        # x = keras.layers.GlobalAveragePooling2D()(in0)
+        x = keras.layers.Dense(1, use_bias=False,activation=keras.activations.relu)(x)
+        kernel = keras.layers.Dense(1, use_bias=False,activation=keras.activations.hard_sigmoid,name='layer_11')(x)
+        begin_senet= keras.layers.Multiply(name='layer_10')([in0,kernel])    #给通道加权重
+     
+    
+       
+    
+    #     conv0 = keras.layers.
+        
+        
+        conv0 = keras.layers.Conv2D(num_filter1, kernel1_size, strides=1, padding='same',name='layer_9')(begin_senet)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_8')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter2, kernel2_size, strides=1, padding='same',name='layer_7')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_6')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter3, kernel3_size, strides=1, padding='same',name='layer_5')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_4')(conv0)
+        conv0 = keras.layers.GlobalAveragePooling2D(name='layer_3')(conv0)
+        conv0 = keras.layers.Dense(64, activation='relu',name='layer_2')(conv0)
+        out = keras.layers.Dense(1, activation='relu',name='layer_1')(conv0)
+    
+        
+        
+    
+    
+    
+        model = keras.models.Model(inputs=in0, outputs=[out])    
+    
+        return model
+    
+    
+    # ##############shuaffle the data
+    np.random.seed(seed)
+    index=np.arange(X_train.shape[0])
+    np.random.shuffle(index,)
+    
+     
+    X_train=X_train[index]#X_train是训练集，y_train是训练标签
+    Y_train=Y_train[index]
+    
+    #X_train, Xtest, Y_train, ytest = train_test_split(X_train, Y_train, test_size=0.7, random_state=0)
+    
+    
+    if __name__ == '__main__':
+    
+        error_record=[]
+        index_record=[]
+        unbalanced_penalty_score_record=[]
+        error_range_left_record=[]
+        error_range_right_record=[]
+        index_min_val_loss_record,min_val_loss_record=[],[]
+        
+        if os.path.exists(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name)):os.remove(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name))
+    
+     
+    
+
+    
+        rul_pred_array_list=[]
+        true_out_array_list=[]          
+        error_pred_array_list=[]
+                    
+    #######             single  output                
+    
+        for i in range(run_times):
+            print('xxx')
+ 
+            model=FCN_model()
+            plot_model(model, to_file=r"F:\桌面11.17\project\RUL\Flatten.png", show_shapes=True)#########to_file='Flatten.png',r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5
+            
+            optimizer = keras.optimizers.Adam()
+            model.compile(loss='mse',#loss=root_mean_squared_error,
+                          optimizer=optimizer,
+                          metrics=[root_mean_squared_error])
+             
+            reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = 'loss', factor=0.5,
+                              patience=patience_reduce_lr, min_lr=0.0001) 
+            
+
+    #                  verbose=1, validation_split=VALIDATION_SPLIT, callbacks = [reduce_lr])   
+            model_name='{}_dataset_{}_log{}_time{}'.format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+            earlystopping=keras.callbacks.EarlyStopping(monitor='loss',patience=patience,verbose=1)
+            modelcheckpoint=keras.callbacks.ModelCheckpoint(monitor='loss',filepath=r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),save_best_only=True,verbose=1)
+            hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+                      verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+    #        hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+    #                  verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+            log = pd.DataFrame(hist.history)
+            log.to_excel(r"F:\桌面11.17\project\RUL\experiments_result\log\{}_dataset_{}_log{}_time{}.xlsx".format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S')))
+            
+            print(hist.history.keys())
+            epochs=range(len(hist.history['loss']))
+            plt.figure()
+            plt.plot(epochs,hist.history['loss'],'b',label='Training loss')
+            plt.plot(epochs,hist.history['val_loss'],'r',label='Validation val_loss')
+            plt.title('Traing and Validation loss')
+            plt.legend()
+            plt.show()
+
+    
+            
+    #        model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error,'Smooth':Smooth,'SeBlock':SeBlock})
+            model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error})
+            for layer in model.layers:
+                layer.trainable=False        
+    #        score = model.evaluate(X_test, Y_test)  ############forbid evaluate!!!!!!!!!!!!!!!!!!
+    #        print('score[1]:{}'.format(score[1]))    ############forbid evaluate!!!!!!!!!!!!!!!!!!    
+            
+            Y_pred=model.predict(X_test)
+    #        rmse=root_mean_squared_error(Y_test,Y_pred)
+    #        with tf.Session() as sess:
+    #            print(rmse.eval())
+            rmse_value=rmse(Y_test,Y_pred)
+            # print('rmse:{}'.format(rmse_value))
+            
+
+            rul_pred_array=np.array(Y_pred)
+            rul_pred_array=rul_pred_array.reshape(rul_pred_array.shape[0])
+            
+            # print(rul_pred_array.shape)
+            
+            true_out_array=np.array(Y_test)
+            
+            error_pred_array=rul_pred_array-true_out_array
+            
+            error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+                # print(sol.x)
+                
+            # print(error_pred_array.sum())
+            # print("xxxxx")
+            # print(error_pred_array)
+                
+                
+            fig, ax = plt.subplots()
+            # 在生成的坐标系下画折线图
+            ax.plot(error_pred_array, linewidth=1)
+            
+            
+            
+            # 显示图形
+            plt.show()   
+             
+            
+            # print(i)
+            # print("rul_pred_array") 
+            # print(list(rul_pred_array))
+            # print("true_out_array") 
+            # print(list(true_out_array))
+            # print("error_pred_array") 
+            # print(list(error_pred_array))  
+            
+            rul_pred_array_list.append(rul_pred_array)
+            true_out_array_list.append(true_out_array)            
+            error_pred_array_list.append(error_pred_array)
+        rul_pred_array=np.mean(rul_pred_array_list,axis=0)
+        true_out_array=np.mean(true_out_array_list,axis=0)
+        error_pred_array=np.mean(error_pred_array_list,axis=0)
+
+
+        print(i)
+        print("rul_pred_array") 
+        print(list(rul_pred_array))
+        print("true_out_array") 
+        print(list(true_out_array))
+        print("error_pred_array") 
+        print(list(error_pred_array))  
+
+
+
+
+ 
\ No newline at end of file
diff --git a/code/comparison experiments/nasa/DPA_our_method/DPA.py b/code/comparison experiments/nasa/DPA_our_method/DPA.py
new file mode 100644
index 0000000..69223a2
--- /dev/null
+++ b/code/comparison experiments/nasa/DPA_our_method/DPA.py	
@@ -0,0 +1,503 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 16:46:05 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 22:07:49 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 20:41:41 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 18 13:04:56 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:49:17 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:26:02 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 14:28:22 2022
+
+@author: Administrator
+"""
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 31 18:17:31 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 14:50:53 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 28 14:55:56 2022
+
+@author: Administrator
+
+"""
+
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-0.45
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+
+    
+
+
+
+    path = last_last_last_last_path+r'\dataset\nasa\B0005_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_35=matdata["B0005_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    
+    # print(x)
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0006_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_36=matdata["B0006_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    
+    # print(x)
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0007_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_37=matdata["B0007_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    # print(x)
+    
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0018_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_38=matdata["B0018_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="B0005_2")
+    ax.plot(CS2_36, linewidth=1,c='g',label="B0006_2")
+    ax.plot(CS2_37, linewidth=1,c='y',label="B0007_2")
+    ax.plot(CS2_38, linewidth=1,c='r',label="B0018_2")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_nasa_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_nasa_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35,CS2_36,CS2_37,CS2_38
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+import scipy.io as sio
+mat_array=np.zeros((max(len(CS2_36),len(CS2_36),len(CS2_37),len(CS2_37)),4))
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+mat_array[:len(CS2_36),0]=CS2_36
+mat_array[:len(CS2_36),1]=CS2_36
+mat_array[:len(CS2_37),2]=CS2_37
+mat_array[:len(CS2_37),3]=CS2_37
+mat_array=-mat_array
+count=[len(CS2_36),len(CS2_36),len(CS2_37),len(CS2_37)]
+sio.savemat('nasaData03061018.mat', {'KFY': mat_array,'count': count})
+
+
+print(CS2_35[0])
+print(CS2_36[0])
+print(CS2_37[0])
+print(CS2_38[0])
+
+
+# 1.8564874208181574
+# 2.035337591005598
+# 1.89105229539079
+# 1.8550045207910817
+print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+print(CS2_35)
+
+
+print(CS2_35[0])
+print(CS2_36[0])
+print(CS2_37[0])
+print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return CS235
+
+
+
+
+def get_input_out(CS235,CS237,CS236):
+    min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    input_list=[]
+    output_list=[]
+    
+    true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS238=get_health_list(CS2_38,shed)  
+    # print(len(CS236_health))   
+    
+    # print(min(len(CS235),len(CS236),len(CS237)))
+    
+    # print("ccccccccccccccccccccccccc")
+    
+    
+    
+    for i in range(2,len(CS236_health)):
+
+        
+        
+        
+        # one_input.append(np.array([len(CS235_health),len(CS237_health)]))
+        
+        
+
+
+
+
+
+
+
+
+        y = [CS235[0], CS235[min_len-1]]
+        x = [0, (min_len-1)*(min_len-1)]
+        slope_0, intercept_0, r_value, p_value, std_err = linregress(x, y)
+
+        
+        y = [CS237[0], CS237[min_len-1]]
+        x = [0,(min_len-1)*(min_len-1)]
+        slope_1, intercept_1, r_value, p_value, std_err = linregress(x, y)
+        
+        
+        
+        
+        
+        
+
+        one_input=[]
+        one_input.append(np.array(CS235[:i]))
+        one_input.append(np.array(CS237[:i]))
+        
+        one_input.append("CS238")
+    
+        min_len_train=min(len(CS235),len(CS237))
+            
+        one_input.append(np.array(CS235[:min_len_train]))
+            
+      
+        one_input.append(np.array(CS237[:min_len_train]))
+        
+        one_input.append(np.array(CS236[:i]))
+        
+        
+        
+        
+        
+        input_list.append(one_input)
+        
+        one_out=[]
+        one_out.append(np.array(CS236[:i]))
+        
+        output_list.append(one_out)
+        
+        true_out_list.append(max(len(CS236_health)-i-1,0))
+
+
+
+
+
+    
+    true_out_array=np.array(true_out_list)
+    return input_list[:len(CS236_health)],output_list[:len(CS236_health)],true_out_array[:len(CS236_health)]
+
+
+
+
+
+
+import random
+
+pred_list=[]
+error_list=[]
+
+for xxx in range(1):
+
+    for i in range(1):
+        
+        
+        if i==0:
+            
+            input_list,output_list,true_out_array=get_input_out(CS2_36,CS2_36,CS2_37)######第三个变量是测试集 ，前两个是训练集
+            
+      
+        
+        rul_pred_list=[]
+        
+        regress_error_list=[]
+        
+        for j in range(len(input_list)):
+            
+            # print(j)
+            # print("jjjjjjjjjjjjjjjjjjjjjjjjjj")
+            index=j
+            def objective(x):
+                
+                integrate_array=input_list[index][0]*x[0]+input_list[index][1]*x[1]+x[2]
+                
+                error = np.sum(np.square(integrate_array - output_list[index][0]))/len(integrate_array)
+                
+                return error    
+            
+            # def objective(x):   #############加入时间权重    np.arange(1,len(integrate_array)+1))
+                
+            #     integrate_array=input_list[index][0]*x[0]+input_list[index][1]*x[1]
+                
+            #     error = np.sum(np.dot(np.square(integrate_array - output_list[index][0]),np.arange(1,len(integrate_array)+1)))/len(integrate_array)
+                
+            #     return error           
+            
+            
+            # x0=np.ones(3)/3
+    
+            
+            x0=np.ones(3)
+            x0[2]=0
+     
+            
+            
+    
+            # sol = minimize(objective, x0 ) 
+            sol = minimize(objective, x0 ,method='BFGS', options={'maxiter':(1+1)*200,'gtol':1e-5 }  )     
+            u1=sol.x[0] 
+            u2=sol.x[1]
+            c1=input_list[j][3]
+    
+            c2=input_list[j][4]
+            
+            offset_vertical=sol.x[2]
+    
+    
+    
+            
+
+            
+            
+
+            
+            ######补偿误差
+            pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1] +offset_vertical
+            # pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve)[-20:] )      
+            pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve)[int(-min(j*0.2,20)):] )    
+    
+    #############预测曲线          
+            # plt.plot(range(len(pred_curve)), pred_curve)
+            # plt.show()
+     
+    #############
+    
+            
+            ful_pred=len(pred_curve)
+            for ii in range(len(pred_curve)):
+                
+                # if i == len(pred_curve)-1:
+                    
+                #     ful_pred==i
+                
+                if pred_curve[ii]<shed:
+                    
+                    ful_pred=ii
+                    
+                    break
+    
+    
+            rul_pred_list.append(ful_pred-j-1) #######(-1  because of  begining from 2)
+    
+    
+    
+    
+    
+    #############实时的拟合曲线与实际曲线的所有点的误差        
+            # pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1]                
+            # plt.plot(range(len(input_list[j][5])), input_list[j][5] - pred_online_cruve)
+            # plt.show()
+    
+    #################
+    
+    
+                    
+        
+        rul_pred_array=np.array(rul_pred_list)
+        
+        error_pred_array=rul_pred_array-true_out_array
+        
+        error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+            # print(sol.x)
+            
+        # print(error_pred_array.sum())
+        # print("xxxxx")
+        # print(error_pred_array)
+            
+            
+        fig, ax = plt.subplots()
+        # 在生成的坐标系下画折线图
+        ax.plot(error_pred_array, linewidth=1)
+        
+        
+        
+        # 显示图形
+        plt.show()   
+         
+        
+        plt.show()   
+         
+        pred_list.append(rul_pred_array)
+        error_list.append(error_pred_array)
+        print(xxx)
+print(pred_list)
+
+print(error_list)
+        
diff --git a/code/comparison experiments/nasa/prediction_value_and_error_nasa_6_7 .py b/code/comparison experiments/nasa/prediction_value_and_error_nasa_6_7 .py
new file mode 100644
index 0000000..336ddc9
--- /dev/null
+++ b/code/comparison experiments/nasa/prediction_value_and_error_nasa_6_7 .py	
@@ -0,0 +1,247 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 26 22:25:42 2023
+
+@author: Administrator
+"""
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+Si=[71, 69, 70, 70, 71, 71, 71, 72, 70, 71, 71, 70, 70, 70, 71, 71, 69, 70, 70, 78, 78, 79, 77, 77, 78, 76, 76, 77, 75, 75, 84, 79, 77, 77, 75, 75, 74, 72, 72, 71, 71, 71, 73, 71, 69, 67, 67, 75, 76, 73, 71, 69, 68, 67, 65, 63, 63, 62, 61, 59, 58, 56, 56, 54, 55, 52, 51, 50, 50, 49, 48, 47, 46, 45, 44, 41, 41, 43, 41, 39, 38, 38, 38, 37, 35, 34, 34, 34, 33, 59, 42, 40, 37, 35, 34, 33, 32, 31, 29, 30, 29, 27, 29, 32, 29, 28, 25, 24, 24, 24, 22, 21, 21, 21, 20, 18, 17, 17, 17, 23, 24, 20, 18, 16, 15, 14, 12, 12, 11, 10, 10, 8, 10, 14, 10, 9, 7, 7, 6, 5, 3, 2, 2, 1, 1, 0]
+     
+Zhang=[13, 14, 0, 0, 0, 0, 110, 110, 111, 107, 107, 107, 104, 103, 103, 103, 102, 100, 99, 99, 104, 103, 102, 100, 99, 98, 96, 95, 94, 92, 91, 94, 91, 89, 88, 86, 85, 84, 82, 81, 80, 79, 78, 77, 76, 74, 73, 72, 73, 72, 70, 69, 68, 66, 65, 64, 62, 61, 60, 59, 58, 57, 55, 54, 53, 52, 51, 50, 49, 48, 47, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 29, 26, 25, 24, 23, 21, 20, 19, 18, 17, 16, 15, 14, 13, 13, 12, 11, 9, 8, 7, 7, 5, 4, 3, 2, 1, 3, 2, 2, 2, 4, 5, 3, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+DCNN= [57.339287, 57.25831, 57.15761, 57.047688, 56.928425, 56.810535, 56.652794, 56.4737, 56.27003, 56.015602, 55.72647, 55.40636, 55.068806, 54.719685, 54.3308, 53.926434, 53.510517, 53.212105, 52.997364, 52.891346, 52.80108, 52.72648, 52.663036, 52.56969, 52.478546, 52.38543, 52.293232, 52.18978, 52.198536, 52.23408, 52.26294, 52.253807, 52.181602, 52.055214, 51.86372, 51.531544, 51.07071, 50.437958, 49.785507, 49.115562, 48.49276, 47.86419, 47.21305, 46.5372, 45.79558, 45.12745, 44.505184, 43.8757, 43.178116, 42.550537, 41.94214, 41.325264, 40.69163, 40.00733, 39.31803, 38.625923, 37.908638, 37.14468, 36.329353, 35.464035, 34.554016, 33.61296, 32.659557, 31.629812, 30.516083, 29.269232, 28.037952, 26.867065, 25.731054, 24.624647, 23.532375, 22.459345, 21.41357, 20.369366, 19.333063, 18.356356, 17.415073, 16.511877, 15.626776, 14.786319, 13.977507, 13.229878, 12.504001, 11.829389, 11.20617, 10.643696, 10.1399975, 9.894609, 9.670118, 9.4657955, 9.25506, 9.017349, 8.77429, 8.547444, 8.3368, 8.147097, 7.9911995, 7.8670015, 7.7494125, 7.6352553, 7.5484657, 7.521201, 7.5170774, 7.517745, 7.4741716, 7.371932, 7.205838, 6.888617, 6.7507043, 6.644236, 6.5664268, 6.5109453, 6.4640822, 6.422563, 6.3869457, 6.3602304, 6.3409386, 6.3370457, 6.347509, 6.3441095, 6.311991, 6.259068, 6.2178974, 6.1849155, 6.161479, 6.142614, 6.119878, 6.095611, 6.081123, 6.069086, 6.0580683, 6.0548735, 6.0503526, 6.0437365, 6.044513, 6.050529, 6.0505323, 6.026494, 5.9942036, 5.9718723, 5.9554787, 5.94131, 5.929776, 5.920268]
+       
+TaFCN=[48.62757, 48.34951, 48.031307, 47.75051, 47.505318, 47.315323, 46.915966, 46.467438, 45.987305, 45.285408, 44.56667, 43.913097, 43.3266, 42.817684, 42.285423, 41.82386, 41.382454, 41.310577, 41.35674, 41.48274, 41.587048, 41.687763, 41.765377, 41.5903, 41.43409, 41.324287, 41.112087, 40.94556, 41.206577, 41.341602, 41.35398, 41.196545, 40.693386, 40.052048, 39.44436, 38.927773, 38.623405, 38.423164, 38.231583, 38.0102, 37.906548, 37.76582, 37.465088, 37.105694, 36.77346, 36.699375, 36.813576, 36.813114, 36.502308, 36.10009, 35.610615, 35.183647, 34.825752, 34.430153, 34.079258, 33.771492, 33.33358, 32.623283, 31.595785, 30.413513, 29.335812, 28.310238, 27.569086, 26.907282, 26.118702, 25.008286, 23.897512, 22.790833, 21.652403, 20.552004, 19.580124, 18.756695, 18.099827, 17.56187, 17.0804, 16.579056, 16.024097, 15.455345, 14.85289, 14.226654, 13.545972, 12.874486, 12.196519, 11.540399, 10.893389, 10.25353, 9.64235, 9.150061, 8.729932, 8.4631605, 8.293872, 8.054728, 7.8718925, 7.6463456, 7.3506303, 7.0144186, 6.7147284, 6.436738, 6.090365, 5.6473603, 5.1784697, 4.757666, 4.536981, 4.5889664, 4.799363, 4.790672, 4.504275, 3.9159093, 3.5652516, 3.251304, 2.9597673, 2.7006633, 2.4766564, 2.2807224, 2.0876417, 1.8946636, 1.7427313, 1.6875105, 1.7341236, 1.8317935, 1.8746793, 1.8137833, 1.701638, 1.566111, 1.4212495, 1.2773049, 1.1455412, 1.0421529, 0.97265613, 0.91955197, 0.8968814, 0.92210615, 0.9597454, 0.99099314, 0.98373014, 0.97556525, 0.96815044, 0.9587822, 0.9497689, 0.94432753, 0.9511595, 0.9647751, 0.98576736, 1.0064211]
+
+Our=[62, 166, 165, 164, 163, 162, 161, 160, 159, 158, 157, 156, 155, 154, 153, 152, 151, 150, 149, 148, 147, 146, 145, 144, 143, 142, 141, 140, 139, 138, 137, 136, 135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 70, 69, 68, 67, 66, 64, 62, 61, 59, 58, 56, 55, 54, 52, 50, 49, 47, 46, 45, 44, 43, 42, 41, 39, 38, 37, 36, 34, 33, 32, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 14, 13, 12, 11, 10, 9, 6, 5, 4, 3, 2]
+
+groud_truth=[822.0, 821.0, 820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+
+
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our))-5
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+ax.plot(Si[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='-.')
+ax.plot(Zhang[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='-.')
+ax.plot(DCNN[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='-.')
+ax.plot(TaFCN[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='-.')
+
+
+ax.plot(Our[-min_len:], linewidth=2,c='r',label="Our",linestyle='-.')
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+error_rate=0.2
+error_rate_1=0.4
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1-error_rate),color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1+error_rate_1),color="#FFCCFF",label='Error band ({:.0%},{:.0%})'.format(error_rate,error_rate_1))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1-error_rate), np.array(groud_truth[-min_len:])*(1-error_rate_1),color="#FFB9E6")# color="#CCEEFF")
+       
+plt.xlabel('Charging cycle times (cycle)',font1) #X轴标签
+plt.ylabel("Predicted RUL (cycle)",font1) #Y轴标签
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_nasa_6_7.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_nasa_6_7.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
+Si_error=[74.0, 75.0, 73.0, 72.0, 70.0, 69.0, 68.0, 66.0, 67.0, 65.0, 64.0, 64.0, 63.0, 62.0, 60.0, 59.0, 60.0, 58.0, 57.0, 48.0, 47.0, 45.0, 46.0, 45.0, 43.0, 44.0, 43.0, 41.0, 42.0, 41.0, 31.0, 35.0, 36.0, 35.0, 36.0, 35.0, 35.0, 36.0, 35.0, 35.0, 34.0, 33.0, 30.0, 31.0, 32.0, 33.0, 32.0, 23.0, 21.0, 23.0, 24.0, 25.0, 25.0, 25.0, 26.0, 27.0, 26.0, 26.0, 26.0, 27.0, 27.0, 28.0, 27.0, 28.0, 26.0, 28.0, 28.0, 28.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 29.0, 28.0, 25.0, 26.0, 27.0, 27.0, 26.0, 25.0, 25.0, 26.0, 26.0, 25.0, 24.0, 24.0, 3.0, 13.0, 14.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 18.0, 16.0, 16.0, 17.0, 14.0, 10.0, 12.0, 12.0, 14.0, 14.0, 13.0, 12.0, 13.0, 13.0, 12.0, 11.0, 11.0, 12.0, 12.0, 11.0, 10.0, 3.0, 1.0, 4.0, 5.0, 6.0, 6.0, 6.0, 7.0, 6.0, 6.0, 6.0, 5.0, 6.0, 3.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 0.0, 0.0]
+     
+# Zhang_error=[132.0, 130.0, 143.0, 142.0, 141.0, 140.0, 25.0, 23.0, 22.0, 25.0, 24.0, 23.0, 25.0, 24.0, 23.0, 22.0, 22.0, 24.0, 23.0, 22.0, 16.0, 16.0, 16.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0, 19.0, 19.0, 15.0, 17.0, 18.0, 18.0, 19.0, 19.0, 19.0, 20.0, 20.0, 21.0, 20.0, 21.0, 20.0, 20.0, 21.0, 21.0, 22.0, 19.0, 19.0, 20.0, 20.0, 21.0, 21.0, 21.0, 22.0, 22.0, 22.0, 22.0, 22.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 25.0, 25.0, 24.0, 24.0, 25.0, 25.0, 25.0, 24.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 21.0, 23.0, 23.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 23.0, 19.0, 20.0, 20.0, 19.0, 19.0, 18.0, 17.0, 16.0, 16.0, 15.0, 14.0, 13.0, 12.0, 10.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+# Zhang_error=[132.0, 130.0, 143.0, 142.0, 141.0, 140.0, 34.0, 33.0, 31.0, 34.0, 33.0, 32.0, 34.0, 33.0, 32.0, 32.0, 31.0, 33.0, 32.0, 32.0, 26.0, 26.0, 26.0, 27.0, 27.0, 27.0, 28.0, 28.0, 28.0, 29.0, 28.0, 25.0, 27.0, 28.0, 28.0, 29.0, 28.0, 29.0, 30.0, 30.0, 30.0, 30.0, 30.0, 29.0, 30.0, 30.0, 31.0, 31.0, 29.0, 28.0, 29.0, 30.0, 30.0, 30.0, 30.0, 31.0, 31.0, 31.0, 31.0, 31.0, 32.0, 32.0, 32.0, 32.0, 33.0, 32.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 34.0, 34.0, 33.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 30.0, 32.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 30.0, 29.0, 28.0, 27.0, 27.0, 26.0, 26.0, 25.0, 24.0, 21.0, 19.0, 20.0, 20.0, 19.0, 19.0, 18.0, 17.0, 16.0, 16.0, 15.0, 14.0, 13.0, 12.0, 10.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]        
+
+Zhang_error=[132.0, 130.0, 143.0, 142.0, 141.0, 140.0, 29.0, 28.0, 26.0, 29.0, 28.0, 27.0, 29.0, 29.0, 28.0, 27.0, 27.0, 28.0, 28.0, 27.0, 21.0, 21.0, 21.0, 22.0, 22.0, 22.0, 23.0, 23.0, 23.0, 24.0, 24.0, 20.0, 22.0, 23.0, 23.0, 24.0, 24.0, 24.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 26.0, 26.0, 26.0, 24.0, 24.0, 25.0, 25.0, 25.0, 26.0, 26.0, 26.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 26.0, 28.0, 28.0, 28.0, 28.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 28.0, 28.0, 28.0, 29.0, 29.0, 29.0, 28.0, 29.0, 29.0, 29.0, 29.0, 29.0, 26.0, 26.0, 25.0, 24.0, 21.0, 19.0, 20.0, 20.0, 19.0, 19.0, 18.0, 17.0, 16.0, 16.0, 15.0, 14.0, 13.0, 12.0, 10.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+DCNN_error=[85.66071243286133, 84.74169616699218, 83.84238739013672, 82.95231475830079, 82.07157592773437, 81.18946533203125, 80.34720382690429, 79.52630081176758, 78.72996520996094, 77.98440399169922, 77.27352600097656, 76.59363861083985, 75.93119201660156, 75.28031387329102, 74.66920013427735, 74.07356796264648, 73.48948135375977, 72.78789367675782, 72.00263442993165, 71.10865478515625, 70.19891510009765, 69.27352447509766, 68.33696823120117, 67.43030700683593, 66.52145538330078, 65.61456604003907, 64.70676651000977, 63.81021728515625, 62.80146484375, 61.76591415405274, 60.73706283569336, 59.74619369506836, 58.81839599609375, 57.9447883605957, 57.13628311157227, 56.4684555053711, 55.92929229736328, 55.56204528808594, 55.214490509033205, 54.884436798095706, 54.507240295410156, 54.13581314086914, 53.786947631835936, 53.462798309326175, 53.204419708251955, 52.872552490234376, 52.494815063476565, 52.12429962158203, 51.82188720703125, 51.44946212768555, 51.05785980224609, 50.67473449707031, 50.30836791992188, 49.992671203613284, 49.681966400146486, 49.37407684326172, 49.09136276245117, 48.85531845092773, 48.67064514160156, 48.535963439941405, 48.44598388671875, 48.38703842163086, 48.34044342041015, 48.370186996459964, 48.48391571044922, 48.73076858520508, 48.96204605102539, 49.132934188842775, 49.268944549560544, 49.37535285949707, 49.46762580871582, 49.540653991699216, 49.58642768859863, 49.63063507080078, 49.66693878173828, 49.64364433288574, 49.584926986694335, 49.48812198638916, 49.373224067687985, 49.213680267333984, 49.02249336242676, 48.77012176513672, 48.49599914550781, 48.17061157226563, 47.79383087158203, 47.356303215026855, 46.86000232696533, 46.10538997650146, 45.32988109588623, 44.53420505523682, 43.74493885040283, 42.98265132904053, 42.225710010528566, 41.45255584716797, 40.66320114135742, 39.852903270721434, 39.008800983428955, 38.1329984664917, 37.25058765411377, 36.3647442817688, 35.45153408050537, 34.47879848480225, 33.48292217254639, 32.48225469589234, 31.525828170776368, 30.628067874908446, 29.79416208267212, 29.111382389068602, 28.24929552078247, 27.35576367378235, 26.433572912216185, 25.489054441452026, 24.53591799736023, 23.577436399459838, 22.61305456161499, 21.639769506454467, 20.65906114578247, 19.662954139709473, 18.65249094963074, 17.655890703201294, 16.688009214401244, 15.74093189239502, 14.782102847099305, 13.815084385871888, 12.838520717620849, 11.857385849952697, 10.880122590065003, 9.904388523101806, 8.918877053260804, 7.930914044380188, 6.941931653022766, 5.945126533508301, 4.94964747428894, 3.95626380443573, 2.9554864168167114, 2.2371399879455565, 1.9821931362152099, 2.1853905439376833, 2.4001699924468993, 2.989093780517578, 3.581574892997742, 4.1749168872833256, 4.929776287078857, 5.920267915725708]
+
+
+TaFCN_error=[94.37242698669434, 93.65049057006836, 92.96869316101075, 92.2494888305664, 91.49468116760254, 90.68467445373535, 90.08403282165527, 89.53256225585938, 89.01269264221192, 88.7145896911621, 88.43333168029785, 88.0869010925293, 87.67339859008788, 87.18231735229492, 86.7145767211914, 86.1761432647705, 85.6175479888916, 84.68942298889161, 83.64326286315918, 82.51726150512695, 81.41295356750489, 80.31223831176757, 79.23462028503418, 78.40969848632812, 77.56590957641602, 76.67571334838867, 75.88791160583496, 75.05444221496582, 73.79342079162598, 72.65840225219726, 71.6460174560547, 70.80345306396484, 70.30661468505859, 69.947953414917, 69.55564155578614, 69.07222480773926, 68.37660140991211, 67.57683944702148, 66.76841812133789, 65.98979949951172, 65.09345016479492, 64.23417778015137, 63.53491439819336, 62.89430618286133, 62.22654075622559, 61.300627899169925, 60.18642578125, 59.18688278198242, 58.49769592285156, 57.899910354614256, 57.38938827514649, 56.81635284423828, 56.174248123168944, 55.56985168457031, 54.920742416381835, 54.22850875854492, 53.666421508789064, 53.37671318054199, 53.40421562194824, 53.5864875793457, 53.66418876647949, 53.68976173400879, 53.430914306640624, 53.09272079467773, 52.88129806518555, 52.99171447753906, 53.10248641967773, 53.209167289733884, 53.3475959777832, 53.447995567321776, 53.41987762451172, 53.243306159973145, 52.900174903869626, 52.43813056945801, 51.91959896087646, 51.42094459533691, 50.97590274810791, 50.54465503692627, 50.14710960388184, 49.77334642410278, 49.454029369354245, 49.12551403045654, 48.80348196029663, 48.45960168838501, 48.10661067962646, 47.746470260620114, 47.357649898529054, 46.84993925094604, 46.27006855010986, 45.536839389801024, 44.70612773895264, 43.9452730178833, 43.128107929229735, 42.35365495681763, 41.64937000274658, 40.98558130264282, 40.28527126312256, 39.56326279640198, 38.90963549613953, 38.35263981819153, 37.8215304851532, 37.24233393669128, 36.46301898956299, 35.41103367805481, 34.200636863708496, 33.20932807922363, 32.495725345611575, 32.08409080505371, 31.434748125076293, 30.748696088790894, 30.040232682228087, 29.29933657646179, 28.523343563079834, 27.719277477264406, 26.912358009815215, 26.10533664226532, 25.257268822193147, 24.31248949766159, 23.265876507759096, 22.16820651292801, 21.125320422649384, 20.186216878890992, 19.298361909389495, 18.433889085054396, 17.578750485181807, 16.722695219516755, 15.854458782076836, 14.957847067713738, 14.027343833446503, 13.08044799566269, 12.103118631243706, 11.077893844246864, 10.04025466442108, 9.009006845951081, 8.016269916296006, 7.0244347035884855, 6.0318495854735374, 5.041217774152756, 4.050231209397316, 3.055672472715378, 2.0488404870033263, 1.1157315105199814, 0.6154629394412041, 1.006421086192131]
+
+Our_error=[81, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 19, 19, 19, 19, 19, 18, 17, 17, 16, 16, 15, 15, 15, 14, 13, 13, 12, 12, 12, 12, 12, 12, 12, 11, 11, 11, 11, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2]
+
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our))-5
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+
+ax.plot(np.arange(min_len,0,-1),Si_error[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Zhang_error[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),DCNN_error[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),TaFCN_error[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Our_error[-min_len:], linewidth=2,c='r',label="Our",linestyle='--')
+# ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+
+def rmse(x):
+    s=0
+    for i in x:
+        s+=i*i
+    r=sqrt(s/len(x))
+    return r
+
+def aae(x):
+    return np.mean(x)
+
+
+def score(Y_test,Y_pred) :
+      
+    s=0    
+    for i in range(len(Y_pred)):
+        if Y_pred[i]>Y_test[i]:
+            s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+        else:
+            s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+    # print('unbalanced_penalty_score{}'.format(s))
+    return s  
+  
+def error_range(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+     
+    error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return error_range
+
+        
+        
+def error_list(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+
+    error_list=Y_test-Y_pred
+    # Y_test =np.array(Y_test)  
+    # Y_pred =np.array(Y_pred) 
+     
+    # error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return list(error_list)
+
+print(list((error_list(groud_truth[-min_len:],Si[-min_len:]),error_list(groud_truth[-min_len:],Zhang[-min_len:]),error_list(groud_truth[-min_len:],DCNN[-min_len:]),error_list(groud_truth[-min_len:],TaFCN[-min_len:]),error_list(groud_truth[-min_len:],Our[-min_len:]))))
+        
+print(list((rmse(Si_error[-min_len:]),rmse(Zhang_error[-min_len:]),rmse(DCNN_error[-min_len:]),rmse(TaFCN_error[-min_len:]),rmse(Our_error[-min_len:]))))
+print(list((aae(Si_error[-min_len:]),aae(Zhang_error[-min_len:]),aae(DCNN_error[-min_len:]),aae(TaFCN_error[-min_len:]),aae(Our_error[-min_len:]))))
+print(list((score(groud_truth[-min_len:],Si[-min_len:]),score(groud_truth[-min_len:],Zhang[-min_len:]),score(groud_truth[-min_len:],DCNN[-min_len:]),score(groud_truth[-min_len:],TaFCN[-min_len:]),score(groud_truth[-min_len:],Our[-min_len:]))))
+print(list((error_range(groud_truth[-min_len:],Si[-min_len:]),error_range(groud_truth[-min_len:],Zhang[-min_len:]),error_range(groud_truth[-min_len:],DCNN[-min_len:]),error_range(groud_truth[-min_len:],TaFCN[-min_len:]),error_range(groud_truth[-min_len:],Our[-min_len:]))))
+
+
+
+def predcition(targets,predictions):
+    targets=np.array(targets)
+    predictions=np.array(predictions)
+    
+    
+    # targets=np.array(targets)
+    # predictions=np.array(predictions)
+
+    
+
+    smape = np.sum( np.abs(predictions - targets)) /np.sum( np.abs( targets)) * 100
+    print(np.sum( np.abs(predictions - targets)))
+    
+    print(np.sum( np.abs( targets)))
+    
+    print(smape)
+    y=100-smape
+    
+    # print(y)
+    return y 
+
+print(list((predcition(groud_truth[-min_len:],Si[-min_len:]),predcition(groud_truth[-min_len:],Zhang[-min_len:]),predcition(groud_truth[-min_len:],DCNN[-min_len:]),predcition(groud_truth[-min_len:],TaFCN[-min_len:]),predcition(groud_truth[-min_len:],Our[-min_len:]))))
+
+
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+error_rate=0.4
+error_rate_1=0.8
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.array(np.arange(min_len,0,-1))*(error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+plt.xlabel('Actual RUL (cycle)',font1) #X轴标签
+plt.ylabel("Absolute prediction error (cycle)",font1) #Y轴标签
+plt.gca().invert_xaxis()
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_nasa_6_7.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_nasa_6_7.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
diff --git a/code/comparison experiments/nasa/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt b/code/comparison experiments/nasa/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt
new file mode 100644
index 0000000..c84ed61
--- /dev/null
+++ b/code/comparison experiments/nasa/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt	
@@ -0,0 +1,806 @@
+2023-03-22 19:18:03,735 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,736 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,825 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,826 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,460 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,461 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:20:00,213 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+2023-03-22 19:20:00,235 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+2023-03-22 19:20:00,241 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+2023-03-22 19:20:00,258 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+2023-03-22 19:20:00,312 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+2023-03-22 19:20:00,313 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+2023-03-22 19:20:00,314 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+2023-03-22 19:20:02,670 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+2023-03-22 19:20:02,671 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+2023-03-22 19:20:02,901 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+2023-03-22 19:20:02,940 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+2023-03-22 19:20:02,990 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+2023-03-22 19:20:03,741 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+2023-03-22 19:20:03,983 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+2023-03-22 19:20:04,011 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+2023-03-22 19:20:04,216 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
+
+2023-03-22 19:22:47,451 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,452 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,545 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,546 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:52,870 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,872 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
+2023-03-22 19:22:52,873 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,879 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,888 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'YouYuan' (SIMYOU.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal heavy normal>) = 10.43
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrak.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibril.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Deng.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STFangsong' (STFANGSO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal black normal>) = 11.525
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal demi condensed>) = 10.44
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'icomoon' (iqiyi_logov5.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXihei' (STXIHEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrakb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextrab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal medium normal>) = 11.145
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,929 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXinwei' (STXINWEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal demi normal>) = 11.24
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra' (mtextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,938 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariblk.ttf) normal normal black normal>) = 6.888636363636364
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,949 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STHupo' (STHUPO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal semibold normal>) = 11.24
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger' (Symbol Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclid.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZYaoTi' (FZYTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal bold condensed>) = 7.8986363636363635
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger' (Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger Expert' (Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'KaiTi' (simkai.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZShuTi' (FZSTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal heavy normal>) = 11.43
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'LiSu' (SIMLI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,978 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Unicode MS' (ARIALUNI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STLiti' (STLITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,994 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STKaiti' (STKAITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger Expert' (Symbol Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal semibold normal>) = 10.24
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal medium condensed>) = 10.344999999999999
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal black normal>) = 11.525
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXingkai' (STXINGKA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STZhongsong' (STZHONGS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Fences' (fences.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 7.613636363636363
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal bold condensed>) = 6.8986363636363635
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FangSong' (simfang.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 6.613636363636363
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STSong' (STSONG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Mincho' (MSMINCHO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra Tiger' (Mt Extra Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,040 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STCaiyun' (STCAIYUN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,042 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,057 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal medium normal>) = 10.145
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWAD.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,063 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal regular normal>) = 10.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal bold condensed>) = 11.535
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWDB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimHei' (simhei.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,071 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('C:\\Users\\Administrator\\anaconda3\\envs\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000.
+2023-03-22 19:23:05,774 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_428:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,776 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_427:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,778 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_426:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,779 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_425:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,781 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_424:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,782 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_423:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,783 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_422:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,784 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_421:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,785 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_420:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,786 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_419:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,787 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_418:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,788 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_417:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,789 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_416:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,791 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_415:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,792 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_414:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,793 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_413:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,794 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_412:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,795 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_411:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,796 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_410:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,798 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_409:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,799 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_408:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:24:47,221 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,222 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,315 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,316 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,162 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,163 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,082 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,176 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,177 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,332 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,333 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,407 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,505 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,506 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,215 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,216 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,143 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,243 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,244 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,337 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,338 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,432 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,433 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,264 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,265 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,578 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,579 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,674 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,675 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,053 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,054 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,145 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,912 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,913 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,422 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,423 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,515 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,517 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,455 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,456 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,550 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,551 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,409 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,496 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,497 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,883 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,884 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,970 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,971 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,988 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,989 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,080 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,943 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,944 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,041 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,042 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,890 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,891 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,985 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,984 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,071 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,072 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,916 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,917 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,073 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,074 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,166 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,168 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,894 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,897 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,005 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
diff --git a/code/comparison experiments/nasa/tafcn2022/TaFCN.py b/code/comparison experiments/nasa/tafcn2022/TaFCN.py
new file mode 100644
index 0000000..955864b
--- /dev/null
+++ b/code/comparison experiments/nasa/tafcn2022/TaFCN.py	
@@ -0,0 +1,730 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 22 16:37:59 2023
+
+@author: Administrator
+"""
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-0.45
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+# print(os.path.abspath(os.path.join(os.getcwd(), "../../../")))
+# last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+# def get_data_list(CS2_35_cap_dropOutlier):
+#     worksheet = xlrd.open_workbook('F:\桌面11.17\project\RUL_guassion\handled_dataset\CACLE\{}.xlsx'.format(CS2_35_cap_dropOutlier))
+#     sheet_names= worksheet.sheet_names()
+#     print(sheet_names)
+#     CS2_35=[]
+#     for sheet_name in sheet_names:
+#         sheet = worksheet.sheet_by_name(sheet_name)
+#         rows = sheet.nrows # 获取行数
+#         cols = sheet.ncols # 获取列数，尽管没用到
+#         all_content = []
+    
+    
+#         CS2_35 = sheet.col_values(0) # 获取第二列内容， 数据格式为此数据的原有格式（原：字符串，读取：字符串；  原：浮点数， 读取：浮点数）
+
+
+
+#     fig, ax = plt.subplots()
+#     # 在生成的坐标系下画折线图
+#     ax.plot(CS2_35, linewidth=1)
+#     # 显示图形
+#     plt.show()
+    
+#     return CS2_35
+
+# CS2_35=get_data_list("CS2_35_cap_dropOutlier")
+# CS2_36=get_data_list("CS2_36_cap_dropOutlier")
+# CS2_37=get_data_list("CS2_37_cap_dropOutlier")
+# CS2_38=get_data_list("CS2_38_cap_dropOutlier")
+
+
+def get_data_list():
+
+    
+
+
+
+    path = last_last_last_last_path+r'\dataset\nasa\B0005_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_35=matdata["B0005_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    
+    # print(x)
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0006_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_36=matdata["B0006_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    
+    # print(x)
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0007_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_37=matdata["B0007_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+    # print(x)
+    
+    
+    
+    path = last_last_last_last_path+r'\dataset\nasa\B0018_2.mat'
+    
+    matdata = scio.loadmat(path)
+    
+    CS2_38=matdata["B0018_2"][0]
+    # plt.plot(range(len(x)),x)
+    # plt.show()
+
+        
+        
+
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="B0005_2")
+    ax.plot(CS2_36, linewidth=1,c='g',label="B0006_2")
+    ax.plot(CS2_37, linewidth=1,c='y',label="B0007_2")
+    ax.plot(CS2_38, linewidth=1,c='r',label="B0018_2")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_nasa_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_nasa_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35,CS2_36,CS2_37,CS2_38
+# CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+# print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+# print(CS2_35)
+
+
+# print(CS2_35[0])
+# print(CS2_36[0])
+# print(CS2_37[0])
+# print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return list(CS235)
+
+# CS235=get_health_list(CS2_35,shed) 
+# CS236=get_health_list(CS2_36,shed) 
+# CS237=get_health_list(CS2_37,shed) 
+# CS238=get_health_list(CS2_38,shed)   
+
+# fig, ax = plt.subplots()
+# # 在生成的坐标系下画折线图
+# ax.plot(CS235, linewidth=1)
+# ax.plot(CS236, linewidth=1)
+# ax.plot(CS237, linewidth=1)
+# ax.plot(CS238, linewidth=1)
+
+# # 显示图形
+# plt.show()
+
+def get_input_out_2(CS236,CS237,time_windows):
+
+    
+    # CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    # CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    # for i in range(time_windows-1-2):
+    #     CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    # CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    # for i in range(len(CS235_health)-time_windows+1):        
+    #     x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+    #     y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+
+
+
+
+
+
+
+
+
+def get_input_out_3(CS235,CS236,CS237,time_windows):
+    # min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    # input_list=[]
+    # output_list=[]
+    
+    # true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    CS237_health=get_health_list(CS237,shed)     
+    
+    print(len(CS237_health))
+    print("hhhhhhhhhhhh")
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    for i in range(time_windows-1-2):
+        CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    for i in range(len(CS235_health)-time_windows+1):        
+        x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+        y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    print(y_test_array.shape)
+    print("jjjjjjjjjj")
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+    
+    
+    
+    
+    
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_3(CS2_35,CS2_36,CS2_37,20)      
+
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+
+
+
+
+
+
+
+#import tensorflow as tf
+import os
+import logging
+import numpy as np
+#from numpy import trans
+import matplotlib.pyplot as plt
+#import tensorflow as tf
+# import CMAPSSDataset
+import pandas as pd
+import datetime
+import keras
+from keras.layers import Lambda
+import math
+import keras.backend as K
+import tensorflow as tf
+from tfdeterminism import patch
+from sklearn.model_selection import train_test_split
+from keras.utils.vis_utils import plot_model
+patch()
+# tf.random.set_seed(0)
+#import keras
+#flags = tf.flags
+#flags.DEFINE_string("weights", None, 'weights of the network')################# the file path of weights
+#flags.DEFINE_integer("epochs", 100, 'train epochs')
+#flags.DEFINE_integer("batch_size", 32, 'batch size for train/test')
+#flags.DEFINE_integer("sequence_length", 32, 'sequence length')
+#flags.DEFINE_boolean('debug', False, 'debugging mode or not')
+#FLAGS = flags.FLAGS
+
+def root_mean_squared_error(y_true, y_pred):
+        return K.sqrt(K.mean(K.square(y_pred - y_true),axis=0))##################  axis=0
+
+def rmse(predictions, targets):
+    return np.sqrt(((predictions - targets) ** 2).mean())
+
+
+
+
+    
+segment=3
+
+
+
+run_times=10
+
+
+
+nb_epochs=2000           #200
+batch_size=64    ## 64        #####300
+# sequence_length=31    ############# min31  max303
+
+patience=50
+patience_reduce_lr=20
+
+
+
+
+
+seed=2
+
+
+
+num_filter1=64
+num_filter2=128
+num_filter3=64
+
+
+
+kernel1_size=8
+kernel2_size=5
+kernel3_size=3
+
+
+
+sequence_length=20
+
+
+
+
+
+
+
+             
+# X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_37,sequence_length)  
+# 
+
+
+# X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_38,sequence_length) 
+
+X_train , Y_train , X_test ,  Y_test =get_input_out_2(CS2_36,CS2_37,sequence_length) 
+
+
+
+for FD in [1]: ######['1','2','3','4']
+    # if max_life==110 and FD=='1':
+    #     continue
+    # if max_life==110 and FD=='2':
+        # continue
+    
+    FD_feature_columns=[]
+ 
+
+
+
+
+
+
+
+
+
+
+
+    method_name='grid_FD{}_TaFCN_npseed{}_segment_{}'.format(FD,seed,segment)
+    # method_name='FCN_RUL_1out_train_split_test'
+    dataset='cmapssd'
+    
+    
+    def unbalanced_penalty_score_1out(Y_test,Y_pred) :
+          
+        s=0    
+        for i in range(len(Y_pred)):
+            if Y_pred[i]>Y_test[i]:
+                s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+            else:
+                s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+        print('unbalanced_penalty_score{}'.format(s))
+        return s  
+      
+    def error_range_1out(Y_test,Y_pred) :           
+        error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+        print('error range{}'.format(error_range))
+        return error_range
+    
+ 
+    
+    X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1,1)
+    
+    X_test=X_test.reshape(X_test.shape[0],X_train.shape[1],1,1)
+    
+    # x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+    
+    
+    import six
+    
+    import keras.backend as K
+    from keras.utils.generic_utils import deserialize_keras_object
+    from keras.utils.generic_utils import serialize_keras_object
+    from tensorflow.python.ops import math_ops
+    from tensorflow.python.util.tf_export import tf_export
+    
+    
+    
+    
+    
+    from tensorflow.python.ops import math_ops
+    
+
+    
+
+    
+    
+    #########np.greater_equal([4, 2, 1], [2, 2, 2])array([ True, True, False])
+    #############tf.cast( ) 或者K.cast( ) 是执行 tensorflow 中的张量数据类型转换，比如读入的图片是int8类型的，一定要在训练的时候把图片的数据格式转换为float32.
+    
+    ################reduce_sum reduce dimensinality and get sum
+
+
+    
+
+            #return inputs*x   
+    
+
+    
+    
+
+
+    # reshape_size=len(FD_feature_columns)*int((sequence_length/3))
+    def FCN_model():
+    #    in0 = keras.Input(shape=(sequence_length,train_feature_slice.shape[1]))  # shape: (batch_size, 3, 2048)
+    #    in0_shaped= keras.layers.Reshape((train_feature_slice.shape[1],sequence_length,1))(in0)   
+    
+        in0 = keras.Input(shape=(X_train.shape[1],X_train.shape[2],X_train.shape[3]),name='layer_13')  # shape: (batch_size, 3, 2048)
+    #    begin_senet=SeBlock()(in0)
+        x = keras.layers.AveragePooling2D(pool_size=(int(sequence_length/segment), 1), strides=int(sequence_length/segment),name='layer_12')(in0)
+        # x = keras.layers.Reshape((-1,1))(x) 
+        
+        # x = keras.layers.Reshape((len(FD_feature_columns)*int((sequence_length/3)),))(x)             
+        x = keras.layers.Reshape((-1,))(x)               
+        # x = keras.layers.GlobalAveragePooling2D()(in0)
+        x = keras.layers.Dense(1, use_bias=False,activation=keras.activations.relu)(x)
+        kernel = keras.layers.Dense(1, use_bias=False,activation=keras.activations.hard_sigmoid,name='layer_11')(x)
+        begin_senet= keras.layers.Multiply(name='layer_10')([in0,kernel])    #给通道加权重
+     
+    
+       
+    
+    #     conv0 = keras.layers.
+        
+        
+        conv0 = keras.layers.Conv2D(num_filter1, kernel1_size, strides=1, padding='same',name='layer_9')(begin_senet)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_8')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter2, kernel2_size, strides=1, padding='same',name='layer_7')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_6')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter3, kernel3_size, strides=1, padding='same',name='layer_5')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_4')(conv0)
+        conv0 = keras.layers.GlobalAveragePooling2D(name='layer_3')(conv0)
+        conv0 = keras.layers.Dense(64, activation='relu',name='layer_2')(conv0)
+        out = keras.layers.Dense(1, activation='relu',name='layer_1')(conv0)
+    
+        
+        
+    
+    
+    
+        model = keras.models.Model(inputs=in0, outputs=[out])    
+    
+        return model
+    
+    
+    # ##############shuaffle the data
+    np.random.seed(seed)
+    index=np.arange(X_train.shape[0])
+    np.random.shuffle(index,)
+    
+     
+    X_train=X_train[index]#X_train是训练集，y_train是训练标签
+    Y_train=Y_train[index]
+    
+    #X_train, Xtest, Y_train, ytest = train_test_split(X_train, Y_train, test_size=0.7, random_state=0)
+    
+    
+    if __name__ == '__main__':
+    
+        error_record=[]
+        index_record=[]
+        unbalanced_penalty_score_record=[]
+        error_range_left_record=[]
+        error_range_right_record=[]
+        index_min_val_loss_record,min_val_loss_record=[],[]
+        
+        if os.path.exists(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name)):os.remove(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name))
+    
+     
+    
+
+    
+        rul_pred_array_list=[]
+        true_out_array_list=[]          
+        error_pred_array_list=[]
+                    
+    #######             single  output                
+    
+        for i in range(run_times):
+            print('xxx')
+ 
+            model=FCN_model()
+            plot_model(model, to_file=r"F:\桌面11.17\project\RUL\Flatten.png", show_shapes=True)#########to_file='Flatten.png',r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5
+            
+            optimizer = keras.optimizers.Adam()
+            model.compile(loss='mse',#loss=root_mean_squared_error,
+                          optimizer=optimizer,
+                          metrics=[root_mean_squared_error])
+             
+            reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = 'loss', factor=0.5,
+                              patience=patience_reduce_lr, min_lr=0.0001) 
+            
+
+    #                  verbose=1, validation_split=VALIDATION_SPLIT, callbacks = [reduce_lr])   
+            model_name='{}_dataset_{}_log{}_time{}'.format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+            earlystopping=keras.callbacks.EarlyStopping(monitor='loss',patience=patience,verbose=1)
+            modelcheckpoint=keras.callbacks.ModelCheckpoint(monitor='loss',filepath=r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),save_best_only=True,verbose=1)
+            hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+                      verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+    #        hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+    #                  verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+            log = pd.DataFrame(hist.history)
+            log.to_excel(r"F:\桌面11.17\project\RUL\experiments_result\log\{}_dataset_{}_log{}_time{}.xlsx".format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S')))
+            
+            print(hist.history.keys())
+            epochs=range(len(hist.history['loss']))
+            plt.figure()
+            plt.plot(epochs,hist.history['loss'],'b',label='Training loss')
+            plt.plot(epochs,hist.history['val_loss'],'r',label='Validation val_loss')
+            plt.title('Traing and Validation loss')
+            plt.legend()
+            plt.show()
+
+    
+            
+    #        model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error,'Smooth':Smooth,'SeBlock':SeBlock})
+            model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error})
+            for layer in model.layers:
+                layer.trainable=False        
+    #        score = model.evaluate(X_test, Y_test)  ############forbid evaluate!!!!!!!!!!!!!!!!!!
+    #        print('score[1]:{}'.format(score[1]))    ############forbid evaluate!!!!!!!!!!!!!!!!!!    
+            
+            Y_pred=model.predict(X_test)
+    #        rmse=root_mean_squared_error(Y_test,Y_pred)
+    #        with tf.Session() as sess:
+    #            print(rmse.eval())
+            rmse_value=rmse(Y_test,Y_pred)
+            # print('rmse:{}'.format(rmse_value))
+            
+
+            rul_pred_array=np.array(Y_pred)
+            rul_pred_array=rul_pred_array.reshape(rul_pred_array.shape[0])
+            
+            # print(rul_pred_array.shape)
+            
+            true_out_array=np.array(Y_test)
+            
+            error_pred_array=rul_pred_array-true_out_array
+            
+            error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+                # print(sol.x)
+                
+            # print(error_pred_array.sum())
+            # print("xxxxx")
+            # print(error_pred_array)
+                
+                
+            fig, ax = plt.subplots()
+            # 在生成的坐标系下画折线图
+            ax.plot(error_pred_array, linewidth=1)
+            
+            
+            
+            # 显示图形
+            plt.show()   
+             
+            
+            # print(i)
+            # print("rul_pred_array") 
+            # print(list(rul_pred_array))
+            # print("true_out_array") 
+            # print(list(true_out_array))
+            # print("error_pred_array") 
+            # print(list(error_pred_array))  
+            
+            rul_pred_array_list.append(rul_pred_array)
+            true_out_array_list.append(true_out_array)            
+            error_pred_array_list.append(error_pred_array)
+        rul_pred_array=np.mean(rul_pred_array_list,axis=0)
+        true_out_array=np.mean(true_out_array_list,axis=0)
+        error_pred_array=np.mean(error_pred_array_list,axis=0)
+
+
+        print(i)
+        print("rul_pred_array") 
+        print(list(rul_pred_array))
+        print("true_out_array") 
+        print(list(true_out_array))
+        print("error_pred_array") 
+        print(list(error_pred_array))  
+
+
diff --git a/code/comparison experiments/xiandao/DPA_our_method/DPA.py b/code/comparison experiments/xiandao/DPA_our_method/DPA.py
new file mode 100644
index 0000000..7f3b9b6
--- /dev/null
+++ b/code/comparison experiments/xiandao/DPA_our_method/DPA.py	
@@ -0,0 +1,418 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 20 16:46:05 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 22:07:49 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 19 20:41:41 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 18 13:04:56 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:49:17 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 16:26:02 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 14:28:22 2022
+
+@author: Administrator
+"""
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 31 18:17:31 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 14:50:53 2022
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 28 14:55:56 2022
+
+@author: Administrator
+
+"""
+
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=-175
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+
+    
+ 
+    CS2_35=[39.6435188,41.73550651,43.70386749,45.59752512,47.44376057,49.22414953,50.94063898,52.59512744,54.18946618,55.72546025,57.20486968,58.62941047,60.00075568,61.32053643,62.59034292,63.81172539,64.98619508,66.11522516,67.20025165,68.24267429,69.24385741,70.2051308,71.12779051,72.01309967,72.86228928,73.67655898,74.4570778,75.20507849,75.94243447,76.66800439,77.38074463,78.07970525,78.76402602,79.43293254,80.08573257,80.72181228,81.3406328,81.9417267,82.52469474,83.08920252,83.63497739,84.16180538,84.66952821,85.15804041,85.62728657,86.07725857,86.50799299,86.91956854,87.31210365,87.68575403,88.04071039,88.37719619,88.69546552,88.99580098,89.27851166,89.54393124,89.79241606,90.02434336,90.24010948,90.44012824,90.62482925,90.79465641,90.95006638,91.09152714,91.21951663,91.33452138,91.43703525,91.52755821,91.6065952,91.67465492,91.73224887,91.77989023,91.81809293,91.84737073,91.86823627,91.88120029,91.88688229,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.99348548,92.12454651,92.25519913,92.38528194,92.51464058,92.64312776,92.77060337,92.89693457,93.02199582,93.14566892,93.26784304,93.38841476,93.50728797,93.62437397,93.73959135,93.85286598,93.96413095,94.0733265,94.18039996,94.28530563,94.3880047,94.48846517,94.58666168,94.68257544,94.77619407,94.87901865,95.03217647,95.18676599,95.34278076,95.50021455,95.65906133,95.81931527,95.98097074,96.14402231,96.3084647,96.49096621,96.71010318,96.93286811,97.15923479,97.38917506,97.62265881,97.85965413,98.10012733,98.34404302,98.59136424,98.84205245,99.09606759,99.35336831,99.61391177,99.87765393,100.1445496,100.4145523,100.6876147,100.9636882,101.2427235,101.5246703,101.8094774,102.0970931,102.3874649,102.6805395,102.9762632,103.2745819,103.5754409,103.8787851,104.1845593,104.4927078,104.8031748,105.1159044,105.4308406,105.7479272,106.0671083,106.3883279,106.7115303,107.0366596,107.3636608,107.6924786,108.0230583,108.3553457,108.6892868,109.0248284,109.3619176,109.7005022,110.0405307,110.3819523,110.7247168,111.0687749,111.4140781,111.7605789,112.1082306,112.4569874,112.8068047,113.1576388,113.5094471,113.8621881,114.2158217,114.5703086,114.9256111,115.2816925,115.6385177,115.9960525,116.3542646,116.7131227,117.072597,117.4326594,117.793283,118.1544425,118.5161143,118.8782761,119.2409074,119.6039892,119.9675041,120.3314367,120.6957727,121.0604999,121.4256078,121.7910874,122.1569315,122.5231349,122.8896939,123.2566067,123.6238731,123.9914949,124.3594757,124.7278208,125.0965374,125.4656343,125.8351225,126.2050145,126.5753248,126.9460695,127.3172667,127.6889364,128.0611002,128.4337816,128.8070058,129.1807998,129.5551926,129.9302146,130.3058983,130.6822776,131.0593884,131.4372681,131.8159558,132.1954926,132.5759206,132.9572842,133.3396289,133.723002,134.1074524,134.4930303,134.8797876,135.2677775,135.6570549,136.0476757,136.4396975,136.8331792,137.2281807,137.6247635,138.0229903,138.4229246,138.8246317,139.2281774,139.6336288,140.0410542,140.4505224,140.8621036,141.2758687,141.6918894,142.1102381,142.5309883,142.9542136,143.3799888,143.808389,144.2394899,144.6733675,145.1100985,145.5497597,145.9924284,146.4381821,146.8870983,147.3392549,147.7947297,148.2536005,148.7159452,149.1818414,149.6513666,150.124598,150.6016127,151.0824871,151.5672974,152.0561191,152.5490275,153.0460967,153.5474006,154.0530121,154.5630033,155.0774454,155.5964086,156.119962,156.6481737,157.1811105,157.7188382,158.2614209,158.8089216,159.3614017,159.9189212,160.4815383,161.0493096,161.6222903,162.2005332,162.7840897,163.373009,163.9673384,164.5671231,165.1724063]    
+         
+    CS2_36=[24.21603668,27.76392576,31.05812474,34.11272657,36.94126062,39.55670907,41.97152296,44.19763785,46.24648927,48.12902763,49.85573305,51.43662963,52.88129959,54.19889697,55.39816113,56.48742985,57.47465218,58.36740102,59.17288536,59.89796229,60.54914868,61.13263263,61.65428459,62.11966834,62.53405155,62.90241619,63.22946866,63.51964968,63.77986482,64.02124681,64.24612443,64.45667649,64.6549378,64.84280487,65.02204165,65.19428489,65.36104955,65.52373394,65.68362478,65.84190207,65.99964387,66.15783094,66.31735119,66.47900406,66.64350478,66.81148844,66.983514,67.16006816,67.34156907,67.52837002,67.72076293,67.91898175,68.12320577,68.33356283,68.55013239,68.77294851,69.00200277,69.23724705,69.47859619,69.72593064,69.97909895,70.23792017,70.50218623,70.77166413,71.04609814,71.32521189,71.62235332,72.01186156,72.41093197,72.81906332,73.23573249,73.66039698,74.09249736,74.53145956,74.97669714,75.42761342,75.88360347,76.34405616,76.80835594,77.27588467,77.7460233,78.21815351,78.69165919,79.16592799,79.64035262,80.11433222,80.58727357,81.05859228,81.52771389,81.9940749,82.45712376,82.91632177,83.37114395,83.82107982,84.26563414,84.7043276,85.13669743,85.562298,85.98070136,86.39149766,86.79429563,87.18872298,87.57442667,87.95107329,88.31834924,88.67596103,89.02363537,89.3611194,89.6881807,90.00460745,90.3102084,90.60481293,90.88827096,91.16045296,91.42124983,91.67057279,91.90835323,92.13454258,92.34911212,92.55205274,92.74337473,92.92310751,93.0942088,93.25737267,93.41259973,93.55990377,93.69931161,93.8308629,93.95460987,94.07061711,94.17896129,94.27973097,94.3730263,94.45895874,94.53765081,94.60923579,94.67537072,94.73773813,94.79638462,94.85136201,94.90272718,94.95054198,94.99487308,95.03579181,95.07337407,95.10770016,95.13885463,95.16692615,95.19200739,95.21419481,95.23358857,95.25029236,95.26441323,95.27606148,95.28535047,95.29239651,95.29731865,95.30023858,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.33521881,95.37337294,95.41511465,95.55435079,95.71377564,95.88296678,96.06207811,96.25125625,96.4506404,96.6603623,96.97094911,97.36193722,97.76950693,98.19375769,98.6347772,99.09264146,99.56741468,100.0591493,100.567886,101.0936539,101.6364702,102.1963406,102.7732591,103.3672081,103.9781587,104.6060704,105.2508912,105.9125583,106.5909971,107.2861225,107.997838,108.7260364,109.4706,110.2314,111.0082975,111.8011431,112.6097773,113.4340305,114.2737233,115.1286665,115.9986614,116.8835001,117.7829651,118.6968305,119.6248611,120.5668133,121.5224352,122.4914666,123.4736392,124.4686773,125.4762972,126.4962082,127.5281125,128.5717052,129.6266751,130.6927044,131.7694691,132.8566394,133.9538799,135.0608497,136.1772027,137.3025878,138.4366496,139.5790278,140.7293584,141.8872733,143.0524007,144.2243656,145.4027897,146.587292,147.7774889,148.9729945,150.1734206,151.3783777,152.5874742,153.8003175,155.0165141,156.2356696,157.457389,158.6812774,159.9069395,161.1339807,162.3620068,163.5906241,164.8194404,166.0480645,167.2761068,168.5031796,169.7288971,170.9528758,172.1747347,173.3940956,174.6105832,175.8238256,177.033454,178.2391036,179.4404132,180.6370259,181.828589,183.0147543,184.1951785,185.3695228,186.5374541,187.6986441,188.8527702,189.9995156,191.1385693,192.2696262,193.3923874,194.5065607,195.6118601,196.7080065,197.7947275,198.8717578,199.938839,200.9957205,202.0421585,203.0779171]
+            
+    CS2_37=[29.10206571,37.72371374,46.30597344,54.85235886,56.62552062,58.36924525,60.0864704,61.77995584,63.99136847,66.1840545,68.36028042,70.5221612,71.42482968,72.3169529,73.20022769,74.07622309,74.94638596,75.81204639,76.67442293,77.53462772,78.06032948,78.58578435,79.1118146,79.63915516,80.16845805,80.70029674,81.23517027,81.7735073,82.31567006,82.86195812,83.41261208,83.96781717,84.43206697,84.90108576,85.37491275,85.85354489,86.33693981,86.82501875,87.3176694,87.81474849,88.31608449,88.82148004,89.33071439,89.84354565,90.3597131,90.87893921,91.40093175,91.92538572,92.45198521,92.98040518,93.51031314,94.04137091,94.57323595,95.10556296,95.63800534,96.17021635,96.70185045,97.23256457,97.76201907,98.28987884,98.81581444,99.33950279,99.86062827,100.3788833,100.799267,101.2161926,101.6293813,102.0385652,102.4434876,102.8439039,103.2395815,103.6303009,104.0158552,104.396051,104.7707085,105.139662,105.5027592,105.8598624,106.2108481,106.5556068,106.8940438,107.2260783,107.5516441,107.8706892,108.1831756,108.4890795,108.788391,109.0811137,109.367265,109.6468755,109.9199887,110.186661,110.4469615,110.7009711,110.9487829,111.1905014,111.3580991,111.5198456,111.6758782,111.8263436,111.9713985,112.1112087,112.2459489,112.3782155,112.5104821,112.6427487,112.7750153,112.9072819,113.0395485,113.1718151,113.3040817,113.4363483,113.5686149,113.7008816,113.8331482,113.9654148,114.0976814,114.229948,114.3622146,114.4944812,114.6267478,114.7590144,114.891281,115.0235477,115.1558143,115.2880809,115.4203475,115.5526141,115.6475159,115.7424177,115.8373195,115.9322214,116.0271232,116.122025,116.2169268,116.3118286,116.4067305,116.5016323,116.5965341,116.6914359,116.7863377,116.8812396,116.9761414,117.0710432,117.165945,117.2608468,117.3557486,117.4506505,117.5455523,117.6404541,117.735356,117.8302578,117.9251596,118.0200614,118.1149632,118.209865,118.3047668,118.3996687,118.4945705,118.5894723,118.7430791,118.8966859,119.0502928,119.2038996,119.3575064,119.5111132,119.66472,119.8183269,119.9719337,120.1255405,120.3410954,120.6199926,120.9031699,121.1905016,121.4818542,121.7770873,122.0760531,122.3785971,122.6845585,122.9937702,123.3060594,123.6212477,123.9391517,124.259583,124.582349,124.9072533,125.2340954,125.5626719,125.8927766,126.2242007,126.5567339,126.890164,127.5350476,128.1804012,128.8260108,129.4716627,130.1171441,130.7622433,131.4067503,132.0504575,132.6931595,133.3346546,133.9747441,134.613234,135.2499343,135.8846604,136.5172331,137.1474793,137.775232,138.4003314,139.0226251,139.6419683,140.2582245,140.871266,141.4809741,142.0872396,142.6899633,143.2890561,143.8844398,144.4760473,145.0638225,145.6477213,146.2277117,146.8037739,147.7231425,148.6385812,149.5501089,150.4577574,151.3615723,152.2616127,153.1579516,154.0506758,154.9398859,155.8256967,156.7082371,157.5876498,158.4640915,159.3377331,160.2087588,161.0773667,161.9437682,162.808188,163.6708636,164.532045,165.3919945,166.2509863,167.1093059,167.9672494,168.8251236,169.6832447,170.5419378,171.4015367,172.2623823,173.1248225,173.9892108,174.8559057,175.4924136,176.1319554,176.7748977,177.4216077,178.0724516,178.7277933,179.3879931,180.0534062,180.724381,181.4012578,182.0843664,182.7740249,183.4705376,184.1741929,184.8852616,185.6039941,186.3306187,187.0653393,187.8083325,188.5597454,189.3196931,190.0882554,190.8654747,191.6513525,192.4458466,193.2488681,194.0602778,194.8798829,195.7074339,196.5426204,197.385068,198.2343338,198.9538561,199.6790907,200.4093662,201.1439264,201.8819257,202.6224249,203.3643859,204.1066673,204.8480193,205.5870782,206.3223613,207.0522618,207.7750424,208.4888303,209.1916107,209.8812214,210.5553457,211.2115068,211.8470607,212.4591899,213.0448962,213.6009936,214.1241015,214.6106366,215.0568061,215.4995219,215.9422376]       
+    
+    CS2_38=[29.10206571,37.72371374,46.30597344,54.85235886,56.62552062,58.36924525,60.0864704,61.77995584,63.99136847,66.1840545,68.36028042,70.5221612,71.42482968,72.3169529,73.20022769,74.07622309,74.94638596,75.81204639,76.67442293,77.53462772,78.06032948,78.58578435,79.1118146,79.63915516,80.16845805,80.70029674,81.23517027,81.7735073,82.31567006,82.86195812,83.41261208,83.96781717,84.43206697,84.90108576,85.37491275,85.85354489,86.33693981,86.82501875,87.3176694,87.81474849,88.31608449,88.82148004,89.33071439,89.84354565,90.3597131,90.87893921,91.40093175,91.92538572,92.45198521,92.98040518,93.51031314,94.04137091,94.57323595,95.10556296,95.63800534,96.17021635,96.70185045,97.23256457,97.76201907,98.28987884,98.81581444,99.33950279,99.86062827,100.3788833,100.799267,101.2161926,101.6293813,102.0385652,102.4434876,102.8439039,103.2395815,103.6303009,104.0158552,104.396051,104.7707085,105.139662,105.5027592,105.8598624,106.2108481,106.5556068,106.8940438,107.2260783,107.5516441,107.8706892,108.1831756,108.4890795,108.788391,109.0811137,109.367265,109.6468755,109.9199887,110.186661,110.4469615,110.7009711,110.9487829,111.1905014,111.3580991,111.5198456,111.6758782,111.8263436,111.9713985,112.1112087,112.2459489,112.3782155,112.5104821,112.6427487,112.7750153,112.9072819,113.0395485,113.1718151,113.3040817,113.4363483,113.5686149,113.7008816,113.8331482,113.9654148,114.0976814,114.229948,114.3622146,114.4944812,114.6267478,114.7590144,114.891281,115.0235477,115.1558143,115.2880809,115.4203475,115.5526141,115.6475159,115.7424177,115.8373195,115.9322214,116.0271232,116.122025,116.2169268,116.3118286,116.4067305,116.5016323,116.5965341,116.6914359,116.7863377,116.8812396,116.9761414,117.0710432,117.165945,117.2608468,117.3557486,117.4506505,117.5455523,117.6404541,117.735356,117.8302578,117.9251596,118.0200614,118.1149632,118.209865,118.3047668,118.3996687,118.4945705,118.5894723,118.7430791,118.8966859,119.0502928,119.2038996,119.3575064,119.5111132,119.66472,119.8183269,119.9719337,120.1255405,120.3410954,120.6199926,120.9031699,121.1905016,121.4818542,121.7770873,122.0760531,122.3785971,122.6845585,122.9937702,123.3060594,123.6212477,123.9391517,124.259583,124.582349,124.9072533,125.2340954,125.5626719,125.8927766,126.2242007,126.5567339,126.890164,127.5350476,128.1804012,128.8260108,129.4716627,130.1171441,130.7622433,131.4067503,132.0504575,132.6931595,133.3346546,133.9747441,134.613234,135.2499343,135.8846604,136.5172331,137.1474793,137.775232,138.4003314,139.0226251,139.6419683,140.2582245,140.871266,141.4809741,142.0872396,142.6899633,143.2890561,143.8844398,144.4760473,145.0638225,145.6477213,146.2277117,146.8037739,147.7231425,148.6385812,149.5501089,150.4577574,151.3615723,152.2616127,153.1579516,154.0506758,154.9398859,155.8256967,156.7082371,157.5876498,158.4640915,159.3377331,160.2087588,161.0773667,161.9437682,162.808188,163.6708636,164.532045,165.3919945,166.2509863,167.1093059,167.9672494,168.8251236,169.6832447,170.5419378,171.4015367,172.2623823,173.1248225,173.9892108,174.8559057,175.4924136,176.1319554,176.7748977,177.4216077,178.0724516,178.7277933,179.3879931,180.0534062,180.724381,181.4012578,182.0843664,182.7740249,183.4705376,184.1741929,184.8852616,185.6039941,186.3306187,187.0653393,187.8083325,188.5597454,189.3196931,190.0882554,190.8654747,191.6513525,192.4458466,193.2488681,194.0602778,194.8798829,195.7074339,196.5426204,197.385068,198.2343338,198.9538561,199.6790907,200.4093662,201.1439264,201.8819257,202.6224249,203.3643859,204.1066673,204.8480193,205.5870782,206.3223613,207.0522618,207.7750424,208.4888303,209.1916107,209.8812214,210.5553457,211.2115068,211.8470607,212.4591899,213.0448962,213.6009936,214.1241015,214.6106366,215.0568061,215.4995219,215.9422376]       
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="c1")
+    ax.plot(CS2_36, linewidth=1,c='g',label="c4")
+    ax.plot(CS2_37, linewidth=1,c='y',label="c6")
+    ax.plot(CS2_38, linewidth=1,c='r',label="c6")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35[:-5],CS2_36[:-5],CS2_37[:-5],CS2_38[:-5]
+
+
+
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+print(CS2_35)
+
+CS2_35=list(-(np.array(CS2_35)-CS2_35[0]))
+CS2_36=list(-(np.array(CS2_36)-CS2_36[0]))
+CS2_37=list(-(np.array(CS2_37)-CS2_37[0]))
+CS2_38=list(-(np.array(CS2_38)-CS2_38[0]))
+
+print(CS2_35)
+
+
+print(CS2_35[0])
+print(CS2_36[0])
+print(CS2_37[0])
+print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]<shed:                           ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return CS235
+
+
+
+
+def get_input_out(CS235,CS237,CS236):
+    min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    input_list=[]
+    output_list=[]
+    
+    true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+
+    
+    
+    
+    for i in range(2,len(CS236_health)):
+
+        
+        
+        
+        # one_input.append(np.array([len(CS235_health),len(CS237_health)]))
+        
+        
+
+
+
+
+
+
+
+
+        y = [CS235[0], CS235[min_len-1]]
+        x = [0, (min_len-1)*(min_len-1)]
+        slope_0, intercept_0, r_value, p_value, std_err = linregress(x, y)
+
+        
+        y = [CS237[0], CS237[min_len-1]]
+        x = [0,(min_len-1)*(min_len-1)]
+        slope_1, intercept_1, r_value, p_value, std_err = linregress(x, y)
+        
+        
+        
+        
+        
+        
+
+        one_input=[]
+        one_input.append(np.array(CS235[:i]))
+        one_input.append(np.array(CS237[:i]))
+        
+        one_input.append("CS238")
+    
+        min_len_train=min(len(CS235),len(CS237))
+            
+        one_input.append(np.array(CS235[:min_len_train]))
+            
+      
+        one_input.append(np.array(CS237[:min_len_train]))
+        
+        one_input.append(np.array(CS236[:i]))
+        
+        
+        
+        
+        
+        input_list.append(one_input)
+        
+        one_out=[]
+        one_out.append(np.array(CS236[:i]))
+        
+        output_list.append(one_out)
+        
+        true_out_list.append(max(len(CS236_health)-i-1,0))
+
+
+
+
+
+    
+    true_out_array=np.array(true_out_list)
+    return input_list[:len(CS236_health)],output_list[:len(CS236_health)],true_out_array[:len(CS236_health)]
+
+
+
+#
+
+
+for i in range(2):
+    
+    
+
+    if i==1:
+        
+        input_list,output_list,true_out_array=get_input_out(CS2_35,CS2_36,CS2_37)######第三个变量是测试集 ，前两个是训练集        
+
+
+    
+    rul_pred_list=[]
+    
+    regress_error_list=[]
+    
+    for j in range(len(input_list)):
+        
+        # print(j)
+        # print("jjjjjjjjjjjjjjjjjjjjjjjjjj")
+        index=j
+        def objective(x):
+            
+            integrate_array=input_list[index][0]*x[0]+input_list[index][1]*x[1]+x[2]
+            
+            error = np.sum(np.square(integrate_array - output_list[index][0]))/len(integrate_array)
+            
+            return error    
+        
+
+
+        
+        x0=np.ones(3)
+        x0[2]=0
+ 
+        
+        
+
+        # sol = minimize(objective, x0 ) 
+        sol = minimize(objective, x0 ,method='BFGS', options={'maxiter':(1+1)*200,'gtol':1e-5 }  )
+        u1=sol.x[0] 
+        u2=sol.x[1]
+        c1=input_list[j][3]
+
+        c2=input_list[j][4]
+        
+        offset_vertical=sol.x[2]
+
+
+
+        
+
+        pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1] +offset_vertical
+        # pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve)[-2:] )   
+        pred_curve=u1*c1+u2*c2  + offset_vertical +      np.mean((input_list[j][5] - pred_online_cruve)[int(-min(j*0.2,20)):] ) 
+
+
+#############预测曲线          
+        # plt.plot(range(len(pred_curve)), pred_curve)
+        # plt.show()
+ 
+#############
+
+        
+        ful_pred=len(pred_curve)
+        for ii in range(len(pred_curve)):
+            
+            # if i == len(pred_curve)-1:
+                
+            #     ful_pred==i
+            
+            if pred_curve[ii]<shed:
+                
+                ful_pred=ii
+                
+                break
+
+
+        rul_pred_list.append(ful_pred-j-1) #######(-1  because of  begining from 2)
+
+
+
+
+
+#############实时的拟合曲线与实际曲线的所有点的误差        
+        # pred_online_cruve=u1*input_list[j][0] +u2*input_list[j][1]                
+        # plt.plot(range(len(input_list[j][5])), input_list[j][5] - pred_online_cruve)
+        # plt.show()
+
+#################
+
+
+                
+    
+    rul_pred_array=np.array(rul_pred_list)
+    
+    error_pred_array=rul_pred_array-true_out_array
+    
+    error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+        # print(sol.x)
+        
+    # print(error_pred_array.sum())
+    # print("xxxxx")
+    # print(error_pred_array)
+        
+        
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(error_pred_array, linewidth=1)
+    
+    
+    
+    # 显示图形
+    plt.show()   
+     
+    
+    print(i)
+    print("rul_pred_array") 
+    print(list(rul_pred_array))
+    print("true_out_array") 
+    print(list(true_out_array))
+    print("error_pred_array") 
+    print(list(error_pred_array))    
+
diff --git a/code/comparison experiments/xiandao/prediction_value_and_error_xiandao_c1_c4_to_c6.py b/code/comparison experiments/xiandao/prediction_value_and_error_xiandao_c1_c4_to_c6.py
new file mode 100644
index 0000000..e6cfcfd
--- /dev/null
+++ b/code/comparison experiments/xiandao/prediction_value_and_error_xiandao_c1_c4_to_c6.py	
@@ -0,0 +1,253 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 26 22:25:42 2023
+
+@author: Administrator
+"""
+
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+Si=[355, 340, 326, 311, 307, 304, 301, 297, 293, 289, 285, 281, 279, 277, 275, 273, 272, 270, 268, 266, 265, 264, 262, 261, 260, 259, 257, 256, 255, 254, 252, 251, 250, 249, 248, 247, 245, 244, 243, 242, 241, 240, 238, 237, 236, 235, 234, 233, 231, 230, 229, 228, 226, 225, 224, 223, 222, 220, 219, 218, 217, 216, 214, 213, 212, 211, 210, 209, 208, 207, 206, 205, 205, 204, 203, 202, 201, 200, 199, 198, 197, 197, 196, 195, 194, 193, 193, 192, 191, 190, 190, 189, 188, 188, 187, 186, 186, 185, 185, 184, 184, 183, 183, 183, 182, 182, 181, 181, 180, 180, 179, 179, 178, 178, 178, 177, 177, 176, 176, 175, 175, 174, 174, 174, 173, 173, 172, 172, 171, 171, 171, 170, 170, 170, 169, 169, 169, 168, 168, 167, 167, 167, 166, 166, 166, 165, 165, 165, 164, 164, 163, 163, 163, 162, 162, 162, 161, 161, 161, 160, 160, 159, 159, 158, 158, 158, 157, 157, 156, 156, 155, 155, 154, 153, 153, 152, 151, 150, 150, 149, 148, 148, 147, 146, 146, 145, 144, 143, 143, 142, 141, 140, 139, 138, 137, 135, 134, 133, 132, 131, 129, 128, 127, 126, 124, 123, 122, 121, 120, 118, 117, 116, 115, 114, 112, 111, 110, 109, 108, 107, 106, 104, 103, 102, 101, 99, 97, 96, 94, 92, 91, 89, 87, 86, 84, 83, 81, 79, 78, 76, 75, 73, 72, 70, 68, 67, 65, 64, 62, 61, 59, 58, 56, 54, 53, 51, 50, 49, 48, 47, 45, 44, 43, 42, 41, 39, 38, 37, 36, 34, 33, 32, 31, 29, 28, 27, 25, 24, 22, 21, 20, 18, 17, 15, 14, 12, 11, 9, 8, 7, 6, 4, 3, 2, 0]
+     
+Zhang=[158, 159, 308, 224, 162, 156, 151, 146, 142, 136, 130, 125, 120, 120, 119, 119, 119, 118, 118, 118, 117, 118, 119, 119, 120, 120, 121, 121, 122, 122, 122, 123, 123, 123, 124, 124, 125, 125, 125, 126, 126, 126, 126, 126, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 126, 126, 126, 126, 126, 126, 126, 125, 126, 126, 126, 126, 126, 126, 126, 126, 126, 127, 127, 127, 127, 127, 127, 127, 128, 128, 128, 128, 128, 129, 129, 129, 129, 130, 130, 130, 131, 131, 131, 131, 132, 133, 133, 134, 134, 135, 135, 136, 137, 137, 138, 138, 139, 140, 140, 141, 141, 142, 142, 143, 143, 144, 145, 145, 146, 146, 147, 147, 148, 148, 149, 149, 150, 150, 151, 152, 152, 153, 153, 154, 154, 155, 156, 156, 157, 157, 158, 159, 159, 160, 160, 161, 161, 162, 162, 163, 163, 164, 164, 165, 166, 166, 167, 167, 167, 168, 168, 168, 169, 169, 169, 169, 170, 170, 170, 170, 169, 169, 169, 169, 168, 168, 168, 167, 167, 166, 166, 166, 165, 165, 164, 164, 163, 163, 162, 162, 160, 159, 157, 155, 154, 152, 151, 149, 147, 146, 144, 143, 141, 140, 138, 137, 135, 134, 132, 131, 129, 128, 127, 125, 124, 123, 121, 120, 119, 117, 116, 115, 113, 110, 108, 106, 104, 102, 100, 98, 96, 93, 91, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 71, 69, 67, 65, 63, 62, 60, 58, 56, 55, 53, 52, 50, 49, 48, 47, 45, 44, 43, 42, 40, 39, 38, 36, 35, 34, 32, 31, 30, 28, 27, 25, 24, 22, 21, 19, 18, 16, 15, 13, 12, 10, 9, 7, 6, 5, 3, 2, 1]
+
+Hu=[316, 221, 188, 0, 183, 183, 181, 180, 187, 187, 185, 183, 185, 193, 194, 196, 198, 198, 200, 200, 200, 205, 207, 208, 208, 206, 211, 210, 212, 212, 214, 214, 215, 215, 214, 216, 217, 217, 216, 216, 218, 218, 218, 217, 218, 217, 218, 217, 218, 237, 232, 228, 225, 222, 219, 217, 215, 213, 211, 209, 208, 206, 205, 204, 203, 203, 202, 201, 201, 200, 199, 198, 198, 197, 196, 195, 195, 194, 193, 193, 192, 191, 191, 190, 189, 189, 188, 188, 187, 186, 186, 185, 184, 184, 183, 183, 183, 182, 182, 181, 181, 181, 180, 180, 179, 179, 179, 178, 178, 177, 177, 176, 176, 175, 174, 174, 173, 173, 172, 171, 171, 170, 169, 169, 168, 167, 167, 166, 165, 165, 164, 164, 163, 162, 162, 161, 160, 160, 159, 158, 158, 157, 156, 155, 155, 154, 153, 152, 152, 151, 150, 149, 149, 148, 147, 146, 146, 145, 144, 143, 142, 142, 141, 140, 139, 138, 137, 136, 135, 134, 133, 132, 131, 130, 129, 128, 127, 125, 124, 123, 122, 121, 120, 119, 117, 116, 115,119,120,121,122,122, 124, 123, 124, 125, 124, 124, 123, 122, 120, 118, 117, 115, 114, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100, 99, 98, 97, 97, 94, 92, 89, 87, 85, 82, 80, 78, 76, 74, 73, 71, 69, 67, 66, 64, 63, 61, 60, 58, 57, 55, 54, 53, 51, 50, 49, 47, 46, 45, 43, 42, 41, 40, 39, 38, 37, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 28, 27, 26, 25, 24, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 11, 10, 9, 7 ,6 ,5, 4, 3, 2, 1, 0]        
+DCNN=[293.71826, 282.54242, 279.9193, 279.42642, 281.7723, 282.49982, 280.8286, 278.4371, 275.55255, 272.5454, 270.04382, 267.67554, 265.7529, 264.26883, 263.75766, 264.6298, 266.00943, 265.91074, 262.8673, 258.97083, 255.78398, 253.37302, 251.05667, 248.84615, 246.51001, 243.96347, 241.32593, 238.49959, 235.61723, 232.73686, 229.81897, 226.75534, 223.66226, 220.6268, 217.51285, 214.72266, 212.21048, 209.76314, 207.32886, 204.91028, 202.5071, 200.10727, 197.71072, 195.29793, 192.85397, 190.41064, 188.001, 185.67691, 183.41365, 181.14655, 178.8627, 176.56201, 174.2572, 171.94705, 169.62402, 167.28758, 164.93806, 162.58493, 160.21855, 157.83241, 155.43945, 153.04044, 150.65906, 148.2537, 145.85309, 143.48569, 141.13405, 138.80827, 136.52158, 134.27548, 132.04646, 129.84085, 127.65794, 125.51308, 123.419655, 121.35057, 119.29183, 117.26587, 115.305336, 113.46, 111.69757, 109.97882, 108.31112, 106.69755, 105.136375, 103.62703, 102.16173, 100.73247, 99.34051, 97.98482, 96.67435, 95.39641, 94.162094, 92.97775, 91.848114, 90.752396, 89.708015, 88.719124, 87.76563, 86.86572, 86.0307, 85.237625, 84.48041, 83.75493, 83.06505, 82.40947, 81.79132, 81.201355, 80.63197, 80.08755, 79.56968, 79.090195, 78.64073, 78.20221, 77.77282, 77.35111, 76.93582, 76.52748, 76.1237, 75.72313, 75.32444, 74.927795, 74.53616, 74.1491, 73.764114, 73.38293, 73.0135, 72.64239, 72.27529, 71.92018, 71.56847, 71.22337, 70.88965, 70.565475, 70.24925, 69.93886, 69.63402, 69.33477, 69.04368, 68.75808, 68.47375, 68.19264, 67.91753, 67.657936, 67.40932, 67.16538, 66.9235, 66.6836, 66.44458, 66.20731, 65.972275, 65.73852, 65.50614, 65.27462, 65.04403, 64.81419, 64.58451, 64.35496, 64.11837, 63.88611, 63.65055, 63.404297, 63.16114, 62.916004, 62.662636, 62.400185, 62.128277, 61.84784, 61.55303, 61.247032, 60.930187, 60.59635, 60.2578, 59.922108, 59.56124, 59.159393, 58.71778, 58.259235, 57.79026, 57.30367, 56.79196, 56.263527, 55.73451, 55.205524, 54.66266, 54.08277, 53.463306, 52.818462, 52.164207, 51.50429, 50.808178, 50.132328, 49.449036, 48.733692, 48.074234, 47.430973, 46.753647, 46.039967, 45.298492, 44.525066, 43.71769, 42.874043, 41.97477, 41.08634, 40.232136, 39.384865, 38.51236, 37.53138, 36.443245, 35.36508, 34.315514, 33.291683, 32.31306, 31.396597, 30.516644, 29.685324, 28.884039, 28.117405, 27.3988, 26.721777, 26.09158, 25.505644, 24.938002, 24.410057, 23.923883, 23.452187, 23.018642, 22.598112, 22.191236, 21.79636, 21.401001, 21.01373, 20.643208, 20.288727, 19.950794, 19.631191, 19.358683, 19.136791, 18.926184, 18.646757, 18.290215, 17.916155, 17.553234, 17.201807, 16.861252, 16.530056, 16.20242, 15.878375, 15.564667, 15.2593975, 14.955856, 14.6604185, 14.369702, 14.086255, 13.816765, 13.554998, 13.295621, 13.047714, 12.797143, 12.550102, 12.306377, 12.070278, 11.843157, 11.627325, 11.422579, 11.231264, 11.050455, 10.880195, 10.708807, 10.543206, 10.381536, 10.242185, 10.118925, 9.999968, 9.884528, 9.769572, 9.656583, 9.543007, 9.429403, 9.316863, 9.2063675, 9.0976715, 8.992862, 8.891032, 8.794586, 8.700773, 8.6111, 8.523294, 8.4372835, 8.352652, 8.267385, 8.184611, 8.104288]
+
+TaFCN=[271.70255, 278.1811, 279.59442, 275.33707, 267.25534, 261.51987, 260.51773, 238.3278, 237.01205, 234.81128, 232.41821, 229.69409, 237.60434, 246.31145, 245.9124, 242.31522, 238.98984, 239.53242, 238.17119, 234.6303, 231.75859, 230.65923, 229.42007, 227.88896, 225.91232, 223.30527, 220.18137, 216.91129, 213.83635, 211.04514, 208.11935, 205.14246, 202.21788, 199.30307, 196.46918, 193.85356, 191.47385, 189.29922, 187.06943, 184.82101, 182.58586, 180.38762, 178.2265, 176.09373, 173.98222, 171.87532, 169.83109, 167.8489, 165.90646, 164.00354, 162.06941, 160.08308, 158.06302, 155.97736, 153.83836, 151.64383, 149.4, 147.09041, 144.70346, 142.23907, 139.76718, 137.32303, 134.88516, 132.30402, 129.47746, 126.41914, 123.15102, 119.79266, 116.47392, 113.26123, 110.1855, 107.295166, 104.67407, 102.346146, 100.26659, 98.400764, 96.679276, 95.06999, 93.610306, 92.29664, 91.12023, 90.0634, 89.060646, 88.09447, 87.15912, 86.26463, 85.40833, 84.58616, 83.79849, 83.037704, 82.30241, 81.58846, 80.8876, 80.20327, 79.54628, 78.92237, 78.35119, 77.8232, 77.326294, 76.86369, 76.43638, 76.041245, 75.668625, 75.312355, 74.943954, 74.55795, 74.14744, 73.7102, 73.24769, 72.76615, 72.28685, 71.83478, 71.42007, 71.03729, 70.658226, 70.287575, 69.93013, 69.59055, 69.26376, 68.94182, 68.61912, 68.29785, 67.979996, 67.670845, 67.367905, 67.07418, 66.79194, 66.520874, 66.26416, 66.016014, 65.77409, 65.54114, 65.31154, 65.08749, 64.863, 64.63673, 64.405075, 64.16662, 63.91802, 63.65744, 63.38681, 63.109577, 62.83499, 62.579243, 62.34947, 62.139534, 61.92353, 61.703693, 61.4919, 61.2826, 61.072685, 60.863045, 60.655426, 60.448875, 60.243813, 60.037895, 59.831825, 59.628296, 59.41889, 59.199974, 58.970276, 58.730965, 58.484978, 58.251503, 58.02663, 57.797356, 57.533226, 57.259857, 56.995583, 56.75218, 56.511005, 56.280952, 56.02528, 55.727135, 55.328846, 54.80796, 54.025734, 52.947945, 51.7165, 50.447968, 49.398903, 48.74472, 48.401524, 48.10969, 47.855225, 47.561546, 47.189754, 46.73313, 46.239426, 45.740154, 45.21382, 44.649128, 44.050213, 43.42681, 42.797615, 42.15772, 41.50396, 40.84182, 40.175087, 39.50308, 38.83703, 38.194683, 37.601017, 37.091846, 36.675438, 36.32338, 35.943676, 35.391064, 34.592407, 33.607433, 32.63714, 31.693567, 30.77666, 29.894413, 29.0312, 28.205399, 27.417526, 26.664394, 25.94607, 25.26581, 24.619286, 24.000116, 23.412292, 22.85373, 22.318396, 21.823658, 21.366789, 20.947159, 20.56236, 20.204248, 19.872768, 19.57027, 19.289059, 19.028408, 18.786219, 18.572918, 18.400135, 18.272484, 18.15736, 18.017448, 17.804714, 17.511547, 17.225128, 16.947369, 16.678188, 16.410032, 16.146355, 15.886775, 15.634738, 15.386587, 15.142054, 14.900332, 14.661756, 14.424039, 14.190425, 13.962222, 13.738963, 13.521327, 13.304758, 13.08531, 12.870285, 12.657373, 12.447558, 12.243441, 12.044533, 11.851888, 11.663368, 11.479512, 11.294484, 11.104896, 10.922815, 10.759542, 10.626402, 10.533262, 10.441904, 10.35272, 10.267083, 10.183947, 10.10316, 10.024042, 9.946326, 9.871294, 9.799982, 9.7310505, 9.6642, 9.599112, 9.539144, 9.482988, 9.429937, 9.38101, 9.3336115, 9.287108, 9.241938]
+
+Our=[106, 12, 16, 306, 305, 304, 303, 302, 296, 288, 281, 277, 274, 271, 268, 266, 264, 263, 261, 259, 258, 257, 256, 255, 254, 253, 252, 251, 250, 250, 249, 248, 248, 247, 246, 246, 245, 245, 244, 244, 243, 243, 242, 242, 241, 241, 240, 239, 239, 238, 238, 237, 237, 236, 235, 235, 234, 233, 232, 231, 231, 230, 229, 228, 226, 225, 224, 223, 221, 220, 218, 216, 215, 213, 211, 209, 207, 205, 204, 202, 200, 199, 197, 195, 194, 193, 191, 190, 189, 188, 187, 186, 185, 184, 184, 183, 182, 181, 180, 180, 179, 178, 178, 177, 177, 176, 175, 175, 174, 174, 173, 172, 172, 171, 171, 170, 169, 169, 168, 167, 167, 166, 165, 164, 164, 163, 162, 161, 160, 160, 159, 158, 157, 156, 156, 155, 154, 153, 152, 151, 151, 150, 149, 148, 147, 146, 146, 145, 144, 143, 142, 141, 141, 140, 139, 138, 137, 136, 135, 135, 134, 133, 132, 131, 130, 129, 128, 128, 127, 126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 101, 100, 99, 98, 97, 96, 95, 93, 92, 91, 90, 89, 88, 87, 86, 84, 83, 82, 81, 80, 79, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 66, 65, 64, 63, 62, 61, 60, 59, 57, 56, 55, 54, 53, 51, 50, 49, 48, 46, 45, 44, 42, 41, 40, 38, 37, 36, 35, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 19, 18, 17, 16, 16, 15, 14, 14, 13, 12, 12, 11, 10, 10, 9, 8, 8, 7, 6, 6, 5, 4, 3, 3, 2, 1, 0, 0]
+
+groud_truth=[822.0, 821.0, 820.0, 819.0, 818.0, 817.0, 816.0, 815.0, 814.0, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 807.0, 806.0, 805.0, 804.0, 803.0, 802.0, 801.0, 800.0, 799.0, 798.0, 797.0, 796.0, 795.0, 794.0, 793.0, 792.0, 791.0, 790.0, 789.0, 788.0, 787.0, 786.0, 785.0, 784.0, 783.0, 782.0, 781.0, 780.0, 779.0, 778.0, 777.0, 776.0, 775.0, 774.0, 773.0, 772.0, 771.0, 770.0, 769.0, 768.0, 767.0, 766.0, 765.0, 764.0, 763.0, 762.0, 761.0, 760.0, 759.0, 758.0, 757.0, 756.0, 755.0, 754.0, 753.0, 752.0, 751.0, 750.0, 749.0, 748.0, 747.0, 746.0, 745.0, 744.0, 743.0, 742.0, 741.0, 740.0, 739.0, 738.0, 737.0, 736.0, 735.0, 734.0, 733.0, 732.0, 731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+
+
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our),len(Hu))-10
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+ax.plot(Si[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='-.')
+ax.plot(Zhang[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='-.')
+ax.plot(Hu[-min_len:], linewidth=2,c='#A67D3D',label="Hu' model",linestyle='-.')
+ax.plot(DCNN[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='-.')
+ax.plot(TaFCN[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='-.')
+
+
+ax.plot(Our[-min_len:], linewidth=2,c='r',label="Our",linestyle='-.')
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+error_rate=0.1
+error_rate_1=0.4
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1-error_rate),color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1+error_rate), np.array(groud_truth[-min_len:])*(1+error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+# plt.fill_between(range(min_len), np.array(groud_truth[-min_len:])*(1-error_rate), np.array(groud_truth[-min_len:])*(1-error_rate_1),color="#E7E7E7")# color="#CCEEFF")
+       
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看   
+plt.xlabel('Cutting times (pass)',font1) #X轴标签
+plt.ylabel("Predicted RUL (Pass)",font1) #Y轴标签
+plt.legend()
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_xiandao_c1_c4_to_c6.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\prediction_rul_xiandao_c1_c4_to_c6.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
+
+Si_error=[61.0, 47.0, 34.0, 20.0, 17.0, 15.0, 13.0, 10.0, 7.0, 4.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 7.0, 8.0, 9.0, 9.0, 9.0, 10.0, 10.0, 10.0, 10.0, 11.0, 11.0, 11.0, 11.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 15.0, 15.0, 15.0, 15.0, 16.0, 16.0, 16.0, 16.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 16.0, 16.0, 16.0, 16.0, 16.0, 15.0, 15.0, 15.0, 15.0, 14.0, 14.0, 14.0, 13.0, 13.0, 13.0, 12.0, 12.0, 11.0, 11.0, 10.0, 10.0, 9.0, 8.0, 8.0, 7.0, 7.0, 6.0, 6.0, 5.0, 5.0, 4.0, 4.0, 3.0, 2.0, 2.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 2.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 6.0, 7.0, 7.0, 8.0, 9.0, 9.0, 10.0, 11.0, 11.0, 12.0, 12.0, 13.0, 14.0, 14.0, 15.0, 16.0, 16.0, 17.0, 18.0, 18.0, 19.0, 19.0, 20.0, 21.0, 21.0, 22.0, 23.0, 23.0, 24.0, 25.0, 25.0, 26.0, 26.0, 27.0, 27.0, 28.0, 29.0, 29.0, 30.0, 30.0, 31.0, 31.0, 32.0, 32.0, 32.0, 33.0, 33.0, 33.0, 33.0, 34.0, 34.0, 34.0, 35.0, 35.0, 35.0, 36.0, 36.0, 36.0, 36.0, 37.0, 37.0, 37.0, 37.0, 37.0, 37.0, 37.0, 36.0, 36.0, 36.0, 36.0, 36.0, 35.0, 35.0, 35.0, 35.0, 34.0, 34.0, 34.0, 34.0, 34.0, 33.0, 33.0, 33.0, 33.0, 33.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 31.0, 31.0, 31.0, 31.0, 30.0, 29.0, 29.0, 28.0, 27.0, 27.0, 26.0, 25.0, 25.0, 24.0, 24.0, 23.0, 22.0, 22.0, 21.0, 21.0, 20.0, 20.0, 19.0, 18.0, 18.0, 17.0, 17.0, 16.0, 16.0, 15.0, 15.0, 14.0, 13.0, 13.0, 12.0, 12.0, 12.0, 12.0, 12.0, 11.0, 11.0, 11.0, 11.0, 11.0, 10.0, 10.0, 10.0, 10.0, 9.0, 9.0, 9.0, 9.0, 8.0, 8.0, 8.0, 7.0, 7.0, 6.0, 6.0, 6.0, 5.0, 5.0, 4.0, 4.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0]
+     
+Zhang_error=[136.0, 134.0, 16.0, 67.0, 128.0, 133.0, 137.0, 141.0, 144.0, 149.0, 154.0, 158.0, 162.0, 161.0, 161.0, 160.0, 159.0, 159.0, 158.0, 157.0, 157.0, 155.0, 153.0, 152.0, 150.0, 149.0, 147.0, 146.0, 144.0, 143.0, 142.0, 140.0, 139.0, 138.0, 136.0, 135.0, 133.0, 132.0, 131.0, 129.0, 128.0, 127.0, 126.0, 125.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 105.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 85.0, 84.0, 83.0, 82.0, 81.0, 79.0, 78.0, 77.0, 76.0, 74.0, 73.0, 72.0, 70.0, 69.0, 68.0, 67.0, 65.0, 63.0, 62.0, 60.0, 59.0, 57.0, 56.0, 54.0, 52.0, 51.0, 49.0, 48.0, 46.0, 44.0, 43.0, 41.0, 40.0, 38.0, 37.0, 35.0, 34.0, 32.0, 30.0, 29.0, 27.0, 26.0, 24.0, 23.0, 21.0, 20.0, 18.0, 17.0, 15.0, 14.0, 12.0, 10.0, 9.0, 7.0, 6.0, 4.0, 3.0, 1.0, 1.0, 2.0, 4.0, 5.0, 7.0, 9.0, 10.0, 12.0, 13.0, 15.0, 16.0, 18.0, 19.0, 21.0, 22.0, 24.0, 25.0, 27.0, 29.0, 30.0, 32.0, 33.0, 34.0, 36.0, 37.0, 38.0, 40.0, 41.0, 42.0, 43.0, 45.0, 46.0, 47.0, 48.0, 48.0, 49.0, 50.0, 51.0, 51.0, 52.0, 53.0, 53.0, 54.0, 54.0, 55.0, 56.0, 56.0, 57.0, 57.0, 58.0, 58.0, 59.0, 59.0, 60.0, 59.0, 59.0, 58.0, 57.0, 57.0, 56.0, 56.0, 55.0, 54.0, 54.0, 53.0, 53.0, 52.0, 52.0, 51.0, 51.0, 50.0, 50.0, 49.0, 49.0, 48.0, 48.0, 48.0, 47.0, 47.0, 47.0, 46.0, 46.0, 46.0, 45.0, 45.0, 45.0, 44.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 33.0, 32.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 23.0, 22.0, 21.0, 20.0, 19.0, 19.0, 18.0, 17.0, 16.0, 16.0, 15.0, 15.0, 14.0, 14.0, 14.0, 14.0, 13.0, 13.0, 13.0, 13.0, 12.0, 12.0, 12.0, 11.0, 11.0, 11.0, 10.0, 10.0, 10.0, 9.0, 9.0, 8.0, 8.0, 7.0, 7.0, 6.0, 6.0, 5.0, 5.0, 4.0, 4.0, 3.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0]
+DCNN_error=[7.393548583984375, 16.046270751953124, 17.860125732421874, 18.937994384765624, 18.0043701171875, 16.4022216796875, 16.5332763671875, 17.64781799316406, 20.272140502929688, 23.47820129394531, 26.05144348144531, 28.385659790039064, 30.131185913085936, 30.8243896484375, 30.142233276367186, 27.133575439453125, 22.267117309570313, 17.242642211914063, 14.824691772460938, 14.7977294921875, 16.21601257324219, 17.626968383789062, 18.94335021972656, 20.153851318359376, 21.489996337890624, 23.036529541015625, 24.674069213867188, 26.500421142578126, 28.382763671875, 30.263116455078126, 32.18103942871094, 34.24466552734375, 36.33772277832031, 38.37321166992187, 40.487161254882814, 42.27734375, 43.78951721191406, 45.23685913085937, 46.671136474609376, 48.089727783203124, 49.49290161132812, 50.892730712890625, 52.289285278320314, 53.702081298828126, 55.146017456054686, 56.58934326171875, 57.9989990234375, 59.323089599609375, 60.586361694335935, 61.85346984863281, 63.13728637695313, 64.43798217773437, 65.74280700683593, 67.0529541015625, 68.37596740722657, 69.71241149902343, 71.06193542480469, 72.4150634765625, 73.7814453125, 75.16759033203125, 76.56054992675782, 77.9595733642578, 79.34095153808593, 80.74630126953124, 82.14690856933593, 83.5143035888672, 84.86595458984375, 86.19172973632813, 87.47842407226562, 88.72452392578126, 89.95353698730469, 91.15914611816406, 92.34206085205078, 93.48692169189454, 94.58034057617188, 95.6494369506836, 96.70816650390626, 97.7341323852539, 98.69466094970703, 99.53999481201171, 100.30242614746093, 101.02117767333985, 101.68887329101562, 102.30245819091797, 102.86361694335938, 103.3729736328125, 103.83826904296875, 104.26753234863281, 104.65949096679688, 105.01519165039062, 105.32564849853516, 105.6035888671875, 105.83790283203125, 106.02224731445312, 106.15188751220703, 106.24760589599609, 106.29198303222657, 106.28087768554687, 106.23437194824218, 106.1342788696289, 105.96930541992188, 105.76238250732422, 105.51959228515625, 105.24507141113281, 104.93495178222656, 104.59053344726563, 104.20867614746093, 103.79864654541015, 103.36803436279297, 102.9124542236328, 102.43032379150391, 101.90979766845703, 101.35925750732422, 100.79779815673828, 100.22717895507813, 99.64889221191406, 99.06417388916016, 98.47251434326172, 97.87629852294921, 97.27687225341796, 96.67556457519531, 96.07220306396485, 95.46383666992188, 94.85089416503907, 94.23588104248047, 93.61707611083985, 92.9865005493164, 92.35761108398438, 91.7247085571289, 91.07981719970704, 90.43153228759766, 89.77662658691406, 89.1103530883789, 88.43452758789063, 87.75074005126953, 87.06114196777344, 86.36598129272461, 85.66523132324218, 84.95632095336914, 84.24191665649414, 83.52625350952148, 82.8073600769043, 82.0824806213379, 81.3420654296875, 80.59068145751954, 79.83462219238281, 79.0764991760254, 78.31640014648437, 77.55542373657227, 76.7926856994629, 76.02772216796875, 75.26147994995117, 74.49385833740234, 73.72537841796876, 72.95596847534179, 72.18580856323243, 71.4154899597168, 70.64504165649414, 69.88162689208984, 69.11389617919922, 68.34945297241211, 67.59569854736328, 66.83885803222657, 66.0839942932129, 65.3373649597168, 64.59981384277344, 63.87172164916992, 63.15215835571289, 62.44697570800781, 61.75296478271484, 61.0698127746582, 60.40365295410156, 59.74220199584961, 59.07789306640625, 58.438758087158206, 57.840606689453125, 57.28221893310547, 56.74076538085937, 56.20974273681641, 55.69633026123047, 55.20803298950195, 54.736470794677736, 54.26548767089844, 53.79447860717774, 53.33733978271484, 52.91722640991211, 52.53669128417969, 52.18153686523438, 51.835792541503906, 51.495714569091795, 51.19182205200195, 50.86767501831055, 50.55096664428711, 50.266310119628905, 49.92576446533203, 49.569026947021484, 49.24635543823242, 48.96003189086914, 48.701506805419925, 48.47493286132813, 48.282311248779294, 48.12596130371094, 48.02522964477539, 47.91366119384766, 47.76786499023437, 47.61513214111328, 47.48764190673828, 47.46861801147461, 47.556758117675784, 47.634918212890625, 47.68448753356934, 47.70831718444824, 47.6869384765625, 47.603401565551756, 47.48335609436035, 47.314675903320314, 47.115958786010744, 46.88259696960449, 46.60119781494141, 46.278222274780276, 45.90841903686523, 45.49435615539551, 45.06199722290039, 44.589942932128906, 44.07611770629883, 43.54781379699707, 42.98135757446289, 42.401889419555665, 41.80876426696777, 41.20363922119141, 40.59899978637695, 39.986270904541016, 39.35679244995117, 38.71127281188965, 38.04920616149902, 37.36880760192871, 36.64131889343262, 35.863210678100586, 35.07381706237793, 34.353241729736325, 33.709785270690915, 33.08384628295899, 32.446764373779295, 31.798194885253906, 31.138747215270996, 30.46994285583496, 29.797580909729003, 29.12162380218506, 28.4353328704834, 27.740602111816408, 27.044142723083496, 26.339580535888672, 25.630298614501953, 24.913745307922362, 24.18323631286621, 23.445000839233398, 22.70437889099121, 21.952284622192384, 21.202856636047365, 20.449898147583006, 19.693622303009032, 18.929722118377686, 18.15684280395508, 17.37267436981201, 16.57742099761963, 15.76873550415039, 14.949545288085938, 14.119805526733398, 13.291192722320556, 12.456794261932373, 11.618465042114257, 10.757815837860107, 9.881074619293212, 9.000032997131347, 8.11547212600708, 7.230427265167236, 6.423256492614746, 5.914760589599609, 5.406037998199463, 4.895994758605957, 4.383648109436035, 3.8692431449890137, 3.492104244232178, 3.3128729820251466, 3.1299845695495607, 2.9906350135803224, 3.15742712020874, 3.6641798973083497, 4.437283611297607, 5.352651309967041, 6.267384624481201, 7.184611415863037, 8.104288482666016]
+        
+TaFCN_error=[39.375006103515624, 45.959756469726564, 47.59447326660156, 44.13709716796875, 36.85532836914062, 40.58560638427734, 39.57156066894531, 59.840201187133786, 59.87359046936035, 59.26974220275879, 58.55359230041504, 57.954834365844725, 45.58396911621094, 34.42735290527344, 39.944984436035156, 38.78539123535156, 38.45716247558594, 38.278334045410155, 36.98129425048828, 38.42877044677734, 40.241407775878905, 40.340765380859374, 40.57994079589844, 41.11107635498047, 42.08767547607422, 43.69473876953125, 45.81863708496094, 48.08873596191406, 50.16363372802734, 51.954855346679686, 53.88062438964844, 55.85754547119141, 57.78212280273438, 59.696913146972655, 61.5308334350586, 63.14643249511719, 64.5261474609375, 65.70077667236328, 66.93054504394532, 68.17899932861329, 69.41412506103515, 70.6123779296875, 71.77348937988282, 72.90626220703125, 74.01777038574218, 75.1246841430664, 76.16891021728516, 77.15109710693359, 78.09354248046876, 78.99646301269532, 79.9305923461914, 80.91692352294922, 81.93697662353516, 83.02264556884765, 84.16164093017578, 85.35616912841797, 86.5999984741211, 87.90959014892579, 89.296533203125, 90.76092987060547, 92.23280334472656, 93.6769790649414, 95.11483535766601, 96.6959945678711, 98.52251434326172, 100.58084335327149, 102.84898529052734, 105.20732345581055, 107.52607879638671, 109.73876647949218, 111.81450500488282, 113.70483474731445, 115.3259292602539, 116.65385665893555, 117.73341751098633, 118.59923477172852, 119.32072677612305, 119.9300048828125, 120.38969039916992, 120.70336685180663, 120.8797607421875, 120.93659744262695, 120.93934936523438, 120.9055274963379, 120.84087677001953, 120.73537673950196, 120.59166641235352, 120.41384429931641, 120.20150604248047, 119.9622932434082, 119.69759674072266, 119.41153030395508, 119.11239242553711, 118.79673233032227, 118.45371704101562, 118.07762832641602, 117.64880981445313, 117.17680435180664, 116.6737060546875, 116.13630523681641, 115.56363143920899, 114.95875625610351, 114.3313705444336, 113.68764953613281, 113.05604476928711, 112.44205322265626, 111.85256576538086, 111.28980712890625, 110.75231475830078, 110.23385238647461, 109.7131477355957, 109.16521987915038, 108.57993011474609, 107.96271286010742, 107.34177017211914, 106.71242446899414, 106.06986999511719, 105.40945053100586, 104.73624267578126, 104.0581787109375, 103.3808807373047, 102.70214614868163, 102.01999740600586, 101.32915344238282, 100.63209533691406, 99.92581481933594, 99.20806427001953, 98.47912902832032, 97.73584060668945, 96.983984375, 96.22590713500976, 95.45886306762695, 94.68846817016602, 93.91251678466797, 93.1370002746582, 92.3632713317871, 91.59492073059081, 90.83337669372558, 90.08197822570801, 89.34256057739258, 88.61319198608399, 87.89042739868164, 87.16500396728516, 86.42075958251954, 85.65053443908691, 84.8604663848877, 84.07646331787109, 83.29631271362305, 82.50809860229492, 81.71740188598633, 80.92731246948242, 80.13696250915527, 79.34457206726074, 78.55112152099609, 77.75618858337403, 76.9621036529541, 76.16817779541016, 75.37170791625977, 74.58110885620117, 73.80002708435059, 73.02972145080567, 72.26903381347657, 71.51502304077148, 70.74850006103516, 69.9733715057373, 69.20264129638672, 68.4667724609375, 67.74014778137207, 67.00442123413086, 66.24781913757325, 65.48899765014649, 64.71904640197754, 63.97471885681152, 63.27286071777344, 62.67115173339844, 62.192037963867186, 61.974264526367186, 62.052056884765626, 62.28350257873535, 62.55203132629394, 62.601100540161134, 62.255282974243165, 61.59847755432129, 60.89030723571777, 60.14477615356445, 59.438452529907224, 58.81024398803711, 58.26687049865723, 57.76057319641113, 57.259843063354495, 56.78617858886719, 56.35087280273437, 55.94978408813476, 55.57318801879883, 55.2023868560791, 54.842282104492185, 54.49604034423828, 54.158179473876956, 53.82491493225098, 53.496921157836915, 53.16297035217285, 52.80531883239746, 52.39898490905762, 51.90814971923828, 51.32456550598145, 50.67661895751953, 50.056321716308595, 49.608934783935545, 49.40759391784668, 49.39256553649902, 49.36286334991455, 49.3064323425293, 49.22334098815918, 49.105588722229, 48.96880054473877, 48.79460144042969, 48.58247318267822, 48.33560409545898, 48.053932189941406, 47.73418979644775, 47.380713081359865, 46.999884605407715, 46.587709999084474, 46.146269989013675, 45.681603622436526, 45.176340866088864, 44.63321075439453, 44.052842712402345, 43.43763942718506, 42.79575080871582, 42.12723026275635, 41.42973117828369, 40.71093940734863, 39.97159433364868, 39.21378154754639, 38.4270806312561, 37.599864864349364, 36.727515888214114, 35.842638206481936, 34.9825496673584, 34.195284843444824, 33.488454055786136, 32.77487277984619, 32.052630519866945, 31.321811866760253, 30.58996686935425, 29.853644561767577, 29.11322441101074, 28.365260314941406, 27.613411235809327, 26.857947158813477, 26.099666118621826, 25.338243007659912, 24.57596092224121, 23.809574794769286, 23.19328317642212, 22.61654214859009, 22.034177017211913, 21.450747776031495, 20.87019500732422, 20.28521842956543, 19.698130893707276, 19.107946395874023, 18.51206374168396, 17.91097264289856, 17.30361752510071, 16.692136383056642, 16.075992631912232, 15.461021232604981, 14.8506094455719, 14.23269009590149, 13.595963144302369, 12.929102373123168, 12.222241401672363, 11.513602018356323, 10.802784299850464, 10.088422060012817, 9.37155876159668, 8.652344608306885, 7.931461906433105, 7.2091785907745365, 6.48420991897583, 5.820221424102783, 5.438226413726807, 5.225995826721191, 5.193736934661866, 5.414930820465088, 5.78898606300354, 6.241176199913025, 6.981009769439697, 7.733611226081848, 8.4871089220047, 9.241937613487243]
+
+Our_error=[186, 279, 274, 17, 17, 17, 17, 17, 12, 5, 1, 4, 6, 8, 10, 11, 12, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 13, 13, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 7, 7, 6, 6, 6, 5, 5, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, 5, 6, 7, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 14, 14, 14, 14, 14, 13, 13, 13, 12, 12, 11, 11, 11, 10, 10, 9, 9, 9, 8, 8, 7, 7, 7, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 7, 7, 7, 6, 6, 6, 5, 5, 5, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0]
+Hu_error=[]
+for i in range(len(Hu)):    
+    Hu_error.append(abs(len(Hu)-i-Hu[i]))
+    
+
+fig, ax = plt.subplots(figsize = (7, 4))
+min_len=min(len(Si),len(Zhang),len(TaFCN),len(Our))-10
+
+fig, ax = plt.subplots(figsize = (7, 4))
+# 在生成的坐标系下画折线图
+
+ax.plot(np.arange(min_len,0,-1),Si_error[-min_len:], linewidth=2,c='b',label="Si' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Zhang_error[-min_len:], linewidth=2,c='g',label="Zhang' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Hu_error[-min_len:], linewidth=2,c='#A67D3D',label="Hu' model",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),DCNN_error[-min_len:], linewidth=2,c='#E47833',label="DCNN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),TaFCN_error[-min_len:], linewidth=2,c='y',label="TaFCN",linestyle='--')
+ax.plot(np.arange(min_len,0,-1),Our_error[-min_len:], linewidth=2,c='r',label="Our",linestyle='--')
+# ax.plot(groud_truth[-min_len:], linewidth=1,c='black',label="Groud truth")
+
+
+def rmse(x):
+    s=0
+    for i in x:
+        s+=i*i
+    r=sqrt(s/len(x))
+    return r
+
+def aae(x):
+    return np.mean(x)
+
+
+def score(Y_test,Y_pred) :
+      
+    s=0    
+    for i in range(len(Y_pred)):
+        if Y_pred[i]>Y_test[i]:
+            s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+        else:
+            s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+    # print('unbalanced_penalty_score{}'.format(s))
+    return s  
+  
+def error_range(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+     
+    error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return error_range
+
+        
+def error_list(Y_test,Y_pred) : 
+    Y_test =np.array(Y_test)  
+    Y_pred =np.array(Y_pred) 
+
+    error_list=Y_test-Y_pred
+    # Y_test =np.array(Y_test)  
+    # Y_pred =np.array(Y_pred) 
+     
+    # error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+    # print('error range{}'.format(error_range))
+    return list(error_list)
+
+print(list((error_list(groud_truth[-min_len:],Si[-min_len:]),error_list(groud_truth[-min_len:],Zhang[-min_len:]),error_list(groud_truth[-min_len:],Hu[-min_len:]),error_list(groud_truth[-min_len:],DCNN[-min_len:]),error_list(groud_truth[-min_len:],TaFCN[-min_len:]),error_list(groud_truth[-min_len:],Our[-min_len:]))))
+        
+print(list((rmse(Si_error[-min_len:]),rmse(Zhang_error[-min_len:]),rmse(Hu_error[-min_len:]),rmse(DCNN_error[-min_len:]),rmse(TaFCN_error[-min_len:]),rmse(Our_error[-min_len:]))))
+print(list((aae(Si_error[-min_len:]),aae(Zhang_error[-min_len:]),aae(Hu_error[-min_len:]),aae(DCNN_error[-min_len:]),aae(TaFCN_error[-min_len:]),aae(Our_error[-min_len:]))))
+print(list((score(groud_truth[-min_len:],Si[-min_len:]),score(groud_truth[-min_len:],Zhang[-min_len:]),score(groud_truth[-min_len:],Hu[-min_len:]),score(groud_truth[-min_len:],DCNN[-min_len:]),score(groud_truth[-min_len:],TaFCN[-min_len:]),score(groud_truth[-min_len:],Our[-min_len:]))))
+print(list((error_range(groud_truth[-min_len:],Si[-min_len:]),error_range(groud_truth[-min_len:],Zhang[-min_len:]),error_range(groud_truth[-min_len:],Hu[-min_len:]),error_range(groud_truth[-min_len:],DCNN[-min_len:]),error_range(groud_truth[-min_len:],TaFCN[-min_len:]),error_range(groud_truth[-min_len:],Our[-min_len:]))))
+# print
+def predcition(targets,predictions):
+    targets=np.array(targets)
+    predictions=np.array(predictions)
+    
+    
+    # targets=np.array(targets)
+    # predictions=np.array(predictions)
+
+    
+
+    smape = np.sum( np.abs(predictions - targets)) /np.sum( np.abs( targets)) * 100
+    print(np.sum( np.abs(predictions - targets)))
+    
+    print(np.sum( np.abs( targets)))
+    
+    print(smape)
+    y=100-smape
+    
+    # print(y)
+    return y 
+
+print(list((predcition(groud_truth[-min_len:],Si[-min_len:]),predcition(groud_truth[-min_len:],Zhang[-min_len:]),predcition(groud_truth[-min_len:],Hu[-min_len:]),predcition(groud_truth[-min_len:],DCNN[-min_len:]),predcition(groud_truth[-min_len:],TaFCN[-min_len:]),predcition(groud_truth[-min_len:],Our[-min_len:]))))
+
+# 显示图形
+font1 = { 
+'weight' : 'normal',
+'size' : 14,
+}
+    
+      
+    #设置横纵坐标的名称以及对应字体格式
+font2 = {#'family' : 'Times New Roman',
+'weight' : 'normal',
+'size' : 30,
+}
+
+# plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC")# color="#CCEEFF")
+error_rate=0.1
+error_rate_1=0.6
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.arange(min_len,0,-1)*0,color="#CCCCCC",label='Error band (±{:.0%})'.format(error_rate))# color="#CCEEFF")
+plt.fill_between(np.arange(min_len,0,-1), np.array(np.arange(min_len,0,-1))*(error_rate), np.array(np.arange(min_len,0,-1))*(error_rate_1),color="#E7E7E7",label='Error band (±{:.0%})'.format(error_rate_1))# color="#CCEEFF")
+    
+plt.xlabel('Actual RUL (pass)',font1) #X轴标签
+plt.ylabel("Absolute prediction error (pass)",font1) #Y轴标签
+plt.gca().invert_xaxis()
+plt.grid(alpha=0.5,linestyle='-.') #网格线，更好看
+
+plt.legend(loc="upper right")
+# plt.ylim((-10,150))
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_xiandao_c1_c4_to_c6.eps',dpi=800,format='eps',bbox_inches = 'tight')
+plt.savefig(last_last_last_path+r'\figure\by_code\The_absolute_error_of_predicted_RUL_xiandao_c1_c4_to_c6.png',dpi=800,format='png',bbox_inches = 'tight')
+plt.show()
+
diff --git a/code/comparison experiments/xiandao/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt b/code/comparison experiments/xiandao/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt
new file mode 100644
index 0000000..c84ed61
--- /dev/null
+++ b/code/comparison experiments/xiandao/tafcn2022/LOGS-LSTM-Keras-CMAPSS.txt	
@@ -0,0 +1,806 @@
+2023-03-22 19:18:03,735 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,736 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,825 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:18:03,826 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,460 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,461 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:19:58,553 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:20:00,213 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+2023-03-22 19:20:00,235 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+2023-03-22 19:20:00,241 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+2023-03-22 19:20:00,258 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+2023-03-22 19:20:00,312 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+2023-03-22 19:20:00,313 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+2023-03-22 19:20:00,314 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+2023-03-22 19:20:02,670 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+2023-03-22 19:20:02,671 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+2023-03-22 19:20:02,901 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+2023-03-22 19:20:02,940 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+2023-03-22 19:20:02,990 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+2023-03-22 19:20:03,741 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+2023-03-22 19:20:03,983 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+2023-03-22 19:20:04,011 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+2023-03-22 19:20:04,216 - tensorflow - WARNING -From C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
+
+2023-03-22 19:22:47,451 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,452 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,545 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:47,546 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:22:52,870 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,871 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,872 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
+2023-03-22 19:22:52,873 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,874 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,875 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,876 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,877 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,878 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,879 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
+2023-03-22 19:22:52,880 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,881 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,882 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,883 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,884 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335
+2023-03-22 19:22:52,885 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,886 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,887 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,888 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,889 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,890 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'YouYuan' (SIMYOU.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,891 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal heavy normal>) = 10.43
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,892 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,893 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,894 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrak.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,895 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibril.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,896 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Deng.ttf) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,897 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STFangsong' (STFANGSO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,898 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,899 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,900 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal black normal>) = 11.525
+2023-03-22 19:22:52,901 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,902 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,903 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,904 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,905 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,906 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,907 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,908 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,909 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,910 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,911 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,912 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal demi condensed>) = 10.44
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,913 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'icomoon' (iqiyi_logov5.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
+2023-03-22 19:22:52,914 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,915 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,916 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,917 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,918 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXihei' (STXIHEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,919 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,920 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,921 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,922 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:52,923 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,924 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Fraktur' (eufrakb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextrab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,925 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal medium normal>) = 11.145
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,926 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,927 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,928 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,929 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,930 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXinwei' (STXINWEI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,931 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal demi normal>) = 11.24
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,932 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Extra' (euextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,933 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,934 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,935 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,936 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra' (mtextra.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,937 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,938 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,939 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,940 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,941 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,942 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,943 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariblk.ttf) normal normal black normal>) = 6.888636363636364
+2023-03-22 19:22:52,944 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,945 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,946 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,947 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,948 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,949 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,950 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consolab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,951 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,952 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STHupo' (STHUPO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,953 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,954 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,955 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,956 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal semibold normal>) = 11.24
+2023-03-22 19:22:52,957 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger' (Symbol Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,958 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclid.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,959 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZYaoTi' (FZYTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,960 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,961 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,962 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal bold condensed>) = 7.8986363636363635
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger' (Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,963 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,964 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,965 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,966 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tiger Expert' (Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,967 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,968 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,969 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,970 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'KaiTi' (simkai.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,971 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,972 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FZShuTi' (FZSTK.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,973 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,974 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,975 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,976 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal heavy normal>) = 11.43
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,977 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'LiSu' (SIMLI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,978 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,979 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,980 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial Unicode MS' (ARIALUNI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal book normal>) = 11.05
+2023-03-22 19:22:52,981 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,982 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,983 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,984 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,985 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,986 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,987 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STLiti' (STLITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,988 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:52,989 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
+2023-03-22 19:22:52,990 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal regular normal>) = 10.05
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,991 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
+2023-03-22 19:22:52,992 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,993 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,994 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STKaiti' (STKAITI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,995 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal medium normal>) = 10.145
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:52,996 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,997 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:52,998 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:52,999 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Symbol Tiger Expert' (Symbol Tiger Expert.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,000 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal semibold normal>) = 10.24
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,001 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,002 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal medium condensed>) = 10.344999999999999
+2023-03-22 19:22:53,003 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,004 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal black normal>) = 11.525
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,005 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,006 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,007 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,008 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,009 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal demi normal>) = 10.24
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal bold normal>) = 11.335
+2023-03-22 19:22:53,010 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal bold condensed>) = 10.535
+2023-03-22 19:22:53,011 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,012 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal book normal>) = 10.05
+2023-03-22 19:22:53,013 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,014 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,015 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STXingkai' (STXINGKA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,016 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STZhongsong' (STZHONGS.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
+2023-03-22 19:22:53,017 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,018 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Fences' (fences.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,019 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Uighur' (MSUIGHUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,020 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,021 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 7.613636363636363
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
+2023-03-22 19:22:53,022 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,023 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,024 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,025 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,026 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,027 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,028 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,029 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal bold condensed>) = 6.8986363636363635
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'FangSong' (simfang.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'DengXian' (Dengl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,030 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,031 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,032 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsymb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 6.613636363636363
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,033 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,034 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STSong' (STSONG.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,035 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,036 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid' (euclidi.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal light normal>) = 11.24
+2023-03-22 19:22:53,037 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MS Mincho' (MSMINCHO.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,038 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math One' (eumat1.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,039 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'MT Extra Tiger' (Mt Extra Tiger.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,040 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'STCaiyun' (STCAIYUN.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,041 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,042 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,057 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Math Two' (eumat2b.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,058 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,059 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal medium normal>) = 10.145
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,060 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWAD.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal demibold normal>) = 11.24
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,061 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Euclid Symbol' (eucsym.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,062 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,063 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal regular normal>) = 10.05
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal bold condensed>) = 11.535
+2023-03-22 19:22:53,064 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,065 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal black normal>) = 10.525
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee' (LEELAWDB.TTF) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,066 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal demibold normal>) = 10.24
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,067 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal bold normal>) = 11.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Constantia' (constanb.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,068 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal bold normal>) = 10.335
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,069 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'SimHei' (simhei.ttf) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal light normal>) = 10.24
+2023-03-22 19:22:53,070 - matplotlib.font_manager - DEBUG -findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
+2023-03-22 19:22:53,071 - matplotlib.font_manager - DEBUG -findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('C:\\Users\\Administrator\\anaconda3\\envs\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000.
+2023-03-22 19:23:05,774 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_428:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,776 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_427:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,778 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_426:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,779 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_425:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,781 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_424:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,782 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_423:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,783 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_422:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,784 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_421:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,785 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_420:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,786 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_419:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,787 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_418:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,788 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_417:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,789 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_416:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,791 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_415:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,792 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_414:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,793 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_413:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,794 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_412:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,795 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_411:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,796 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_410:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,798 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_409:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:23:05,799 - tensorflow - ERROR -==================================
+Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
+<tf.Tensor 'IsVariableInitialized_408:0' shape=() dtype=bool>
+If you want to mark it as used call its "mark_used()" method.
+It was originally created here:
+  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\optimizers.py", line 126, in set_weights
+    param_values = K.batch_get_value(params)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 2420, in batch_get_value
+    return get_session().run(ops)  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in get_session
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\keras\backend\tensorflow_backend.py", line 199, in <listcomp>
+    [tf.is_variable_initialized(v) for v in candidate_vars])  File "C:\Users\Administrator\anaconda3\envs\python36\lib\site-packages\tensorflow_core\python\util\tf_should_use.py", line 198, in wrapped
+    return _add_should_use_warning(fn(*args, **kwargs))
+==================================
+2023-03-22 19:24:47,221 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,222 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,315 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:24:47,316 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,075 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,162 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:31:54,163 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,082 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,176 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:37:41,177 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,240 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,332 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:47:59,333 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,010 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:48:39,097 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,407 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,505 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:49:25,506 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,126 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,215 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:52:45,216 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,143 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,243 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:03,244 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,337 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,338 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,432 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 19:54:23,433 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,170 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,264 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:32,265 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,578 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,579 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,674 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:03:53,675 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,053 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,054 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,144 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:05:20,145 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,912 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:20,913 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:06:21,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,422 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,423 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,515 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:11:44,517 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,455 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,456 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,550 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:19:27,551 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,408 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,409 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,496 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:24:30,497 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,883 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,884 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,970 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:47,971 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,988 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:51,989 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,080 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:35:52,081 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,943 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:13,944 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,041 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:44:14,042 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,890 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,891 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:49:13,985 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,983 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:44,984 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,071 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 20:58:45,072 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,916 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:58,917 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,006 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 21:08:59,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,073 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,074 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,166 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-22 22:33:22,168 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,894 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:19,897 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,005 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
+2023-03-23 10:01:20,007 - matplotlib.backends.backend_ps - WARNING -The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
diff --git a/code/comparison experiments/xiandao/tafcn2022/TaFCN.py b/code/comparison experiments/xiandao/tafcn2022/TaFCN.py
new file mode 100644
index 0000000..9807390
--- /dev/null
+++ b/code/comparison experiments/xiandao/tafcn2022/TaFCN.py	
@@ -0,0 +1,682 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 22 16:37:59 2023
+
+@author: Administrator
+"""
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+
+shed=175
+
+import numpy as np
+import pandas as pd
+import os
+import pickle
+import scipy as sp
+import datetime
+
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../..")))
+last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../.."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "..")))
+last_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../..")))
+last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../.."))
+
+
+print(os.path.abspath(os.path.join(os.getcwd(), "../../../..")))
+last_last_last_last_path=os.path.abspath(os.path.join(os.getcwd(), "../../../.."))
+
+
+
+
+
+def get_data_list():
+
+    
+ 
+    CS2_35=[39.6435188,41.73550651,43.70386749,45.59752512,47.44376057,49.22414953,50.94063898,52.59512744,54.18946618,55.72546025,57.20486968,58.62941047,60.00075568,61.32053643,62.59034292,63.81172539,64.98619508,66.11522516,67.20025165,68.24267429,69.24385741,70.2051308,71.12779051,72.01309967,72.86228928,73.67655898,74.4570778,75.20507849,75.94243447,76.66800439,77.38074463,78.07970525,78.76402602,79.43293254,80.08573257,80.72181228,81.3406328,81.9417267,82.52469474,83.08920252,83.63497739,84.16180538,84.66952821,85.15804041,85.62728657,86.07725857,86.50799299,86.91956854,87.31210365,87.68575403,88.04071039,88.37719619,88.69546552,88.99580098,89.27851166,89.54393124,89.79241606,90.02434336,90.24010948,90.44012824,90.62482925,90.79465641,90.95006638,91.09152714,91.21951663,91.33452138,91.43703525,91.52755821,91.6065952,91.67465492,91.73224887,91.77989023,91.81809293,91.84737073,91.86823627,91.88120029,91.88688229,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.88839744,91.99348548,92.12454651,92.25519913,92.38528194,92.51464058,92.64312776,92.77060337,92.89693457,93.02199582,93.14566892,93.26784304,93.38841476,93.50728797,93.62437397,93.73959135,93.85286598,93.96413095,94.0733265,94.18039996,94.28530563,94.3880047,94.48846517,94.58666168,94.68257544,94.77619407,94.87901865,95.03217647,95.18676599,95.34278076,95.50021455,95.65906133,95.81931527,95.98097074,96.14402231,96.3084647,96.49096621,96.71010318,96.93286811,97.15923479,97.38917506,97.62265881,97.85965413,98.10012733,98.34404302,98.59136424,98.84205245,99.09606759,99.35336831,99.61391177,99.87765393,100.1445496,100.4145523,100.6876147,100.9636882,101.2427235,101.5246703,101.8094774,102.0970931,102.3874649,102.6805395,102.9762632,103.2745819,103.5754409,103.8787851,104.1845593,104.4927078,104.8031748,105.1159044,105.4308406,105.7479272,106.0671083,106.3883279,106.7115303,107.0366596,107.3636608,107.6924786,108.0230583,108.3553457,108.6892868,109.0248284,109.3619176,109.7005022,110.0405307,110.3819523,110.7247168,111.0687749,111.4140781,111.7605789,112.1082306,112.4569874,112.8068047,113.1576388,113.5094471,113.8621881,114.2158217,114.5703086,114.9256111,115.2816925,115.6385177,115.9960525,116.3542646,116.7131227,117.072597,117.4326594,117.793283,118.1544425,118.5161143,118.8782761,119.2409074,119.6039892,119.9675041,120.3314367,120.6957727,121.0604999,121.4256078,121.7910874,122.1569315,122.5231349,122.8896939,123.2566067,123.6238731,123.9914949,124.3594757,124.7278208,125.0965374,125.4656343,125.8351225,126.2050145,126.5753248,126.9460695,127.3172667,127.6889364,128.0611002,128.4337816,128.8070058,129.1807998,129.5551926,129.9302146,130.3058983,130.6822776,131.0593884,131.4372681,131.8159558,132.1954926,132.5759206,132.9572842,133.3396289,133.723002,134.1074524,134.4930303,134.8797876,135.2677775,135.6570549,136.0476757,136.4396975,136.8331792,137.2281807,137.6247635,138.0229903,138.4229246,138.8246317,139.2281774,139.6336288,140.0410542,140.4505224,140.8621036,141.2758687,141.6918894,142.1102381,142.5309883,142.9542136,143.3799888,143.808389,144.2394899,144.6733675,145.1100985,145.5497597,145.9924284,146.4381821,146.8870983,147.3392549,147.7947297,148.2536005,148.7159452,149.1818414,149.6513666,150.124598,150.6016127,151.0824871,151.5672974,152.0561191,152.5490275,153.0460967,153.5474006,154.0530121,154.5630033,155.0774454,155.5964086,156.119962,156.6481737,157.1811105,157.7188382,158.2614209,158.8089216,159.3614017,159.9189212,160.4815383,161.0493096,161.6222903,162.2005332,162.7840897,163.373009,163.9673384,164.5671231,165.1724063]    
+         
+    CS2_36=[24.21603668,27.76392576,31.05812474,34.11272657,36.94126062,39.55670907,41.97152296,44.19763785,46.24648927,48.12902763,49.85573305,51.43662963,52.88129959,54.19889697,55.39816113,56.48742985,57.47465218,58.36740102,59.17288536,59.89796229,60.54914868,61.13263263,61.65428459,62.11966834,62.53405155,62.90241619,63.22946866,63.51964968,63.77986482,64.02124681,64.24612443,64.45667649,64.6549378,64.84280487,65.02204165,65.19428489,65.36104955,65.52373394,65.68362478,65.84190207,65.99964387,66.15783094,66.31735119,66.47900406,66.64350478,66.81148844,66.983514,67.16006816,67.34156907,67.52837002,67.72076293,67.91898175,68.12320577,68.33356283,68.55013239,68.77294851,69.00200277,69.23724705,69.47859619,69.72593064,69.97909895,70.23792017,70.50218623,70.77166413,71.04609814,71.32521189,71.62235332,72.01186156,72.41093197,72.81906332,73.23573249,73.66039698,74.09249736,74.53145956,74.97669714,75.42761342,75.88360347,76.34405616,76.80835594,77.27588467,77.7460233,78.21815351,78.69165919,79.16592799,79.64035262,80.11433222,80.58727357,81.05859228,81.52771389,81.9940749,82.45712376,82.91632177,83.37114395,83.82107982,84.26563414,84.7043276,85.13669743,85.562298,85.98070136,86.39149766,86.79429563,87.18872298,87.57442667,87.95107329,88.31834924,88.67596103,89.02363537,89.3611194,89.6881807,90.00460745,90.3102084,90.60481293,90.88827096,91.16045296,91.42124983,91.67057279,91.90835323,92.13454258,92.34911212,92.55205274,92.74337473,92.92310751,93.0942088,93.25737267,93.41259973,93.55990377,93.69931161,93.8308629,93.95460987,94.07061711,94.17896129,94.27973097,94.3730263,94.45895874,94.53765081,94.60923579,94.67537072,94.73773813,94.79638462,94.85136201,94.90272718,94.95054198,94.99487308,95.03579181,95.07337407,95.10770016,95.13885463,95.16692615,95.19200739,95.21419481,95.23358857,95.25029236,95.26441323,95.27606148,95.28535047,95.29239651,95.29731865,95.30023858,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.30128046,95.33521881,95.37337294,95.41511465,95.55435079,95.71377564,95.88296678,96.06207811,96.25125625,96.4506404,96.6603623,96.97094911,97.36193722,97.76950693,98.19375769,98.6347772,99.09264146,99.56741468,100.0591493,100.567886,101.0936539,101.6364702,102.1963406,102.7732591,103.3672081,103.9781587,104.6060704,105.2508912,105.9125583,106.5909971,107.2861225,107.997838,108.7260364,109.4706,110.2314,111.0082975,111.8011431,112.6097773,113.4340305,114.2737233,115.1286665,115.9986614,116.8835001,117.7829651,118.6968305,119.6248611,120.5668133,121.5224352,122.4914666,123.4736392,124.4686773,125.4762972,126.4962082,127.5281125,128.5717052,129.6266751,130.6927044,131.7694691,132.8566394,133.9538799,135.0608497,136.1772027,137.3025878,138.4366496,139.5790278,140.7293584,141.8872733,143.0524007,144.2243656,145.4027897,146.587292,147.7774889,148.9729945,150.1734206,151.3783777,152.5874742,153.8003175,155.0165141,156.2356696,157.457389,158.6812774,159.9069395,161.1339807,162.3620068,163.5906241,164.8194404,166.0480645,167.2761068,168.5031796,169.7288971,170.9528758,172.1747347,173.3940956,174.6105832,175.8238256,177.033454,178.2391036,179.4404132,180.6370259,181.828589,183.0147543,184.1951785,185.3695228,186.5374541,187.6986441,188.8527702,189.9995156,191.1385693,192.2696262,193.3923874,194.5065607,195.6118601,196.7080065,197.7947275,198.8717578,199.938839,200.9957205,202.0421585,203.0779171]
+            
+    CS2_37=[29.10206571,37.72371374,46.30597344,54.85235886,56.62552062,58.36924525,60.0864704,61.77995584,63.99136847,66.1840545,68.36028042,70.5221612,71.42482968,72.3169529,73.20022769,74.07622309,74.94638596,75.81204639,76.67442293,77.53462772,78.06032948,78.58578435,79.1118146,79.63915516,80.16845805,80.70029674,81.23517027,81.7735073,82.31567006,82.86195812,83.41261208,83.96781717,84.43206697,84.90108576,85.37491275,85.85354489,86.33693981,86.82501875,87.3176694,87.81474849,88.31608449,88.82148004,89.33071439,89.84354565,90.3597131,90.87893921,91.40093175,91.92538572,92.45198521,92.98040518,93.51031314,94.04137091,94.57323595,95.10556296,95.63800534,96.17021635,96.70185045,97.23256457,97.76201907,98.28987884,98.81581444,99.33950279,99.86062827,100.3788833,100.799267,101.2161926,101.6293813,102.0385652,102.4434876,102.8439039,103.2395815,103.6303009,104.0158552,104.396051,104.7707085,105.139662,105.5027592,105.8598624,106.2108481,106.5556068,106.8940438,107.2260783,107.5516441,107.8706892,108.1831756,108.4890795,108.788391,109.0811137,109.367265,109.6468755,109.9199887,110.186661,110.4469615,110.7009711,110.9487829,111.1905014,111.3580991,111.5198456,111.6758782,111.8263436,111.9713985,112.1112087,112.2459489,112.3782155,112.5104821,112.6427487,112.7750153,112.9072819,113.0395485,113.1718151,113.3040817,113.4363483,113.5686149,113.7008816,113.8331482,113.9654148,114.0976814,114.229948,114.3622146,114.4944812,114.6267478,114.7590144,114.891281,115.0235477,115.1558143,115.2880809,115.4203475,115.5526141,115.6475159,115.7424177,115.8373195,115.9322214,116.0271232,116.122025,116.2169268,116.3118286,116.4067305,116.5016323,116.5965341,116.6914359,116.7863377,116.8812396,116.9761414,117.0710432,117.165945,117.2608468,117.3557486,117.4506505,117.5455523,117.6404541,117.735356,117.8302578,117.9251596,118.0200614,118.1149632,118.209865,118.3047668,118.3996687,118.4945705,118.5894723,118.7430791,118.8966859,119.0502928,119.2038996,119.3575064,119.5111132,119.66472,119.8183269,119.9719337,120.1255405,120.3410954,120.6199926,120.9031699,121.1905016,121.4818542,121.7770873,122.0760531,122.3785971,122.6845585,122.9937702,123.3060594,123.6212477,123.9391517,124.259583,124.582349,124.9072533,125.2340954,125.5626719,125.8927766,126.2242007,126.5567339,126.890164,127.5350476,128.1804012,128.8260108,129.4716627,130.1171441,130.7622433,131.4067503,132.0504575,132.6931595,133.3346546,133.9747441,134.613234,135.2499343,135.8846604,136.5172331,137.1474793,137.775232,138.4003314,139.0226251,139.6419683,140.2582245,140.871266,141.4809741,142.0872396,142.6899633,143.2890561,143.8844398,144.4760473,145.0638225,145.6477213,146.2277117,146.8037739,147.7231425,148.6385812,149.5501089,150.4577574,151.3615723,152.2616127,153.1579516,154.0506758,154.9398859,155.8256967,156.7082371,157.5876498,158.4640915,159.3377331,160.2087588,161.0773667,161.9437682,162.808188,163.6708636,164.532045,165.3919945,166.2509863,167.1093059,167.9672494,168.8251236,169.6832447,170.5419378,171.4015367,172.2623823,173.1248225,173.9892108,174.8559057,175.4924136,176.1319554,176.7748977,177.4216077,178.0724516,178.7277933,179.3879931,180.0534062,180.724381,181.4012578,182.0843664,182.7740249,183.4705376,184.1741929,184.8852616,185.6039941,186.3306187,187.0653393,187.8083325,188.5597454,189.3196931,190.0882554,190.8654747,191.6513525,192.4458466,193.2488681,194.0602778,194.8798829,195.7074339,196.5426204,197.385068,198.2343338,198.9538561,199.6790907,200.4093662,201.1439264,201.8819257,202.6224249,203.3643859,204.1066673,204.8480193,205.5870782,206.3223613,207.0522618,207.7750424,208.4888303,209.1916107,209.8812214,210.5553457,211.2115068,211.8470607,212.4591899,213.0448962,213.6009936,214.1241015,214.6106366,215.0568061,215.4995219,215.9422376]       
+    
+    CS2_38=[29.10206571,37.72371374,46.30597344,54.85235886,56.62552062,58.36924525,60.0864704,61.77995584,63.99136847,66.1840545,68.36028042,70.5221612,71.42482968,72.3169529,73.20022769,74.07622309,74.94638596,75.81204639,76.67442293,77.53462772,78.06032948,78.58578435,79.1118146,79.63915516,80.16845805,80.70029674,81.23517027,81.7735073,82.31567006,82.86195812,83.41261208,83.96781717,84.43206697,84.90108576,85.37491275,85.85354489,86.33693981,86.82501875,87.3176694,87.81474849,88.31608449,88.82148004,89.33071439,89.84354565,90.3597131,90.87893921,91.40093175,91.92538572,92.45198521,92.98040518,93.51031314,94.04137091,94.57323595,95.10556296,95.63800534,96.17021635,96.70185045,97.23256457,97.76201907,98.28987884,98.81581444,99.33950279,99.86062827,100.3788833,100.799267,101.2161926,101.6293813,102.0385652,102.4434876,102.8439039,103.2395815,103.6303009,104.0158552,104.396051,104.7707085,105.139662,105.5027592,105.8598624,106.2108481,106.5556068,106.8940438,107.2260783,107.5516441,107.8706892,108.1831756,108.4890795,108.788391,109.0811137,109.367265,109.6468755,109.9199887,110.186661,110.4469615,110.7009711,110.9487829,111.1905014,111.3580991,111.5198456,111.6758782,111.8263436,111.9713985,112.1112087,112.2459489,112.3782155,112.5104821,112.6427487,112.7750153,112.9072819,113.0395485,113.1718151,113.3040817,113.4363483,113.5686149,113.7008816,113.8331482,113.9654148,114.0976814,114.229948,114.3622146,114.4944812,114.6267478,114.7590144,114.891281,115.0235477,115.1558143,115.2880809,115.4203475,115.5526141,115.6475159,115.7424177,115.8373195,115.9322214,116.0271232,116.122025,116.2169268,116.3118286,116.4067305,116.5016323,116.5965341,116.6914359,116.7863377,116.8812396,116.9761414,117.0710432,117.165945,117.2608468,117.3557486,117.4506505,117.5455523,117.6404541,117.735356,117.8302578,117.9251596,118.0200614,118.1149632,118.209865,118.3047668,118.3996687,118.4945705,118.5894723,118.7430791,118.8966859,119.0502928,119.2038996,119.3575064,119.5111132,119.66472,119.8183269,119.9719337,120.1255405,120.3410954,120.6199926,120.9031699,121.1905016,121.4818542,121.7770873,122.0760531,122.3785971,122.6845585,122.9937702,123.3060594,123.6212477,123.9391517,124.259583,124.582349,124.9072533,125.2340954,125.5626719,125.8927766,126.2242007,126.5567339,126.890164,127.5350476,128.1804012,128.8260108,129.4716627,130.1171441,130.7622433,131.4067503,132.0504575,132.6931595,133.3346546,133.9747441,134.613234,135.2499343,135.8846604,136.5172331,137.1474793,137.775232,138.4003314,139.0226251,139.6419683,140.2582245,140.871266,141.4809741,142.0872396,142.6899633,143.2890561,143.8844398,144.4760473,145.0638225,145.6477213,146.2277117,146.8037739,147.7231425,148.6385812,149.5501089,150.4577574,151.3615723,152.2616127,153.1579516,154.0506758,154.9398859,155.8256967,156.7082371,157.5876498,158.4640915,159.3377331,160.2087588,161.0773667,161.9437682,162.808188,163.6708636,164.532045,165.3919945,166.2509863,167.1093059,167.9672494,168.8251236,169.6832447,170.5419378,171.4015367,172.2623823,173.1248225,173.9892108,174.8559057,175.4924136,176.1319554,176.7748977,177.4216077,178.0724516,178.7277933,179.3879931,180.0534062,180.724381,181.4012578,182.0843664,182.7740249,183.4705376,184.1741929,184.8852616,185.6039941,186.3306187,187.0653393,187.8083325,188.5597454,189.3196931,190.0882554,190.8654747,191.6513525,192.4458466,193.2488681,194.0602778,194.8798829,195.7074339,196.5426204,197.385068,198.2343338,198.9538561,199.6790907,200.4093662,201.1439264,201.8819257,202.6224249,203.3643859,204.1066673,204.8480193,205.5870782,206.3223613,207.0522618,207.7750424,208.4888303,209.1916107,209.8812214,210.5553457,211.2115068,211.8470607,212.4591899,213.0448962,213.6009936,214.1241015,214.6106366,215.0568061,215.4995219,215.9422376]       
+
+
+    fig, ax = plt.subplots()
+    # 在生成的坐标系下画折线图
+    ax.plot(CS2_35, linewidth=1,c='b',label="c1")
+    ax.plot(CS2_36, linewidth=1,c='g',label="c4")
+    ax.plot(CS2_37, linewidth=1,c='y',label="c6")
+    ax.plot(CS2_38, linewidth=1,c='r',label="c6")
+
+    # 显示图形
+    font1 = { 
+    'weight' : 'normal',
+    'size' : 14,
+    }
+        
+          
+        #设置横纵坐标的名称以及对应字体格式
+    font2 = {#'family' : 'Times New Roman',
+    'weight' : 'normal',
+    'size' : 30,
+    }
+        
+    plt.xlabel('Cycle',font1) #X轴标签
+    plt.ylabel("Capacity (Ah)",font1) #Y轴标签
+    plt.legend()
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.eps',dpi=800,format='eps',bbox_inches = 'tight')
+    plt.savefig(last_last_last_last_path+r'\figure\by_code\Dataset_IGBT_curves_comparision.png',dpi=800,format='png',bbox_inches = 'tight')
+    plt.show()
+    
+    return CS2_35,CS2_36,CS2_37,CS2_38
+
+
+
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+CS2_35,CS2_36,CS2_37,CS2_38=get_data_list()
+
+
+# print(CS2_35)
+
+CS2_35=list(np.array(CS2_35)-CS2_35[0])
+CS2_36=list(np.array(CS2_36)-CS2_36[0])
+CS2_37=list(np.array(CS2_37)-CS2_37[0])
+CS2_38=list(np.array(CS2_38)-CS2_38[0])
+
+# print(CS2_35)
+
+
+# print(CS2_35[0])
+# print(CS2_36[0])
+# print(CS2_37[0])
+# print(CS2_38[0])
+
+def get_health_list(CS2_35,shed):
+    for i in range(len(CS2_35)):
+        if CS2_35[i]>shed: 
+            print("aaaaa")                          ########################################   小于门槛值
+            CS235=CS2_35[0:i]
+            return list(CS235)
+        else:
+            
+            if i==len(CS2_35)-1: 
+                print("aaaaa")                          ########################################   小于门槛值
+                CS235=CS2_35[0:i]
+                return list(CS235)
+
+# CS235=get_health_list(CS2_35,shed) 
+# CS236=get_health_list(CS2_36,shed) 
+# CS237=get_health_list(CS2_37,shed) 
+# CS238=get_health_list(CS2_38,shed)   
+
+# fig, ax = plt.subplots()
+# # 在生成的坐标系下画折线图
+# ax.plot(CS235, linewidth=1)
+# ax.plot(CS236, linewidth=1)
+# ax.plot(CS237, linewidth=1)
+# ax.plot(CS238, linewidth=1)
+
+# # 显示图形
+# plt.show()
+
+def get_input_out_2(CS236,CS237,time_windows):
+
+    
+    # CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    # CS235_health=list(reversed(CS235_health))
+    print(CS236_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    # for i in range(time_windows-1-2):
+    #     CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    # CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    # for i in range(len(CS235_health)-time_windows+1):        
+    #     x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+    #     y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+
+
+
+
+
+
+
+
+
+
+
+
+def get_input_out_3(CS235,CS236,CS237,time_windows):
+    # min_len=min(len(CS235),len(CS236),len(CS237))
+    
+    # input_list=[]
+    # output_list=[]
+    
+    # true_out_list=[]
+    
+    CS235_health=get_health_list(CS235,shed) 
+    CS237_health=get_health_list(CS237,shed) 
+    CS236_health=get_health_list(CS236,shed) 
+    # CS235_health[::-1] 
+    
+    # print(CS235_health)
+    
+    CS235_health=list(reversed(CS235_health))
+    # print(CS23_health)
+    
+    
+    
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))
+    
+    for i in range(time_windows-1-2):
+        CS235_health.append(0)
+        
+    for i in range(time_windows-1-2):
+        CS236_health.append(0)    
+    
+    for i in range(time_windows-1-2):
+        CS237_health.append(0)
+        
+    CS235_health=list(reversed(CS235_health))
+    CS236_health=list(reversed(CS236_health))
+    CS237_health=list(reversed(CS237_health))   
+    
+    x_train_list=[]
+    y_train_list=[]
+    for i in range(len(CS235_health)-time_windows+1):        
+        x_train_list.append(np.array(CS235_health[i:i+time_windows]))
+        y_train_list.append(len(CS235_health)-time_windows+1-1-i) 
+               
+    for i in range(len(CS236_health)-time_windows+1):        
+        x_train_list.append(np.array(CS236_health[i:i+time_windows]))  
+        y_train_list.append(len(CS236_health)-time_windows+1-1-i)        
+    x_train_array=np.array(x_train_list)
+    y_train_array=np.array(y_train_list)
+    
+    
+    x_test_list=[]
+    y_test_list=[]
+    for i in range(len(CS237_health)-time_windows+1):        
+        x_test_list.append(np.array(CS237_health[i:i+time_windows]))
+        y_test_list.append(len(CS237_health)-time_windows+1-1-i) 
+                  
+    x_test_array=np.array(x_test_list)
+    y_test_array=np.array(y_test_list)   
+    
+    return x_train_array , y_train_array , x_test_array ,  y_test_array
+    
+    
+    
+    
+    
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_3(CS2_35,CS2_36,CS2_37,20)      
+
+# x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+
+
+
+
+
+
+
+#import tensorflow as tf
+import os
+import logging
+import numpy as np
+#from numpy import trans
+import matplotlib.pyplot as plt
+#import tensorflow as tf
+# import CMAPSSDataset
+import pandas as pd
+import datetime
+import keras
+from keras.layers import Lambda
+import math
+import keras.backend as K
+import tensorflow as tf
+from tfdeterminism import patch
+from sklearn.model_selection import train_test_split
+from keras.utils.vis_utils import plot_model
+patch()
+# tf.random.set_seed(0)
+#import keras
+#flags = tf.flags
+#flags.DEFINE_string("weights", None, 'weights of the network')################# the file path of weights
+#flags.DEFINE_integer("epochs", 100, 'train epochs')
+#flags.DEFINE_integer("batch_size", 32, 'batch size for train/test')
+#flags.DEFINE_integer("sequence_length", 32, 'sequence length')
+#flags.DEFINE_boolean('debug', False, 'debugging mode or not')
+#FLAGS = flags.FLAGS
+
+def root_mean_squared_error(y_true, y_pred):
+        return K.sqrt(K.mean(K.square(y_pred - y_true),axis=0))##################  axis=0
+
+def rmse(predictions, targets):
+    return np.sqrt(((predictions - targets) ** 2).mean())
+
+
+
+
+    
+segment=3
+
+
+
+run_times=10
+
+
+
+nb_epochs=2000           #200
+batch_size=64    ## 64        #####300
+# sequence_length=31    ############# min31  max303
+
+patience=50
+patience_reduce_lr=20
+
+
+
+
+
+seed=2
+
+
+
+num_filter1=64
+num_filter2=128
+num_filter3=64
+
+
+
+kernel1_size=8
+kernel2_size=5
+kernel3_size=3
+
+
+
+sequence_length=20
+
+
+
+
+
+
+
+             
+X_train , Y_train , X_test ,  Y_test =get_input_out_3(CS2_35,CS2_36,CS2_37,sequence_length)  
+
+# rul_pred_array
+# [713.244, 713.1562, 712.77234, 712.6178, 712.6362, 712.1615, 711.78345, 711.2615, 708.99744, 708.87, 711.11, 712.4278, 713.49414, 714.25867, 713.402, 711.72095, 711.26874, 710.88086, 710.22986, 709.77014, 709.3306, 710.3753, 711.87366, 711.3405, 709.3842, 706.19525, 702.10974, 699.5227, 699.2441, 698.87585, 701.22906, 703.2003, 700.235, 699.10095, 696.52496, 693.44666, 693.0563, 696.2302, 698.7272, 695.3815, 694.4967, 693.5123, 693.38306, 693.2394, 694.91754, 697.03656, 693.40564, 691.52985, 690.39124, 691.87085, 691.4027, 697.944, 698.5906, 695.2054, 700.6422, 705.2433, 703.1223, 700.4419, 699.7397, 699.2757, 701.4927, 702.37616, 702.47864, 705.78375, 701.02783, 696.3351, 691.7626, 693.1491, 686.65295, 681.7866, 689.0198, 688.1178, 688.4046, 687.5969, 688.75806, 689.65076, 688.8744, 688.23584, 686.7946, 687.5255, 686.79346, 685.3927, 684.9672, 683.8795, 682.6698, 682.51025, 683.8211, 682.92114, 680.70654, 679.4169, 678.1807, 674.47833, 670.88367, 668.7057, 666.5606, 669.7509, 672.69556, 673.4862, 674.9121, 674.3078, 672.216, 671.7014, 670.01697, 668.50116, 666.4166, 669.5475, 673.41077, 674.2728, 671.56934, 666.7858, 657.87476, 652.3662, 652.8141, 659.29144, 664.42487, 661.59357, 659.1002, 657.7722, 653.1403, 647.9372, 651.46344, 654.8484, 656.52966, 660.3795, 665.4159, 665.53253, 663.9164, 661.2416, 662.7247, 663.3247, 664.1227, 667.2366, 669.012, 668.1583, 664.56287, 663.0799, 659.5332, 651.3357, 646.80853, 648.03955, 644.49786, 646.05493, 649.70776, 651.1151, 646.3118, 644.782, 644.1628, 646.97546, 653.35034, 655.55914, 651.2461, 647.23236, 647.4703, 650.45416, 653.9027, 652.37897, 647.29944, 641.7139, 635.1615, 633.4523, 634.6832, 641.3993, 641.96686, 638.1969, 639.8759, 637.06177, 635.131, 631.55237, 631.87006, 635.26575, 638.7335, 642.37933, 646.0248, 648.5171, 647.1953, 645.5355, 645.90814, 645.80865, 646.49054, 647.4532, 649.38434, 647.8402, 644.8619, 643.1564, 643.4628, 637.869, 633.56604, 632.9404, 631.04736, 626.7531, 627.45593, 629.57587, 628.3112, 623.5763, 617.7104, 615.6481, 619.1083, 627.56067, 632.7553, 630.5792, 626.2737, 624.652, 626.09863, 625.9995, 625.6986, 627.5331, 622.86456, 618.31537, 615.52716, 615.3958, 616.89294, 616.6007, 618.57007, 621.04364, 621.85986, 619.1908, 619.1992, 619.8617, 625.7473, 630.04663, 629.1188, 629.27875, 630.44775, 631.568, 629.7825, 627.8963, 627.9581, 626.93616, 627.9811, 630.30066, 633.31964, 631.2836, 626.3009, 627.468, 630.504, 629.2702, 625.2847, 621.13684, 617.03406, 611.2758, 610.44275, 611.9093, 611.60657, 607.73096, 601.79846, 605.56006, 612.6711, 622.30334, 629.7209, 629.4262, 623.1507, 622.5094, 623.0492, 617.2604, 605.971, 601.8469, 599.1995, 599.5541, 597.9122, 595.98914, 589.1527, 584.6663, 583.79626, 585.5099, 594.225, 601.0638, 608.88794, 605.9352, 599.1792, 595.4628, 595.3343, 594.90283, 593.2749, 597.38654, 593.98474, 586.7595, 577.71985, 575.28845, 577.2362, 579.4049, 580.1428, 583.24426, 587.7101, 589.4106, 588.6268, 587.16614, 586.3199, 586.5416, 586.0021, 585.6371, 586.47034, 584.5841, 581.5365, 578.49664, 579.96155, 578.78345, 573.5273, 572.7939, 573.76306, 569.73926, 566.2865, 565.7433, 567.62, 561.5143, 555.05096, 549.80835, 548.40906, 547.1733, 547.8942, 549.353, 552.2807, 555.75916, 554.5836, 553.7981, 553.36707, 554.37335, 550.27716, 546.5861, 548.1832, 549.82166, 547.61584, 541.95703, 538.0464, 541.4823, 544.5839, 547.6454, 549.9669, 552.8439, 553.7388, 553.7318, 553.8903, 553.8476, 552.896, 549.4304, 544.7288, 540.0086, 540.3739, 540.5376, 535.44745, 533.04846, 532.8993, 529.39636, 521.69885, 519.7277, 519.0194, 515.3288, 508.64273, 501.18637, 497.21533, 496.0961, 495.97577, 495.23126, 496.37384, 491.4583, 484.74423, 478.80316, 474.34277, 469.4146, 469.03345, 468.89053, 465.17154, 464.96143, 461.1883, 456.01862, 454.92343, 455.3499, 452.15942, 446.37433, 443.41284, 441.24835, 436.5005, 430.64005, 426.2313, 427.85553, 428.28995, 432.5515, 432.6935, 431.76157, 434.09854, 434.56488, 434.49228, 435.09393, 436.50278, 436.3396, 431.79022, 428.1424, 427.97223, 430.00546, 432.72232, 429.26474, 430.43076, 429.89224, 426.63614, 423.2202, 423.28696, 422.1862, 416.34985, 411.7933, 407.31915, 403.97333, 399.66638, 399.04205, 397.17175, 393.88266, 388.6202, 384.48453, 381.4451, 375.44244, 373.07654, 375.6656, 375.3813, 372.5932, 372.71783, 371.48724, 366.39764, 363.39453, 360.81076, 357.25214, 353.15027, 350.0936, 353.66705, 350.39478, 346.71588, 345.26254, 344.14606, 342.3794, 343.16806, 340.99188, 338.32416, 340.9997, 341.55597, 342.8888, 333.07767, 324.71643, 320.0458, 316.3312, 314.40756, 316.67877, 314.3625, 308.9753, 303.88425, 299.55035, 299.30145, 299.43622, 298.1392, 296.75464, 291.98093, 284.17374, 283.42746, 287.1516, 284.4586, 278.20605, 278.40546, 274.96393, 270.15665, 264.2525, 267.82062, 269.34906, 266.54993, 262.69067, 261.7164, 256.78983, 253.13374, 256.29172, 260.89005, 259.48962, 257.92438, 260.8764, 261.3296, 260.27756, 260.27078, 264.4813, 267.294, 265.554, 264.16806, 265.41373, 265.86288, 262.76962, 259.8485, 263.51636, 262.80365, 261.8983, 255.401, 253.71756, 253.90579, 252.634, 248.46863, 246.09912, 241.2253, 237.07373, 233.98064, 234.85242, 234.5695, 235.1449, 232.24678, 228.7192, 222.28073, 216.94296, 219.00131, 222.97478, 219.77766, 215.06314, 214.49088, 212.39633, 205.1956, 198.69612, 201.54987, 205.50398, 198.35611, 195.1422, 192.6241, 190.18031, 187.43675, 186.1347, 189.1068, 187.79317, 183.29237, 184.31013, 184.47766, 182.97884, 181.37143, 181.35753, 185.40533, 182.61295, 175.84111, 175.50711, 178.02734, 180.84474, 187.77629, 189.38794, 185.81625, 183.46696, 180.11668, 179.86276, 182.08359, 179.6784, 174.4248, 172.5172, 170.37212, 170.69522, 167.39456, 167.28006, 169.23067, 172.59262, 165.2148, 161.39572, 161.88583, 160.81479, 160.64876, 159.30542, 155.43123, 149.88556, 146.71269, 145.40408, 147.36478, 148.45169, 147.17987, 144.73495, 140.19785, 138.08614, 137.14355, 138.66843, 140.37465, 141.23547, 139.433, 138.74422, 138.839, 138.0947, 128.92258, 128.39798, 126.71358, 128.17403, 127.819595, 129.33855, 129.65257, 129.29678, 121.39711, 120.12527, 117.69694, 119.05394, 117.72056, 119.64604, 123.27684, 122.55349, 119.29464, 122.29118, 123.68431, 124.139786, 124.98164, 126.66221, 129.3624, 127.10695, 122.94556, 120.55647, 118.81523, 117.802895, 119.91113, 118.65028, 114.92422, 113.490135, 110.923645, 107.33518, 105.965164, 103.69718, 104.137695, 103.06416, 103.24333, 102.462845, 102.53288, 100.935425, 98.75389, 98.23696, 97.19185, 95.53534, 96.70274, 95.66011, 90.46946, 83.95983, 83.20836, 82.16189, 82.77049, 82.20778, 83.26946, 85.21086, 84.03151, 79.75421, 78.6595, 79.69268, 80.109375, 80.9441, 81.87271, 80.89349, 80.56523, 80.79472, 79.93695, 79.7756, 81.50333, 84.42182, 85.74151, 87.307785, 88.265854, 89.66879, 88.91675, 83.158646, 77.42551, 73.047905, 73.4345, 72.8579, 70.774414, 69.25677, 67.67607, 66.21152, 66.07107, 66.04902, 67.13377, 68.20207, 69.87503, 70.18053, 71.12348, 69.15323, 66.54096, 63.878376, 61.638508, 61.73644, 61.025383, 61.52482, 57.93728, 55.16337, 54.173695, 54.281433, 55.20214, 55.941143, 56.94815, 56.06418, 55.936054, 55.102997, 53.799873, 54.237335, 55.870228, 57.047535, 57.02363, 57.892677, 58.243458, 57.36081, 57.534332, 57.350136, 58.411877, 59.96099, 60.542988, 61.49874, 61.587837, 62.12456, 61.822002, 61.691826, 59.3513, 58.01903, 60.52827, 59.774464, 59.4804, 58.18931, 56.108692, 54.756462, 53.438488, 53.59255, 53.52561, 55.051807, 54.463898, 54.91088, 54.076794, 51.721935, 50.54023, 50.204033, 50.869514, 50.632153, 52.1, 51.59037, 50.37258, 47.418568, 43.07737, 40.435974, 44.509205, 43.924797, 36.716595, 29.19551, 28.993393, 28.50917, 28.568375, 23.621105, 19.200375, 17.296505, 15.499324, 15.344284]
+# true_out_array
+# [731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+# error_pred_array
+# [128.44404296875, 129.1562255859375, 129.57232666015625, 130.21776123046874, 131.03623657226564, 131.361474609375, 131.78346557617186, 132.06145629882812, 130.59747314453125, 131.2699951171875, 134.30995483398436, 136.42778930664062, 138.29417114257814, 139.85870361328125, 139.80201416015626, 138.92095947265625, 139.26876831054688, 139.68087158203124, 139.82987060546876, 140.17011108398438, 140.5306823730469, 142.37528076171876, 144.67363891601562, 144.94056396484376, 143.78418579101563, 141.395263671875, 138.10977783203126, 136.32273559570314, 136.84407958984374, 137.27587280273437, 140.42905883789064, 143.20032348632813, 141.0349609375, 140.7008850097656, 138.9249267578125, 136.6466552734375, 137.05631103515626, 141.0302001953125, 144.32713012695314, 141.7814147949219, 141.69661865234374, 141.51235961914062, 142.18303833007812, 142.83936157226563, 145.3175476074219, 148.23655395507814, 145.4056823730469, 144.32981567382814, 143.99124145507812, 146.2708740234375, 146.60265502929687, 153.9439453125, 155.39049682617187, 152.805322265625, 159.04217529296875, 164.44320068359374, 163.122314453125, 161.24185791015626, 161.33970336914064, 161.6756591796875, 164.69271240234374, 166.376171875, 167.27867431640624, 171.38377685546874, 167.42784423828124, 163.53504638671876, 159.76257934570313, 161.94910278320313, 156.25293579101563, 152.18658447265625, 160.21983032226564, 160.11781005859376, 161.20457763671874, 161.1969421386719, 163.158056640625, 164.8507507324219, 164.8743896484375, 165.03582763671875, 164.39459228515625, 165.92542114257813, 165.99351196289064, 165.3927001953125, 165.76724243164062, 165.4794494628906, 165.06981201171874, 165.7102478027344, 167.82107543945312, 167.7211181640625, 166.30652465820313, 165.81692504882812, 165.3806640625, 162.47828979492186, 159.68365478515625, 158.30562744140624, 156.96064453125, 160.95086059570312, 164.69556884765626, 166.28624267578124, 168.51207275390624, 168.70781860351562, 167.41607055664062, 167.7013671875, 166.81691284179686, 166.10112915039062, 164.81658935546875, 168.7474365234375, 173.4107421875, 175.07284545898438, 173.16929931640624, 169.1858642578125, 161.07476806640625, 156.36621704101563, 157.614111328125, 164.8914306640625, 170.82486572265626, 168.79354248046874, 167.10023803710936, 166.57221069335938, 162.74031982421874, 158.33716430664063, 162.66343383789064, 166.84840087890626, 169.3296875, 173.97955322265625, 179.81593627929686, 180.73256225585936, 179.9164306640625, 178.04166259765626, 180.3246276855469, 181.72474365234376, 183.3226806640625, 187.23653564453124, 189.81199340820314, 189.75829467773437, 186.96285400390624, 186.27989501953124, 183.53319091796874, 176.135693359375, 172.40855102539064, 174.4395263671875, 171.69788208007813, 174.05496215820312, 178.50775756835938, 180.7151123046875, 176.71175537109374, 175.98196411132812, 176.1627990722656, 179.77540283203126, 186.95033569335936, 189.95911865234376, 186.44608154296876, 183.23233032226562, 184.2703125, 188.05416870117188, 192.3027587890625, 191.57894287109374, 187.299462890625, 182.51393432617186, 176.76150512695312, 175.85219116210936, 177.8832580566406, 185.39928588867187, 186.7668212890625, 183.79692993164062, 186.27594604492188, 184.26180419921874, 183.13094482421874, 180.35236206054688, 181.47009887695313, 185.66575927734374, 189.93357543945314, 194.37932739257812, 198.82473754882812, 202.11707763671876, 201.59533081054687, 200.73551025390626, 201.90814819335938, 202.6086669921875, 204.0905334472656, 205.853125, 208.584326171875, 207.840185546875, 205.6618408203125, 204.75636596679686, 205.86284790039062, 201.06906127929688, 197.566015625, 197.74049072265626, 196.64743041992188, 193.15314331054688, 194.65590209960936, 197.5759033203125, 197.11129150390624, 193.17633666992188, 188.1103942871094, 186.8481201171875, 191.10829467773436, 200.36063232421876, 206.35532836914064, 204.979248046875, 201.4736572265625, 200.651904296875, 202.89857788085936, 203.5995361328125, 204.0985595703125, 206.7330749511719, 202.86459350585938, 199.1153991699219, 197.12714233398438, 197.79583740234375, 200.09288330078124, 200.60069580078124, 203.37008056640624, 206.64364013671874, 208.25989990234376, 206.3908203125, 207.1991760253906, 208.6616638183594, 215.34730224609376, 220.44661865234374, 220.31876220703126, 221.2787658691406, 223.2477783203125, 225.16803588867188, 224.18244018554688, 223.0962707519531, 223.95819091796875, 223.7361572265625, 225.5809814453125, 228.70062866210938, 232.5196044921875, 231.28363037109375, 227.1009094238281, 229.06804809570312, 232.90401611328124, 232.47023315429686, 229.2846923828125, 225.93682861328125, 222.6341064453125, 217.67577514648437, 217.64271240234376, 219.90932006835936, 220.40651245117186, 217.33093872070313, 212.1984130859375, 216.7600891113281, 224.6710693359375, 235.10327758789063, 243.3209228515625, 243.8262145996094, 238.3506652832031, 238.50936889648438, 239.84925537109376, 234.86040649414062, 224.3709716796875, 221.04690551757812, 219.19954833984374, 220.35408935546874, 219.5121826171875, 218.38910522460938, 212.35267944335936, 208.6663391113281, 208.59633178710936, 211.1099060058594, 220.62493896484375, 228.26380004882813, 236.88788452148438, 234.73519287109374, 228.77924194335938, 225.8628173828125, 226.5342590332031, 226.90288696289062, 226.07484130859376, 230.986474609375, 228.3847412109375, 221.959521484375, 213.71985473632813, 212.0884582519531, 214.83616333007814, 217.8049072265625, 219.34277954101563, 223.2443115234375, 228.51013793945313, 231.01055908203125, 231.02674560546876, 230.36621704101563, 230.319921875, 231.341650390625, 231.60202026367188, 232.03707885742188, 233.6703125, 232.58411865234376, 230.33648071289062, 228.09664916992188, 230.361572265625, 229.98341674804686, 225.527294921875, 225.59389038085936, 227.36303100585937, 224.13927001953124, 221.486474609375, 221.7433349609375, 224.4200012207031, 219.11431274414062, 213.45095825195312, 209.00838623046874, 208.40909423828126, 207.97328491210936, 209.49424438476564, 211.7530029296875, 215.48071899414063, 219.75919189453126, 219.38366088867187, 219.39805908203124, 219.7670166015625, 221.57329711914062, 218.27713928222656, 215.38619995117188, 217.78320922851563, 220.22161865234375, 218.81575927734374, 213.95699462890624, 210.84642333984374, 215.08223876953124, 218.98389282226563, 222.84539184570312, 225.966943359375, 229.64389038085938, 231.33877563476562, 232.13187255859376, 233.09029541015624, 233.84762573242188, 233.69597778320312, 231.03038024902344, 227.12875671386718, 223.20855407714845, 224.3739013671875, 225.33755493164062, 221.0474884033203, 219.4484619140625, 220.09935913085937, 217.39642333984375, 210.4988006591797, 209.3277130126953, 209.41944580078126, 206.5288055419922, 200.64272766113282, 193.9864044189453, 190.81531982421876, 190.4960479736328, 191.1758270263672, 191.2312774658203, 193.17384643554686, 189.05829467773438, 183.14420166015626, 178.00317993164063, 174.34280395507812, 170.2145751953125, 170.63343505859376, 171.29056396484376, 168.37156982421874, 168.96141967773437, 165.9882781982422, 161.61859741210938, 161.32342529296875, 162.5499053955078, 160.15941467285157, 155.1743133544922, 153.01282348632813, 151.64830627441407, 147.70049133300782, 142.64002075195313, 139.03127746582032, 141.45548095703126, 142.68993530273437, 147.75152282714845, 148.69349365234376, 148.5615661621094, 151.69853820800782, 152.9649200439453, 153.81736450195314, 157.3365936279297, 159.5700653076172, 160.51480407714843, 157.65391540527344, 156.36669921875, 156.91981506347656, 159.59076538085938, 161.93418884277344, 156.6475830078125, 157.63075256347656, 157.8922332763672, 155.43609619140625, 152.82025756835938, 153.6869323730469, 153.38614807128906, 148.3498504638672, 144.593310546875, 140.919140625, 138.3848114013672, 138.43084411621095, 139.2867004394531, 138.78878784179688, 135.881689453125, 130.7374481201172, 125.21468811035156, 123.36256103515625, 120.77804565429688, 121.08883972167969, 123.76382446289062, 124.33162841796874, 122.85722351074219, 122.69735107421874, 120.98337097167969, 118.21698303222657, 117.51797790527344, 115.70404052734375, 113.69694213867187, 111.7427490234375, 110.54600524902344, 113.37464904785156, 110.89859008789062, 108.78515014648437, 108.27554626464844, 107.58105163574218, 108.38932495117187, 110.79601135253907, 109.48285522460938, 107.82490234375, 107.927880859375, 108.97590026855468, 111.93962097167969, 104.71879577636719, 98.88610229492187, 95.48537902832031, 93.4391098022461, 92.43889923095703, 94.54204406738282, 94.04161376953125, 89.48624877929687, 87.54637451171875, 85.1763687133789, 85.46596069335938, 83.41571197509765, 82.24242553710937, 82.65549774169922, 81.96833190917968, 77.42306518554688, 73.37626647949219, 74.61889953613282, 74.33442993164063, 70.08006591796875, 70.17378845214844, 68.523876953125, 67.44596099853516, 64.40172271728515, 62.305781555175784, 62.582759094238284, 62.288308715820314, 59.56385040283203, 58.42617340087891, 56.59038543701172, 57.13045654296875, 58.46919403076172, 62.009735107421875, 62.288961791992186, 62.60346984863281, 63.756675720214844, 64.31104278564453, 63.68323669433594, 65.1746322631836, 68.74009704589844, 72.00096130371094, 73.0018096923828, 74.72274322509766, 76.55504913330078, 76.93199005126954, 74.1617431640625, 71.23601684570312, 73.07925109863281, 74.16602783203125, 74.73312835693359, 67.87274932861328, 64.4855712890625, 64.35745239257812, 64.40265655517578, 62.546145629882815, 61.62525329589844, 58.22956237792969, 54.56965942382813, 53.017523193359374, 52.260223388671875, 51.804151916503905, 52.96812744140625, 50.410589599609374, 48.50082550048828, 46.18961029052734, 43.16055755615234, 42.50442962646484, 43.547607421875, 42.01837005615234, 40.051091003417966, 40.26797637939453, 39.73948364257812, 35.7941177368164, 33.56692810058594, 33.799974060058595, 34.135353088378906, 31.901150512695313, 35.163623046875, 36.728091430664065, 35.85277252197265, 34.73910064697266, 35.26514892578125, 33.18161163330078, 35.66581268310547, 38.808161926269534, 37.1342529296875, 38.10228424072265, 36.09913940429688, 33.47134094238281, 31.581875610351563, 31.80333709716797, 33.94527893066406, 37.12687225341797, 39.096003723144534, 40.56741943359375, 38.86300354003906, 38.79838409423828, 37.158447265625, 35.35722198486328, 34.99322509765625, 30.52393798828125, 29.56664581298828, 30.214495849609374, 31.07225341796875, 35.158688354492185, 36.791377258300784, 37.01826171875, 35.98007659912109, 33.92915802001953, 30.763630676269532, 28.889382934570314, 33.1811767578125, 32.430564880371094, 33.08864288330078, 30.35515594482422, 30.292254638671874, 29.416169738769533, 29.665750122070314, 28.238555908203125, 30.783740234375, 33.424522399902344, 35.67454223632812, 32.70137939453125, 31.500950622558594, 31.14190673828125, 32.862875366210936, 32.80213928222656, 33.91386108398437, 35.51348876953125, 35.19645538330078, 34.090284729003905, 31.9422119140625, 31.731845092773437, 30.219270324707033, 29.38343505859375, 31.694297790527344, 35.45694198608398, 35.507929992675784, 36.740089416503906, 34.987589263916014, 33.60873794555664, 31.36730041503906, 29.463785552978514, 31.421296691894533, 34.578975677490234, 35.89346923828125, 37.41142349243164, 34.549791717529295, 34.44340438842774, 32.61220245361328, 30.436394500732423, 29.689669036865233, 30.471735382080077, 28.523981475830077, 29.39884796142578, 28.60289764404297, 26.280330657958984, 26.365684509277344, 25.228802490234376, 24.91222686767578, 25.318479919433592, 26.076097106933595, 27.41505584716797, 28.35756607055664, 27.798690795898438, 28.33661346435547, 28.374998474121092, 27.666127014160157, 28.767979431152344, 31.29453048706055, 30.258948516845702, 30.296824645996093, 29.947371673583984, 30.238136291503906, 30.971700286865236, 29.090316009521484, 28.796878051757812, 30.330371856689453, 31.1436408996582, 30.836636352539063, 29.630431365966796, 27.953913116455077, 26.47283935546875, 26.084770965576173, 28.61912307739258, 31.80632629394531, 32.24363899230957, 32.92632713317871, 32.293766784667966, 29.404439544677736, 28.091866302490235, 26.69931106567383, 27.80881271362305, 28.237080383300782, 29.41628189086914, 30.58887481689453, 30.57209358215332, 27.072730255126952, 24.282626342773437, 24.19184799194336, 25.151026916503906, 25.748144912719727, 26.460292053222656, 27.08810501098633, 26.49458236694336, 26.118693542480468, 26.133878326416017, 23.82370834350586, 23.8682861328125, 24.35929946899414, 24.204568481445314, 22.88933410644531, 23.439949417114256, 24.84860610961914, 23.697040557861328, 21.685837173461913, 20.62164649963379, 19.64074287414551, 21.276799392700195, 22.3033390045166, 20.912229156494142, 19.005928421020506, 18.23721389770508, 19.668947601318358, 20.3134407043457, 18.602231979370117, 18.784926223754884, 17.733196258544922, 19.60085563659668, 17.897478103637695, 15.909258270263672, 15.024677658081055, 16.250395202636717, 16.844846725463867, 17.259196853637697, 17.483677291870116, 17.413933944702148, 17.06965866088867, 15.612966537475586, 13.30797233581543, 13.638027954101563, 15.00291748046875, 15.96730842590332, 15.564126968383789, 15.092281723022461, 15.439710998535157, 17.067489624023438, 17.219091796875, 17.09698028564453, 18.12844467163086, 19.08882865905762, 18.960807418823244, 20.043313598632814, 20.550130462646486, 22.56386604309082, 24.76099052429199, 26.142984771728514, 27.89873924255371, 28.787831115722657, 30.124557876586913, 30.622003173828126, 31.29182662963867, 29.751293563842772, 29.219031524658202, 32.52827262878418, 32.574463272094725, 33.08040237426758, 32.58930587768555, 31.308691787719727, 30.75646553039551, 30.23848648071289, 31.192547225952147, 31.925611877441405, 34.25181083679199, 34.46389884948731, 35.71088409423828, 35.67679214477539, 34.12193336486816, 33.740227127075194, 34.20403289794922, 35.66951789855957, 36.23215293884277, 38.499999618530275, 38.79036903381348, 38.372582244873044, 36.21856575012207, 32.67736930847168, 30.835976219177248, 35.70920181274414, 35.92479496002197, 29.516596603393555, 22.79550838470459, 23.393392753601074, 23.709171104431153, 24.568376541137695, 20.421105766296385, 16.800374507904053, 15.696505260467529, 14.699323272705078, 15.344282913208009]
+
+
+
+# X_train , Y_train , X_test ,  Y_test =get_input_out_2(CS2_36,CS2_37,sequence_length) 
+
+# rul_pred_array
+# [837.80176, 837.24915, 836.0985, 835.1865, 834.171, 831.6792, 830.0959, 828.72284, 827.1708, 828.74915, 833.5259, 837.6029, 839.40125, 838.516, 835.5311, 834.0419, 834.4539, 834.3083, 834.4248, 835.0781, 836.8628, 840.0043, 841.5062, 841.3164, 840.7996, 835.9998, 831.28827, 828.24133, 825.0877, 823.05615, 827.07684, 830.9129, 826.1222, 820.116, 816.5462, 812.44714, 812.2042, 818.64197, 821.87415, 817.87054, 814.32324, 814.03235, 814.24255, 815.6897, 818.61096, 819.88855, 816.17413, 814.18555, 813.46625, 811.41705, 809.61316, 823.10614, 827.9305, 832.0934, 838.3833, 835.25684, 826.89685, 824.1669, 824.80615, 826.2469, 825.97375, 825.47644, 826.2298, 829.8377, 824.3927, 814.0811, 804.9259, 803.8266, 798.60583, 794.55994, 788.93463, 787.74695, 787.10266, 786.9579, 788.6291, 789.12146, 788.7956, 788.65594, 787.98376, 787.7869, 787.4193, 785.84863, 784.2899, 783.3592, 782.6781, 782.5883, 782.7114, 781.39197, 778.50653, 776.30554, 774.43774, 769.98376, 765.09827, 761.0909, 758.27893, 762.47253, 766.6632, 768.11035, 771.1355, 771.82855, 769.9547, 768.65063, 767.7777, 767.20184, 768.2813, 776.0491, 783.0934, 784.0543, 778.5939, 769.866, 759.96466, 755.77185, 755.9667, 759.7628, 763.4088, 761.4069, 759.246, 755.4714, 751.8372, 746.307, 746.9851, 749.64575, 754.19836, 763.266, 768.8457, 768.3845, 765.516, 761.75653, 762.1595, 765.52185, 769.8377, 774.5597, 776.60443, 774.9713, 769.2555, 765.879, 761.5434, 754.5543, 750.92426, 748.6974, 744.4597, 747.07733, 751.97485, 751.05237, 744.63635, 741.6457, 739.8928, 743.1419, 748.3386, 748.71643, 746.2126, 743.7751, 749.9761, 755.4025, 760.1794, 755.59424, 747.63696, 744.9779, 743.6888, 744.02216, 738.1444, 739.98865, 741.2545, 739.688, 741.96063, 740.3068, 738.9793, 733.61755, 732.89575, 735.01324, 737.87366, 744.0665, 750.4198, 752.5987, 749.7184, 745.22296, 743.4749, 745.1821, 748.05664, 752.5392, 755.74335, 752.3256, 746.1864, 742.79474, 742.8424, 738.0526, 734.63293, 733.69006, 729.743, 724.3812, 727.11395, 730.4349, 725.73474, 719.5135, 713.78235, 711.3633, 714.5849, 724.1707, 726.0887, 723.68536, 719.73676, 723.77185, 731.1858, 732.5577, 727.0497, 725.54175, 722.8721, 724.2011, 725.693, 721.3984, 713.9076, 713.63477, 719.02893, 720.92004, 722.9181, 721.7645, 723.63544, 723.1545, 725.8424, 726.0764, 726.098, 727.3862, 732.78644, 737.1093, 731.4772, 724.819, 724.57214, 727.36707, 733.7621, 736.74805, 735.2433, 729.8451, 725.99896, 728.2697, 732.671, 730.26196, 724.553, 719.4757, 713.83203, 706.24524, 706.07556, 708.5496, 705.8734, 700.6906, 694.7765, 698.49963, 706.4443, 714.70337, 720.0395, 719.0912, 711.4469, 717.6629, 726.08875, 722.4636, 707.59314, 700.13947, 700.2371, 705.937, 705.33014, 696.8265, 680.61725, 674.3334, 675.8751, 679.6162, 680.45056, 686.2073, 699.19617, 705.1449, 702.249, 694.6117, 690.85284, 693.4259, 698.31506, 701.0963, 694.381, 690.0285, 685.5152, 681.71985, 678.55133, 679.72156, 679.5952, 680.9029, 686.68896, 692.74646, 694.7502, 691.7017, 688.0243, 686.4175, 684.2723, 683.68665, 687.05927, 688.0519, 685.1408, 680.30676, 680.0392, 679.203, 674.10754, 673.06757, 670.85266, 663.4579, 660.07086, 661.6087, 664.3247, 658.4527, 653.60754, 648.4374, 645.88525, 642.9193, 647.4533, 647.6902, 648.9184, 655.72864, 661.09174, 662.8973, 660.25824, 658.77124, 651.9669, 645.95404, 641.4798, 643.6891, 645.3373, 644.35846, 639.1144, 640.7921, 647.9606, 656.98364, 662.3551, 665.0342, 662.33374, 658.2845, 654.632, 651.263, 652.33466, 651.2839, 645.83057, 638.94165, 637.42145, 637.6391, 632.96716, 628.61066, 625.057, 617.15515, 607.1124, 606.40765, 606.7627, 603.7588, 599.1633, 595.5037, 592.4302, 589.8393, 594.1935, 594.7594, 594.3848, 589.8862, 584.2606, 581.8957, 577.9967, 575.41486, 574.81323, 572.1382, 564.6007, 564.935, 565.2028, 562.3564, 564.17267, 565.2894, 555.48615, 544.46985, 541.6184, 542.78, 539.9745, 540.6187, 536.6134, 532.9326, 534.992, 539.6227, 541.6517, 540.8759, 540.4313, 538.4404, 535.7714, 536.2209, 537.91956, 539.75793, 536.50055, 533.95984, 533.32874, 534.3157, 535.5425, 529.07684, 529.19293, 527.3878, 523.2802, 517.39795, 519.9137, 521.82117, 516.69824, 511.86084, 510.09515, 507.99042, 503.3416, 505.612, 508.00235, 511.09024, 506.8199, 499.01456, 493.63516, 488.65527, 484.68814, 488.91644, 492.49506, 490.09464, 489.3327, 486.55963, 481.72565, 477.89062, 473.54794, 468.58148, 465.17743, 457.8881, 461.88077, 465.0978, 468.0187, 469.15268, 465.9466, 460.5245, 457.95782, 448.4376, 445.6657, 447.6626, 447.4087, 448.13672, 440.1419, 429.206, 417.83276, 410.1504, 402.2289, 403.70035, 399.9311, 385.98904, 370.2232, 361.2846, 357.2215, 350.3539, 337.56946, 341.03964, 349.50006, 342.4695, 330.21597, 324.4836, 323.48126, 318.607, 316.2666, 322.44763, 327.63644, 317.72977, 307.72253, 305.83047, 309.39008, 305.17157, 306.18823, 311.93277, 317.1166, 318.5666, 315.66107, 315.3028, 317.08438, 319.15094, 316.88544, 316.11615, 317.4477, 314.2443, 310.57816, 309.8604, 310.06833, 309.6522, 308.2667, 303.12314, 298.10745, 296.00406, 295.6236, 298.24164, 291.21344, 284.68805, 282.92078, 283.36652, 280.23514, 279.29538, 283.66913, 284.80566, 278.15463, 274.29877, 273.7735, 274.1728, 266.60797, 265.63663, 269.96237, 269.29, 262.02777, 254.71655, 254.32861, 257.1684, 254.08496, 251.00009, 248.8509, 247.51059, 244.7972, 238.3995, 241.2109, 243.4803, 238.43742, 240.2468, 247.16353, 256.4035, 257.5721, 253.87263, 252.46355, 247.14302, 240.34244, 236.79861, 234.01321, 230.16426, 224.40865, 220.68164, 217.1976, 216.90457, 217.57133, 219.36206, 220.85445, 223.63394, 220.04932, 221.4126, 223.48564, 215.58223, 213.97168, 218.74963, 212.65393, 209.27693, 213.51538, 223.19809, 219.27303, 207.41757, 204.41428, 208.11926, 202.10565, 203.76837, 206.14401, 204.57497, 199.64435, 194.41296, 185.01364, 179.24785, 180.00243, 178.63344, 177.56592, 180.66995, 190.8287, 185.23721, 173.443, 170.01222, 171.9204, 172.43979, 166.97333, 167.90237, 172.20445, 167.35245, 159.11557, 152.87354, 148.34, 148.05777, 145.93689, 150.0643, 153.3365, 152.08842, 145.25537, 139.10936, 136.17459, 133.54681, 131.36452, 136.02701, 141.73312, 144.29788, 145.52328, 144.09383, 143.05441, 140.90955, 141.65207, 142.61777, 143.05325, 142.13441, 140.08275, 139.749, 139.21175, 135.5651, 131.44594, 130.41434, 133.28102, 134.03891, 129.08243, 124.14127, 121.78352, 118.52834, 116.22025, 114.89071, 117.4739, 122.4624, 125.23069, 119.878174, 115.19771, 113.853195, 109.7183, 106.796486, 110.15104, 111.5376, 113.53227, 109.74314, 104.40353, 100.48576, 99.54789, 96.54811, 95.850975, 99.040726, 101.072136, 104.11758, 105.560745, 98.821815, 94.70805, 94.50092, 93.032394, 93.755356, 97.3617, 99.48755, 100.01741, 99.60188, 97.74243, 98.64872, 98.56181, 99.196655, 100.063965, 101.35634, 101.67732, 100.95293, 101.127594, 95.87315, 90.71084, 88.89375, 89.44081, 89.83371, 89.904655, 88.566956, 86.29763, 85.30243, 84.03261, 83.97205, 85.75832, 87.39823, 89.20949, 88.45851, 86.41845, 84.415306, 81.50712, 77.80872, 77.339676, 79.81583, 81.13301, 81.05561, 77.23153, 72.89038, 72.391945, 71.07969, 70.87498, 72.261375, 75.0746, 76.70819, 76.56844, 73.12854, 71.438965, 70.28694, 71.26936, 72.242935, 74.03232, 75.545364, 76.54635, 76.94473, 75.84058, 74.42658, 75.3491, 76.233475, 76.89633, 78.132034, 78.789154, 78.465004, 77.8358, 78.098366, 76.230125, 74.119095, 76.35391, 77.10805, 78.140526, 76.72907, 73.803, 73.808334, 72.25221, 70.95018, 70.440605, 72.982834, 75.666725, 77.45668, 74.26027, 71.673676, 70.28499, 68.34596, 67.44149, 67.59604, 70.240616, 71.528824, 71.34795, 66.34916, 61.83895, 59.476116, 61.63697, 61.775856, 58.013466, 54.73246, 54.357708, 50.997528, 50.980225, 44.46351, 38.584133, 37.092083, 36.34773, 37.76707]
+# true_out_array
+# [731.0, 730.0, 729.0, 728.0, 727.0, 726.0, 725.0, 724.0, 723.0, 722.0, 721.0, 720.0, 719.0, 718.0, 717.0, 716.0, 715.0, 714.0, 713.0, 712.0, 711.0, 710.0, 709.0, 708.0, 707.0, 706.0, 705.0, 704.0, 703.0, 702.0, 701.0, 700.0, 699.0, 698.0, 697.0, 696.0, 695.0, 694.0, 693.0, 692.0, 691.0, 690.0, 689.0, 688.0, 687.0, 686.0, 685.0, 684.0, 683.0, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 676.0, 675.0, 674.0, 673.0, 672.0, 671.0, 670.0, 669.0, 668.0, 667.0, 666.0, 665.0, 664.0, 663.0, 662.0, 661.0, 660.0, 659.0, 658.0, 657.0, 656.0, 655.0, 654.0, 653.0, 652.0, 651.0, 650.0, 649.0, 648.0, 647.0, 646.0, 645.0, 644.0, 643.0, 642.0, 641.0, 640.0, 639.0, 638.0, 637.0, 636.0, 635.0, 634.0, 633.0, 632.0, 631.0, 630.0, 629.0, 628.0, 627.0, 626.0, 625.0, 624.0, 623.0, 622.0, 621.0, 620.0, 619.0, 618.0, 617.0, 616.0, 615.0, 614.0, 613.0, 612.0, 611.0, 610.0, 609.0, 608.0, 607.0, 606.0, 605.0, 604.0, 603.0, 602.0, 601.0, 600.0, 599.0, 598.0, 597.0, 596.0, 595.0, 594.0, 593.0, 592.0, 591.0, 590.0, 589.0, 588.0, 587.0, 586.0, 585.0, 584.0, 583.0, 582.0, 581.0, 580.0, 579.0, 578.0, 577.0, 576.0, 575.0, 574.0, 573.0, 572.0, 571.0, 570.0, 569.0, 568.0, 567.0, 566.0, 565.0, 564.0, 563.0, 562.0, 561.0, 560.0, 559.0, 558.0, 557.0, 556.0, 555.0, 554.0, 553.0, 552.0, 551.0, 550.0, 549.0, 548.0, 547.0, 546.0, 545.0, 544.0, 543.0, 542.0, 541.0, 540.0, 539.0, 538.0, 537.0, 536.0, 535.0, 534.0, 533.0, 532.0, 531.0, 530.0, 529.0, 528.0, 527.0, 526.0, 525.0, 524.0, 523.0, 522.0, 521.0, 520.0, 519.0, 518.0, 517.0, 516.0, 515.0, 514.0, 513.0, 512.0, 511.0, 510.0, 509.0, 508.0, 507.0, 506.0, 505.0, 504.0, 503.0, 502.0, 501.0, 500.0, 499.0, 498.0, 497.0, 496.0, 495.0, 494.0, 493.0, 492.0, 491.0, 490.0, 489.0, 488.0, 487.0, 486.0, 485.0, 484.0, 483.0, 482.0, 481.0, 480.0, 479.0, 478.0, 477.0, 476.0, 475.0, 474.0, 473.0, 472.0, 471.0, 470.0, 469.0, 468.0, 467.0, 466.0, 465.0, 464.0, 463.0, 462.0, 461.0, 460.0, 459.0, 458.0, 457.0, 456.0, 455.0, 454.0, 453.0, 452.0, 451.0, 450.0, 449.0, 448.0, 447.0, 446.0, 445.0, 444.0, 443.0, 442.0, 441.0, 440.0, 439.0, 438.0, 437.0, 436.0, 435.0, 434.0, 433.0, 432.0, 431.0, 430.0, 429.0, 428.0, 427.0, 426.0, 425.0, 424.0, 423.0, 422.0, 421.0, 420.0, 419.0, 418.0, 417.0, 416.0, 415.0, 414.0, 413.0, 412.0, 411.0, 410.0, 409.0, 408.0, 407.0, 406.0, 405.0, 404.0, 403.0, 402.0, 401.0, 400.0, 399.0, 398.0, 397.0, 396.0, 395.0, 394.0, 393.0, 392.0, 391.0, 390.0, 389.0, 388.0, 387.0, 386.0, 385.0, 384.0, 383.0, 382.0, 381.0, 380.0, 379.0, 378.0, 377.0, 376.0, 375.0, 374.0, 373.0, 372.0, 371.0, 370.0, 369.0, 368.0, 367.0, 366.0, 365.0, 364.0, 363.0, 362.0, 361.0, 360.0, 359.0, 358.0, 357.0, 356.0, 355.0, 354.0, 353.0, 352.0, 351.0, 350.0, 349.0, 348.0, 347.0, 346.0, 345.0, 344.0, 343.0, 342.0, 341.0, 340.0, 339.0, 338.0, 337.0, 336.0, 335.0, 334.0, 333.0, 332.0, 331.0, 330.0, 329.0, 328.0, 327.0, 326.0, 325.0, 324.0, 323.0, 322.0, 321.0, 320.0, 319.0, 318.0, 317.0, 316.0, 315.0, 314.0, 313.0, 312.0, 311.0, 310.0, 309.0, 308.0, 307.0, 306.0, 305.0, 304.0, 303.0, 302.0, 301.0, 300.0, 299.0, 298.0, 297.0, 296.0, 295.0, 294.0, 293.0, 292.0, 291.0, 290.0, 289.0, 288.0, 287.0, 286.0, 285.0, 284.0, 283.0, 282.0, 281.0, 280.0, 279.0, 278.0, 277.0, 276.0, 275.0, 274.0, 273.0, 272.0, 271.0, 270.0, 269.0, 268.0, 267.0, 266.0, 265.0, 264.0, 263.0, 262.0, 261.0, 260.0, 259.0, 258.0, 257.0, 256.0, 255.0, 254.0, 253.0, 252.0, 251.0, 250.0, 249.0, 248.0, 247.0, 246.0, 245.0, 244.0, 243.0, 242.0, 241.0, 240.0, 239.0, 238.0, 237.0, 236.0, 235.0, 234.0, 233.0, 232.0, 231.0, 230.0, 229.0, 228.0, 227.0, 226.0, 225.0, 224.0, 223.0, 222.0, 221.0, 220.0, 219.0, 218.0, 217.0, 216.0, 215.0, 214.0, 213.0, 212.0, 211.0, 210.0, 209.0, 208.0, 207.0, 206.0, 205.0, 204.0, 203.0, 202.0, 201.0, 200.0, 199.0, 198.0, 197.0, 196.0, 195.0, 194.0, 193.0, 192.0, 191.0, 190.0, 189.0, 188.0, 187.0, 186.0, 185.0, 184.0, 183.0, 182.0, 181.0, 180.0, 179.0, 178.0, 177.0, 176.0, 175.0, 174.0, 173.0, 172.0, 171.0, 170.0, 169.0, 168.0, 167.0, 166.0, 165.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 158.0, 157.0, 156.0, 155.0, 154.0, 153.0, 152.0, 151.0, 150.0, 149.0, 148.0, 147.0, 146.0, 145.0, 144.0, 143.0, 142.0, 141.0, 140.0, 139.0, 138.0, 137.0, 136.0, 135.0, 134.0, 133.0, 132.0, 131.0, 130.0, 129.0, 128.0, 127.0, 126.0, 125.0, 124.0, 123.0, 122.0, 121.0, 120.0, 119.0, 118.0, 117.0, 116.0, 115.0, 114.0, 113.0, 112.0, 111.0, 110.0, 109.0, 108.0, 107.0, 106.0, 105.0, 104.0, 103.0, 102.0, 101.0, 100.0, 99.0, 98.0, 97.0, 96.0, 95.0, 94.0, 93.0, 92.0, 91.0, 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, 80.0, 79.0, 78.0, 77.0, 76.0, 75.0, 74.0, 73.0, 72.0, 71.0, 70.0, 69.0, 68.0, 67.0, 66.0, 65.0, 64.0, 63.0, 62.0, 61.0, 60.0, 59.0, 58.0, 57.0, 56.0, 55.0, 54.0, 53.0, 52.0, 51.0, 50.0, 49.0, 48.0, 47.0, 46.0, 45.0, 44.0, 43.0, 42.0, 41.0, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 31.0, 30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
+# error_pred_array
+# [106.8017578125, 107.24916381835938, 107.09857788085938, 107.18652954101563, 107.17105712890626, 105.679150390625, 105.09588623046875, 104.722900390625, 104.17068481445312, 106.74915161132813, 112.5259033203125, 117.60294189453126, 120.4012939453125, 120.51604614257812, 118.531201171875, 118.04191284179687, 119.45393676757813, 120.308251953125, 121.4248046875, 123.0781982421875, 125.86275634765624, 130.00436401367188, 132.5062683105469, 133.3163635253906, 133.7996032714844, 129.99985961914064, 126.28825073242187, 124.241259765625, 122.08763427734375, 121.05615234375, 126.07679443359375, 130.9129638671875, 127.12218627929687, 122.11602172851562, 119.54619140625, 116.44710083007813, 117.20416259765625, 124.6419189453125, 128.87418212890626, 125.87058715820312, 123.323291015625, 124.03239135742187, 125.24253540039062, 127.6897705078125, 131.61094970703124, 133.88858032226562, 131.1741516113281, 130.18557739257812, 130.466259765625, 129.41697998046874, 128.61314697265624, 143.10620727539063, 148.93040771484374, 154.093310546875, 161.38328857421874, 159.25676879882812, 153.34186401367188, 153.0009338378906, 154.77952270507814, 154.2467834472656, 154.97373046875, 155.47647094726562, 157.2298583984375, 161.83776245117187, 157.39271240234376, 148.08115844726564, 139.92596435546875, 139.82662353515624, 135.60584106445313, 132.55995483398436, 128.39576416015626, 129.30995483398436, 128.9197570800781, 129.02015380859376, 131.6291015625, 133.12144165039064, 133.79563598632814, 134.65589599609376, 134.9836853027344, 135.78692626953125, 136.41942749023437, 135.84865112304686, 135.2898742675781, 135.3591552734375, 135.678125, 136.58834228515624, 137.71140747070314, 137.39193115234374, 135.50650024414062, 134.30554809570313, 133.43773193359374, 129.983740234375, 126.09824829101562, 123.090869140625, 121.27896728515626, 126.47249145507813, 131.66317749023438, 134.11036376953126, 138.1355224609375, 139.82849731445313, 138.95473022460936, 138.65062255859374, 138.77760009765626, 139.20184936523438, 141.28125610351563, 150.04909057617186, 158.093359375, 160.05431518554687, 155.5939208984375, 147.86602172851562, 138.9646423339844, 135.77183837890624, 136.9666748046875, 141.76278686523438, 146.40884399414062, 145.40692749023438, 144.246044921875, 141.47144165039063, 138.8371337890625, 134.30701904296876, 135.98505249023438, 141.79879760742188, 148.11781005859376, 155.26597900390624, 161.8457458496094, 162.38451538085937, 160.51602172851562, 157.75650634765626, 159.15952758789064, 163.5218505859375, 168.8377197265625, 174.559619140625, 177.6044189453125, 176.97130126953124, 172.2554504394531, 169.879052734375, 166.54342651367188, 160.55439453125, 157.9242431640625, 156.6973449707031, 153.459716796875, 157.07733764648438, 162.97486572265626, 163.05238647460936, 157.6363525390625, 155.6457092285156, 154.89284057617186, 159.14182739257814, 165.3385986328125, 166.71649169921875, 165.98580322265624, 163.775048828125, 170.97605590820314, 177.4025390625, 183.17938842773438, 179.59425659179686, 172.63693237304688, 170.97788696289064, 170.6887939453125, 172.0221923828125, 167.1444580078125, 169.98863525390624, 172.2545654296875, 171.97409057617188, 174.96064453125, 174.30679321289062, 173.97936401367187, 169.61762084960938, 169.89578857421876, 173.01317138671874, 176.9396728515625, 184.0664794921875, 191.419921875, 194.59866943359376, 192.71838989257813, 189.2229431152344, 188.47494506835938, 191.18211059570314, 195.05662841796874, 200.53916015625, 204.74340209960937, 202.32561645507812, 197.18637084960938, 194.794775390625, 195.84238891601564, 192.05261840820313, 189.63292846679687, 189.690087890625, 186.74298706054688, 182.38124389648436, 186.11392211914062, 190.43483276367186, 186.73477783203126, 181.5135009765625, 176.7824279785156, 175.36327514648437, 179.58489990234375, 190.1707763671875, 196.873583984375, 198.3995788574219, 192.51524047851564, 193.77179565429688, 202.18579711914063, 204.55764770507812, 200.049755859375, 199.54173583984374, 197.8720275878906, 200.201123046875, 202.69306030273438, 199.39838256835938, 192.90757446289064, 193.63469848632812, 200.0288818359375, 202.9200927734375, 205.9181396484375, 205.76450805664064, 208.63541259765626, 209.154443359375, 212.8423645019531, 214.07640380859374, 215.0979736328125, 217.38623046875, 223.78648681640624, 229.1092956542969, 224.47726440429688, 218.8189453125, 219.5721923828125, 223.36707763671876, 230.76204833984374, 234.74806518554686, 234.2433349609375, 229.8450439453125, 226.9989990234375, 230.26968383789062, 235.67100830078124, 234.2619384765625, 229.55297241210937, 225.4757568359375, 220.8319580078125, 214.24515380859376, 215.07557373046876, 218.5496032714844, 216.87337646484374, 212.69066162109374, 207.77647705078124, 212.4995910644531, 221.44427490234375, 230.7033935546875, 237.03948974609375, 237.09111633300782, 230.44684448242188, 237.6629150390625, 247.08877563476562, 244.46361083984374, 230.5930908203125, 224.139453125, 225.2371032714844, 231.93701782226563, 232.33006591796874, 224.82645263671876, 209.61727600097657, 204.33335876464844, 206.87513122558593, 211.6161682128906, 213.45057983398436, 220.20721435546875, 234.19617919921876, 241.14498901367188, 239.24900512695314, 232.61168212890624, 229.85286865234374, 233.42586669921874, 239.31504516601564, 243.09632568359376, 237.38095092773438, 234.02857666015626, 230.51520080566405, 227.71993408203124, 225.55133056640625, 227.72156372070313, 228.59523010253906, 230.9028778076172, 237.68890686035155, 244.74642944335938, 247.75022583007814, 245.701708984375, 243.0243347167969, 242.41748046875, 241.27228393554688, 241.6866424560547, 246.05927429199218, 248.05186462402344, 246.14089965820312, 242.30682983398438, 243.03912963867188, 243.2030029296875, 239.10759582519532, 239.06760864257814, 237.85259704589845, 231.45787963867187, 229.0707977294922, 231.60869750976562, 235.32467041015624, 230.45263061523437, 226.60755004882813, 222.43735961914064, 220.88528137207032, 218.9193145751953, 224.45324096679687, 225.69020080566406, 227.91840209960938, 235.72862548828124, 242.0917236328125, 244.8972595214844, 243.25823669433595, 242.7712829589844, 236.96685180664062, 231.95407409667968, 228.4798126220703, 231.68909301757813, 234.33731079101562, 234.3584716796875, 230.1144226074219, 232.7921142578125, 240.96051635742188, 250.98364868164063, 257.3550537109375, 261.0341827392578, 259.3337371826172, 256.2845489501953, 253.63208923339843, 251.26297912597656, 253.33463134765626, 253.28392028808594, 248.8305694580078, 242.9416961669922, 242.42147521972657, 243.63910827636718, 239.96717834472656, 236.61067810058594, 234.05697021484374, 227.15514221191407, 219.05592346191406, 219.67520446777343, 220.3845672607422, 218.2250213623047, 216.08983154296874, 214.55523681640625, 211.94017944335937, 208.79502563476564, 213.193505859375, 214.759423828125, 215.38479614257812, 211.88626708984376, 207.2621276855469, 209.0108673095703, 206.70924072265626, 204.54959106445312, 203.4871032714844, 200.42527770996094, 193.78063049316407, 194.9349395751953, 198.00064697265626, 196.13331604003906, 197.17262268066406, 199.28938903808594, 190.48612060546876, 180.46985778808593, 178.94498596191406, 182.30372314453126, 182.17596740722655, 183.37083129882814, 178.88883056640626, 174.93262939453126, 177.99194946289063, 183.62265014648438, 186.6516906738281, 186.87590637207032, 187.43128967285156, 186.44041442871094, 184.77142333984375, 186.22093200683594, 188.91959228515626, 191.75789794921874, 189.50049438476563, 187.9597900390625, 188.32879333496095, 190.31566162109374, 192.54249877929686, 187.0768280029297, 190.5132598876953, 192.28074340820314, 190.25094604492188, 186.3477783203125, 188.43851623535156, 189.0863067626953, 183.9326934814453, 180.43062438964844, 179.03975830078124, 175.9904815673828, 172.34158020019532, 175.61199951171875, 179.00237731933595, 183.0902587890625, 179.81983947753906, 173.01455993652343, 168.6351104736328, 164.6552764892578, 161.68812255859376, 166.91649169921874, 171.495068359375, 170.09461364746093, 170.3326843261719, 168.55962524414062, 164.7256591796875, 161.89059448242188, 158.54799194335936, 154.58148498535155, 152.17744140625, 145.88811950683595, 150.88079528808595, 155.09781188964843, 159.01866760253907, 161.1527130126953, 158.9465362548828, 154.52452087402344, 152.95787658691407, 144.4376007080078, 142.6656707763672, 145.66258850097657, 146.40871887207032, 148.13670349121094, 141.14189758300782, 131.87977905273436, 123.6688720703125, 118.89546813964844, 113.04057312011719, 114.23306884765626, 110.44611511230468, 97.51891784667968, 85.08323364257812, 78.68525085449218, 75.26891784667968, 69.14007415771485, 59.50851287841797, 60.639642333984376, 68.37369995117187, 62.735595703125, 54.932237243652345, 51.954647827148435, 51.54478912353515, 48.50131530761719, 49.14748077392578, 55.10923614501953, 60.109623718261716, 51.883201599121094, 45.67605438232422, 44.85676574707031, 47.547821044921875, 47.58889617919922, 48.23987731933594, 54.58482360839844, 59.22719116210938, 60.001705932617185, 55.666220092773436, 54.1843994140625, 55.517295837402344, 57.72265777587891, 55.46001434326172, 54.91736755371094, 56.44767456054687, 54.2442626953125, 51.57813415527344, 51.860400390625, 53.068338012695314, 53.72517852783203, 53.53219146728516, 50.85009613037109, 48.34832916259766, 46.63679962158203, 46.587641906738284, 48.92315826416016, 45.197218322753905, 42.47053527832031, 42.42797698974609, 43.77884063720703, 42.41496276855469, 43.43930206298828, 47.83823852539062, 49.602532958984376, 44.99979858398437, 44.47834167480469, 45.724876403808594, 46.012307739257814, 41.54290924072266, 39.32746124267578, 44.60581665039062, 46.11036376953125, 40.866015625, 39.80544586181641, 38.91185607910156, 43.62619171142578, 43.48525543212891, 40.63693695068359, 39.722059631347655, 40.381719970703124, 40.92910919189453, 40.43625946044922, 45.1573974609375, 52.02257232666015, 48.56901397705078, 48.05445556640625, 52.17318878173828, 55.466845703125, 55.739654541015625, 54.01840362548828, 54.051690673828126, 48.23794097900391, 41.82695617675781, 39.976698303222655, 38.78358154296875, 36.631352233886716, 32.10371704101563, 29.16675262451172, 27.327642822265624, 25.854829406738283, 26.493132019042967, 30.202236938476563, 31.807963562011718, 34.1703857421875, 33.280259704589845, 32.92369384765625, 36.733647155761716, 36.5574462890625, 40.62167510986328, 45.30859222412109, 44.16117553710937, 41.90992279052735, 43.72123718261719, 47.174137878417966, 44.556575012207034, 42.87532043457031, 44.29874114990234, 45.69954223632813, 41.068791198730466, 38.023095703125, 40.871473693847655, 40.962510681152345, 34.4193115234375, 30.350871276855468, 28.587992858886718, 28.949136352539064, 34.29034576416016, 35.244712829589844, 32.48330535888672, 35.492636108398436, 38.915185546875, 31.491596984863282, 31.710682678222657, 33.706322479248044, 37.92001190185547, 41.377970123291014, 38.551904296875, 36.301979064941406, 35.198622131347655, 33.89222183227539, 30.950508880615235, 28.624395751953124, 28.092375183105467, 28.319123840332033, 29.985074615478517, 28.703287506103514, 29.460755157470704, 27.660344696044923, 26.011257934570313, 23.915016174316406, 24.5432861328125, 25.5913444519043, 26.988822174072265, 24.569823455810546, 25.98686752319336, 24.42647933959961, 19.939124298095702, 17.767887115478516, 17.874130249023438, 15.76476593017578, 16.114447021484374, 16.529776763916015, 16.479144287109374, 16.948919677734374, 15.333244323730469, 13.940868377685547, 13.623796081542968, 15.400931549072265, 17.845833587646485, 19.182776641845702, 18.436087799072265, 17.064998626708984, 17.26526794433594, 19.717427062988282, 21.24462432861328, 22.28343276977539, 22.651512908935548, 21.249842834472656, 18.98738784790039, 21.115738677978516, 19.83998794555664, 20.588232421875, 20.658695220947266, 22.07738494873047, 20.25640106201172, 19.66504821777344, 20.30720672607422, 21.67621765136719, 20.724027252197267, 18.372946166992186, 17.78198699951172, 19.340448760986327, 20.073881530761717, 20.24507369995117, 19.188165283203126, 19.993731689453124, 18.949485778808594, 17.413033294677735, 14.311641693115234, 13.426365661621094, 16.404698181152344, 17.533022308349608, 16.289517211914063, 16.230094146728515, 15.966808319091797, 16.082582092285158, 13.866648864746093, 12.27248764038086, 12.003680419921874, 13.715357208251953, 13.196182250976562, 11.97830810546875, 12.697190856933593, 14.522168731689453, 15.576066589355468, 15.711257934570312, 15.452623748779297, 12.863646697998046, 12.036328125, 12.553289794921875, 13.430391693115235, 15.031000900268555, 14.38227424621582, 14.745211410522462, 14.62278938293457, 15.559734725952149, 15.446501922607421, 15.371404647827148, 16.57732391357422, 17.396385955810548, 17.248658752441408, 17.507235717773437, 17.300888061523438, 16.413942718505858, 14.680451202392579, 13.553557586669921, 14.59645881652832, 16.375525283813477, 16.628854751586914, 16.157353591918945, 14.618912506103516, 12.806095123291016, 13.34145736694336, 12.702802276611328, 12.91172981262207, 15.199021530151366, 17.664461517333983, 18.75383529663086, 19.56844291687012, 17.617464065551758, 18.16679801940918, 17.549903869628906, 18.36036605834961, 20.252636337280272, 23.172793579101562, 25.545364379882812, 27.5463436126709, 28.944729614257813, 28.84058532714844, 28.426579666137695, 30.349101257324218, 32.23348808288574, 33.89633636474609, 36.13203010559082, 37.789146423339844, 38.4650016784668, 38.83580894470215, 40.09836616516113, 39.23013458251953, 38.11909370422363, 41.35390853881836, 43.108053588867186, 45.14053115844727, 44.72906761169433, 42.80300025939941, 43.80832977294922, 43.25221633911133, 42.950191497802734, 43.440609741210935, 46.98283348083496, 50.66671600341797, 53.45667915344238, 51.26027565002441, 49.67367324829102, 49.284981918334964, 48.34595794677735, 48.44149742126465, 49.59604263305664, 53.24061279296875, 55.528814697265624, 56.34794807434082, 52.34915771484375, 48.83895835876465, 47.47611427307129, 50.63696479797363, 51.775851821899415, 49.01346702575684, 46.732459449768065, 47.35771083831787, 44.997528076171875, 45.98022804260254, 40.463508224487306, 35.58413314819336, 35.09208116531372, 35.34773006439209, 37.767073440551755]
+
+
+
+
+for FD in [1]: ######['1','2','3','4']
+    # if max_life==110 and FD=='1':
+    #     continue
+    # if max_life==110 and FD=='2':
+        # continue
+    
+    FD_feature_columns=[]
+ 
+
+
+
+
+
+
+
+
+
+
+
+    method_name='grid_FD{}_TaFCN_npseed{}_segment_{}'.format(FD,seed,segment)
+    # method_name='FCN_RUL_1out_train_split_test'
+    dataset='cmapssd'
+    
+    
+    def unbalanced_penalty_score_1out(Y_test,Y_pred) :
+          
+        s=0    
+        for i in range(len(Y_pred)):
+            if Y_pred[i]>Y_test[i]:
+                s=s+math.exp((Y_pred[i]-Y_test[i])/10)-1
+            else:
+                s=s+math.exp((Y_test[i]-Y_pred[i])/13)-1    
+        print('unbalanced_penalty_score{}'.format(s))
+        return s  
+      
+    def error_range_1out(Y_test,Y_pred) :           
+        error_range=(Y_test-Y_pred).min(),(Y_test-Y_pred).max()
+        print('error range{}'.format(error_range))
+        return error_range
+    
+ 
+    print(X_train.shape)
+    X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1,1)
+    
+    X_test=X_test.reshape(X_test.shape[0],X_train.shape[1],1,1)
+    
+    # x_train_array , y_train_array , x_test_array ,  y_test_array=get_input_out_2(CS2_36,CS2_37,20)   
+
+    
+    
+    import six
+    
+    import keras.backend as K
+    from keras.utils.generic_utils import deserialize_keras_object
+    from keras.utils.generic_utils import serialize_keras_object
+    from tensorflow.python.ops import math_ops
+    from tensorflow.python.util.tf_export import tf_export
+    
+    
+    
+    
+    
+    from tensorflow.python.ops import math_ops
+    
+
+    
+
+    
+    
+    #########np.greater_equal([4, 2, 1], [2, 2, 2])array([ True, True, False])
+    #############tf.cast( ) 或者K.cast( ) 是执行 tensorflow 中的张量数据类型转换，比如读入的图片是int8类型的，一定要在训练的时候把图片的数据格式转换为float32.
+    
+    ################reduce_sum reduce dimensinality and get sum
+
+
+    
+
+            #return inputs*x   
+    
+
+    
+    
+
+
+    # reshape_size=len(FD_feature_columns)*int((sequence_length/3))
+    def FCN_model():
+    #    in0 = keras.Input(shape=(sequence_length,train_feature_slice.shape[1]))  # shape: (batch_size, 3, 2048)
+    #    in0_shaped= keras.layers.Reshape((train_feature_slice.shape[1],sequence_length,1))(in0)   
+    
+        in0 = keras.Input(shape=(X_train.shape[1],X_train.shape[2],X_train.shape[3]),name='layer_13')  # shape: (batch_size, 3, 2048)
+    #    begin_senet=SeBlock()(in0)
+        x = keras.layers.AveragePooling2D(pool_size=(int(sequence_length/segment), 1), strides=int(sequence_length/segment),name='layer_12')(in0)
+        # x = keras.layers.Reshape((-1,1))(x) 
+        
+        # x = keras.layers.Reshape((len(FD_feature_columns)*int((sequence_length/3)),))(x)             
+        x = keras.layers.Reshape((-1,))(x)               
+        # x = keras.layers.GlobalAveragePooling2D()(in0)
+        x = keras.layers.Dense(1, use_bias=False,activation=keras.activations.relu)(x)
+        kernel = keras.layers.Dense(1, use_bias=False,activation=keras.activations.hard_sigmoid,name='layer_11')(x)
+        begin_senet= keras.layers.Multiply(name='layer_10')([in0,kernel])    #给通道加权重
+     
+    
+       
+    
+    #     conv0 = keras.layers.
+        
+        
+        conv0 = keras.layers.Conv2D(num_filter1, kernel1_size, strides=1, padding='same',name='layer_9')(begin_senet)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_8')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter2, kernel2_size, strides=1, padding='same',name='layer_7')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_6')(conv0)
+        
+    #    conv0 = keras.layers.Dropout(dropout)(conv0)
+        conv0 = keras.layers.Conv2D(num_filter3, kernel3_size, strides=1, padding='same',name='layer_5')(conv0)
+        conv0 = keras.layers.BatchNormalization()(conv0)
+        conv0 = keras.layers.Activation('relu',name='layer_4')(conv0)
+        conv0 = keras.layers.GlobalAveragePooling2D(name='layer_3')(conv0)
+        conv0 = keras.layers.Dense(64, activation='relu',name='layer_2')(conv0)
+        out = keras.layers.Dense(1, activation='relu',name='layer_1')(conv0)
+    
+        
+        
+    
+    
+    
+        model = keras.models.Model(inputs=in0, outputs=[out])    
+    
+        return model
+    
+    
+    # ##############shuaffle the data
+    np.random.seed(seed)
+    index=np.arange(X_train.shape[0])
+    np.random.shuffle(index,)
+    
+     
+    X_train=X_train[index]#X_train是训练集，y_train是训练标签
+    Y_train=Y_train[index]
+    
+    #X_train, Xtest, Y_train, ytest = train_test_split(X_train, Y_train, test_size=0.7, random_state=0)
+    
+    
+    if __name__ == '__main__':
+    
+        error_record=[]
+        index_record=[]
+        unbalanced_penalty_score_record=[]
+        error_range_left_record=[]
+        error_range_right_record=[]
+        index_min_val_loss_record,min_val_loss_record=[],[]
+        
+        if os.path.exists(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name)):os.remove(r"F:\桌面11.17\project\RUL\experiments_result\method_error_txt\{}.txt".format(method_name))
+    
+     
+    
+
+    
+        rul_pred_array_list=[]
+        true_out_array_list=[]          
+        error_pred_array_list=[]
+                    
+    #######             single  output                
+    
+        for i in range(run_times):
+            print('xxx')
+ 
+            model=FCN_model()
+            plot_model(model, to_file=r"F:\桌面11.17\project\RUL\Flatten.png", show_shapes=True)#########to_file='Flatten.png',r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5
+            
+            optimizer = keras.optimizers.Adam()
+            model.compile(loss='mse',#loss=root_mean_squared_error,
+                          optimizer=optimizer,
+                          metrics=[root_mean_squared_error])
+             
+            reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = 'loss', factor=0.5,
+                              patience=patience_reduce_lr, min_lr=0.0001) 
+            
+
+    #                  verbose=1, validation_split=VALIDATION_SPLIT, callbacks = [reduce_lr])   
+            model_name='{}_dataset_{}_log{}_time{}'.format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+            earlystopping=keras.callbacks.EarlyStopping(monitor='loss',patience=patience,verbose=1)
+            modelcheckpoint=keras.callbacks.ModelCheckpoint(monitor='loss',filepath=r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),save_best_only=True,verbose=1)
+            hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+                      verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+    #        hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epochs,
+    #                  verbose=1, validation_data=(X_test, Y_test), callbacks = [reduce_lr,earlystopping,modelcheckpoint])   
+            log = pd.DataFrame(hist.history)
+            log.to_excel(r"F:\桌面11.17\project\RUL\experiments_result\log\{}_dataset_{}_log{}_time{}.xlsx".format(method_name,dataset,i,datetime.datetime.now().strftime('%Y%m%d%H%M%S')))
+            
+            print(hist.history.keys())
+            epochs=range(len(hist.history['loss']))
+            plt.figure()
+            plt.plot(epochs,hist.history['loss'],'b',label='Training loss')
+            plt.plot(epochs,hist.history['val_loss'],'r',label='Validation val_loss')
+            plt.title('Traing and Validation loss')
+            plt.legend()
+            plt.show()
+
+    
+            
+    #        model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error,'Smooth':Smooth,'SeBlock':SeBlock})
+            model=keras.models.load_model(r"F:\桌面11.17\project\RUL\model\FCN_RUL_1out_train_valid_test\{}.h5".format(model_name),custom_objects={'root_mean_squared_error': root_mean_squared_error})
+            for layer in model.layers:
+                layer.trainable=False        
+    #        score = model.evaluate(X_test, Y_test)  ############forbid evaluate!!!!!!!!!!!!!!!!!!
+    #        print('score[1]:{}'.format(score[1]))    ############forbid evaluate!!!!!!!!!!!!!!!!!!    
+            
+            Y_pred=model.predict(X_test)
+    #        rmse=root_mean_squared_error(Y_test,Y_pred)
+    #        with tf.Session() as sess:
+    #            print(rmse.eval())
+            rmse_value=rmse(Y_test,Y_pred)
+            # print('rmse:{}'.format(rmse_value))
+            
+
+            rul_pred_array=np.array(Y_pred)
+            rul_pred_array=rul_pred_array.reshape(rul_pred_array.shape[0])
+            
+            # print(rul_pred_array.shape)
+            
+            true_out_array=np.array(Y_test)
+            
+            error_pred_array=rul_pred_array-true_out_array
+            
+            error_pred_array=np.maximum(error_pred_array, -error_pred_array)
+                # print(sol.x)
+                
+            # print(error_pred_array.sum())
+            # print("xxxxx")
+            # print(error_pred_array)
+                
+                
+            fig, ax = plt.subplots()
+            # 在生成的坐标系下画折线图
+            ax.plot(error_pred_array, linewidth=1)
+            
+            
+            
+            # 显示图形
+            plt.show()   
+             
+            
+            # print(i)
+            # print("rul_pred_array") 
+            # print(list(rul_pred_array))
+            # print("true_out_array") 
+            # print(list(true_out_array))
+            # print("error_pred_array") 
+            # print(list(error_pred_array))  
+            
+            rul_pred_array_list.append(rul_pred_array)
+            true_out_array_list.append(true_out_array)            
+            error_pred_array_list.append(error_pred_array)
+        rul_pred_array=np.mean(rul_pred_array_list,axis=0)
+        true_out_array=np.mean(true_out_array_list,axis=0)
+        error_pred_array=np.mean(error_pred_array_list,axis=0)
+
+
+        print(i)
+        print("rul_pred_array") 
+        print(list(rul_pred_array))
+        print("true_out_array") 
+        print(list(true_out_array))
+        print("error_pred_array") 
+        print(list(error_pred_array))  
+
+
+
diff --git a/code/data_read/read_mat_for_IGBT.py b/code/data_read/read_mat_for_IGBT.py
new file mode 100644
index 0000000..8169ef6
--- /dev/null
+++ b/code/data_read/read_mat_for_IGBT.py
@@ -0,0 +1,161 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 14 15:25:26 2023
+
+@author: Administrator
+"""
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][4][0]
+
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
+
+
+invaribale_index=3    #########dtype=[('dt', 'O'), ('gateSignalVoltage', 'O'), ('gateEmitterVoltage', 'O'), ('collectorEmitterVoltage', 'O'), ('collectorEmitterCurrentSignal', 'O')]),
+
+
+
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 3\Device3  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 4\Device4  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 5\Device5  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+# epoch_index=6
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# # x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][epoch_index][2][0][0][invaribale_index][0]
+
+# print(x)
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
diff --git a/code/data_read/test.py b/code/data_read/test.py
new file mode 100644
index 0000000..232838e
--- /dev/null
+++ b/code/data_read/test.py
@@ -0,0 +1,170 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 14 17:20:51 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 14 15:25:26 2023
+
+@author: Administrator
+"""
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][4][0]
+
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
+
+
+invaribale_index=3    #########dtype=[('dt', 'O'), ('gateSignalVoltage', 'O'), ('gateEmitterVoltage', 'O'), ('collectorEmitterVoltage', 'O'), ('collectorEmitterCurrentSignal', 'O')]),
+
+
+
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 3\Device3  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+# print()
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 4\Device4  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+print(Vces_list)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 5\Device5  1.mat'
+matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+Vces_list=[]
+
+for i in range(len(matdata["measurement"][0][0][1][0])):
+
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+    Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+    # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+# print(x)
+
+plt.plot(range(len(Vces_list)),Vces_list)
+plt.show()
+
+
+
+
+
+# epoch_index=9
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# # x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][epoch_index][2][0][0][invaribale_index][0]
+
+# print(x)
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
diff --git a/code/data_read/test_for_batarry.py b/code/data_read/test_for_batarry.py
new file mode 100644
index 0000000..89f3d03
--- /dev/null
+++ b/code/data_read/test_for_batarry.py
@@ -0,0 +1,248 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 14 20:18:57 2023
+
+@author: Administrator
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 14 15:25:26 2023
+
+@author: Administrator
+"""
+
+import numpy as np
+
+import scipy as sp
+
+import math
+
+from numpy import matmul as mm
+from math import sqrt,pi,log, exp
+
+import xlrd
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.optimize import minimize, rosen, rosen_der
+
+from scipy.stats import linregress
+
+from scipy.stats import norm
+
+
+import scipy.io as scio
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][4][0]
+
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
+
+
+invaribale_index=3    #########dtype=[('dt', 'O'), ('gateSignalVoltage', 'O'), ('gateEmitterVoltage', 'O'), ('collectorEmitterVoltage', 'O'), ('collectorEmitterCurrentSignal', 'O')]),
+
+
+
+
+
+###########feng tian
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\fengtian\training.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["training"]
+plt.plot(range(len(x)),x)
+plt.show()
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\fengtian\test.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["test"]
+plt.plot(range(len(x)),x)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+#########  nasa
+
+
+
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\nasa\B0005_2.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["B0005_2"][0]
+plt.plot(range(len(x)),x)
+# plt.show()
+
+print(x)
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\nasa\B0006_2.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["B0006_2"][0]
+plt.plot(range(len(x)),x)
+# plt.show()
+
+print(x)
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\nasa\B0007_2.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["B0007_2"][0]
+plt.plot(range(len(x)),x)
+# plt.show()
+print(x)
+
+
+
+path = r'F:\桌面11.17\project\RUL_guassion\handled_dataset\nasa\B0018_2.mat'
+
+matdata = scio.loadmat(path)
+
+x=matdata["B0018_2"][0]
+plt.plot(range(len(x)),x)
+plt.show()
+
+
+
+
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# Vces_list=[]
+
+# for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])
+#     Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+# # print(x)
+
+# plt.plot(range(len(Vces_list)),Vces_list)
+# plt.show()
+
+
+
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 3\Device3  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# Vces_list=[]
+
+# for i in range(len(matdata["measurement"][0][0][1][0])):
+    
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+#     Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+# # print(x)
+
+# plt.plot(range(len(Vces_list)),Vces_list)
+# plt.show()
+
+
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 4\Device4  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# Vces_list=[]
+
+# for i in range(len(matdata["measurement"][0][0][1][0])):
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+#     Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+# # print(x)
+
+# plt.plot(range(len(Vces_list)),Vces_list)
+# plt.show()
+
+
+
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 5\Device5  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# Vces_list=[]
+
+# for i in range(len(matdata["measurement"][0][0][1][0])):
+
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][110000])    
+#     Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][-1])
+#     # Vces_list.append(matdata["measurement"][0][0][1][0][i][2][0][0][invaribale_index][0][50000])
+    
+    
+# # print(x)
+
+# plt.plot(range(len(Vces_list)),Vces_list)
+# plt.show()
+
+
+
+
+
+# epoch_index=6
+
+
+# path = r'F:\桌面11.17\project\RUL_with_highly_small_sample\dataset\IGBTAgingData_04022009\Data\Thermal Overstress Aging with Square Signal at gate and SMU data\Aging Data\Device 2\Device2  1.mat'
+# matdata = scio.loadmat(path)
+# # print(matdata["measurement"][0][0][1][0][0][2][0][0][4])
+# # x=matdata["measurement"][0][0][1][0][0][2][0][0][invaribale_index][0]
+
+# x=matdata["measurement"][0][0][1][0][epoch_index][2][0][0][invaribale_index][0]
+
+# print(x)
+# plt.plot(range(0,len(x),10000),x[range(0,len(x),10000)])
+# plt.show()
+
diff --git a/code/desktop.ini b/code/desktop.ini
new file mode 100644
index 0000000..84caab7
--- /dev/null
+++ b/code/desktop.ini
@@ -0,0 +1,2 @@
+[.ShellClassInfo]
+IconResource=C:\Users\Administrator\AppData\Roaming\baidu\BaiduNetdisk\autobackup.ico,0
diff --git a/dataset/CACLE/CS2_35_cap.xlsx b/dataset/CACLE/CS2_35_cap.xlsx
new file mode 100644
index 0000000..ef566a4
Binary files /dev/null and b/dataset/CACLE/CS2_35_cap.xlsx differ
diff --git a/dataset/CACLE/CS2_35_cap_dropOutlier.xlsx b/dataset/CACLE/CS2_35_cap_dropOutlier.xlsx
new file mode 100644
index 0000000..67c61c3
Binary files /dev/null and b/dataset/CACLE/CS2_35_cap_dropOutlier.xlsx differ
diff --git a/dataset/CACLE/CS2_36_cap.xlsx b/dataset/CACLE/CS2_36_cap.xlsx
new file mode 100644
index 0000000..7a927aa
Binary files /dev/null and b/dataset/CACLE/CS2_36_cap.xlsx differ
diff --git a/dataset/CACLE/CS2_36_cap_dropOutlier.xlsx b/dataset/CACLE/CS2_36_cap_dropOutlier.xlsx
new file mode 100644
index 0000000..5c6dd60
Binary files /dev/null and b/dataset/CACLE/CS2_36_cap_dropOutlier.xlsx differ
diff --git a/dataset/CACLE/CS2_37_cap.xlsx b/dataset/CACLE/CS2_37_cap.xlsx
new file mode 100644
index 0000000..7073aaa
Binary files /dev/null and b/dataset/CACLE/CS2_37_cap.xlsx differ
diff --git a/dataset/CACLE/CS2_37_cap_dropOutlier.xlsx b/dataset/CACLE/CS2_37_cap_dropOutlier.xlsx
new file mode 100644
index 0000000..6397cb5
Binary files /dev/null and b/dataset/CACLE/CS2_37_cap_dropOutlier.xlsx differ
diff --git a/dataset/CACLE/CS2_38_cap.xlsx b/dataset/CACLE/CS2_38_cap.xlsx
new file mode 100644
index 0000000..ee08727
Binary files /dev/null and b/dataset/CACLE/CS2_38_cap.xlsx differ
diff --git a/dataset/CACLE/CS2_38_cap_dropOutlier.xlsx b/dataset/CACLE/CS2_38_cap_dropOutlier.xlsx
new file mode 100644
index 0000000..044961a
Binary files /dev/null and b/dataset/CACLE/CS2_38_cap_dropOutlier.xlsx differ
diff --git a/dataset/IGBTAgingData_04022009/Readme.doc b/dataset/IGBTAgingData_04022009/Readme.doc
new file mode 100644
index 0000000..217c35a
Binary files /dev/null and b/dataset/IGBTAgingData_04022009/Readme.doc differ
diff --git a/dataset/IGBTAgingData_04022009/Sonnenfeld_Goebel_Celaya.pdf b/dataset/IGBTAgingData_04022009/Sonnenfeld_Goebel_Celaya.pdf
new file mode 100644
index 0000000..1f565f6
Binary files /dev/null and b/dataset/IGBTAgingData_04022009/Sonnenfeld_Goebel_Celaya.pdf differ
diff --git a/dataset/nasa/B0005_2.mat b/dataset/nasa/B0005_2.mat
new file mode 100644
index 0000000..2262432
Binary files /dev/null and b/dataset/nasa/B0005_2.mat differ
diff --git a/dataset/nasa/B0006_2.mat b/dataset/nasa/B0006_2.mat
new file mode 100644
index 0000000..10c18b6
Binary files /dev/null and b/dataset/nasa/B0006_2.mat differ
diff --git a/dataset/nasa/B0007_2.mat b/dataset/nasa/B0007_2.mat
new file mode 100644
index 0000000..1dff18c
Binary files /dev/null and b/dataset/nasa/B0007_2.mat differ
diff --git a/dataset/nasa/B0018_2.mat b/dataset/nasa/B0018_2.mat
new file mode 100644
index 0000000..4bf7914
Binary files /dev/null and b/dataset/nasa/B0018_2.mat differ
diff --git a/dataset/xiandao/c1.txt b/dataset/xiandao/c1.txt
new file mode 100644
index 0000000..db924dc
--- /dev/null
+++ b/dataset/xiandao/c1.txt
@@ -0,0 +1,315 @@
+39.6435188
+41.73550651
+43.70386749
+45.59752512
+47.44376057
+49.22414953
+50.94063898
+52.59512744
+54.18946618
+55.72546025
+57.20486968
+58.62941047
+60.00075568
+61.32053643
+62.59034292
+63.81172539
+64.98619508
+66.11522516
+67.20025165
+68.24267429
+69.24385741
+70.2051308
+71.12779051
+72.01309967
+72.86228928
+73.67655898
+74.4570778
+75.20507849
+75.94243447
+76.66800439
+77.38074463
+78.07970525
+78.76402602
+79.43293254
+80.08573257
+80.72181228
+81.3406328
+81.9417267
+82.52469474
+83.08920252
+83.63497739
+84.16180538
+84.66952821
+85.15804041
+85.62728657
+86.07725857
+86.50799299
+86.91956854
+87.31210365
+87.68575403
+88.04071039
+88.37719619
+88.69546552
+88.99580098
+89.27851166
+89.54393124
+89.79241606
+90.02434336
+90.24010948
+90.44012824
+90.62482925
+90.79465641
+90.95006638
+91.09152714
+91.21951663
+91.33452138
+91.43703525
+91.52755821
+91.6065952
+91.67465492
+91.73224887
+91.77989023
+91.81809293
+91.84737073
+91.86823627
+91.88120029
+91.88688229
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.88839744
+91.99348548
+92.12454651
+92.25519913
+92.38528194
+92.51464058
+92.64312776
+92.77060337
+92.89693457
+93.02199582
+93.14566892
+93.26784304
+93.38841476
+93.50728797
+93.62437397
+93.73959135
+93.85286598
+93.96413095
+94.0733265
+94.18039996
+94.28530563
+94.3880047
+94.48846517
+94.58666168
+94.68257544
+94.77619407
+94.87901865
+95.03217647
+95.18676599
+95.34278076
+95.50021455
+95.65906133
+95.81931527
+95.98097074
+96.14402231
+96.3084647
+96.49096621
+96.71010318
+96.93286811
+97.15923479
+97.38917506
+97.62265881
+97.85965413
+98.10012733
+98.34404302
+98.59136424
+98.84205245
+99.09606759
+99.35336831
+99.61391177
+99.87765393
+100.1445496
+100.4145523
+100.6876147
+100.9636882
+101.2427235
+101.5246703
+101.8094774
+102.0970931
+102.3874649
+102.6805395
+102.9762632
+103.2745819
+103.5754409
+103.8787851
+104.1845593
+104.4927078
+104.8031748
+105.1159044
+105.4308406
+105.7479272
+106.0671083
+106.3883279
+106.7115303
+107.0366596
+107.3636608
+107.6924786
+108.0230583
+108.3553457
+108.6892868
+109.0248284
+109.3619176
+109.7005022
+110.0405307
+110.3819523
+110.7247168
+111.0687749
+111.4140781
+111.7605789
+112.1082306
+112.4569874
+112.8068047
+113.1576388
+113.5094471
+113.8621881
+114.2158217
+114.5703086
+114.9256111
+115.2816925
+115.6385177
+115.9960525
+116.3542646
+116.7131227
+117.072597
+117.4326594
+117.793283
+118.1544425
+118.5161143
+118.8782761
+119.2409074
+119.6039892
+119.9675041
+120.3314367
+120.6957727
+121.0604999
+121.4256078
+121.7910874
+122.1569315
+122.5231349
+122.8896939
+123.2566067
+123.6238731
+123.9914949
+124.3594757
+124.7278208
+125.0965374
+125.4656343
+125.8351225
+126.2050145
+126.5753248
+126.9460695
+127.3172667
+127.6889364
+128.0611002
+128.4337816
+128.8070058
+129.1807998
+129.5551926
+129.9302146
+130.3058983
+130.6822776
+131.0593884
+131.4372681
+131.8159558
+132.1954926
+132.5759206
+132.9572842
+133.3396289
+133.723002
+134.1074524
+134.4930303
+134.8797876
+135.2677775
+135.6570549
+136.0476757
+136.4396975
+136.8331792
+137.2281807
+137.6247635
+138.0229903
+138.4229246
+138.8246317
+139.2281774
+139.6336288
+140.0410542
+140.4505224
+140.8621036
+141.2758687
+141.6918894
+142.1102381
+142.5309883
+142.9542136
+143.3799888
+143.808389
+144.2394899
+144.6733675
+145.1100985
+145.5497597
+145.9924284
+146.4381821
+146.8870983
+147.3392549
+147.7947297
+148.2536005
+148.7159452
+149.1818414
+149.6513666
+150.124598
+150.6016127
+151.0824871
+151.5672974
+152.0561191
+152.5490275
+153.0460967
+153.5474006
+154.0530121
+154.5630033
+155.0774454
+155.5964086
+156.119962
+156.6481737
+157.1811105
+157.7188382
+158.2614209
+158.8089216
+159.3614017
+159.9189212
+160.4815383
+161.0493096
+161.6222903
+162.2005332
+162.7840897
+163.373009
+163.9673384
+164.5671231
+165.1724063
diff --git a/dataset/xiandao/c1_average.xlsx b/dataset/xiandao/c1_average.xlsx
new file mode 100644
index 0000000..d54d4b4
Binary files /dev/null and b/dataset/xiandao/c1_average.xlsx differ
diff --git a/dataset/xiandao/c1_wear.csv b/dataset/xiandao/c1_wear.csv
new file mode 100644
index 0000000..2d7e3e4
--- /dev/null
+++ b/dataset/xiandao/c1_wear.csv
@@ -0,0 +1,316 @@
+cut,flute_1,flute_2,flute_3,
+1,32.31711361,48.89261732,37.72082548,39.6435188
+2,37.914879,49.57081504,37.72082548,41.73550651
+3,43.08790971,50.30286727,37.72082548,43.70386749
+4,47.8590723,51.08365203,37.84985103,45.59752512
+5,52.25032922,51.90828793,38.17266456,47.44376057
+6,56.28276562,52.77212655,38.61755643,49.22414953
+7,59.97661561,53.6707451,39.17455623,50.94063898
+8,63.3512879,54.5999392,39.83415523,52.59512744
+9,66.4253909,55.55571585,40.58729178,54.18946618
+10,69.21675719,56.53428648,41.42533708,55.72546025
+11,71.74246752,57.53206029,42.34008123,57.20486968
+12,74.01887416,58.54563763,43.32371962,58.62941047
+13,76.06162381,59.57180359,44.36883963,60.00075568
+14,77.88567991,60.60752172,45.46840765,61.32053643
+15,79.50534445,61.64992794,46.61575637,62.59034292
+16,80.93427925,62.69632451,47.8045724,63.81172539
+17,82.18552678,63.74417426,49.0288842,64.98619508
+18,83.27153045,64.79109482,50.28305022,66.11522516
+19,84.20415441,65.83485314,51.56174741,67.20025165
+20,84.99470288,66.87336002,52.85995997,68.24267429
+21,85.653939,67.90466485,54.17296838,69.24385741
+22,86.19210327,68.92695045,55.49633868,70.2051308
+23,86.61893145,69.93852803,56.82591204,71.12779051
+24,86.94367205,70.93783235,58.15779461,72.01309967
+25,87.17510345,71.92341685,59.48834755,72.86228928
+26,87.32155046,72.89394911,60.81417738,73.67655898
+27,87.39090058,73.84820624,62.13212659,74.4570778
+28,87.39090058,74.78507049,63.43926441,75.20507849
+29,87.39090058,75.70352498,64.73287786,75.94243447
+30,87.39090058,76.6026495,66.0104631,76.66800439
+31,87.39090058,77.48161641,67.26971691,77.38074463
+32,87.39090058,78.33968677,68.50852841,78.07970525
+33,87.39090058,79.17620637,69.72497111,78.76402602
+34,87.39090058,79.99060207,70.91729498,79.43293254
+35,87.39090058,80.78237816,72.08391897,80.08573257
+36,87.39090058,81.55111275,73.22342351,80.72181228
+37,87.39090058,82.2964544,74.33454341,81.3406328
+38,87.39090058,83.01811874,75.41616079,81.9417267
+39,87.39090058,83.71588526,76.46729838,82.52469474
+40,87.39090058,84.38959411,77.48711287,83.08920252
+41,87.39090058,85.03914309,78.47488851,83.63497739
+42,87.39090058,85.66448465,79.43003091,84.16180538
+43,87.39090058,86.26562303,80.35206101,84.66952821
+44,87.39090058,86.84261145,81.24060921,85.15804041
+45,87.39090058,87.39554943,82.09540971,85.62728657
+46,87.39090058,87.92458016,82.91629497,86.07725857
+47,87.39090058,88.42988794,83.70319044,86.50799299
+48,87.39090058,88.91169574,84.45610931,86.91956854
+49,87.39090058,89.37026282,85.17514756,87.31210365
+50,87.39090058,89.80588243,85.86047909,87.68575403
+51,87.39090058,90.21887955,86.51235104,88.04071039
+52,87.39090058,90.60960878,87.13107922,88.37719619
+53,87.39090058,90.97845225,87.71704374,88.69546552
+54,87.39090058,91.32581757,88.27068479,88.99580098
+55,87.39090058,91.65213593,88.79249847,89.27851166
+56,87.39090058,91.95786023,89.2830329,89.54393124
+57,87.39090058,92.24346325,89.74288435,89.79241606
+58,87.39090058,92.50943591,90.17269359,90.02434336
+59,87.39090058,92.7562856,90.57314227,90.24010948
+60,87.39090058,92.98453457,90.94494956,90.44012824
+61,87.39090058,93.19471836,91.2888688,90.62482925
+62,87.39090058,93.38738431,91.60568433,90.79465641
+63,87.39090058,93.56309014,91.89620842,90.95006638
+64,87.39090058,93.72240252,92.16127833,91.09152714
+65,87.39090058,93.86589581,92.40175351,91.21951663
+66,87.39090058,93.99415074,92.61851282,91.33452138
+67,87.39090058,94.10775318,92.81245198,91.43703525
+68,87.39090058,94.20729304,92.98448102,91.52755821
+69,87.39090058,94.29336308,93.13552193,91.6065952
+70,87.39090058,94.36655788,93.26650631,91.67465492
+71,87.39090058,94.42747281,93.37837323,91.73224887
+72,87.39090058,94.47670304,93.47206707,91.77989023
+73,87.39090058,94.51484266,93.54853556,91.81809293
+74,87.39090058,94.54248373,93.60872787,91.84737073
+75,87.39090058,94.5602155,93.65359272,91.86823627
+76,87.39090058,94.56862356,93.68407674,91.88120029
+77,87.39090058,94.56862356,93.70112274,91.88688229
+78,87.39090058,94.56862356,93.70566818,91.88839744
+79,87.39090058,94.56862356,93.70566818,91.88839744
+80,87.39090058,94.56862356,93.70566818,91.88839744
+81,87.39090058,94.56862356,93.70566818,91.88839744
+82,87.39090058,94.56862356,93.70566818,91.88839744
+83,87.39090058,94.56862356,93.70566818,91.88839744
+84,87.39090058,94.56862356,93.70566818,91.88839744
+85,87.39090058,94.56862356,93.70566818,91.88839744
+86,87.39090058,94.56862356,93.70566818,91.88839744
+87,87.39090058,94.56862356,93.70566818,91.88839744
+88,87.39090058,94.56862356,93.70566818,91.88839744
+89,87.39090058,94.56862356,93.70566818,91.88839744
+90,87.39090058,94.56862356,93.70566818,91.88839744
+91,87.39090058,94.56862356,93.70566818,91.88839744
+92,87.39090058,94.56862356,93.70566818,91.88839744
+93,87.39090058,94.56862356,93.70566818,91.88839744
+94,87.39090058,94.56862356,93.70566818,91.88839744
+95,87.39090058,94.56862356,93.70566818,91.88839744
+96,87.39090058,94.56862356,93.70566818,91.88839744
+97,87.39090058,94.56862356,93.70566818,91.88839744
+98,87.39090058,94.56862356,93.70566818,91.88839744
+99,87.39090058,94.56862356,93.70566818,91.88839744
+100,87.39090058,94.56862356,93.70566818,91.88839744
+101,87.7061647,94.56862356,93.70566818,91.99348548
+102,88.0993478,94.56862356,93.70566818,92.12454651
+103,88.49130564,94.56862356,93.70566818,92.25519913
+104,88.88155409,94.56862356,93.70566818,92.38528194
+105,89.26963001,94.56862356,93.70566818,92.51464058
+106,89.65509154,94.56862356,93.70566818,92.64312776
+107,90.03751838,94.56862356,93.70566818,92.77060337
+108,90.41651198,94.56862356,93.70566818,92.89693457
+109,90.79169571,94.56862356,93.70566818,93.02199582
+110,91.16271501,94.56862356,93.70566818,93.14566892
+111,91.52923739,94.56862356,93.70566818,93.26784304
+112,91.89095253,94.56862356,93.70566818,93.38841476
+113,92.24757218,94.56862356,93.70566818,93.50728797
+114,92.59883018,94.56862356,93.70566818,93.62437397
+115,92.94448231,94.56862356,93.70566818,93.73959135
+116,93.2843062,94.56862356,93.70566818,93.85286598
+117,93.61810111,94.56862356,93.70566818,93.96413095
+118,93.94568777,94.56862356,93.70566818,94.0733265
+119,94.26690815,94.56862356,93.70566818,94.18039996
+120,94.58162515,94.56862356,93.70566818,94.28530563
+121,94.88972237,94.56862356,93.70566818,94.3880047
+122,95.19110376,94.56862356,93.70566818,94.48846517
+123,95.48569329,94.56862356,93.70566818,94.58666168
+124,95.77343457,94.56862356,93.70566818,94.68257544
+125,96.05429047,94.56862356,93.70566818,94.77619407
+126,96.32824273,94.56862356,93.74018966,94.87901865
+127,96.59529149,94.56862356,93.93261436,95.03217647
+128,96.85545491,94.56862356,94.13621951,95.18676599
+129,97.10876863,94.56862356,94.3509501,95.34278076
+130,97.35528535,94.56862356,94.57673474,95.50021455
+131,97.59507432,94.56862356,94.8134861,95.65906133
+132,97.82822082,94.56862356,95.06110142,95.81931527
+133,98.05482567,94.56862356,95.31946299,95.98097074
+134,98.27500469,94.56862356,95.58843867,96.14402231
+135,98.48888816,94.56862356,95.86788239,96.3084647
+136,98.69662026,94.61864372,96.15763466,96.49096621
+137,98.89835857,94.77442788,96.45752308,96.71010318
+138,99.09427345,94.936968,96.76736287,96.93286811
+139,99.2845475,95.1061995,97.08695738,97.15923479
+140,99.469375,95.28205156,97.41609862,97.38917506
+141,99.64896132,95.46444735,97.75456776,97.62265881
+142,99.82352237,95.65330429,98.10213572,97.85965413
+143,99.99328397,95.84853439,98.45856362,98.10012733
+144,100.1584813,96.05004442,98.82360334,98.34404302
+145,100.3193584,96.25773627,99.19699805,98.59136424
+146,100.4761675,96.47150713,99.57848272,98.84205245
+147,100.6291683,96.69124982,99.96778466,99.09606759
+148,100.7786279,96.91685304,100.364624,99.35336831
+149,100.9248194,97.1482016,100.7687143,99.61391177
+150,101.0680222,97.3851767,101.1797629,99.87765393
+151,101.208521,97.62765621,101.5974715,100.1445496
+152,101.3466052,97.87551489,102.0215369,100.4145523
+153,101.4825683,98.12862466,102.4516511,100.6876147
+154,101.6167078,98.38685484,102.887502,100.9636882
+155,101.7493242,98.65007241,103.328774,101.2427235
+156,101.8807204,98.91814226,103.7751482,101.5246703
+157,102.0112017,99.1909274,104.2263032,101.8094774
+158,102.1410748,99.46828924,104.6819154,102.0970931
+159,102.2706475,99.75008778,105.1416593,102.3874649
+160,102.400228,100.0361819,105.6052085,102.6805395
+161,102.5301247,100.3264295,106.0722354,102.9762632
+162,102.6606455,100.6206878,106.5424124,103.2745819
+163,102.7920973,100.9188136,107.0154117,103.5754409
+164,102.9247859,101.2206632,107.4909062,103.8787851
+165,103.0590149,101.5260932,107.9685697,104.1845593
+166,103.1950858,101.8349601,108.4480774,104.4927078
+167,103.3332975,102.1471208,108.9291061,104.8031748
+168,103.4739456,102.4624327,109.4113349,105.1159044
+169,103.6173222,102.7807541,109.8944454,105.4308406
+170,103.7637155,103.101944,110.3781221,105.7479272
+171,103.9134094,103.4258627,110.8620528,106.0671083
+172,104.0666829,103.7523718,111.3459291,106.3883279
+173,104.2238103,104.0813342,111.8294463,106.7115303
+174,104.3850601,104.4126146,112.3123042,107.0366596
+175,104.5506955,104.7460794,112.7942074,107.3636608
+176,104.7209733,105.0815971,113.2748654,107.6924786
+177,104.896144,105.4190381,113.7539929,108.0230583
+178,105.0764515,105.7582753,114.2313103,108.3553457
+179,105.2621329,106.0991838,114.7065438,108.6892868
+180,105.4534178,106.4416414,115.179426,109.0248284
+181,105.6505285,106.7855285,115.6496958,109.3619176
+182,105.8536796,107.1307284,116.1170987,109.7005022
+183,106.0630776,107.4771272,116.5813874,110.0405307
+184,106.2789212,107.8246142,117.0423215,110.3819523
+185,106.5014004,108.1730817,117.4996682,110.7247168
+186,106.7306968,108.5224254,117.9532024,111.0687749
+187,106.9669833,108.8725444,118.4027067,111.4140781
+188,107.210424,109.2233411,118.8479717,111.7605789
+189,107.4611738,109.5747216,119.2887964,112.1082306
+190,107.7193786,109.9265956,119.724988,112.4569874
+191,107.985175,110.2788767,120.1563624,112.8068047
+192,108.2586902,110.631482,120.5827442,113.1576388
+193,108.5400419,110.9843326,121.0039667,113.5094471
+194,108.8293382,111.3373537,121.4198724,113.8621881
+195,109.1266779,111.6904743,121.8303128,114.2158217
+196,109.4321497,112.0436276,122.2351485,114.5703086
+197,109.7458328,112.3967508,122.6342497,114.9256111
+198,110.0677966,112.7497853,123.0274957,115.2816925
+199,110.3981009,113.1026766,123.4147755,115.6385177
+200,110.7367954,113.4553746,123.7959876,115.9960525
+201,111.0839203,113.8078334,124.1710401,116.3542646
+202,111.4395061,114.1600113,124.5398507,116.7131227
+203,111.8035733,114.5118709,124.9023469,117.072597
+204,112.176133,114.8633792,125.2584659,117.4326594
+205,112.5571865,115.2145076,125.6081548,117.793283
+206,112.9467257,115.5652317,125.9513701,118.1544425
+207,113.344733,115.9155313,126.2880785,118.5161143
+208,113.7511812,116.2653909,126.6182561,118.8782761
+209,114.1660341,116.614799,126.941889,119.2409074
+210,114.5892461,116.9637486,127.2589728,119.6039892
+211,115.0207626,117.3122369,127.5695129,119.9675041
+212,115.4605201,117.6602655,127.8735244,120.3314367
+213,115.9084463,118.00784,128.1710319,120.6957727
+214,116.36446,118.3549704,128.4620693,121.0604999
+215,116.828472,118.701671,128.7466804,121.4256078
+216,117.3003841,119.04796,129.024918,121.7910874
+217,117.7800905,119.3938598,129.2968443,122.1569315
+218,118.2674771,119.7393969,129.5625308,122.5231349
+219,118.7624221,120.0846019,129.8220578,122.8896939
+220,119.2647961,120.4295091,130.0755148,123.2566067
+221,119.7744621,120.774157,130.3230002,123.6238731
+222,120.2912763,121.1185876,130.5646208,123.9914949
+223,120.8150876,121.4628472,130.8004924,124.3594757
+224,121.3457382,121.8069852,131.030739,124.7278208
+225,121.8830639,122.1510551,131.2554931,125.0965374
+226,122.4268941,122.4951138,131.474895,125.4656343
+227,122.9770523,122.8392217,131.6890935,125.8351225
+228,123.5333561,123.1834427,131.8982448,126.2050145
+229,124.0956175,123.5278439,132.1025129,126.5753248
+230,124.6636433,123.8724957,132.3020694,126.9460695
+231,125.2372354,124.2174717,132.4970931,127.3172667
+232,125.8161908,124.5628486,132.6877699,127.6889364
+233,126.4003021,124.908706,132.8742926,128.0611002
+234,126.9893575,125.2551264,133.0568608,128.4337816
+235,127.5831416,125.6021951,133.2356806,128.8070058
+236,128.1814351,125.9500002,133.4109642,129.1807998
+237,128.7840154,126.2986322,133.5829302,129.5551926
+238,129.3906569,126.6481841,133.7518028,129.9302146
+239,130.0011312,126.9987515,133.9178121,130.3058983
+240,130.6152074,127.350432,134.0811934,130.6822776
+241,131.2326523,127.7033255,134.2421873,131.0593884
+242,131.8532309,128.0575339,134.4010394,131.4372681
+243,132.4767066,128.413161,134.5579999,131.8159558
+244,133.1028415,128.7703127,134.7133235,132.1954926
+245,133.7313964,129.1290961,134.8672693,132.5759206
+246,134.3621319,129.4896204,135.0201002,132.9572842
+247,134.9948077,129.851996,135.172083,133.3396289
+248,135.6291837,130.2163346,135.3234878,133.723002
+249,136.2650196,130.5827494,135.4745881,134.1074524
+250,136.902076,130.9513544,135.6256605,134.4930303
+251,137.540114,131.3222648,135.776984,134.8797876
+252,138.1788957,131.6955966,135.9288403,135.2677775
+253,138.8181847,132.0714667,136.0815132,135.6570549
+254,139.4577462,132.4499923,136.2352886,136.0476757
+255,140.0973471,132.8312914,136.390454,136.4396975
+256,140.7367569,133.2154822,136.5472984,136.8331792
+257,141.375747,133.6026833,136.7061117,137.2281807
+258,142.0140922,133.9930132,136.8671851,137.6247635
+259,142.6515699,134.3865908,137.0308101,138.0229903
+260,143.2879608,134.7835344,137.1972787,138.4229246
+261,143.9230495,135.1839625,137.3668831,138.8246317
+262,144.556624,135.5879928,137.5399153,139.2281774
+263,145.1884767,135.995743,137.7166668,139.6336288
+264,145.8184043,136.4073297,137.8974285,140.0410542
+265,146.4462078,136.8228691,138.0824903,140.4505224
+266,147.0716935,137.2424764,138.272141,140.8621036
+267,147.6946724,137.6662659,138.4666678,141.2758687
+268,148.3149609,138.0943508,138.6663564,141.6918894
+269,148.9323811,138.526843,138.8714903,142.1102381
+270,149.5467607,138.9638531,139.082351,142.5309883
+271,150.1579333,139.4054903,139.2992173,142.9542136
+272,150.765739,139.8518621,139.5223654,143.3799888
+273,151.3700239,140.3030746,139.7520686,143.808389
+274,151.970641,140.7592317,139.9885969,144.2394899
+275,152.5674499,141.2204358,140.2322168,144.6733675
+276,153.1603172,141.686787,140.4831912,145.1100985
+277,153.7491167,142.1583834,140.741779,145.5497597
+278,154.3337295,142.6353208,141.008235,145.9924284
+279,154.914044,143.1176927,141.2828095,146.4381821
+280,155.4899564,143.6055902,141.5657483,146.8870983
+281,156.0613707,144.0991016,141.8572923,147.3392549
+282,156.6281987,144.5983129,142.1576774,147.7947297
+283,157.1903603,145.1033072,142.467134,148.2536005
+284,157.7477835,145.6141647,142.7858874,148.7159452
+285,158.3004046,146.1309628,143.1141568,149.1818414
+286,158.8481684,146.6537756,143.4521558,149.6513666
+287,159.3910281,147.1826743,143.8000917,150.124598
+288,159.9289454,147.717727,144.1581657,150.6016127
+289,160.4618907,148.2589982,144.5265724,151.0824871
+290,160.9898432,148.8065493,144.9054997,151.5672974
+291,161.5127908,149.3604379,145.2951287,152.0561191
+292,162.0307303,149.9207185,145.6956336,152.5490275
+293,162.5436673,150.4874416,146.1071812,153.0460967
+294,163.0516165,151.0606542,146.5299311,153.5474006
+295,163.5546014,151.6403995,146.9640355,154.0530121
+296,164.0526545,152.2267169,147.4096385,154.5630033
+297,164.5458173,152.8196419,147.866877,155.0774454
+298,165.0341403,153.419206,148.3358794,155.5964086
+299,165.5176828,154.0254367,148.8167664,156.119962
+300,165.9965134,154.6383575,149.3096502,156.6481737
+301,166.4707092,155.2579876,149.8146348,157.1811105
+302,166.9403565,155.8843423,150.3318158,157.7188382
+303,167.4055503,156.5174323,150.8612801,158.2614209
+304,167.8663945,157.1572644,151.4031059,158.8089216
+305,168.3230015,157.8038408,151.9573629,159.3614017
+306,168.7754925,158.4571595,152.5241115,159.9189212
+307,169.2239972,159.1172142,153.1034034,160.4815383
+308,169.6686538,159.7839938,153.6952813,161.0493096
+309,170.109609,160.4574833,154.2997786,161.6222903
+310,170.5470173,161.1376626,154.9169196,162.2005332
+311,170.9810419,161.8245077,155.5467195,162.7840897
+312,171.4118535,162.5179895,156.1891839,163.373009
+313,171.839631,163.2180749,156.8443093,163.9673384
+314,172.2645609,163.9247258,157.5120827,164.5671231
+315,172.6868372,164.6378999,158.1924817,165.1724063
diff --git a/dataset/xiandao/c4.txt b/dataset/xiandao/c4.txt
new file mode 100644
index 0000000..1862211
--- /dev/null
+++ b/dataset/xiandao/c4.txt
@@ -0,0 +1,315 @@
+24.21603668
+27.76392576
+31.05812474
+34.11272657
+36.94126062
+39.55670907
+41.97152296
+44.19763785
+46.24648927
+48.12902763
+49.85573305
+51.43662963
+52.88129959
+54.19889697
+55.39816113
+56.48742985
+57.47465218
+58.36740102
+59.17288536
+59.89796229
+60.54914868
+61.13263263
+61.65428459
+62.11966834
+62.53405155
+62.90241619
+63.22946866
+63.51964968
+63.77986482
+64.02124681
+64.24612443
+64.45667649
+64.6549378
+64.84280487
+65.02204165
+65.19428489
+65.36104955
+65.52373394
+65.68362478
+65.84190207
+65.99964387
+66.15783094
+66.31735119
+66.47900406
+66.64350478
+66.81148844
+66.983514
+67.16006816
+67.34156907
+67.52837002
+67.72076293
+67.91898175
+68.12320577
+68.33356283
+68.55013239
+68.77294851
+69.00200277
+69.23724705
+69.47859619
+69.72593064
+69.97909895
+70.23792017
+70.50218623
+70.77166413
+71.04609814
+71.32521189
+71.62235332
+72.01186156
+72.41093197
+72.81906332
+73.23573249
+73.66039698
+74.09249736
+74.53145956
+74.97669714
+75.42761342
+75.88360347
+76.34405616
+76.80835594
+77.27588467
+77.7460233
+78.21815351
+78.69165919
+79.16592799
+79.64035262
+80.11433222
+80.58727357
+81.05859228
+81.52771389
+81.9940749
+82.45712376
+82.91632177
+83.37114395
+83.82107982
+84.26563414
+84.7043276
+85.13669743
+85.562298
+85.98070136
+86.39149766
+86.79429563
+87.18872298
+87.57442667
+87.95107329
+88.31834924
+88.67596103
+89.02363537
+89.3611194
+89.6881807
+90.00460745
+90.3102084
+90.60481293
+90.88827096
+91.16045296
+91.42124983
+91.67057279
+91.90835323
+92.13454258
+92.34911212
+92.55205274
+92.74337473
+92.92310751
+93.0942088
+93.25737267
+93.41259973
+93.55990377
+93.69931161
+93.8308629
+93.95460987
+94.07061711
+94.17896129
+94.27973097
+94.3730263
+94.45895874
+94.53765081
+94.60923579
+94.67537072
+94.73773813
+94.79638462
+94.85136201
+94.90272718
+94.95054198
+94.99487308
+95.03579181
+95.07337407
+95.10770016
+95.13885463
+95.16692615
+95.19200739
+95.21419481
+95.23358857
+95.25029236
+95.26441323
+95.27606148
+95.28535047
+95.29239651
+95.29731865
+95.30023858
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.30128046
+95.33521881
+95.37337294
+95.41511465
+95.55435079
+95.71377564
+95.88296678
+96.06207811
+96.25125625
+96.4506404
+96.6603623
+96.97094911
+97.36193722
+97.76950693
+98.19375769
+98.6347772
+99.09264146
+99.56741468
+100.0591493
+100.567886
+101.0936539
+101.6364702
+102.1963406
+102.7732591
+103.3672081
+103.9781587
+104.6060704
+105.2508912
+105.9125583
+106.5909971
+107.2861225
+107.997838
+108.7260364
+109.4706
+110.2314
+111.0082975
+111.8011431
+112.6097773
+113.4340305
+114.2737233
+115.1286665
+115.9986614
+116.8835001
+117.7829651
+118.6968305
+119.6248611
+120.5668133
+121.5224352
+122.4914666
+123.4736392
+124.4686773
+125.4762972
+126.4962082
+127.5281125
+128.5717052
+129.6266751
+130.6927044
+131.7694691
+132.8566394
+133.9538799
+135.0608497
+136.1772027
+137.3025878
+138.4366496
+139.5790278
+140.7293584
+141.8872733
+143.0524007
+144.2243656
+145.4027897
+146.587292
+147.7774889
+148.9729945
+150.1734206
+151.3783777
+152.5874742
+153.8003175
+155.0165141
+156.2356696
+157.457389
+158.6812774
+159.9069395
+161.1339807
+162.3620068
+163.5906241
+164.8194404
+166.0480645
+167.2761068
+168.5031796
+169.7288971
+170.9528758
+172.1747347
+173.3940956
+174.6105832
+175.8238256
+177.033454
+178.2391036
+179.4404132
+180.6370259
+181.828589
+183.0147543
+184.1951785
+185.3695228
+186.5374541
+187.6986441
+188.8527702
+189.9995156
+191.1385693
+192.2696262
+193.3923874
+194.5065607
+195.6118601
+196.7080065
+197.7947275
+198.8717578
+199.938839
+200.9957205
+202.0421585
+203.0779171
diff --git a/dataset/xiandao/c4_average.xlsx b/dataset/xiandao/c4_average.xlsx
new file mode 100644
index 0000000..d54d4b4
Binary files /dev/null and b/dataset/xiandao/c4_average.xlsx differ
diff --git a/dataset/xiandao/c4_wear.csv b/dataset/xiandao/c4_wear.csv
new file mode 100644
index 0000000..3253258
--- /dev/null
+++ b/dataset/xiandao/c4_wear.csv
@@ -0,0 +1,316 @@
+cut,flute_1,flute_2,flute_3,
+1,31.41635516,19.48369225,21.74806264,24.21603668
+2,34.8927704,23.473047,24.92595987,27.76392576
+3,38.10284657,27.17286849,27.89865916,31.05812474
+4,41.06102301,30.59930868,30.67784802,34.11272657
+5,43.78119133,33.76786983,33.27472071,36.94126062
+6,46.27671037,36.69342355,35.6999933,39.55670907
+7,48.56042098,39.39022937,37.96391852,41.97152296
+8,50.64466037,41.87195302,40.07630017,44.19763785
+9,52.54127629,44.15168423,42.04650728,46.24648927
+10,54.26164088,46.24195416,43.88348786,48.12902763
+11,55.81666418,48.15475251,45.59578247,49.85573305
+12,57.21680745,49.90154413,47.19153732,51.43662963
+13,58.47209617,51.49328539,48.67851721,52.88129959
+14,59.59213275,52.9404401,50.06411807,54.19889697
+15,60.58610902,54.25299511,51.35537927,55.39816113
+16,61.46281844,55.44047554,52.55899557,56.48742985
+17,62.23066801,56.51195964,53.68132888,57.47465218
+18,62.89769001,57.47609342,54.72841962,58.36740102
+19,63.47155339,58.34110474,55.70599795,59.17288536
+20,63.959575,59.1148173,56.61949457,59.89796229
+21,64.36873051,59.80466414,57.4740514,60.54914868
+22,64.70566514,60.41770086,58.27453188,61.13263263
+23,64.9767041,60.96061858,59.02553109,61.65428459
+24,65.1878629,61.43975654,59.73138559,62.11966834
+25,65.34485725,61.86111439,60.39618301,62.53405155
+26,65.45311297,62.23036422,61.02377139,62.90241619
+27,65.51777545,62.55286224,61.6177683,63.22946866
+28,65.54371906,62.83366026,62.18156971,63.51964968
+29,65.54371906,63.07751676,62.71835863,63.77986482
+30,65.54371906,63.28890784,63.23111352,64.02124681
+31,65.54371906,63.47203771,63.72261651,64.24612443
+32,65.54371906,63.6308491,64.19546132,64.45667649
+33,65.54371906,63.76903326,64.65206107,64.6549378
+34,65.54371906,63.89003975,65.09465581,64.84280487
+35,65.54371906,63.99708603,65.52531985,65.02204165
+36,65.54371906,64.09316671,65.9459689,65.19428489
+37,65.54371906,64.18106257,66.35836702,65.36104955
+38,65.54371906,64.26334943,66.76413333,65.52373394
+39,65.54371906,64.34240665,67.16474862,65.68362478
+40,65.54371906,64.42042549,67.56156165,65.84190207
+41,65.54371906,64.49941721,67.95579535,65.99964387
+42,65.54371906,64.58122094,68.34855283,66.15783094
+43,65.54371906,64.66751133,68.74082317,66.31735119
+44,65.54371906,64.75980602,69.1334871,66.47900406
+45,65.54371906,64.85947284,69.52732244,66.64350478
+46,65.54371906,64.96773684,69.92300941,66.81148844
+47,65.54371906,65.08568713,70.32113581,66.983514
+48,65.54371906,65.21428344,70.72220197,67.16006816
+49,65.54371906,65.35436259,71.12662555,67.34156907
+50,65.54371906,65.50664473,71.53474627,67.52837002
+51,65.54371906,65.67173932,71.94683041,67.72076293
+52,65.54371906,65.85015104,72.36307514,67.91898175
+53,65.54371906,66.04228544,72.78361282,68.12320577
+54,65.54371906,66.24845443,73.208515,68.33356283
+55,65.54371906,66.46888163,73.63779647,68.55013239
+56,65.54371906,66.70370749,74.07141897,68.77294851
+57,65.54371906,66.95299431,74.50929495,69.00200277
+58,65.54371906,67.21673103,74.95129106,69.23724705
+59,65.54371906,67.4948379,75.39723162,69.47859619
+60,65.54371906,67.787171,75.84690187,69.72593064
+61,65.54371906,68.09352659,76.3000512,69.97909895
+62,65.54371906,68.41364529,76.75639617,70.23792017
+63,65.54371906,68.74721616,77.21562347,70.50218623
+64,65.54371906,69.0938806,77.67739272,70.77166413
+65,65.54371906,69.45323612,78.14133923,71.04609814
+66,65.54371906,69.82483999,78.60707661,71.32521189
+67,65.58464803,70.20821271,79.07419922,71.62235332
+68,65.89045864,70.60284141,79.54228463,72.01186156
+69,66.21371695,71.00818308,80.01089589,72.41093197
+70,66.55393858,71.42366764,80.47958375,72.81906332
+71,66.9106077,71.84870103,80.94788874,73.23573249
+72,67.28317974,72.28266799,81.41534322,73.66039698
+73,67.67108395,72.72493484,81.88147329,74.09249736
+74,68.07372593,73.17485215,82.34580059,74.53145956
+75,68.49049008,73.63175725,82.8078441,74.97669714
+76,68.92074185,74.09497664,83.26712176,75.42761342
+77,69.36383003,74.56382831,83.72315207,75.88360347
+78,69.8190889,75.03762397,84.17545561,76.34405616
+79,70.28584028,75.51567114,84.6235564,76.80835594
+80,70.76339554,75.99727516,85.06698331,77.27588467
+81,71.25105747,76.48174113,85.50527131,77.7460233
+82,71.74812216,76.9683757,85.93796266,78.21815351
+83,72.25388071,77.45648882,86.36460805,78.69165919
+84,72.76762093,77.94539538,86.78476767,79.16592799
+85,73.28862893,78.43441676,87.19801218,79.64035262
+86,73.81619066,78.92288232,87.60392369,80.11433222
+87,74.3495934,79.41013074,88.00209657,80.58727357
+88,74.88812711,79.89551141,88.39213832,81.05859228
+89,75.4310858,80.37838559,88.77367027,81.52771389
+90,75.97776878,80.8581276,89.14632832,81.9940749
+91,76.52748184,81.33412589,89.50976354,82.45712376
+92,77.07953845,81.80578408,89.86364278,82.91632177
+93,77.6332608,82.27252187,90.20764919,83.37114395
+94,78.18798081,82.73377594,90.54148272,83.82107982
+95,78.74304115,83.18900074,90.86486054,84.26563414
+96,79.29779609,83.63766927,91.17751744,84.7043276
+97,79.85161241,84.07927373,91.47920614,85.13669743
+98,80.40387017,84.51332619,91.76969765,85.562298
+99,80.95396348,84.93935911,92.04878148,85.98070136
+100,81.50130122,85.35692589,92.31626586,86.39149766
+101,82.04530765,85.76560133,92.57197792,86.79429563
+102,82.58542306,86.16498203,92.81576385,87.18872298
+103,83.12110433,86.55468673,93.04748896,87.57442667
+104,83.65182542,86.93435665,93.26703779,87.95107329
+105,84.17707788,87.30365573,93.47431411,88.31834924
+106,84.69637128,87.66227089,93.66924092,88.67596103
+107,85.20923356,88.00991211,93.85176045,89.02363537
+108,85.71521147,88.34631267,94.02183405,89.3611194
+109,86.2138708,88.67122916,94.17944214,89.6881807
+110,86.7047967,88.98444158,94.32458407,90.00460745
+111,87.18759393,89.2857533,94.45727798,90.3102084
+112,87.66188705,89.57499109,94.57756064,90.60481293
+113,88.12732061,89.85200505,94.68548722,90.88827096
+114,88.58355929,90.11666848,94.78113112,91.16045296
+115,89.030288,90.3688778,94.86458369,91.42124983
+116,89.46721198,90.60855238,94.935954,91.67057279
+117,89.89405683,90.83563433,94.99536852,91.90835323
+118,90.31056855,91.05008833,95.04297086,92.13454258
+119,90.71651354,91.25190137,95.07892145,92.34911212
+120,91.11167857,91.44108249,95.10339716,92.55205274
+121,91.4958707,91.61766247,95.11659101,92.74337473
+122,91.86891723,91.78169355,95.11871175,92.92310751
+123,92.23066559,91.93324905,95.11871175,93.0942088
+124,92.5809832,92.07242307,95.11871175,93.25737267
+125,92.91975736,92.19933008,95.11871175,93.41259973
+126,93.24689504,92.31410451,95.11871175,93.55990377
+127,93.5623227,92.41690038,95.11871175,93.69931161
+128,93.86598613,92.50789083,95.11871175,93.8308629
+129,94.15785016,92.58726771,95.11871175,93.95460987
+130,94.43789847,92.6552411,95.11871175,94.07061711
+131,94.70613329,92.71203882,95.11871175,94.17896129
+132,94.96257518,92.75790598,95.11871175,94.27973097
+133,95.20726268,92.79310447,95.11871175,94.3730263
+134,95.44025205,92.81791243,95.11871175,94.45895874
+135,95.66161694,92.83262375,95.11871175,94.53765081
+136,95.87144806,92.83754756,95.11871175,94.60923579
+137,96.06985284,92.83754756,95.11871175,94.67537072
+138,96.25695507,92.83754756,95.11871175,94.73773813
+139,96.43289455,92.83754756,95.11871175,94.79638462
+140,96.59782671,92.83754756,95.11871175,94.85136201
+141,96.75192223,92.83754756,95.11871175,94.90272718
+142,96.89536664,92.83754756,95.11871175,94.95054198
+143,97.02835992,92.83754756,95.11871175,94.99487308
+144,97.15111612,92.83754756,95.11871175,95.03579181
+145,97.26386291,92.83754756,95.11871175,95.07337407
+146,97.36684116,92.83754756,95.11871175,95.10770016
+147,97.46030457,92.83754756,95.11871175,95.13885463
+148,97.54451915,92.83754756,95.11871175,95.16692615
+149,97.61976286,92.83754756,95.11871175,95.19200739
+150,97.68632512,92.83754756,95.11871175,95.21419481
+151,97.7445064,92.83754756,95.11871175,95.23358857
+152,97.79461776,92.83754756,95.11871175,95.25029236
+153,97.83698037,92.83754756,95.11871175,95.26441323
+154,97.87192512,92.83754756,95.11871175,95.27606148
+155,97.89979211,92.83754756,95.11871175,95.28535047
+156,97.92093022,92.83754756,95.11871175,95.29239651
+157,97.93569664,92.83754756,95.11871175,95.29731865
+158,97.94445643,92.83754756,95.11871175,95.30023858
+159,97.94758207,92.83754756,95.11871175,95.30128046
+160,97.94758207,92.83754756,95.11871175,95.30128046
+161,97.94758207,92.83754756,95.11871175,95.30128046
+162,97.94758207,92.83754756,95.11871175,95.30128046
+163,97.94758207,92.83754756,95.11871175,95.30128046
+164,97.94758207,92.83754756,95.11871175,95.30128046
+165,97.94758207,92.83754756,95.11871175,95.30128046
+166,97.94758207,92.83754756,95.11871175,95.30128046
+167,97.94758207,92.83754756,95.11871175,95.30128046
+168,97.94758207,92.83754756,95.11871175,95.30128046
+169,97.94758207,92.83754756,95.11871175,95.30128046
+170,97.94758207,92.83754756,95.11871175,95.30128046
+171,97.94758207,92.83754756,95.11871175,95.30128046
+172,97.94758207,92.83754756,95.11871175,95.30128046
+173,97.94758207,92.83754756,95.11871175,95.30128046
+174,97.94758207,92.83754756,95.11871175,95.30128046
+175,97.94758207,92.83754756,95.11871175,95.30128046
+176,97.94758207,92.83754756,95.11871175,95.30128046
+177,97.94758207,92.83754756,95.11871175,95.30128046
+178,97.94758207,92.83754756,95.11871175,95.30128046
+179,97.94758207,92.83754756,95.11871175,95.30128046
+180,97.94758207,92.83754756,95.11871175,95.30128046
+181,97.94758207,92.83754756,95.11871175,95.30128046
+182,97.94758207,92.83754756,95.11871175,95.30128046
+183,97.94758207,92.83754756,95.11871175,95.30128046
+184,97.94758207,92.83754756,95.11871175,95.30128046
+185,97.94758207,92.83754756,95.11871175,95.30128046
+186,97.94758207,92.83754756,95.11871175,95.30128046
+187,97.94758207,92.83754756,95.11871175,95.30128046
+188,97.94758207,92.83754756,95.11871175,95.30128046
+189,97.94758207,92.83754756,95.11871175,95.30128046
+190,97.94758207,92.83754756,95.11871175,95.30128046
+191,97.94758207,92.83754756,95.11871175,95.30128046
+192,97.94758207,92.83754756,95.11871175,95.30128046
+193,97.94758207,92.83754756,95.11871175,95.30128046
+194,97.94758207,92.83754756,95.11871175,95.30128046
+195,97.94758207,92.83754756,95.11871175,95.30128046
+196,97.94758207,92.83754756,95.11871175,95.30128046
+197,97.94758207,92.83754756,95.11871175,95.30128046
+198,98.04939711,92.83754756,95.11871175,95.33521881
+199,98.16385952,92.83754756,95.11871175,95.37337294
+200,98.28908463,92.83754756,95.11871175,95.41511465
+201,98.42538882,93.11895181,95.11871175,95.55435079
+202,98.57308034,93.44953482,95.11871175,95.71377564
+203,98.73245902,93.79772956,95.11871175,95.88296678
+204,98.90381609,94.1637065,95.11871175,96.06207811
+205,99.08743402,94.54762299,95.11871175,96.25125625
+206,99.28358632,94.94962314,95.11871175,96.4506404
+207,99.49253735,95.36983781,95.11871175,96.6603623
+208,99.71454217,95.80838454,95.38992062,96.97094911
+209,99.94984642,96.26536749,95.87059774,97.36193722
+210,100.1986861,96.74087746,96.36895722,97.76950693
+211,100.4612875,97.23499183,96.88499373,98.19375769
+212,100.737867,97.74777459,97.41869002,98.6347772
+213,101.0286311,98.27927631,97.97001698,99.09264146
+214,101.3337761,98.82953419,98.53893374,99.56741468
+215,101.6534881,99.39857206,99.12538773,100.0591493
+216,101.9879429,99.98640043,99.72931479,100.567886
+217,102.337306,100.5930165,100.3506392,101.0936539
+218,102.7017323,101.2184044,100.989274,101.6364702
+219,103.0813662,101.8625349,101.6451207,102.1963406
+220,103.4763417,102.5253658,102.3180698,102.7732591
+221,103.8867818,103.2068418,103.0080008,103.3672081
+222,104.3127991,103.9068949,103.7147822,103.9781587
+223,104.7544953,104.6254442,104.4382717,104.6060704
+224,105.2119613,105.362396,105.1783164,105.2508912
+225,105.6852774,106.1176442,105.9347532,105.9125583
+226,106.1745129,106.8910702,106.7074082,106.5909971
+227,106.6797264,107.6825431,107.4960979,107.2861225
+228,107.2009656,108.4919199,108.3006285,107.997838
+229,107.7382675,109.3190454,109.1207964,108.7260364
+230,108.2916583,110.1637529,109.9563887,109.4706
+231,108.8611533,111.0258638,110.8071828,110.2314
+232,109.4467572,111.9051881,111.6729471,111.0082975
+233,110.048464,112.8015244,112.5534408,111.8011431
+234,110.6662571,113.7146603,113.4484144,112.6097773
+235,111.3001091,114.6443724,114.3576099,113.4340305
+236,111.9499823,115.5904267,115.2807608,114.2737233
+237,112.6158284,116.5525786,116.2175924,115.1286665
+238,113.2975887,117.5305732,117.1678223,115.9986614
+239,113.9951942,118.5241458,118.1311602,116.8835001
+240,114.7085656,119.5330215,119.1073083,117.7829651
+241,115.4376137,120.556916,120.0959617,118.6968305
+242,116.1822389,121.5955358,121.0968085,119.6248611
+243,116.942332,122.648578,122.10953,120.5668133
+244,117.7177737,123.715731,123.133801,121.5224352
+245,118.5084351,124.7966747,124.16929,122.4914666
+246,119.3141778,125.8910804,125.2156595,123.4736392
+247,120.1348537,126.9986117,126.2725664,124.4686773
+248,120.9703056,128.1189241,127.3396619,125.4762972
+249,121.8203669,129.2516656,128.4165922,126.4962082
+250,122.6848622,130.3964771,129.5029982,127.5281125
+251,123.5636069,131.5529925,130.5985163,128.5717052
+252,124.4564079,132.7208389,131.7027786,129.6266751
+253,125.3630635,133.8996371,132.8154126,130.6927044
+254,126.2833633,135.0890018,133.9360422,131.7694691
+255,127.2170889,136.2885418,135.0642876,132.8566394
+256,128.1640137,137.4978605,136.1997656,133.9538799
+257,129.1239033,138.716556,137.3420898,135.0608497
+258,130.0965154,139.9442215,138.4908711,136.1772027
+259,131.0816002,141.1804455,139.6457177,137.3025878
+260,132.0789007,142.4248124,140.8062356,138.4366496
+261,133.0881525,143.6769023,141.9720286,139.5790278
+262,134.1090844,144.9362918,143.1426991,140.7293584
+263,135.1414183,146.202554,144.3178475,141.8872733
+264,136.1848696,147.475259,145.4970734,143.0524007
+265,137.2391473,148.7539741,146.6799753,144.2243656
+266,138.3039542,150.0382639,147.8661509,145.4027897
+267,139.3789872,151.3276912,149.0551976,146.587292
+268,140.4639374,152.6218167,150.2467126,147.7774889
+269,141.5584906,153.9201996,151.4402933,148.9729945
+270,142.6623269,155.2223977,152.6355373,150.1734206
+271,143.7751217,156.5279683,153.832043,151.3783777
+272,144.8965453,157.8364676,155.0294096,152.5874742
+273,146.0262635,159.1474517,156.2272374,153.8003175
+274,147.1639376,160.4604766,157.4251281,155.0165141
+275,148.3092247,161.7750987,158.6226853,156.2356696
+276,149.461778,163.090875,159.8195141,157.457389
+277,150.621247,164.4073631,161.015222,158.6812774
+278,151.7872777,165.724122,162.2094189,159.9069395
+279,152.9595127,167.0407123,163.4017172,161.1339807
+280,154.1375919,168.3566961,164.5917323,162.3620068
+281,155.321152,169.6716378,165.7790826,163.5906241
+282,156.5098275,170.9851038,166.9633899,164.8194404
+283,157.7032503,172.2966636,168.1442796,166.0480645
+284,158.9010505,173.6058893,169.3213807,167.2761068
+285,160.1028562,174.9123562,170.4943265,168.5031796
+286,161.3082938,176.2156432,171.6627542,169.7288971
+287,162.5169886,177.5153329,172.8263058,170.9528758
+288,163.7285644,178.8110119,173.9846277,172.1747347
+289,164.9426445,180.102271,175.1373712,173.3940956
+290,166.1588513,181.3887056,176.2841928,174.6105832
+291,167.3768067,182.6699161,177.4247539,175.8238256
+292,168.5961328,183.9455075,178.5587218,177.033454
+293,169.8164512,185.2150905,179.6857691,178.2391036
+294,171.0373842,186.4782811,180.8055743,179.4404132
+295,172.2585545,187.7347013,181.917822,180.6370259
+296,173.4795854,188.9839787,183.0222029,181.828589
+297,174.7001014,190.2257477,184.1184139,183.0147543
+298,175.9197281,191.4596487,185.2061586,184.1951785
+299,177.1380923,192.685329,186.2851471,185.3695228
+300,178.3548229,193.9024428,187.3550965,186.5374541
+301,179.5695503,195.1106513,188.4157306,187.6986441
+302,180.7819071,196.3096231,189.4667804,188.8527702
+303,181.9915282,197.4990343,190.5079843,189.9995156
+304,183.1980513,198.6785687,191.5390879,191.1385693
+305,184.4011164,199.8479179,192.5598442,192.2696262
+306,185.6003667,201.0067816,193.570014,193.3923874
+307,186.7954486,202.1548679,194.5693657,194.5065607
+308,187.9860118,203.291893,195.5576756,195.6118601
+309,189.1717095,204.417582,196.5347279,196.7080065
+310,190.352199,205.5316685,197.5003149,197.7947275
+311,191.5271412,206.6338951,198.454237,198.8717578
+312,192.6962013,207.7240132,199.3963026,199.938839
+313,193.8590491,208.8017835,200.3263288,200.9957205
+314,195.0153586,209.8669762,201.2441406,202.0421585
+315,196.1648089,210.9193705,202.1495718,203.0779171
diff --git a/dataset/xiandao/c6.txt b/dataset/xiandao/c6.txt
new file mode 100644
index 0000000..6da2bf7
--- /dev/null
+++ b/dataset/xiandao/c6.txt
@@ -0,0 +1,315 @@
+29.10206571
+37.72371374
+46.30597344
+54.85235886
+56.62552062
+58.36924525
+60.0864704
+61.77995584
+63.99136847
+66.1840545
+68.36028042
+70.5221612
+71.42482968
+72.3169529
+73.20022769
+74.07622309
+74.94638596
+75.81204639
+76.67442293
+77.53462772
+78.06032948
+78.58578435
+79.1118146
+79.63915516
+80.16845805
+80.70029674
+81.23517027
+81.7735073
+82.31567006
+82.86195812
+83.41261208
+83.96781717
+84.43206697
+84.90108576
+85.37491275
+85.85354489
+86.33693981
+86.82501875
+87.3176694
+87.81474849
+88.31608449
+88.82148004
+89.33071439
+89.84354565
+90.3597131
+90.87893921
+91.40093175
+91.92538572
+92.45198521
+92.98040518
+93.51031314
+94.04137091
+94.57323595
+95.10556296
+95.63800534
+96.17021635
+96.70185045
+97.23256457
+97.76201907
+98.28987884
+98.81581444
+99.33950279
+99.86062827
+100.3788833
+100.799267
+101.2161926
+101.6293813
+102.0385652
+102.4434876
+102.8439039
+103.2395815
+103.6303009
+104.0158552
+104.396051
+104.7707085
+105.139662
+105.5027592
+105.8598624
+106.2108481
+106.5556068
+106.8940438
+107.2260783
+107.5516441
+107.8706892
+108.1831756
+108.4890795
+108.788391
+109.0811137
+109.367265
+109.6468755
+109.9199887
+110.186661
+110.4469615
+110.7009711
+110.9487829
+111.1905014
+111.3580991
+111.5198456
+111.6758782
+111.8263436
+111.9713985
+112.1112087
+112.2459489
+112.3782155
+112.5104821
+112.6427487
+112.7750153
+112.9072819
+113.0395485
+113.1718151
+113.3040817
+113.4363483
+113.5686149
+113.7008816
+113.8331482
+113.9654148
+114.0976814
+114.229948
+114.3622146
+114.4944812
+114.6267478
+114.7590144
+114.891281
+115.0235477
+115.1558143
+115.2880809
+115.4203475
+115.5526141
+115.6475159
+115.7424177
+115.8373195
+115.9322214
+116.0271232
+116.122025
+116.2169268
+116.3118286
+116.4067305
+116.5016323
+116.5965341
+116.6914359
+116.7863377
+116.8812396
+116.9761414
+117.0710432
+117.165945
+117.2608468
+117.3557486
+117.4506505
+117.5455523
+117.6404541
+117.735356
+117.8302578
+117.9251596
+118.0200614
+118.1149632
+118.209865
+118.3047668
+118.3996687
+118.4945705
+118.5894723
+118.7430791
+118.8966859
+119.0502928
+119.2038996
+119.3575064
+119.5111132
+119.66472
+119.8183269
+119.9719337
+120.1255405
+120.3410954
+120.6199926
+120.9031699
+121.1905016
+121.4818542
+121.7770873
+122.0760531
+122.3785971
+122.6845585
+122.9937702
+123.3060594
+123.6212477
+123.9391517
+124.259583
+124.582349
+124.9072533
+125.2340954
+125.5626719
+125.8927766
+126.2242007
+126.5567339
+126.890164
+127.5350476
+128.1804012
+128.8260108
+129.4716627
+130.1171441
+130.7622433
+131.4067503
+132.0504575
+132.6931595
+133.3346546
+133.9747441
+134.613234
+135.2499343
+135.8846604
+136.5172331
+137.1474793
+137.775232
+138.4003314
+139.0226251
+139.6419683
+140.2582245
+140.871266
+141.4809741
+142.0872396
+142.6899633
+143.2890561
+143.8844398
+144.4760473
+145.0638225
+145.6477213
+146.2277117
+146.8037739
+147.7231425
+148.6385812
+149.5501089
+150.4577574
+151.3615723
+152.2616127
+153.1579516
+154.0506758
+154.9398859
+155.8256967
+156.7082371
+157.5876498
+158.4640915
+159.3377331
+160.2087588
+161.0773667
+161.9437682
+162.808188
+163.6708636
+164.532045
+165.3919945
+166.2509863
+167.1093059
+167.9672494
+168.8251236
+169.6832447
+170.5419378
+171.4015367
+172.2623823
+173.1248225
+173.9892108
+174.8559057
+175.4924136
+176.1319554
+176.7748977
+177.4216077
+178.0724516
+178.7277933
+179.3879931
+180.0534062
+180.724381
+181.4012578
+182.0843664
+182.7740249
+183.4705376
+184.1741929
+184.8852616
+185.6039941
+186.3306187
+187.0653393
+187.8083325
+188.5597454
+189.3196931
+190.0882554
+190.8654747
+191.6513525
+192.4458466
+193.2488681
+194.0602778
+194.8798829
+195.7074339
+196.5426204
+197.385068
+198.2343338
+198.9538561
+199.6790907
+200.4093662
+201.1439264
+201.8819257
+202.6224249
+203.3643859
+204.1066673
+204.8480193
+205.5870782
+206.3223613
+207.0522618
+207.7750424
+208.4888303
+209.1916107
+209.8812214
+210.5553457
+211.2115068
+211.8470607
+212.4591899
+213.0448962
+213.6009936
+214.1241015
+214.6106366
+215.0568061
+215.4995219
+215.9422376
diff --git a/dataset/xiandao/c6_average.xlsx b/dataset/xiandao/c6_average.xlsx
new file mode 100644
index 0000000..d54d4b4
Binary files /dev/null and b/dataset/xiandao/c6_average.xlsx differ
diff --git a/dataset/xiandao/c6_wear.csv b/dataset/xiandao/c6_wear.csv
new file mode 100644
index 0000000..9b335bd
--- /dev/null
+++ b/dataset/xiandao/c6_wear.csv
@@ -0,0 +1,316 @@
+cut,flute_1,flute_2,flute_3,
+1,62.78864802,9.896640679,14.62090844,29.10206571
+2,64.13604296,19.79328136,29.24181689,37.72371374
+3,65.36527295,29.68992204,43.86272533,46.30597344
+4,66.4868801,39.58656272,58.48363377,54.85235886
+5,67.51080822,43.01999443,59.34575921,56.62552062
+6,68.44642494,46.45342614,60.20788466,58.36924525
+7,69.30254323,49.88685786,61.0700101,60.0864704
+8,70.08744241,53.32028957,61.93213554,61.77995584
+9,70.8088887,58.52379832,62.6414184,63.99136847
+10,71.47415519,63.72730707,63.35070125,66.1840545
+11,72.09004135,68.93081581,64.05998411,68.36028042
+12,72.66289207,74.13432456,64.76926696,70.5221612
+13,73.19861618,75.21507024,65.86080262,71.42482968
+14,73.70270453,76.29581591,66.95233827,72.3169529
+15,74.18024756,77.37656158,68.04387392,73.20022769
+16,74.63595243,78.45730726,69.13540957,74.07622309
+17,75.07415974,79.53805293,70.22694522,74.94638596
+18,75.4988597,80.6187986,71.31848087,75.81204639
+19,75.91370798,81.69954428,72.41001653,76.67442293
+20,76.32204104,82.78028995,73.50155218,77.53462772
+21,76.72689103,83.61749165,73.83660575,78.06032948
+22,77.13100039,84.45469334,74.17165933,78.58578435
+23,77.53683587,85.29189504,74.5067129,79.1118146
+24,77.94660226,86.12909673,74.84176648,79.63915516
+25,78.36225568,86.96629843,75.17682005,80.16845805
+26,78.78551648,87.80350012,75.51187363,80.70029674
+27,79.21788179,88.64070181,75.8469272,81.23517027
+28,79.66063762,89.47790351,76.18198078,81.7735073
+29,80.11487062,90.3151052,76.51703435,82.31567006
+30,80.58147952,91.1523069,76.85208793,82.86195812
+31,81.06118616,91.98950859,77.1871415,83.41261208
+32,81.55454615,92.82671029,77.52219508,83.96781717
+33,82.06195924,93.25202035,77.98222132,84.43206697
+34,82.58367929,93.67733042,78.44224757,84.90108576
+35,83.11982397,94.10264048,78.90227381,85.37491275
+36,83.67038407,94.52795055,79.36230005,85.85354489
+37,84.23523251,94.95326061,79.8223263,86.33693981
+38,84.81413304,95.37857068,80.28235254,86.82501875
+39,85.40674866,95.80388075,80.74237878,87.3176694
+40,86.01264963,96.22919081,81.20240503,87.81474849
+41,86.63132132,96.65450088,81.66243127,88.31608449
+42,87.26217167,97.07981094,82.12245751,88.82148004
+43,87.90453839,97.50512101,82.58248376,89.33071439
+44,88.55769588,97.93043107,83.04251,89.84354565
+45,89.22086191,98.35574114,83.50253624,90.3597131
+46,89.89320394,98.7810512,83.96256249,90.87893921
+47,90.57384526,99.20636127,84.42258873,91.40093175
+48,91.26187086,99.63167133,84.88261497,91.92538572
+49,91.956333,100.0569814,85.34264122,92.45198521
+50,92.65625657,100.4822915,85.80266746,92.98040518
+51,93.36064421,100.9076015,86.2626937,93.51031314
+52,94.06848117,101.3329116,86.72271995,94.04137091
+53,94.77873996,101.7582217,87.18274619,94.57323595
+54,95.49038474,102.1835317,87.64277243,95.10556296
+55,96.20237554,102.6088418,88.10279867,95.63800534
+56,96.91367223,103.0341519,88.56282492,96.17021635
+57,97.62323828,103.4594619,89.02285116,96.70185045
+58,98.33004431,103.884772,89.4828774,97.23256457
+59,99.03307145,104.3100821,89.94290365,97.76201907
+60,99.73131453,104.7353921,90.40292989,98.28987884
+61,100.423785,105.1607022,90.86295613,98.81581444
+62,101.1095138,105.5860122,91.32298238,99.33950279
+63,101.7875539,106.0113223,91.78300862,99.86062827
+64,102.4569826,106.4366324,92.24303486,100.3788833
+65,103.1169041,106.4907197,92.79017713,100.799267
+66,103.7664512,106.5448071,93.3373194,101.2161926
+67,104.4047877,106.5988945,93.88446166,101.6293813
+68,105.0311097,106.6529819,94.43160393,102.0385652
+69,105.6446474,106.7070692,94.97874619,102.4434876
+70,106.2446665,106.7611566,95.52588846,102.8439039
+71,106.8304699,106.815244,96.07303072,103.2395815
+72,107.4013983,106.8693313,96.62017299,103.6303009
+73,107.9568315,106.9234187,97.16731525,104.0158552
+74,108.4961893,106.9775061,97.71445752,104.396051
+75,109.0189324,107.0315934,98.26159979,104.7707085
+76,109.524563,107.0856808,98.80874205,105.139662
+77,110.0126251,107.1397682,99.35588432,105.5027592
+78,110.4827052,107.1938555,99.90302658,105.8598624
+79,110.9344325,107.2479429,100.4501688,106.2108481
+80,111.3674791,107.3020303,100.9973111,106.5556068
+81,111.7815604,107.3561177,101.5444534,106.8940438
+82,112.1764343,107.410205,102.0915956,107.2260783
+83,112.5519021,107.4642924,102.6387379,107.5516441
+84,112.9078077,107.5183798,103.1858802,107.8706892
+85,113.2440373,107.5724671,103.7330224,108.1831756
+86,113.5605194,107.6265545,104.2801647,108.4890795
+87,113.8572241,107.6806419,104.827307,108.788391
+88,114.1341627,107.7347292,105.3744492,109.0811137
+89,114.391387,107.7888166,105.9215915,109.367265
+90,114.6289887,107.842904,106.4687338,109.6468755
+91,114.8470987,107.8969914,107.015876,109.9199887
+92,115.0458861,107.9510787,107.5630183,110.186661
+93,115.2255578,108.0051661,108.1101606,110.4469615
+94,115.386357,108.0592535,108.6573028,110.7009711
+95,115.5285627,108.1133408,109.2044451,110.9487829
+96,115.6524886,108.1674282,109.7515874,111.1905014
+97,115.7584818,108.3737331,109.9420823,111.3580991
+98,115.8469217,108.580038,110.1325772,111.5198456
+99,115.9182194,108.786343,110.3230721,111.6758782
+100,115.9728158,108.9926479,110.513567,111.8263436
+101,116.0111807,109.1989528,110.7040619,111.9713985
+102,116.0338116,109.4052577,110.8945568,112.1112087
+103,116.0412323,109.6115626,111.0850517,112.2459489
+104,116.0412323,109.8178676,111.2755466,112.3782155
+105,116.0412323,110.0241725,111.4660415,112.5104821
+106,116.0412323,110.2304774,111.6565364,112.6427487
+107,116.0412323,110.4367823,111.8470313,112.7750153
+108,116.0412323,110.6430872,112.0375262,112.9072819
+109,116.0412323,110.8493922,112.2280211,113.0395485
+110,116.0412323,111.0556971,112.418516,113.1718151
+111,116.0412323,111.262002,112.6090109,113.3040817
+112,116.0412323,111.4683069,112.7995058,113.4363483
+113,116.0412323,111.6746118,112.9900007,113.5686149
+114,116.0412323,111.8809168,113.1804956,113.7008816
+115,116.0412323,112.0872217,113.3709905,113.8331482
+116,116.0412323,112.2935266,113.5614855,113.9654148
+117,116.0412323,112.4998315,113.7519804,114.0976814
+118,116.0412323,112.7061364,113.9424753,114.229948
+119,116.0412323,112.9124414,114.1329702,114.3622146
+120,116.0412323,113.1187463,114.3234651,114.4944812
+121,116.0412323,113.3250512,114.51396,114.6267478
+122,116.0412323,113.5313561,114.7044549,114.7590144
+123,116.0412323,113.737661,114.8949498,114.891281
+124,116.0412323,113.943966,115.0854447,115.0235477
+125,116.0412323,114.1502709,115.2759396,115.1558143
+126,116.0412323,114.3565758,115.4664345,115.2880809
+127,116.0412323,114.5628807,115.6569294,115.4203475
+128,116.0412323,114.7691856,115.8474243,115.5526141
+129,116.0412323,114.840982,116.0603334,115.6475159
+130,116.0412323,114.9127783,116.2732426,115.7424177
+131,116.0412323,114.9845746,116.4861517,115.8373195
+132,116.0412323,115.056371,116.6990608,115.9322214
+133,116.0412323,115.1281673,116.9119699,116.0271232
+134,116.0412323,115.1999636,117.1248791,116.122025
+135,116.0412323,115.27176,117.3377882,116.2169268
+136,116.0412323,115.3435563,117.5506973,116.3118286
+137,116.0412323,115.4153526,117.7636065,116.4067305
+138,116.0412323,115.487149,117.9765156,116.5016323
+139,116.0412323,115.5589453,118.1894247,116.5965341
+140,116.0412323,115.6307416,118.4023338,116.6914359
+141,116.0412323,115.7025379,118.615243,116.7863377
+142,116.0412323,115.7743343,118.8281521,116.8812396
+143,116.0412323,115.8461306,119.0410612,116.9761414
+144,116.0412323,115.9179269,119.2539704,117.0710432
+145,116.0412323,115.9897233,119.4668795,117.165945
+146,116.0412323,116.0615196,119.6797886,117.2608468
+147,116.0412323,116.1333159,119.8926977,117.3557486
+148,116.0412323,116.2051123,120.1056069,117.4506505
+149,116.0412323,116.2769086,120.318516,117.5455523
+150,116.0412323,116.3487049,120.5314251,117.6404541
+151,116.0412323,116.4205013,120.7443343,117.735356
+152,116.0412323,116.4922976,120.9572434,117.8302578
+153,116.0412323,116.5640939,121.1701525,117.9251596
+154,116.0412323,116.6358903,121.3830616,118.0200614
+155,116.0412323,116.7076866,121.5959708,118.1149632
+156,116.0412323,116.7794829,121.8088799,118.209865
+157,116.0412323,116.8512792,122.021789,118.3047668
+158,116.0412323,116.9230756,122.2346982,118.3996687
+159,116.0412323,116.9948719,122.4476073,118.4945705
+160,116.0412323,117.0666682,122.6605164,118.5894723
+161,116.0412323,117.287211,122.9007941,118.7430791
+162,116.0412323,117.5077538,123.1410717,118.8966859
+163,116.0412323,117.7282966,123.3813494,119.0502928
+164,116.0412323,117.9488394,123.6216271,119.2038996
+165,116.0412323,118.1693822,123.8619047,119.3575064
+166,116.0412323,118.389925,124.1021824,119.5111132
+167,116.0412323,118.6104678,124.34246,119.66472
+168,116.0412323,118.8310106,124.5827377,119.8183269
+169,116.0412323,119.0515534,124.8230154,119.9719337
+170,116.0412323,119.2720961,125.063293,120.1255405
+171,116.2270765,119.4926389,125.3035707,120.3410954
+172,116.6029478,119.7131817,125.5438483,120.6199926
+173,116.9916592,119.9337245,125.784126,120.9031699
+174,117.3928337,120.1542673,126.0244037,121.1905016
+175,117.8060712,120.3748101,126.2646813,121.4818542
+176,118.2309499,120.5953529,126.504959,121.7770873
+177,118.6670268,120.8158957,126.7452367,122.0760531
+178,119.1138385,121.0364385,126.9855143,122.3785971
+179,119.5709023,121.2569813,127.225792,122.6845585
+180,120.0377171,121.477524,127.4660696,122.9937702
+181,120.5137642,121.6980668,127.7063473,123.3060594
+182,120.9985086,121.9186096,127.946625,123.6212477
+183,121.4914,122.1391524,128.1869026,123.9391517
+184,121.9918734,122.3596952,128.4271803,124.259583
+185,122.4993512,122.580238,128.6674579,124.582349
+186,123.0132434,122.8007808,128.9077356,124.9072533
+187,123.5329492,123.0213236,129.1480133,125.2340954
+188,124.0578583,123.2418664,129.3882909,125.5626719
+189,124.5873519,123.4624092,129.6285686,125.8927766
+190,125.1208041,123.6829519,129.8688462,126.2242007
+191,125.6575832,123.9034947,130.1091239,126.5567339
+192,126.1970529,124.1240375,130.3494016,126.890164
+193,126.7385738,124.8334912,131.0330779,127.5350476
+194,127.2815045,125.5429449,131.7167543,128.1804012
+195,127.8252031,126.2523985,132.4004307,128.8260108
+196,128.3690289,126.9618522,133.084107,129.4716627
+197,128.912343,127.6713059,133.7677834,130.1171441
+198,129.4545105,128.3807596,134.4514598,130.7622433
+199,129.9949016,129.0902132,135.1351362,131.4067503
+200,130.532893,129.7996669,135.8188125,132.0504575
+201,131.0678691,130.5091206,136.5024889,132.6931595
+202,131.5992242,131.2185743,137.1861653,133.3346546
+203,132.1263628,131.9280279,137.8698416,133.9747441
+204,132.6487023,132.6374816,138.553518,134.613234
+205,133.1656732,133.3469353,139.2371944,135.2499343
+206,133.6767215,134.056389,139.9208707,135.8846604
+207,134.1813097,134.7658426,140.6045471,136.5172331
+208,134.678918,135.4752963,141.2882235,137.1474793
+209,135.1690461,136.18475,141.9718998,137.775232
+210,135.6512144,136.8942036,142.6555762,138.4003314
+211,136.1249653,137.6036573,143.3392526,139.0226251
+212,136.5898649,138.313111,144.022929,139.6419683
+213,137.0455035,139.0225647,144.7066053,140.2582245
+214,137.491498,139.7320183,145.3902817,140.871266
+215,137.9274922,140.441472,146.0739581,141.4809741
+216,138.3531587,141.1509257,146.7576344,142.0872396
+217,138.7681996,141.8603794,147.4413108,142.6899633
+218,139.1723481,142.569833,148.1249872,143.2890561
+219,139.5653693,143.2792867,148.8086635,143.8844398
+220,139.9470615,143.9887404,149.4923399,144.4760473
+221,140.3172571,144.6981941,150.1760163,145.0638225
+222,140.6758236,145.4076477,150.8596926,145.6477213
+223,141.0226648,146.1171014,151.543369,146.2277117
+224,141.3577211,146.8265551,152.2270454,146.8037739
+225,141.680971,147.9628628,153.5255936,147.7231425
+226,141.9924313,149.0991705,154.8241418,148.6385812
+227,142.2921584,150.2354782,156.12269,149.5501089
+228,142.5802481,151.3717859,157.4212382,150.4577574
+229,142.856837,152.5080936,158.7197863,151.3615723
+230,143.1221024,153.6444013,160.0183345,152.2616127
+231,143.3762632,154.780709,161.3168827,153.1579516
+232,143.6195797,155.9170167,162.6154309,154.0506758
+233,143.8523541,157.0533244,163.9139791,154.9398859
+234,144.0749307,158.1896321,165.2125273,155.8256967
+235,144.2876959,159.3259398,166.5110755,156.7082371
+236,144.4910781,160.4622475,167.8096237,157.5876498
+237,144.6855475,161.5985552,169.1081719,158.4640915
+238,144.8716163,162.7348629,170.4067201,159.3377331
+239,145.0498375,163.8711706,171.7052683,160.2087588
+240,145.2208054,165.0074783,173.0038165,161.0773667
+241,145.3851541,166.143786,174.3023646,161.9437682
+242,145.5435575,167.2800937,175.6009128,162.808188
+243,145.6967283,168.4164014,176.899461,163.6708636
+244,145.8454166,169.5527091,178.1980092,164.532045
+245,145.9904093,170.6890168,179.4965574,165.3919945
+246,146.1325289,171.8253245,180.7951056,166.2509863
+247,146.2726316,172.9616322,182.0936538,167.1093059
+248,146.4116064,174.0979399,183.392202,167.9672494
+249,146.550373,175.2342476,184.6907502,168.8251236
+250,146.6898802,176.3705554,185.9892984,169.6832447
+251,146.8311038,177.5068631,187.2878466,170.5419378
+252,146.9750444,178.6431708,188.5863948,171.4015367
+253,147.1227254,179.7794785,189.8849429,172.2623823
+254,147.2751901,180.9157862,191.1834911,173.1248225
+255,147.4334992,182.0520939,192.4820393,173.9892108
+256,147.5987281,183.1884016,193.7805875,174.8559057
+257,147.7719636,183.779055,194.9262222,175.4924136
+258,147.9543008,184.3697084,196.0718569,176.1319554
+259,148.1468398,184.9603618,197.2174916,176.7748977
+260,148.3506817,185.5510152,198.3631263,177.4216077
+261,148.5669251,186.1416687,199.5087611,178.0724516
+262,148.7966619,186.7323221,200.6543958,178.7277933
+263,149.0409732,187.3229755,201.8000305,179.3879931
+264,149.3009244,187.9136289,202.9456652,180.0534062
+265,149.5775609,188.5042823,204.0912999,180.724381
+266,149.871903,189.0949358,205.2369346,181.4012578
+267,150.1849406,189.6855892,206.3825693,182.0843664
+268,150.517628,190.2762426,207.528204,182.7740249
+269,150.870878,190.866896,208.6738387,183.4705376
+270,151.245556,191.4575494,209.8194734,184.1741929
+271,151.6424737,192.0482029,210.9651081,184.8852616
+272,152.0623831,192.6388563,212.1107428,185.6039941
+273,152.5059689,193.2295097,213.2563775,186.3306187
+274,152.9738426,193.8201631,214.4020122,187.0653393
+275,153.4665341,194.4108165,215.5476469,187.8083325
+276,153.9844848,195.0014699,216.6932816,188.5597454
+277,154.5280396,195.5921234,217.8389164,189.3196931
+278,155.0974384,196.1827768,218.9845511,190.0882554
+279,155.6928081,196.7734302,220.1301858,190.8654747
+280,156.3141533,197.3640836,221.2758205,191.6513525
+281,156.9613477,197.954737,222.4214552,192.4458466
+282,157.634124,198.5453905,223.5670899,193.2488681
+283,158.3320649,199.1360439,224.7127246,194.0602778
+284,159.0545922,199.7266973,225.8583593,194.8798829
+285,159.800957,200.3173507,227.003994,195.7074339
+286,160.5702285,200.9080041,228.1496287,196.5426204
+287,161.361283,201.4986576,229.2952634,197.385068
+288,162.1727923,202.089311,230.4408981,198.2343338
+289,163.0032119,203.259123,230.5992333,198.9538561
+290,163.8507685,204.4289351,230.7575685,199.6790907
+291,164.7134478,205.5987472,230.9159037,200.4093662
+292,165.5889811,206.7685593,231.0742389,201.1439264
+293,166.4748318,207.9383713,231.2325741,201.8819257
+294,167.3681819,209.1081834,231.3909093,202.6224249
+295,168.2659177,210.2779955,231.5492445,203.3643859
+296,169.1646147,211.4478075,231.7075797,204.1066673
+297,170.0605234,212.6176196,231.8659149,204.8480193
+298,170.9495527,213.7874317,232.0242501,205.5870782
+299,171.827255,214.9572437,232.1825853,206.3223613
+300,172.688809,216.1270558,232.3409205,207.0522618
+301,173.5290036,217.2968679,232.4992557,207.7750424
+302,174.3422199,218.46668,232.6575909,208.4888303
+303,175.1224141,219.636492,232.8159261,209.1916107
+304,175.8630988,220.8063041,232.9742613,209.8812214
+305,176.5573244,221.9761162,233.1325965,210.5553457
+306,177.1976604,223.1459282,233.2909317,211.2115068
+307,177.776175,224.3157403,233.4492669,211.8470607
+308,178.2844153,225.4855524,233.6076021,212.4591899
+309,178.7133868,226.6553645,233.7659373,213.0448962
+310,179.0535319,227.8251765,233.9242725,213.6009936
+311,179.2947081,228.9949886,234.0826077,214.1241015
+312,179.4261663,230.1648007,234.2409429,214.6106366
+313,179.4365275,231.3346127,234.3992781,215.0568061
+314,179.4365275,232.5044248,234.5576133,215.4995219
+315,179.4365275,233.6742369,234.7159485,215.9422376
diff --git a/figure/by_code/txt.txt b/figure/by_code/txt.txt
new file mode 100644
index 0000000..e69de29