modified the scripts to generate only two figures in folder figure of every...

modified the scripts to generate only two figures in folder figure of every factor-*,and corrected some scripts to avoid divided by zero

Data_4.0/Trigger after_Inflowrate _done.py

@@ -12,15 +12,16 @@ import os
 
 Factors=glob.glob('factor-*')
 for factor in Factors:
-    if factor == 'factor-stepsize':
-        pass
-    else:
+    #if factor == 'factor-stepsize':
+        #pass
+    #else:
     os.chdir(factor)
     print('os.getcwd()')
-        Scripts=glob.glob('*.py')
-        for script in Scripts:
+    print(factor)
+        #Scripts=glob.glob('*.py')
+        #for script in Scripts:
         
-        subprocess.call(["python", "%s"%script,])
+            #subprocess.call(["python", "%s"%script,])
 
     os.chdir('..')
 

Data_4.0/factor-inflowrate/simulation-plotting.py

@@ -186,19 +186,6 @@ for model in Model:
     print(C)
     BIG_C[j]=C
     j=j+1
-    
-    
-    plt.figure()
-    plt.plot(W,MeanNmax)
-    plt.savefig('inflowrate-n')
-
-        
-    plt.figure()
-    plt.plot(W,C,'-or')   
-    plt.savefig('inflowrate-nt')         
-                  
-    print('now leave'+model)
-               
     os.chdir('..')
       
 std1=np.std(BIG_meanNmax)

Data_4.0/factor-stepsize/simulation-plotting-stepsize.py

@@ -158,21 +158,7 @@ for model in Model:
         BIG_C[j]=C
         j=j+1
         
-        plt.figure()
-        plt.plot(W,MeanNmax)
-        plt.savefig('W-N',dpi=300)
-        
-        plt.figure()
-        plt.grid()
-        plt.plot(W,C)
-        plt.savefig('W-Nt',dpi=300)
-        
-        
-        #plt.savefig('W-N/T')
-        
         print('now leave'+model)
-        
-            
         os.chdir('../..')
         
 std1=np.std(BIG_meanNmax)

Data_4.0/factor-update_methods/simulation-plotting-update.py

@@ -110,7 +110,7 @@ for model in Model:
                 else:
                     Nmax_p_m0.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m0[0]=sum(Nmax_p_m0)/len(Nmax_p_m0)
-                    Timespan_p_m0.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_p_m0.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m0[0]=sum(Timespan_p_m0)/len(Timespan_p_m0)
                     c_m0[0]=(meanNmax_m0[0]/meanTimespan_m0[0])
 
@@ -125,7 +125,7 @@ for model in Model:
                 else:
                     Nmax_s_m0.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m0[1]=sum(Nmax_s_m0)/len(Nmax_s_m0)
-                    Timespan_s_m0.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_s_m0.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m0[1]=sum(Timespan_s_m0)/len(Timespan_s_m0)
                     c_m0[1]=(meanNmax_m0[1]/meanTimespan_m0[1])
         os.chdir('../..')
@@ -146,7 +146,7 @@ for model in Model:
                 else:
                     Nmax_p_m1.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m1[0]=sum(Nmax_p_m1)/len(Nmax_p_m1)
-                    Timespan_p_m1.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_p_m1.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m1[0]=sum(Timespan_p_m1)/len(Timespan_p_m1)
                     c_m1[0]=(meanNmax_m1[0]/meanTimespan_m1[0])
 
@@ -161,7 +161,7 @@ for model in Model:
                 else:
                     Nmax_s_m1.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m1[1]=sum(Nmax_s_m1)/len(Nmax_s_m1)
-                    Timespan_s_m1.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_s_m1.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m1[1]=sum(Timespan_s_m1)/len(Timespan_s_m1)
                     c_m1[1]=(meanNmax_m1[1]/meanTimespan_m1[1])
         os.chdir('../..')
@@ -181,7 +181,7 @@ for model in Model:
                 else:
                     Nmax_p_m2.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m2[0]=sum(Nmax_p_m2)/len(Nmax_p_m2)
-                    Timespan_p_m2.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_p_m2.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m2[0]=sum(Timespan_p_m2)/len(Timespan_p_m2)
                     c_m2[0]=(meanNmax_m2[0]/meanTimespan_m2[0])
 
@@ -196,7 +196,7 @@ for model in Model:
                 else:
                     Nmax_s_m2.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m2[1]=sum(Nmax_s_m2)/len(Nmax_s_m2)
-                    Timespan_s_m2.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_s_m2.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m2[1]=sum(Timespan_s_m2)/len(Timespan_s_m2)
                     c_m2[1]=(meanNmax_m2[1]/meanTimespan_m2[1])
         os.chdir('../..')
@@ -217,7 +217,7 @@ for model in Model:
                 else:
                     Nmax_p_m3.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m3[0]=sum(Nmax_p_m3)/len(Nmax_p_m3)
-                    Timespan_p_m3.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_p_m3.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m3[0]=sum(Timespan_p_m3)/len(Timespan_p_m3)
                     c_m3[0]=(meanNmax_m3[0]/meanTimespan_m3[0])
 
@@ -232,7 +232,7 @@ for model in Model:
                 else:
                     Nmax_s_m3.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m3[1]=sum(Nmax_s_m3)/len(Nmax_s_m3)
-                    Timespan_s_m3.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_s_m3.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m3[1]=sum(Timespan_s_m3)/len(Timespan_s_m3)
                     c_m3[1]=(meanNmax_m3[1]/meanTimespan_m3[1])
         os.chdir('../..')
@@ -252,7 +252,7 @@ for model in Model:
                 else:
                     Nmax_p_m4.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m4[0]=sum(Nmax_p_m4)/len(Nmax_p_m4)
-                    Timespan_p_m4.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_p_m4.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m4[0]=sum(Timespan_p_m4)/len(Timespan_p_m4)
                     c_m4[0]=(meanNmax_m4[0]/meanTimespan_m4[0])
 
@@ -267,7 +267,7 @@ for model in Model:
                 else:
                     Nmax_s_m4.append(np.loadtxt(log)[-1][-1])
                     meanNmax_m4[1]=sum(Nmax_s_m4)/len(Nmax_s_m4)
-                    Timespan_s_m4.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[1][1])
+                    Timespan_s_m4.append(np.loadtxt(log)[-1][1]-np.loadtxt(log)[0][1])
                     meanTimespan_m4[1]=sum(Timespan_s_m4)/len(Timespan_s_m4)
                     c_m4[1]=(meanNmax_m4[1]/meanTimespan_m4[1])
         os.chdir('../..')

Data_4.0/factor-width/simulation-plotting-width.py

@@ -174,15 +174,6 @@ for model in Model:
     BIG_C[j]=C
     print(C)
     j=j+1
-          
-    plt.figure()
-    plt.plot(W,MeanNmax)
-    plt.savefig('W-N')
-    
-    plt.figure()
-    plt.plot(W,C)
-    plt.savefig('W-NT')
-    
     print('now leave'+model)