Try Shuffle_sim without G sweep

Shuffle_sim.csh

@@ -11,7 +11,7 @@
 #PBS -V
 
 # -J Specifies Job Array with Indices 
-#PBS -J 1-200
+#PBS -J 1-100
 
 # Show the host on which the job ran
 hostname

Shuffle_validation.py

@@ -90,8 +90,9 @@
 i = int(sys.argv[1])
 
 # FCFC Shuffle
-ParamsDict["ExportSim"] = False 
-# Skimp on computation time and power by reducing to 1.2e4.  Should be fine. 
+# Nah don't save it. 
+ParamsDict["ExportSim"] = False
+# Skimp on computation time and power by reducing to 1.2e5.  
 ParamsDict["Simul_length"] = 1.2e5
 
 # First we must shuffle it randomly. 
@@ -129,22 +130,24 @@
 Best_G = 0
 Best_Sigma = 0  
 
+# Do not sweep G 
+ParamsDict["G"] = 0.7
+
 # Sweep across the range of Sigma values:
 for I in np.arange(6):
 
-    ParamsDict["Sigma"] =I*0.2
-    sigma = ParamsDict["Sigma"] 
-
-    # Heterogeneous Coupling Constants (array)
-    ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma * E_normalised) 
-    ParamsDict["MODEL_c_ee"] = h_ee  * (1 + sigma * E_normalised) 
-    ParamsDict["MODEL_c_ii"] = h_ii  * (1 + sigma * I_normalised) 
-    ParamsDict["MODEL_c_ei"] = h_ei  * (1 + sigma * I_normalised) 
+    ParamsDict["Sigma_e"] =I*0.2
+    sigma_e = ParamsDict["Sigma_e"] 
 
-
-    # Sweep across the range of G values
-    for J in np.arange(29):
-        ParamsDict["G"] = np.array([J * 0.05]) + 0.10
+    for J in np.arange(6):
+        ParamsDict["Sigma_i"] = J*0.2
+        sigma_i = ParamsDict["Sigma_i"] 
+
+        # Heterogeneous Coupling Constants (array)
+        ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma_e * E_normalised) 
+        ParamsDict["MODEL_c_ee"] = h_ee  * (1 + sigma_e * E_normalised) 
+        ParamsDict["MODEL_c_ii"] = h_ii  * (1 + sigma_i * I_normalised) 
+        ParamsDict["MODEL_c_ei"] = h_ei  * (1 + sigma_i * I_normalised) 
 
         ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
                                             a_e=numpy.array([1.0]),a_i=numpy.array([1.0]),b_e=numpy.array([1.5]),b_i=numpy.array([2.8]),tau_e=numpy.array([10.0]),
@@ -155,12 +158,18 @@
         if Score > Best_Score:
             Best_Score = Score
             Best_G = ParamsDict["G"]
-            Best_Sigma = ParamsDict["Sigma"] 
+            Best_Sigma_e = ParamsDict["Sigma_e"] 
+            Best_Sigma_i = ParamsDict["Sigma_i"] 
             print(Best_Score)  
             print(Best_G)   
-            print(Best_Sigma)         
+            print(Best_Sigma_e)  
+            print(Best_Sigma_i)         
+
+date = time.strftime("%Y_%m_%d/")
+# Create new directory which is the date. 
+os.makedirs("do-not-track/" + date,exist_ok=True)
 
 # Now export the information:
 time_now = time.strftime("%Y%m%d-%H%M%S")
-np.savetxt("do-not-track/LCHetVal_Better/" + str(i) + "_" + ParamsDict["name"] + "_Best_" + time_now + "_.csv", [Best_Score,Best_G,Best_Sigma], delimiter="\t")
-np.savetxt("do-not-track/LCHetVal_Better/" + str(i) + "_" + ParamsDict["name"] + "_EIHet_" + time_now + "_.csv", [E_normalised,I_normalised], delimiter="\t")
+np.savetxt("do-not-track/"  + date  + str(i) + "_" + ParamsDict["name"] + "_Best_" + time_now + "_.csv", [Best_Score,Best_G,Best_Sigma_e,Best_Sigma_i], delimiter="\t")
+np.savetxt("do-not-track/" + date + str(i) + "_" + ParamsDict["name"] + "_EIHet_" + time_now + "_.csv", [E_normalised,I_normalised], delimiter="\t")