Develop evaluation codes

evaluation/4Layer Bootstrap.py

@@ -26,12 +26,12 @@ def resultant(sample):
 def H2(x):
     return -x*m.log2(x)-(1-x)*m.log2(1-x)
 
-extremes = 50000
+extremes = None
-maxdt = 500
+maxdt = 300
 
-for dim in [7]:
+for dim in [9]:
     for eps in eps_space:
-        path=f'/cloud/Public/_data/neuropercolation/4lay/steps=1000100/dim={dim:02}/'
+        path=f'/cloud/Public/_data/neuropercolation/4lay/cons=27-3diag_steps=1000100/dim=09/batch=0/'
         
         try:
             with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
@@ -49,6 +49,7 @@ for dim in [7]:
     
         
         all_res = norm(resultant(phase_diff))
+        av_diff = np.arccos(all_res)
         
         try:
             with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
@@ -62,39 +63,75 @@ for dim in [7]:
             with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'w', encoding='utf-8') as f:
                 json.dump(ei, f, indent=1)
         
-        bot_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[ extremes]
+        pha_center = av_diff
-        top_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[-extremes]
+        pha_dev = m.pi/8
+        orth_ind = sorted([i for i,val in enumerate(ei[:-maxdt]) if (pha_center-pha_dev)<=abs(phase_diff[i])<=(pha_center+pha_dev)], key = lambda i: ei[i])
         
-        bot_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] <= bot_ei[1]]
+        if extremes is None:
-        top_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] >= top_ei[1]]
+            extremes = len(orth_ind)//4000*1000
 
-        bot_eis = choose(bot_ei_pool, extremes)
+        print(len(orth_ind))
-        top_eis = choose(top_ei_pool, extremes)
+        #print(all_res, av_diff)
         
-        bot_ind = [enum[0] for enum in bot_eis]
+        
-        top_ind = [enum[0] for enum in top_eis]
+        bot_ind = orth_ind[         :extremes]
+        top_ind = orth_ind[-extremes:        ]
+        
+        # bot_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] <= bot_ei[1]]
+        # top_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] >= top_ei[1]]
+        
+        # bot_eis = choose(bot_ei_pool, extremes)
+        # top_eis = choose(top_ei_pool, extremes)
+        
+        # bot_ind = [enum[0] for enum in bot_ei]
+        # top_ind = [enum[0] for enum in top_ei]
         
         bot_res = []
         top_res = []
-        for dt in range(maxdt):
+        orth_res = []
-            bot_pha = [phase_diff[i+dt] for i in bot_ind]
+        tot_res = []
-            top_pha = [phase_diff[i+dt] for i in top_ind]
+        
+        bot_ei = []
+        top_ei = []
+        orth_ei = []
+        tot_ei = []
+        for dt in range(maxdt):
+            bot_res.append( norm(resultant([phase_diff[i+dt] for i in bot_ind])) )
+            top_res.append( norm( resultant([phase_diff[i+dt] for i in top_ind])) )
+            orth_res.append( norm( resultant([phase_diff[i+dt] for i in orth_ind])) )
+            tot_res.append( norm( resultant(phase_diff[dt:dt-maxdt])) )
+            
+            bot_ei.append( np.average([ei[i+dt] for i in bot_ind]) )
+            top_ei.append( np.average([ei[i+dt] for i in top_ind]) )
+            orth_ei.append( np.average([ei[i+dt] for i in orth_ind]) )
+            tot_ei.append( np.average(ei[dt:dt-maxdt]) )
             
-            bot_res.append( norm(resultant(bot_pha)) )
-            top_res.append( norm(resultant(top_pha)) )
             if dt%100==99:
                 print(f'Done dt={dt}')
             
             
         qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
-                [np.array(range(maxdt))]*3,
+                [np.array(range(maxdt))]*4,
-                [bot_res, top_res, [all_res]*maxdt],
+                [bot_res, top_res, orth_res, tot_res],
-                ['Resultant ev of bottom 100 ei', 'Resultant ev of top 100 ei', 'Average Resultant'],
+                ['Resultant ev of bottom {extremes} ei', 'Resultant ev of top {extremes} ei',
+                 'Resultant ev of phase filtered ei', 'Average Resultant'],
                 x_tag = 'dt',
                 y_tag = 'concentration',
                 export=True,
                 path=path,
-                filename=f'Diachronic Resultant for eps={round(eps,3)} dim={dim} extremes={extremes}.png',
+                filename=f'Diachronic Resultant for eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
+                close=True)
+        
+        qtplot(f'Diachronic ei for dim={dim} with 4 layers',
+                [np.array(range(maxdt))]*4,
+                [bot_ei, top_ei, orth_ei, tot_ei],
+                ['EI ev of bottom {extremes} ei', 'EI ev of top {extremes} ei',
+                 'EI ev of phase filtered ei', 'Average EI'],
+                x_tag = 'dt',
+                y_tag = 'average ei',
+                export=True,
+                path=path,
+                filename=f'Diachronic EI for eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
                 close=True)
         
         print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')

evaluation/4Layer EI to Phase Survey.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug 21 14:59:22 2023
+
+@author: astral
+"""
+
+import json
+import math as m
+import numpy as np
+from numpy.linalg import norm
+from datetime import datetime
+from random import sample as choose
+
+from plot import qtplot
+
+eps_space = list(np.linspace(0.01,0.2,20))
+
+def resultant(sample):
+    phase_x = [m.cos(ind) for ind in sample]
+    phase_y = [m.sin(ind) for ind in sample]
+    
+    return (np.average(phase_x), np.average(phase_y))
+
+def H2(x):
+    return -x*m.log2(x)-(1-x)*m.log2(1-x)
+
+extremes = 50000
+maxdt = 500
+
+for dim in [7]:
+    for eps in eps_space:
+        path=f'/cloud/Public/_data/neuropercolation/4lay/cons=14_steps=1000100/dim={dim:02}/batch=0/'
+        
+        try:
+            with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
+                phase_diff = json.load(f)
+        except:
+            with open(path+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
+                activation = json.load(f)[100:]
+        
+            osc = list(zip(*activation))
+            phase = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
+            phase_diff = (phase[1]-phase[0]+m.pi)%(2*m.pi)-m.pi
+            
+            with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'w', encoding='utf-8') as f:
+                json.dump(list(phase_diff), f, indent=1)
+    
+        
+        all_res = norm(resultant(phase_diff))
+        
+        try:
+            with open(path+f"eps={round(eps,3):.3f}_channelsum.txt", 'r', encoding='utf-8') as f:
+                chasum = json.load(f)
+        except:
+            with open(path+f"eps={round(eps,3):.3f}_channels.txt", 'r', encoding='utf-8') as f:
+                channels = json.load(f)[100:]
+            
+            chasum = [int(np.sum(cha)) for cha in channels]
+            
+            with open(path+f"eps={round(eps,3):.3f}_channelsum.txt", 'w', encoding='utf-8') as f:
+                json.dump(chasum, f, indent=1)
+        
+        maxchas = max(chasum)
+        
+        res = [norm(resultant([pha for i, pha in enumerate(phase_diff) if chasum[i]==n])) for n in range(maxchas+1) if len([pha for i, pha in enumerate(phase_diff) if chasum[i]==n])>=20]
+        #dist = [len([pha for i, pha in enumerate(phase_diff) if chasum[i]==n]) for n in range(maxchas+1)]
+        
+        qtplot(f'Channel-Resultant for dim={dim}',
+                [list(range(len(res)))],
+                [res],
+                [f'Resultant against open channels'],
+                x_tag = 'open channels',
+                y_tag = 'resultant',
+                export=True,
+                path=path,
+                filename=f'Channel-Resultant eps={round(eps,3):.3f} dim={dim}.png',
+                close=True)
+        
+        print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
+        
+    # qtplot(f'Resultant and EI evolution for dim={dim} with 4 layers',
+    #         [[0]+eps_space]*2,
+    #         [max(av_ei)*diff_res, av_ei],
+    #         ['Resultant', 'avEI'],
+    #         export=True,
+    #         path=path,
+    #         filename=f'Resultant and EI for dim={dim}.png',
+    #         close=True)
+        
+        

evaluation/4Layer Phase to EI Survey.py

@@ -29,14 +29,16 @@ def H2(x):
 extremes = 50000
 maxdt = 500
 
-for dim in [7]:
+for dim in [9]:
-    for eps in eps_space:
+    for eps in eps_space[:1]:
-        path=f'/cloud/Public/_data/neuropercolation/4lay/steps=1000100/dim={dim:02}/'
+        path=f'/cloud/Public/_data/neuropercolation/4lay/cons=9-3dist_steps=1000100/dim=09/batch=0/'
         
         try:
             with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
                 phase_diff = json.load(f)
         except:
+        #     None
+        # if True:
             with open(path+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
                 activation = json.load(f)[100:]
         
@@ -62,22 +64,34 @@ for dim in [7]:
             with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'w', encoding='utf-8') as f:
                 json.dump(ei, f, indent=1)
         
-        pha_divs = 8
+        pha_divs = 32
         pha_sort = [0]*pha_divs
         
-        pha_sort = [[pha for pha in list(enumerate(phase_diff))[:-maxdt] if i*m.pi/pha_divs <= abs(pha[1]) <= (i+1)*m.pi/pha_divs] for i in range(pha_divs)]
+        pha_sort = [[pha for pha in list(enumerate(phase_diff))[:-maxdt] if i*m.pi/pha_divs <= abs(pha[1]) <= (i+1)*m.pi/pha_divs]
-        dia_ei = [[np.average([ei[pha[0]+dt] for pha in pha_div]) for dt in range(maxdt)] for pha_div in pha_sort]
+                    for i in range(pha_divs)]
+        dia_ei = [np.average([ei[i] for i, pha in pha_div]) for pha_div in pha_sort]
+        phase_dist = [len(pha)/len(phase[0][:-maxdt]) for pha in pha_sort]  
         
-        qtplot(f'Diachronic EI for dim={dim} with 4 layers',
+        qtplot(f'EI Distribution against phase diff for dim={dim}',
-                [list(range(maxdt))]*pha_divs,
+                [[(div+.5)*m.pi/pha_divs for div in range(pha_divs)]],
-                dia_ei,
+                [dia_ei],
-                [f'EI evolution for initial phase in [{div}pi,{div+1}pi]' for div in range(pha_divs)],
+                [f'EI distribution against phase diff'],
-                x_tag = 'dt',
+                x_tag = 'abs phase',
                 y_tag = 'average EI',
-                colors = 'rgb',
                 export=True,
                 path=path,
-                filename=f'Diachronic EI for eps={round(eps,3)} dim={dim} pha_divs={pha_divs}.png',
+                filename=f'Phase-EI eps={round(eps,3):.3f} dim={dim} pha_divs={pha_divs}.png',
+                close=True)
+        
+        qtplot(f'Phasediff Distribution for dim={dim}',
+                [[(div+.5)*m.pi/pha_divs for div in range(pha_divs)]],
+                [phase_dist],
+                [f'Phase distribution'],
+                x_tag = 'abs phase',
+                y_tag = 'frequency',
+                export=True,
+                path=path,
+                filename=f'Phasediff dist eps={round(eps,3):.3f} dim={dim} pha_divs={pha_divs}.png',
                 close=True)
         
         print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')

evaluation/plot.py

@@ -126,6 +126,8 @@ def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_ta
         # save to file
         exporter.export(path+filename)
         
+        print(f'Saving to {path+filename}')
+    
     def handleAppExit():
         # Add any cleanup tasks here
         print("closing")

examples/Simulate4Layers.py

@@ -15,18 +15,20 @@ eps_space = np.linspace(0.005,0.5,100)
 
 stp = 1000100
 
-for batch in range(1,5):
+for batch in range(5):
-    for dim in [7]:
+    for dim in [8]:
         for eps in eps_space[1:41:2]:
             eps = round(eps,3)
-            cons = [(n,n) for n in range(dim)]+[(n,(n+3)%7) for n in range(dim)]
+            cons = [(n,(n+m)%dim) for n in range(dim) for m in [0,int(dim/2)]]
+            initstate = [[0,0],[0,0]]
             sim = Simulate4Layers(dim,
                            eps,
                            coupling=cons,
+                           init=initstate,
                            steps=stp,
                            draw=None,
                            res=2,
                            save='simple',
-                           path=f'/cloud/Public/_data/neuropercolation/4lay/cons={len(cons)}_steps={stp}/dim={dim:02}/batch={batch}/',
+                           path=f'/cloud/Public/_data/neuropercolation/4lay/cons={len(cons)}-2diag_steps={stp}/dim={dim:02}/batch={batch}/',
                            )
             print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')