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Develop evaluation codes

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This commit is contained in:
timofej 2023-08-27 21:12:43 +02:00
parent 7a5b7eae6e
commit 0f62707e33
5 changed files with 186 additions and 39 deletions
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77
evaluation/4Layer Bootstrap.py
View File

@ -26,12 +26,12 @@ def resultant(sample):
def H2(x):
return -x*m.log2(x)-(1-x)*m.log2(1-x)
extremes = 50000
maxdt = 500
extremes = None
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]
top_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[-extremes]
pha_center = av_diff
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]]
top_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] >= top_ei[1]]
if extremes is None:
extremes = len(orth_ind)//4000*1000
print(len(orth_ind))
#print(all_res, av_diff)
bot_eis = choose(bot_ei_pool, extremes)
top_eis = choose(top_ei_pool, extremes)
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 = []
orth_res = []
tot_res = []
bot_ei = []
top_ei = []
orth_ei = []
tot_ei = []
for dt in range(maxdt):
bot_pha = [phase_diff[i+dt] for i in bot_ind]
top_pha = [phase_diff[i+dt] for i in top_ind]
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,
[bot_res, top_res, [all_res]*maxdt],
['Resultant ev of bottom 100 ei', 'Resultant ev of top 100 ei', 'Average Resultant'],
[np.array(range(maxdt))]*4,
[bot_res, top_res, orth_res, tot_res],
['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()}')

92
evaluation/4Layer EI to Phase Survey.py Normal file
View File

@ -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)

44
evaluation/4Layer Survey.py → evaluation/4Layer Phase to EI Survey.py
View File

@ -29,14 +29,16 @@ def H2(x):
extremes = 50000
maxdt = 500
for dim in [7]:
for eps in eps_space:
path=f'/cloud/Public/_data/neuropercolation/4lay/steps=1000100/dim={dim:02}/'
for dim in [9]:
for eps in eps_space[:1]:
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:]
@ -57,27 +59,39 @@ for dim in [7]:
with open(path+f"eps={round(eps,3):.3f}_channels.txt", 'r', encoding='utf-8') as f:
channels = json.load(f)[100:]
ei = [np.sum(cha)*(1-H2(eps)) for cha in channels]
ei = [np.sum(cha)*(1-H2(eps)) for cha in channels]
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)]
dia_ei = [[np.average([ei[pha[0]+dt] for pha in pha_div]) for dt in range(maxdt)] for pha_div in pha_sort]
qtplot(f'Diachronic EI for dim={dim} with 4 layers',
[list(range(maxdt))]*pha_divs,
dia_ei,
[f'EI evolution for initial phase in [{div}pi,{div+1}pi]' for div in range(pha_divs)],
x_tag = 'dt',
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)]
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'EI Distribution against phase diff for dim={dim}',
[[(div+.5)*m.pi/pha_divs for div in range(pha_divs)]],
[dia_ei],
[f'EI distribution against phase diff'],
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()}')

2
evaluation/plot.py
View File

@ -125,6 +125,8 @@ 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

10
examples/Simulate4Layers.py
View File

@ -15,18 +15,20 @@ eps_space = np.linspace(0.005,0.5,100)
stp = 1000100
for batch in range(1,5):
for dim in [7]:
for batch in range(5):
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()}')