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Trying to understand Resultant evolution

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timofej 2023-08-28 22:04:55 +02:00
parent 0f62707e33
commit 0711f071a5
7 changed files with 679 additions and 41 deletions
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97
evaluation/4Layer Bootstrap.py
View File

@ -23,15 +23,16 @@ def resultant(sample):
return (np.average(phase_x), np.average(phase_y))
def H2(x):
return -x*m.log2(x)-(1-x)*m.log2(1-x)
phase = lambda x,y: (m.atan2(y,x)+m.pi)%(2*m.pi)-m.pi
H2 = lambda x: -x*m.log2(x)-(1-x)*m.log2(1-x)
extremes = None
maxdt = 300
for dim in [9]:
for dim in []:
for eps in eps_space:
path=f'/cloud/Public/_data/neuropercolation/4lay/cons=27-3diag_steps=1000100/dim=09/batch=0/'
path=f'/cloud/Public/_data/neuropercolation/4lay/cons=7-knight_steps=1000100/dim=07/batch=0/'
try:
with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
@ -64,74 +65,106 @@ for dim in [9]:
json.dump(ei, f, indent=1)
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])
pha_dev = m.pi/32
from_sync = lambda i: True if abs(phase_diff[i])<0.08*m.pi else False if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else from_sync(i-1)
dev_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])
dev_in_ind = [i for i in dev_ind if not from_sync(i)]
dev_out_ind = [i for i in dev_ind if from_sync(i)]
devm_in_ind = [i for i in dev_in_ind if phase_diff[i]<0]
devp_in_ind = [i for i in dev_in_ind if phase_diff[i]>0]
devm_out_ind = [i for i in dev_out_ind if phase_diff[i]<0]
devp_out_ind = [i for i in dev_out_ind if phase_diff[i]>0]
if extremes is None:
extremes = len(orth_ind)//4000*1000
extremes = min(len(dev_in_ind),len(dev_out_ind))//1000*100
print(len(orth_ind))
print(len(dev_out_ind), len(dev_in_ind))
#print(all_res, av_diff)
bot_ind = orth_ind[ :extremes]
top_ind = orth_ind[-extremes: ]
bot_in_ind = dev_in_ind[ :extremes]
top_in_ind = dev_in_ind[-extremes: ]
bot_out_ind = dev_out_ind[ :extremes]
top_out_ind = dev_out_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_ind = bot_in_ind + bot_out_ind
top_ind = top_in_ind + top_out_ind
bot_res = []
top_res = []
orth_res = []
dev_res = []
tot_res = []
bot_ph = []
top_ph = []
dev_ph = []
tot_ph = []
bot_ei = []
top_ei = []
orth_ei = []
dev_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_pha = [phase_diff[i+dt] for i in bot_ind]
top_pha = [phase_diff[i+dt] for i in top_ind]
dev_pha = [phase_diff[i+dt] for i in dev_ind]
tot_pha = phase_diff[dt:dt-maxdt]
bot_res.append( norm(resultant(bot_pha)) )
top_res.append( norm(resultant(top_pha)) )
dev_res.append( norm(resultant(dev_pha)) )
tot_res.append( norm(resultant(tot_pha)) )
bot_ph.append( phase(resultant(bot_pha)) )
top_ph.append( phase(resultant(top_pha)) )
dev_ph.append( phase(resultant(dev_pha)) )
tot_ph.append( phase(resultant(tot_pha)) )
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]) )
dev_ei.append( np.average([ei[i+dt] for i in dev_ind]) )
tot_ei.append( np.average(ei[dt:dt-maxdt]) )
if dt%100==99:
print(f'Done dt={dt}')
qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
[np.array(range(maxdt))]*4,
[bot_res, top_res, orth_res, tot_res],
[bot_res, top_res, dev_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):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
path=path+'plots/',
filename=f'Diachronic Resultant balanced eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
close=True)
qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
[np.array(range(maxdt))]*4,
[bot_ph, top_ph, dev_ph, tot_ph],
['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+'plots/',
filename=f'Diachronic Resultant balanced 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],
[bot_ei, top_ei, dev_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',
path=path+'plots/',
filename=f'Diachronic EI balanced 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()}')

153
evaluation/4Layer Percolation small eps.py Normal file
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@ -0,0 +1,153 @@
#!/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 = None
maxdt = 300
for dim in [9]:
for eps in eps_space[:10]:
path=f'/cloud/Public/_data/neuropercolation/4lay/cons=7-knight_steps=1000100/dim=07/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))
av_diff = np.arccos(all_res)
try:
with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
ei = 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:]
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_center = av_diff
pha_dev = m.pi
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])
thres = 0.04*m.pi
last_sync = lambda i: i if abs(phase_diff[i])<0.08*m.pi else last_sync(i-1)
last_orth = lambda i: i if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else last_orth(i-1)
trav_out = [(i,pha) for i,pha in enumerate(phase_diff[:-maxdt]) if abs(pha)<thres and abs(phase_diff[i+1])>thres]
diff00_ind = [last_sync(i) for i,pha in trav_out]
diff00m_ind = [last_sync(i) for i,pha in trav_out if pha<0]
diff00p_ind = [last_sync(i) for i,pha in trav_out if pha>0]
trav_in = [(i,pha) for i,pha in enumerate(phase_diff[:-maxdt]) if m.pi/2-thres<abs(pha)<m.pi/2+thres and abs(phase_diff[i+1])<m.pi/2-thres]
diff90_ind = [last_orth(i) for i,pha in trav_in]
diff90m_ind = [last_orth(i) for i,pha in trav_in if pha<0]
diff90p_ind = [last_orth(i) for i,pha in trav_in if pha>0]
print(f'{len(diff00_ind)}/{len(trav_out)} synced states and {len(diff90_ind)}/{len(trav_in)} orthogonal states')
#print(all_res, av_diff)
in_ei = []
out_ei = []
inm_res = []
inp_res = []
outm_res = []
outp_res = []
for dt in range(maxdt):
inm_res.append( resultant([phase_diff[i+dt] for i in diff90m_ind]) )
outm_res.append( resultant([phase_diff[i+dt] for i in diff00m_ind]) )
inp_res.append( resultant([phase_diff[i+dt] for i in diff90p_ind]) )
outp_res.append( resultant([phase_diff[i+dt] for i in diff00p_ind]) )
in_ei.append( np.average([ei[i+dt] for i in diff90_ind]) )
out_ei.append( np.average([ei[i+dt] for i in diff00_ind]) )
if dt%100==99:
print(f'Done dt={dt}')
im_res = list(zip(*inm_res))
om_res = list(zip(*outm_res))
ip_res = list(zip(*inp_res))
op_res = list(zip(*outp_res))
space = np.linspace(0,2*m.pi,101)
av_ei = np.average(ei)
qtplot(f'Transitions resultants for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
[im_res[0], om_res[0], ip_res[0], op_res[0], np.cos(space)],
[im_res[1], om_res[1], ip_res[1], op_res[1], np.sin(space)],
['Resultant ev inward -', 'Resultant ev outward -',
'Resultant ev inward +', 'Resultant ev outward +',
'Unit Circle'],
x_tag = 'x',
y_tag = 'y',
lw=1,
export=True,
path=path,
filename=f'Transitions resultant pm for eps={round(eps,3):.3f} dim={dim}.png',
close=False)
qtplot(f'Transitions ei for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
[np.array(range(maxdt))]*3,
[in_ei, out_ei, [av_ei]*maxdt],
['EI ev inward', 'EI ev outward', 'Average EI'],
x_tag = 'dt',
y_tag = 'average ei',
export=True,
path=path,
filename=f'Transitions EI for eps={round(eps,3):.3f} dim={dim} .png',
close=False)
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)

266
evaluation/4Layer Resultant Evolution.py Normal file
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@ -0,0 +1,266 @@
#!/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 = None
maxdt = 300
for dim in [9]:
for eps in eps_space:
path=f'/cloud/Public/_data/neuropercolation/4lay/cons=7-knight_steps=1000100/dim=07/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))
av_diff = np.arccos(all_res)
try:
with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
ei = 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:]
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_center = av_diff
pha_dev = m.pi/32
last_sync = lambda i: i if abs(phase_diff[i])<0.08*m.pi else last_sync(i-1)
last_orth = lambda i: i if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else last_orth(i-1)
dev_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])
dev_in_ind = [i for i in dev_ind if last_sync(i)<last_orth(i)]
dev_out_ind = [i for i in dev_ind if last_sync(i)>last_orth(i)]
devm_in_ind = [i for i in dev_in_ind if phase_diff[i]<0]
devp_in_ind = [i for i in dev_in_ind if phase_diff[i]>0]
devm_out_ind = [i for i in dev_out_ind if phase_diff[i]<0]
devp_out_ind = [i for i in dev_out_ind if phase_diff[i]>0]
if extremes is None:
extremes = len(dev_in_ind)//4000*1000
print(len(dev_out_ind), len(dev_in_ind))
#print(all_res, av_diff)
bot_in_ind = dev_in_ind[ :extremes]
top_in_ind = dev_in_ind[-extremes: ]
bot_out_ind = dev_out_ind[ :extremes]
top_out_ind = dev_out_ind[-extremes: ]
bot_ind = bot_in_ind + bot_out_ind
top_ind = top_in_ind + top_out_ind
botm_in_ind = [i for i in bot_in_ind if phase_diff[i]<0]
botp_in_ind = [i for i in bot_in_ind if phase_diff[i]>0]
topm_in_ind = [i for i in top_in_ind if phase_diff[i]<0]
topp_in_ind = [i for i in top_in_ind if phase_diff[i]>0]
botm_out_ind = [i for i in bot_out_ind if phase_diff[i]<0]
botp_out_ind = [i for i in bot_out_ind if phase_diff[i]>0]
topm_out_ind = [i for i in top_out_ind if phase_diff[i]<0]
topp_out_ind = [i for i in top_out_ind if phase_diff[i]>0]
# 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]
botm_in_res = []
topm_in_res = []
devm_in_res = []
botp_in_res = []
topp_in_res = []
devp_in_res = []
botm_out_res = []
topm_out_res = []
devm_out_res = []
botp_out_res = []
topp_out_res = []
devp_out_res = []
bot_ei = []
top_ei = []
dev_ei = []
tot_res = []
tot_ei = []
for dt in range(maxdt):
botm_in_res.append( resultant([phase_diff[i+dt] for i in botm_in_ind]) )
topm_in_res.append( resultant([phase_diff[i+dt] for i in topm_in_ind]) )
devm_in_res.append( resultant([phase_diff[i+dt] for i in devm_in_ind]) )
botp_in_res.append( resultant([phase_diff[i+dt] for i in botp_in_ind]) )
topp_in_res.append( resultant([phase_diff[i+dt] for i in topp_in_ind]) )
devp_in_res.append( resultant([phase_diff[i+dt] for i in devp_in_ind]) )
botm_out_res.append( resultant([phase_diff[i+dt] for i in botm_out_ind]) )
topm_out_res.append( resultant([phase_diff[i+dt] for i in topm_out_ind]) )
devm_out_res.append( resultant([phase_diff[i+dt] for i in devm_out_ind]) )
botp_out_res.append( resultant([phase_diff[i+dt] for i in botp_out_ind]) )
topp_out_res.append( resultant([phase_diff[i+dt] for i in topp_out_ind]) )
devp_out_res.append( resultant([phase_diff[i+dt] for i in devp_out_ind]) )
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]) )
dev_ei.append( np.average([ei[i+dt] for i in dev_ind]) )
tot_res.append( resultant(phase_diff[dt:dt-maxdt]) )
tot_ei.append( np.average(ei[dt:dt-maxdt]) )
if dt%100==99:
print(f'Done dt={dt}')
bm_in_res = list(zip(*botm_in_res))
tm_in_res = list(zip(*topm_in_res))
dm_in_res = list(zip(*devm_in_res))
bp_in_res = list(zip(*botp_in_res))
tp_in_res = list(zip(*topp_in_res))
dp_in_res = list(zip(*devp_in_res))
bm_out_res = list(zip(*botm_out_res))
tm_out_res = list(zip(*topm_out_res))
dm_out_res = list(zip(*devm_out_res))
bp_out_res = list(zip(*botp_out_res))
tp_out_res = list(zip(*topp_out_res))
dp_out_res = list(zip(*devp_out_res))
t_res = list(zip(*tot_res))
space = np.linspace(0,2*m.pi,101)
qtplot(f'Diachronic resultant +/- for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
[bm_in_res[0], tm_in_res[0], dm_in_res[0], bp_in_res[0], tp_in_res[0], dp_in_res[0],
bm_out_res[0], tm_out_res[0], dm_out_res[0], bp_out_res[0], tp_out_res[0], dp_out_res[0],
t_res[0], np.cos(space)],
[bm_in_res[1], tm_in_res[1], dm_in_res[1], bp_in_res[1], tp_in_res[1], dp_in_res[1],
bm_out_res[1], tm_out_res[1], dm_out_res[1], bp_out_res[1], tp_out_res[1], dp_out_res[1],
t_res[1], np.sin(space)],
['Resultant ev in of bottom {extremes} ei -', 'Resultant ev in of top {extremes} ei -',
'Resultant ev in of phase filtered ei -',
'Resultant ev in of bottom {extremes} ei +', 'Resultant ev in of top {extremes} ei +',
'Resultant ev in of phase filtered ei +',
'Resultant ev out of bottom {extremes} ei -', 'Resultant ev out of top {extremes} ei -',
'Resultant ev out of phase filtered ei -',
'Resultant ev out of bottom {extremes} ei +', 'Resultant ev out of top {extremes} ei +',
'Resultant ev out of phase filtered ei +',
'Average Resultant', 'Unit Circle'],
x_tag = 'x',
y_tag = 'y',
lw=1,
export=True,
path=path,
filename=f'Diachronic Resultant filtered pm inout eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
close=True)
qtplot(f'Diachronic resultant +/- for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
[bm_in_res[0], tm_in_res[0], dm_in_res[0], bp_in_res[0], tp_in_res[0], dp_in_res[0],
t_res[0], np.cos(space)],
[bm_in_res[1], tm_in_res[1], dm_in_res[1], bp_in_res[1], tp_in_res[1], dp_in_res[1],
t_res[1], np.sin(space)],
['Resultant ev in of bottom {extremes} ei -', 'Resultant ev in of top {extremes} ei -',
'Resultant ev in of phase filtered ei -',
'Resultant ev in of bottom {extremes} ei +', 'Resultant ev in of top {extremes} ei +',
'Resultant ev in of phase filtered ei +',
'Average Resultant', 'Unit Circle'],
x_tag = 'x',
y_tag = 'y',
lw=1,
export=True,
path=path,
filename=f'Evolution of Resultant filtered pm in eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
close=True)
qtplot(f'Diachronic resultant +/- for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
[bm_out_res[0], tm_out_res[0], dm_out_res[0], bp_out_res[0], tp_out_res[0], dp_out_res[0],
t_res[0], np.cos(space)],
[bm_out_res[1], tm_out_res[1], dm_out_res[1], bp_out_res[1], tp_out_res[1], dp_out_res[1],
t_res[1], np.sin(space)],
['Resultant ev out of bottom {extremes} ei -', 'Resultant ev out of top {extremes} ei -',
'Resultant ev out of phase filtered ei -',
'Resultant ev out of bottom {extremes} ei +', 'Resultant ev out of top {extremes} ei +',
'Resultant ev out of phase filtered ei +',
'Average Resultant', 'Unit Circle'],
x_tag = 'x',
y_tag = 'y',
lw=1,
export=True,
path=path,
filename=f'Evolution of Resultant filtered pm out eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
close=True)
# qtplot(f'Diachronic ei +/- for eps={round(eps,3):.3f} with dim={dim} with 4 layers',
# [np.array(range(maxdt))]*4,
# [bot_ei, top_ei, dev_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 filtered 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()}')
# 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)

181
evaluation/4Layer Resultant Phase Evolution.py Normal file
View File

@ -0,0 +1,181 @@
#!/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))
phase = lambda x,y: (m.atan2(y,x)+m.pi)%(2*m.pi)-m.pi
H2 = lambda x: -x*m.log2(x)-(1-x)*m.log2(1-x)
extremes = None
maxdt = 300
for dim in [7]:
for eps in eps_space:
path=f'/cloud/Public/_data/neuropercolation/4lay/cons=7-knight_steps=1000100/dim=07/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))
av_diff = np.arccos(all_res)
try:
with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
ei = 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:]
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_center = av_diff
pha_dev = m.pi/32
from_sync = lambda i: True if abs(phase_diff[i])<0.08*m.pi else False if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else from_sync(i-1)
dev_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])
dev_in_ind = [i for i in dev_ind if not from_sync(i)]
dev_out_ind = [i for i in dev_ind if from_sync(i)]
devm_in_ind = [i for i in dev_in_ind if phase_diff[i]<0]
devp_in_ind = [i for i in dev_in_ind if phase_diff[i]>0]
devm_out_ind = [i for i in dev_out_ind if phase_diff[i]<0]
devp_out_ind = [i for i in dev_out_ind if phase_diff[i]>0]
if extremes is None:
extremes = min(len(dev_in_ind),len(dev_out_ind))//1000*100
print(len(dev_out_ind), len(dev_in_ind))
#print(all_res, av_diff)
bot_in_ind = dev_in_ind[ :extremes]
top_in_ind = dev_in_ind[-extremes: ]
bot_out_ind = dev_out_ind[ :extremes]
top_out_ind = dev_out_ind[-extremes: ]
bot_ind = bot_in_ind + bot_out_ind
top_ind = top_in_ind + top_out_ind
bot_res = []
top_res = []
dev_res = []
tot_res = []
bot_ph = []
top_ph = []
dev_ph = []
tot_ph = []
bot_ei = []
top_ei = []
dev_ei = []
tot_ei = []
for dt in range(maxdt):
bot_pha = [abs(phase_diff[i+dt]) for i in bot_ind]
top_pha = [abs(phase_diff[i+dt]) for i in top_ind]
dev_pha = [abs(phase_diff[i+dt]) for i in dev_ind]
tot_pha = np.abs(phase_diff[dt:dt-maxdt])
bot_res.append( norm(resultant(bot_pha)) )
top_res.append( norm(resultant(top_pha)) )
dev_res.append( norm(resultant(dev_pha)) )
tot_res.append( norm(resultant(tot_pha)) )
bot_ph.append( phase(*resultant(bot_pha)) )
top_ph.append( phase(*resultant(top_pha)) )
dev_ph.append( phase(*resultant(dev_pha)) )
tot_ph.append( phase(*resultant(tot_pha)) )
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]) )
dev_ei.append( np.average([ei[i+dt] for i in dev_ind]) )
tot_ei.append( np.average(ei[dt:dt-maxdt]) )
if dt%100==99:
print(f'Done dt={dt}')
qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
[np.array(range(maxdt))]*4,
[bot_res, top_res, dev_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+'plots/',
filename=f'Diachronic Resultant Norm balanced eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
close=True)
qtplot(f'Diachronic resultant phase for dim={dim} with 4 layers',
[np.array(range(maxdt))]*4,
[bot_ph, top_ph, dev_ph, tot_ph],
['bottom {extremes} ei', 'top {extremes} ei',
'all filtered ei', 'Average'],
x_tag = 'dt',
y_tag = 'concentration',
export=True,
path=path+'plots/',
filename=f'Diachronic Resultant Phase balanced 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, dev_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+'plots/',
# filename=f'Diachronic EI balanced 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()}')
# 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)

16
evaluation/plot.py
View File

@ -78,11 +78,12 @@ def plot_execute():
def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_tag=None, colors=None, export=False, path=None, filename=None, lw=4, close=False):
linewidth = lw
#app = qt.mkQApp()
if not close:
app = qt.mkQApp()
ph = qt.plot()
ph.showGrid(x=True,y=True)
ph.setXRange(np.min(spaces), np.max(spaces))
ph.setXRange(min([min(sp) for sp in spaces]), max([max(sp) for sp in spaces]))
# ph.setYRange(0.0, 6)
#ph.setTitle(title='Susceptibility density evolution for different automaton sizes', offset=(1000,500))#.format(dim))
@ -100,7 +101,10 @@ def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_ta
if colors=='rgb':
colors=[__get_color(fac/(len(vals)-1)) for fac in range(len(vals))]
elif colors is None:
colors=['r', 'g', 'b', 'y', 'c', 'm', 'w', (100,100,0), (0,100,100), (100,0,100)]
colors=['r', 'g', 'b', 'y', 'c', 'm', 'w',
(100,100,0), (0,100,100), (100,0,100),
(100,200,0), (0,100,200), (200,0,100),
(200,100,0), (0,200,100), (100,0,200)]
for i in range(len(vals)):
@ -134,7 +138,7 @@ def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_ta
if close:
ph.close()
# app.aboutToQuit.connect(handleAppExit)
# app.exec_()
else:
app.aboutToQuit.connect(handleAppExit)
app.exec_()

2
examples/Simulate1Layer.py
View File

@ -15,7 +15,7 @@ eps_space = np.linspace(0.135,0.15,4)
for dim in [49,100]:
for eps in eps_space:
eps = round(eps,3)
Simulate1Layer(dim,
Simulate2Layers(dim,
eps,
res=2,
path=f'/cloud/Public/_data/neuropercolation/1lay/steps=100000/dim={dim:02}/',

3
neuropercolation/automaton.py
View File

@ -152,8 +152,9 @@ class Neurolattice(CellularAutomatonCreator, abc.ABC):
class Neuropercolation(CellularAutomatonCreator, abc.ABC):
""" Cellular automaton with the evolution rules of conways game of life """
def __init__(self, dim, eps, *args, **kwargs):
def __init__(self, dim, eps, init, *args, **kwargs):
super().__init__(dimension=[dim, dim, 2],
init=init,
neighborhood=VonNeumannNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS))
self._evolution_step = 0
self.epsilon = eps