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Add riding eps example

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This commit is contained in:
timofej 2023-12-10 21:47:15 +00:00
parent 6513f4e2b4
commit 076493fc6d
13 changed files with 291 additions and 54 deletions
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3
evaluation/1Layer Suscept.py
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@ -51,6 +51,7 @@ def __calc_chi(mode='density'):
lastkurt=None
for dim in dims:
try:
1/0
with open(path+f"magnets/magnetisation_{mode}_dim={dim:02}.txt", 'r', encoding='utf-8') as f:
magnet = json.load(f)
with open(path+f"suscepts/susceptibility_{mode}_dim={dim:02}.txt", 'r', encoding='utf-8') as f:
@ -87,7 +88,7 @@ def __calc_chi(mode='density'):
else:
raise NotImplementedError
mag = round(abs(mag),6)
mag = round(abs(dmag),6)
fluct = round(mag2-mag**2,6)
tail = round(mag4/mag2**2,6)

71
evaluation/2Layer Sonic.py Normal file
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@ -0,0 +1,71 @@
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 30 14:25:12 2022
@author: timof
"""
import sys
import os
import json
# from plot import qtplot
from scipy.io import wavfile
import math as m
import numpy as np
vect = np.vectorize
@vect
def log2(x):
try:
return m.log2(x)
except ValueError:
if x==0:
return float(0)
else:
raise
def new_folder(path):
if not os.path.exists(path):
os.makedirs(path)
return path
phase = np.vectorize(lambda x,y: (m.atan2(y,x)+m.pi)%(2*m.pi)-m.pi)
diff = np.vectorize(lambda x,y: (y-x+m.pi)%(2*m.pi)-m.pi)
H2 = lambda x: -x*m.log2(x)-(1-x)*m.log2(1-x)
path = '/cloud/Public/_data/neuropercolation/2lay/steps=500000_rising/'
suffix = ''
chi = chr(967)
vareps = chr(949)
vals = [[],[]]
runsteps = 1000100
eps_space = np.linspace(0.005, 0.5, 100)
eps_space = eps_space[1::2]
dims = [121]#+[16,49]
mode='density'
ma=[]
s=[]
k=[]
mk=[]
lastkurt=None
for dim in dims:
dimpath = new_folder(path + f'dim={dim:03d}/')
for epsilon in [0.000]:
with open(dimpath+f"eps={round(epsilon,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
activation = np.array(json.load(f)[::-1])
# activation = list(zip(*activation))
wavfile.write(dimpath+f"eps={round(epsilon,3):.3f}_activation_48k.wav_rev",48000,activation)

10
evaluation/2Layer fft.py
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@ -345,13 +345,13 @@ qtplot(f'Q facor for odd automaton sizes',
# FWHM=json.load(f)
qtplot(f'Dominant frequency',
[[0]+list(eps_space[:-1])]*3,
[[0]+FMAX[-1],[0]+LHM[-1],[0]+UHM[-1]],
[[0]+list(eps_space[:20])]*3,
[[0]+FMAX[:20],[0]+LHM[:20],[0]+UHM[:20]],
# [f'dim={dim}x{dim}' for dim in dims],
['dominant frequency', 'lesser half maximum', 'upper half maximum'],
#colors=['g','r','r'],
['FMAX', 'FMAX-FWHM/2', 'FMAX+FWHM/2'],
colors=['r','r','g'],
y_tag = 'frequency f',
y_log = True,
y_log = False,
export=True,
path=sigpath,
filename=f'FMAX+FWHMto50_plot_{parts}parts_accum={accum}.png',

1
evaluation/2Layer phiplot.py
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@ -89,6 +89,7 @@ qtplot(f"Mean effect integration over noise level",
[[0]+phi for phi in PHI[::-1]],
[f'dim={dim:02d}x{dim:02d}' for dim in dims[::-1]],
y_tag = f'effect integration {varphi}',
x_tag = f'noise level {vareps}',
export=True,
path=dimpath+"",
filename=f'eps={round(epsilon,3):.3f}_evolution.png',

77
evaluation/4Layer Sonic.py Normal file
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@ -0,0 +1,77 @@
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 30 14:25:12 2022
@author: timof
"""
import sys
import os
import json
from plot import qtplot
import math as m
import numpy as np
vect = np.vectorize
@vect
def log2(x):
try:
return m.log2(x)
except ValueError:
if x==0:
return float(0)
else:
raise
def new_folder(path):
if not os.path.exists(path):
os.makedirs(path)
return path
path = '/cloud/Public/_data/neuropercolation/4lay/cons=27-knight_steps=100100_causal/dim=09/batch=0/'
suffix = ''
chi = chr(967)
vareps = chr(949)
vals = [[],[]]
runsteps = 1000100
eps_space = np.linspace(0.005, 0.5, 100)
eps_space = eps_space[1::2]
dims = list(range(3,10))#+[16,49]
mode='density'
ma=[]
s=[]
k=[]
mk=[]
lastkurt=None
for dim in dims[-1:]:
dimpath = new_folder(path + f'dim={dim:02}/')
for epsilon in eps_space[:]:
with open(path+f"eps={round(epsilon,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
phase_diff = np.array(json.load(f)[:500])
with open(path+f"eps={round(epsilon,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
phase_diff = np.array(json.load(f)[:500])
qtplot(f"Phase relation time series for eps={round(epsilon,3):.3f}",
[list(range(500))]*2,
[phase_diff],
x_tag = 'time step',
y_tag = f'phase diffe´rence',
y_range = (-m.pi,m.pi),
export=True,
path=dimpath+"evolution/",
filename=f'eps={round(epsilon,3):.3f}_evolution.png',
close=False)
mode = 'density'
#%%

54
evaluation/4laysign.py
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@ -71,20 +71,20 @@ for _eps in _epses[11:21]:
# sign_p_list.append(sign_p)
# with open(savepath+f"phasediff_means.txt", 'w', encoding='utf-8') as f:
# json.dump(meanlist, f, indent=1)
# with open(savepath+f"phasediff_stds.txt", 'w', encoding='utf-8') as f:
# json.dump(stdlist, f, indent=1)
# with open(savepath+f"phasediff_integrals.txt", 'w', encoding='utf-8') as f:
# json.dump(integrallist, f, indent=1)
# with open(savepath+f"phasediff_cohends.txt", 'w', encoding='utf-8') as f:
# json.dump(cohendlist, f, indent=1)
# with open(savepath+f"phasediff_norm_ps.txt", 'w', encoding='utf-8') as f:
# json.dump(norm_p_list, f, indent=1)
# with open(savepath+f"phasediff_ttest_ps.txt", 'w', encoding='utf-8') as f:
# json.dump(ttest_p_list, f, indent=1)
# with open(savepath+f"phasediff_sign_ps.txt", 'w', encoding='utf-8') as f:
# json.dump(sign_p_list, f, indent=1)
with open(savepath+f"phasediff_means.txt", 'w', encoding='utf-8') as f:
json.dump(meanlist, f, indent=1)
with open(savepath+f"phasediff_stds.txt", 'w', encoding='utf-8') as f:
json.dump(stdlist, f, indent=1)
with open(savepath+f"phasediff_integrals.txt", 'w', encoding='utf-8') as f:
json.dump(integrallist, f, indent=1)
with open(savepath+f"phasediff_cohends.txt", 'w', encoding='utf-8') as f:
json.dump(cohendlist, f, indent=1)
with open(savepath+f"phasediff_norm_ps.txt", 'w', encoding='utf-8') as f:
json.dump(norm_p_list, f, indent=1)
with open(savepath+f"phasediff_ttest_ps.txt", 'w', encoding='utf-8') as f:
json.dump(ttest_p_list, f, indent=1)
with open(savepath+f"phasediff_sign_ps.txt", 'w', encoding='utf-8') as f:
json.dump(sign_p_list, f, indent=1)
# stdlowlist = [meanlist[eps] - stdlist[eps] for eps in range(len(meanlist))]
# stdhighlist = [meanlist[eps] + stdlist[eps] for eps in range(len(meanlist))]
@ -105,19 +105,19 @@ for _eps in _epses[11:21]:
filename=f'Resultant reduction disconnect eps={_eps} dim={_dim} extremes={_ext}.png',
close=True)
# qtplot(f'Mean causal phase reduction for dt={_dt} dim={_dim} with 4 layers',
# [_epsilons]*3,
# [stdlowlist, stdhighlist, meanlist],
# ['Low standard deviation',
# 'High standard deviation',
# 'Mean'],
# colors=['r','r','g'],
# x_tag = f'noise level {chr(949)}',
# y_tag = 'abs phase reduction',
# export=True,
# path=savepath,
# filename=f'Mean phase reduction dim={_dim} extremes={_ext}.png',
# close=True)
qtplot(f'Mean causal phase reduction for dt={_dt} dim={_dim} with 4 layers',
[_epsilons]*3,
[stdlowlist, stdhighlist, meanlist],
['Low standard deviation',
'High standard deviation',
'Mean'],
colors=['r','r','g'],
x_tag = f'noise level {chr(949)}',
y_tag = 'abs phase reduction',
export=True,
path=savepath,
filename=f'Mean phase reduction dim={_dim} extremes={_ext}.png',
close=True)
# qtplot(f'Phase reduction probability for dt={_dt} dim={_dim} with 4 layers',
# [_epsilons],
# [integrallist],

49
evaluation/phi.py
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@ -13,11 +13,12 @@ from numpy.linalg import norm
from datetime import datetime
from random import sample as choose
from random import random
from numba import jit, njit, prange
import itertools
#from numba import jit, njit, prange
from plot import qtplot
# from plot import qtplot
from neuropercolation import Simulate4Layers
# from neuropercolation import Simulate4Layers
eps_space = list(np.linspace(0.01,0.2,20))
def new_folder(path):
@ -29,7 +30,7 @@ phase = np.vectorize(lambda x,y: (m.atan2(y,x)+m.pi)%(2*m.pi)-m.pi)
diff = np.vectorize(lambda x,y: (y-x+m.pi)%(2*m.pi)-m.pi)
H2 = lambda x: -x*m.log2(x)-(1-x)*m.log2(1-x)
@njit
def neighbor(digit0, digit1, lenght):
layer = int(lenght)
dim = int(np.sqrt(layer))
@ -43,9 +44,11 @@ def neighbor(digit0, digit1, lenght):
else:
return False
@njit
def kcomb(zp,zm):
if zp>2:
if zp+zm>5:
val=None
elif zp>2:
val=1
elif zm>2:
val=0
@ -62,7 +65,24 @@ def kcomb(zp,zm):
else:
raise NotImplementedError(zp,zm)
return val
#%%
neighbourcomb = [[(i,j) for j in range(6)] for i in range(6)]
probcombs = [[kcomb(*n) for n in row] for row in neighbourcomb]
posscombs = {val for row in probcombs for val in row}
probcombs1 = [kcomb(n,4-n) for n in range(5)]
eta = lambda eps: 1-eps
etastar = lambda eps,kp: kp*(1-eps)+(1-kp)*eps
epsstar = lambda eps,kp: kp*eps+(1-kp)*(1-eps)
KL = lambda eps,kp: eta(eps)*m.log2(eta(eps)/etastar(eps,kp)) + eps*m.log2(eps/epsstar(eps,kp)) if kp is not None else 0
phi_mat = lambda eps: [[KL(eps,kcomb(i,j)) for j in range(6)] for i in range(6)]
phi_vals = lambda eps,df=4: sorted({sum([KL(eps,kp) for kp in kps]) for kps in itertools.product(*[[1,0.5]]*df)})
phi_sing = lambda eps: KL(eps,kcomb(1,0))
phi_lims = lambda eps: [phi_sing(eps)+val for val in phi_vals(eps)]
#%%
path = new_folder('/cloud/Public/_data/neuropercolation/1lay/mips/')
@ -125,22 +145,29 @@ def MIP(dim,statestr,eps):
return mip,lophi
def calc_mips(dim,eps):
mip = []
def calc_mips(dim,eps,save=True):
mips = [[] for i in range(5)]
statestr='0'*dim**2
lims = phi_lims(eps)
# statestr=statestr.translate(str.maketrans('','','.-='))
for parti in range(1,2**(dim**2-1)):
partstr = bin(parti)[2:].zfill(dim**2)
curphi = phi(dim,statestr,partstr,eps)
mip.append(round(curphi,6))
for cha,mip in enumerate(mips):
if curphi<lims[cha]:
mip.append(parti)
print(f'Part {partstr} in mips[{cha}] with phi={curphi}')
mipath = new_folder(path+f'dim={dim:02d}/')
with open(mipath+f"eps={round(eps,3):.3f}_mips.txt", 'w', encoding='utf-8') as f:
json.dump(mip, f, indent=1)
for cha,mip in enumerate(mips):
with open(mipath+f"eps={round(eps,3):.3f}_mipis_{cha}.txt", 'w', encoding='utf-8') as f:
json.dump(mip, f, indent=1) if save else None
return mips
def smartMIP(dim,statestr,eps):
lophi=np.inf
mip = []
phi_lims = phi_vals(eps)
for parti in range(0,dim**2):
partstr = bin(2**parti)[2:].zfill(dim**2)

5
evaluation/plots/bootstrap_plot.py
View File

@ -23,7 +23,8 @@ for eps in eps_ep:
boots.append(ps)
lower=[0.001]*len(ps)
higher=[0.999]*len(ps)
qtplot(f"Bootstrapping p-value for noise level {eps}",[range(dt+1)],boots[-1:],[f''],y_tag='p-value',y_log=False)
qtplot(f"Bootstrapping p-value for noise level {eps}",[range(dt+1)]*3,[lower,higher,ps],[f'right-sided p-value','p=0.999 limit','p=0.001 limit'],colors=['w','r','g'],y_tag='p-value',y_log=False)

25
evaluation/plots/significance_plot.py Normal file
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@ -0,0 +1,25 @@
from plot import qtplot
import json
import math as ma
import numpy as np
path='/cloud/Public/_data/neuropercolation/4lay/cons=27-knight_steps=1000100_diares/dim=09/batch=0/allpos_ei_samples=10000/dt=1/'
eps_sp=np.linspace(0.005,0.2,40)
eps_ep=eps_sp
extremes=10000
strength=100000
savepath=path+f'extremes={extremes}_bootstrength={strength}/'
with open(path+f"phasediff_means.txt", 'r', encoding='utf-8') as f:
means=json.load(f)
with open(path+f"phasediff_stds.txt", 'r', encoding='utf-8') as f:
stds=json.load(f)
hirim=[means[i]+stds[i] for i in range(len(means))]
lorim=[means[i]-stds[i] for i in range(len(means))]
qtplot(f'Reduction of phase deviation to noise level',[[0]+list(eps_sp[:40])]*3,[[0]+lorim,[0]+means,[0]+hirim],['mean-std','mean','mean+std'],colors=['r','g','r'],y_tag='phase difference',y_log=False)

29
examples/Simulate2Layers rising eps.py Normal file
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@ -0,0 +1,29 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 18 19:05:04 2023
@author: astral
"""
from datetime import datetime
import numpy as np
from neuropercolation import Simulate2Layers
eps_space = np.linspace(0.005,0.5,100)
#eps_space = np.linspace(0.135,0.15,4)
dims=list(range(21,27,2))
for dim in [33]:
for eps in [0]:
eps = round(eps,3)
sim = Simulate2Layers(dim,
eps,
steps=5000,
draw='pygame',
save='simple',
path=f'/cloud/Public/_data/neuropercolation/2lay/steps=500000_rising/dim={dim:02}/',
rising=True,
fps=60,
)
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')

12
examples/Simulate4Layers.py
View File

@ -19,7 +19,7 @@ eps_space = np.linspace(0.005,0.5,100)
#eps_space = np.linspace(0.135,0.15,4)
stp = 1000100
dims = range(9,10)
dims = range(25,26)
batches = 1
path = f'/cloud/Public/_data/neuropercolation/4lay/cons=27-knight_steps=1000100_diares/dim=09/batch=0/'
@ -40,7 +40,7 @@ for dim in dims:
for batch in range(max_bat, max_bat+batches):
savepath = dimpath + f'batch={batch}'
new_folder(savepath)
for eps in [0.3]:#list(eps_space[61::2]):
for eps in [0.05]:#list(eps_space[61::2]):
eps = round(eps,3)
initstate = [[0,0],[0,0]]
sim = Simulate4Layers(dim,
@ -49,10 +49,10 @@ for dim in dims:
init=initstate,
steps=stp,
noeffect=-1,
#fps=20,
draw=None,
res=6,
save='all',
fps=60,
draw='pygame',
res=10,
save=None,
path=path,
)
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')

5
neuropercolation/display.py
View File

@ -248,6 +248,7 @@ class Simulate2Layers:
save=None,
path='/cloud/Public/_data/neuropercolation/test/',
state_to_color_cb=None,
rising=False,
*args, **kwargs):
"""
Creates a window to render a 2D CellularAutomaton.
@ -275,6 +276,7 @@ class Simulate2Layers:
self.__state_list = []
self.__activation_list = []
self.__channel_list = []
self.__rising = rising
self.run(evolutions_per_second=fps, last_evolution_step=steps)
def run(self,
@ -293,6 +295,9 @@ class Simulate2Layers:
while self.__active and self._not_at_the_end(last_evolution_step):
time_ca_start = time.time()
self._cellular_automaton.evolve(evolutions_per_draw)
if self.__rising:
self._cellular_automaton.epsilon = self._cellular_automaton.evolution_step/last_evolution_step*0.5
self._track()
if self.__draw_engine is not None:
time_ca_end = time.time()