99 lines
2.3 KiB
Python
99 lines
2.3 KiB
Python
|
# -*- 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
|
||
|
|
|
||
|
|
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=100100/'
|
||
|
|
suffix = ''
|
||
|
|
|
||
|
|
chi = chr(967)
|
||
|
|
vareps = chr(949)
|
||
|
|
varphi = chr(981)
|
||
|
|
|
||
|
|
vals = [[],[]]
|
||
|
|
|
||
|
|
runsteps = 1000100
|
||
|
|
|
||
|
|
eps_space = np.linspace(0.005, 0.5, 100)
|
||
|
|
eps_space = eps_space[1::2]
|
||
|
|
|
||
|
|
dims = list(range(3,10,2))#+[16,49]
|
||
|
|
|
||
|
|
mode='density'
|
||
|
|
ma=[]
|
||
|
|
s=[]
|
||
|
|
k=[]
|
||
|
|
mk=[]
|
||
|
|
PHI=[]
|
||
|
|
lastkurt=None
|
||
|
|
for dim in dims:
|
||
|
|
phis=[]
|
||
|
|
con_gap = 3
|
||
|
|
cons = [(n,(2*n+m)%dim) for n in range(dim) for m in range(0,dim-2,con_gap)]
|
||
|
|
dimpath = new_folder(path + f'dim={dim:02}/')
|
||
|
|
for epsilon in eps_space:
|
||
|
|
try:
|
||
|
|
with open(dimpath+f"eps={round(epsilon,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
|
||
|
|
ei = np.array(json.load(f)[100:])
|
||
|
|
except:
|
||
|
|
print(f'Calcing phi for eps={epsilon}')
|
||
|
|
with open(dimpath+f"eps={round(epsilon,3):.3f}_channels.txt", 'r', encoding='utf-8') as f:
|
||
|
|
channels = np.array(json.load(f)[100:])
|
||
|
|
|
||
|
|
ei = np.sum(channels,axis=0)*(1-H2(epsilon))
|
||
|
|
ei=list(ei)
|
||
|
|
with open(dimpath+f"eps={round(epsilon,3):.3f}_ei.txt", 'w', encoding='utf-8') as f:
|
||
|
|
json.dump(list(ei),f, ensure_ascii=False,indent=1)
|
||
|
|
|
||
|
|
phi=np.average(ei)
|
||
|
|
phis.append(phi)
|
||
|
|
PHI.append(phis)
|
||
|
|
#%%
|
||
|
|
qtplot(f"Mean effect integration over noise level",
|
||
|
|
[[0]+list(eps_space)]*len(dims),
|
||
|
|
[[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}',
|
||
|
|
export=True,
|
||
|
|
path=dimpath+"",
|
||
|
|
filename=f'eps={round(epsilon,3):.3f}_evolution.png',
|
||
|
|
close=False)
|
||
|
|
|
||
|
|
mode = 'density'
|
||
|
|
#%%
|