neuropercolation/evaluation/2Layer Activity.py

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 30 14:25:12 2022

@author: timofej
"""

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)

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(dimpath+f"eps={round(epsilon,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
            activation = np.array(json.load(f)[:500])
        activation = list(zip(*activation))
        
        qtplot(f"Activity time series for eps={round(epsilon,3):.3f}",
                [list(range(500))]*2,
                activation,
                x_tag = 'time step',
                y_tag = f'activity density',
                y_range = (-1,1),
                export=True,
                path=dimpath+"evolution/",
                filename=f'eps={round(epsilon,3):.3f}_evolution.png',
                close=True) 

mode = 'density'
#%%