neuropercolation/evaluation/4Layer Phase to EI Survey.py

#!/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 [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:]
        
            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}_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_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[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',
                export=True,
                path=path,
                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()}')
        
    # 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)