neuropercolation/evaluation/4Layer Bootstrap.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 = None
maxdt = 300

for dim in [9]:
    for eps in eps_space:
        path=f'/cloud/Public/_data/neuropercolation/4lay/cons=27-3diag_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:
            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/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])
        
        if extremes is None:
            extremes = len(orth_ind)//4000*1000

        print(len(orth_ind))
        #print(all_res, av_diff)
        
        
        bot_ind = orth_ind[         :extremes]
        top_ind = orth_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_res = []
        top_res = []
        orth_res = []
        tot_res = []
        
        bot_ei = []
        top_ei = []
        orth_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_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]) )
            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],
                ['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',
                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],
                ['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',
                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)