#!/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 [7]:
    for eps in eps_space:
        path=f'/cloud/Public/_data/neuropercolation/4lay/steps=1000100/dim={dim:02}/'
        
        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))
        
        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)
        
        bot_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[ extremes]
        top_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[-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_eis]
        top_ind = [enum[0] for enum in top_eis]
        
        bot_res = []
        top_res = []
        for dt in range(maxdt):
            bot_pha = [phase_diff[i+dt] for i in bot_ind]
            top_pha = [phase_diff[i+dt] for i in top_ind]
            
            bot_res.append( norm(resultant(bot_pha)) )
            top_res.append( norm(resultant(top_pha)) )
            if dt%100==99:
                print(f'Done dt={dt}')
            
            
        qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
                [np.array(range(maxdt))]*3,
                [bot_res, top_res, [all_res]*maxdt],
                ['Resultant ev of bottom 100 ei', 'Resultant ev of top 100 ei', 'Average Resultant'],
                x_tag = 'dt',
                y_tag = 'concentration',
                export=True,
                path=path,
                filename=f'Diachronic Resultant for eps={round(eps,3)} dim={dim} extremes={extremes}.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)