#!/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 datetime import datetime

from plot import qtplot

eps_space = list(np.linspace(0.005,0.5,100))

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)

for dim in [7]:
    diff_res = [0]
    av_ei = [0]
    for eps in eps_space:
        path=f'/cloud/Public/_data/neuropercolation/4lay/steps=100000/dim={dim:02}/'
        
        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
        
        res = np.linalg.norm(resultant(phase_diff))
        diff_res.append(res)
        
        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]
        av_ei.append(np.average(ei))
        
        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,
           [diff_res, av_ei],
           ['Resultant', 'avEI'],
           export=True,
           path=path,
           filename=f'Resultant and EI for dim={dim}.png',
           close=True)