2023-09-30 17:53:06 +00:00
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Aug 21 14:59:22 2023
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2023-12-14 19:49:44 +00:00
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@author: timofej
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2023-09-30 17:53:06 +00:00
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"""
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import json
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import math as m
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import numpy as np
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from numpy.linalg import norm
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from datetime import datetime
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from random import sample as choose
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from plot import qtplot
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from neuropercolation import Simulate4Layers
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eps_space = list(np.linspace(0.01,0.2,20))
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def resultant(sample):
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phase_x = [m.cos(ind) for ind in sample]
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phase_y = [m.sin(ind) for ind in sample]
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return (np.average(phase_x), np.average(phase_y))
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phase = np.vectorize(lambda x,y: (m.atan2(y,x)+m.pi)%(2*m.pi)-m.pi)
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diff = np.vectorize(lambda x,y: (y-x+m.pi)%(2*m.pi)-m.pi)
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H2 = lambda x: -x*m.log2(x)-(1-x)*m.log2(1-x)
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extremes = None
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maxdt = 300
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stp = 10100
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batch = 0
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for dim in [9]:
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for eps in eps_space[1:41:2]:
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path=f'/cloud/Public/_data/neuropercolation/4lay/cons=27-knight_steps={stp}/dim={dim:02}/batch={batch}/'
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try:
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with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'r', encoding='utf-8') as f:
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phase_diff = json.load(f)
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except:
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with open(path+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
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activation = json.load(f)[100:]
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osc = list(zip(*activation))
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phase_abs = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
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phase_diff = diff(phase_abs[0],phase_abs[1])
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with open(path+f"eps={round(eps,3):.3f}_phase_diff.txt", 'w', encoding='utf-8') as f:
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json.dump(list(phase_diff), f, indent=1)
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all_res = norm(resultant(phase_diff))
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av_diff = np.arccos(all_res)
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try:
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with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'r', encoding='utf-8') as f:
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ei = json.load(f)
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except:
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with open(path+f"eps={round(eps,3):.3f}_channels.txt", 'r', encoding='utf-8') as f:
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channels = json.load(f)[100:]
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ei = [np.sum(cha)*(1-H2(eps)) for cha in channels]
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with open(path+f"eps={round(eps,3):.3f}_ei.txt", 'w', encoding='utf-8') as f:
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json.dump(ei, f, indent=1)
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pha_center = av_diff
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pha_dev = m.pi/32
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from_sync = lambda i: True if abs(phase_diff[i])<0.08*m.pi else False if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else from_sync(i-1) if i>0 else None
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to_sync = lambda i: True if abs(phase_diff[i])<0.08*m.pi else False if 0.42*m.pi<abs(phase_diff[i])<0.58*m.pi else to_sync(i+1) if i+1<len(phase_diff) else None
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dev_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])
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dev_00 = [i for i in dev_ind if from_sync(i) and to_sync(i) ]
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dev_01 = [i for i in dev_ind if from_sync(i) and to_sync(i) is False]
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dev_10 = [i for i in dev_ind if from_sync(i) is False and to_sync(i) ]
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dev_11 = [i for i in dev_ind if from_sync(i) is False and to_sync(i) is False]
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lens = [len(dev_00),len(dev_01),len(dev_10),len(dev_11)]
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#if not extremes:
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extremes = [100]*4 #[l//2 for l in lens]
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print(lens)
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#print(all_res, av_diff)
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# bot_00 = dev_00[:extremes[0]]
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# bot_01 = dev_01[:extremes[1]]
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# bot_10 = dev_10[:extremes[2]]
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# bot_11 = dev_11[:extremes[3]]
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top_00 = dev_00[-extremes[0]:]
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top_01 = dev_01[-extremes[1]:]
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top_10 = dev_10[-extremes[2]:]
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top_11 = dev_11[-extremes[3]:]
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with open(path+f"eps={round(eps,3):.3f}_states.txt", 'r', encoding='utf-8') as f:
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states = json.load(f)[100:]
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with open(path+f"eps={round(eps,3):.3f}_coupling.txt", 'r', encoding='utf-8') as f:
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coupling = json.load(f)
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coupling = [tuple(edge) for edge in coupling]
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for top,name in [(top_00,'top_00'),(top_01,'top_01'),(top_10,'top_10'),(top_11,'top_11')]:
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for i in top:
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causal_init = states[i].translate(str.maketrans('', '', '.-='))
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path_c = path+f'causal_maxdt={maxdt}/{name}/{i:0{len(str(stp))}}/'
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try:
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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phasediff = json.load(f)
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except:
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sim=Simulate4Layers(dim,
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eps,
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coupling=coupling,
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init=causal_init,
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noeffect=0,
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steps=maxdt,
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draw=None,
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save='all',
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path=path_c,
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)
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with open(path_c+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
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activation = json.load(f)
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osc = list(zip(*activation))
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phase_abs = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
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phasediff = diff(phase_abs[0],phase_abs[1])
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with open(path_c+f"eps={round(eps,3):.3f}_phase_diff.txt", 'w', encoding='utf-8') as f:
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json.dump(list(phasediff), f, indent=1)
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for top,name in [(top_00,'top_00'),(top_01,'top_01'),(top_10,'top_10'),(top_11,'top_11')]:
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for i in top:
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causal_init = states[i].translate(str.maketrans('', '', '.-='))
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path_c = path+f'original_maxdt={maxdt}/{name}/{i:0{len(str(stp))}}/'
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try:
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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phasediff = json.load(f)
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except:
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sim=Simulate4Layers(dim,
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eps,
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coupling=coupling,
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init=causal_init,
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noeffect=-1,
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steps=maxdt,
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draw=None,
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save='all',
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path=path_c,
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)
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with open(path_c+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
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activation = json.load(f)
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osc = list(zip(*activation))
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phase_abs = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
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phasediff = diff(phase_abs[0],phase_abs[1])
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with open(path_c+f"eps={round(eps,3):.3f}_phase_diff.txt", 'w', encoding='utf-8') as f:
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json.dump(list(phasediff), f, indent=1)
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# bot_res = []
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top_res = []
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dis_res = []
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tot_res = []
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# bot_ph = []
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top_ph = []
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dis_ph = []
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tot_ph = []
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# bot_ei = []
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top_ei = []
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# dev_ei = []
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tot_ei = []
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for dt in range(maxdt):
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# bot_pha = [[abs(phase_diff[i+dt]) for i in bot_00],
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# [abs(phase_diff[i+dt]) for i in bot_01],
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# [abs(phase_diff[i+dt]) for i in bot_10],
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# [abs(phase_diff[i+dt]) for i in bot_11]]
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top_pha = [[abs(phase_diff[i+dt]) for i in top_00],
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[abs(phase_diff[i+dt]) for i in top_01],
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[abs(phase_diff[i+dt]) for i in top_10],
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[abs(phase_diff[i+dt]) for i in top_11]]
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dis_00 = []
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dis_01 = []
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dis_10 = []
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dis_11 = []
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for i in top_00:
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path_c = path+f'causal_maxdt={maxdt}/top_00/{i:0{len(str(stp))}}/'
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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dis_00.append(abs(json.load(f)[dt]))
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for i in top_01:
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path_c = path+f'causal_maxdt={maxdt}/top_01/{i:0{len(str(stp))}}/'
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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dis_01.append(abs(json.load(f)[dt]))
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for i in top_10:
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path_c = path+f'causal_maxdt={maxdt}/top_10/{i:0{len(str(stp))}}/'
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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dis_10.append(abs(json.load(f)[dt]))
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for i in top_11:
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path_c = path+f'causal_maxdt={maxdt}/top_11/{i:0{len(str(stp))}}/'
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with open(path_c+f'eps={round(eps,3):.3f}_phase_diff.txt', 'r', encoding='utf-8') as f:
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dis_11.append(abs(json.load(f)[dt]))
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dis_pha = [dis_00, dis_01, dis_10, dis_11]
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tot_pha = np.abs(phase_diff[dt:dt-maxdt])
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# bot_res.append( [norm(resultant(bot_pha[i])) for i in range(4)] )
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top_res.append( [norm(resultant(top_pha[i])) for i in range(4)] )
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dis_res.append( [norm(resultant(dis_pha[i])) for i in range(4)] )
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tot_res.append( norm(resultant(tot_pha)) )
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# bot_ph.append( [phase(*resultant(bot_pha[i])) for i in range(4)] )
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top_ph.append( [phase(*resultant(top_pha[i])) for i in range(4)] )
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dis_ph.append( [phase(*resultant(dis_pha[i])) for i in range(4)] )
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tot_ph.append( phase(*resultant(tot_pha)) )
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# bot_ei.append( [np.average([ei[i+dt] for i in bot]) for bot in [bot_00,bot_01,bot_10,bot_11]] )
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top_ei.append( [np.average([ei[i+dt] for i in top]) for top in [top_00,top_01,top_10,top_11]] )
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# dev_ei.append( [np.average([ei[i+dt] for i in dev]) for dev in [dev_00,dev_01,dev_10,dev_11]] )
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tot_ei.append( np.average(ei[dt:dt-maxdt]) )
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if dt%10==0:
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print(f'Done dt={dt}')
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# bot_res = list(zip(*bot_res))
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top_res = list(zip(*top_res))
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dis_res = list(zip(*dis_res))
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# bot_ph = list(zip(*bot_ph))
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top_ph = list(zip(*top_ph))
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dis_ph = list(zip(*dis_ph))
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# bot_ei = list(zip(*bot_ei))
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top_ei = list(zip(*top_ei))
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# dev_ei = list(zip(*dev_ei))
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plotpath = path+'4waycausal/'
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qtplot(f'Diachronic resultant sync to sync for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_res[0], dis_res[0], tot_res],
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['sync to sync top {extremes} ei',
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'sync to sync dis {extremes} ei', 'Average Resultant'],
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x_tag = 'dt',
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y_tag = 'concentration',
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export=True,
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path=plotpath,
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filename=f'Diachronic Resultant Norm eps={round(eps,3):.3f} sts dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant sync to orth for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_res[1], dis_res[1], tot_res],
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['sync to orth top {extremes} ei', 'sync to orth dis {extremes} ei',
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'Average Resultant'],
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x_tag = 'dt',
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y_tag = 'concentration',
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export=True,
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path=plotpath,
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filename=f'Diachronic Resultant Norm eps={round(eps,3):.3f} sto dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant orth to sync for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_res[2], dis_res[2], tot_res],
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['orth to sync top {extremes} ei', 'orth to sync dis {extremes} ei',
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'Average Resultant'],
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x_tag = 'dt',
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y_tag = 'concentration',
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export=True,
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path=plotpath,
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filename=f'Diachronic Resultant Norm eps={round(eps,3):.3f} ots dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant orth to orth for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_res[3], dis_res[3], tot_res],
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['orth to orth top {extremes} ei', 'orth to orth dis {extremes} ei',
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'Average Resultant'],
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x_tag = 'dt',
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y_tag = 'concentration',
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export=True,
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path=plotpath,
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filename=f'Diachronic Resultant Norm eps={round(eps,3):.3f} oto dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant phase sync to sync for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_ph[0], dis_ph[0], tot_ph],
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['sync to sync top {extremes} ei', 'sync to sync dis {extremes} ei',
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'Average'],
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x_tag = 'dt',
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y_tag = 'phase',
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export=True,
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path=plotpath,
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filename=f'Diachronic Resultant Phase eps={round(eps,3):.3f} sts dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant phase sync to orth for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_ph[1], dis_ph[1], tot_ph],
|
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|
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['sync to orth top {extremes} ei', 'sync to orth dis {extremes} ei',
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'Average'],
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x_tag = 'dt',
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y_tag = 'phase',
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export=True,
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|
path=plotpath,
|
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|
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|
filename=f'Diachronic Resultant Phase eps={round(eps,3):.3f} sto dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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|
qtplot(f'Diachronic resultant phase orth to sync for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*3,
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[top_ph[2], dis_ph[2], tot_ph],
|
|
|
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|
['orth to sync top {extremes} ei', 'orth to sync dos {extremes} ei',
|
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|
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'Average'],
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|
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|
x_tag = 'dt',
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|
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|
y_tag = 'phase',
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|
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|
export=True,
|
|
|
|
|
path=plotpath,
|
|
|
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|
filename=f'Diachronic Resultant Phase eps={round(eps,3):.3f} ots dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
|
|
|
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|
close=True)
|
|
|
|
|
qtplot(f'Diachronic resultant phase orth to orth for dim={dim} with 4 layers',
|
|
|
|
|
[np.array(range(maxdt))]*3,
|
|
|
|
|
[top_ph[3], dis_ph[3], tot_ph],
|
|
|
|
|
['orth to orth top {extremes} ei', 'orth to orth dis {extremes} ei',
|
|
|
|
|
'Average'],
|
|
|
|
|
x_tag = 'dt',
|
|
|
|
|
y_tag = 'phase',
|
|
|
|
|
export=True,
|
|
|
|
|
path=plotpath,
|
|
|
|
|
filename=f'Diachronic Resultant Phase eps={round(eps,3):.3f} oto dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
|
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close=True)
|
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# qtplot(f'Diachronic ei for dim={dim} with 4 layers',
|
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# [np.array(range(maxdt))]*4,
|
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|
# [bot_ei, top_ei, dev_ei, tot_ei],
|
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|
# ['EI ev of bottom {extremes} ei', 'EI ev of top {extremes} ei',
|
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# 'EI ev of phase filtered ei', 'Average EI'],
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# x_tag = 'dt',
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# y_tag = 'average ei',
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# export=True,
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|
# path=path+'plots/',
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|
# filename=f'Diachronic EI balanced for eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
|
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|
# close=True)
|
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|
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
|
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|
# qtplot(f'Resultant and EI evolution for dim={dim} with 4 layers',
|
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|
# [[0]+eps_space]*2,
|
|
|
|
|
# [max(av_ei)*diff_res, av_ei],
|
|
|
|
|
# ['Resultant', 'avEI'],
|
|
|
|
|
# export=True,
|
|
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|
|
# path=path,
|
|
|
|
|
# filename=f'Resultant and EI for dim={dim}.png',
|
|
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|
# close=True)
|
|
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|
