181 lines
6.9 KiB
Python
181 lines
6.9 KiB
Python
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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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@author: astral
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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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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 = lambda x,y: (m.atan2(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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for dim in [7]:
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for eps in eps_space:
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path=f'/cloud/Public/_data/neuropercolation/4lay/cons=7-knight_steps=1000100/dim=07/batch=0/'
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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 = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
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phase_diff = (phase[1]-phase[0]+m.pi)%(2*m.pi)-m.pi
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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)
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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_in_ind = [i for i in dev_ind if not from_sync(i)]
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dev_out_ind = [i for i in dev_ind if from_sync(i)]
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devm_in_ind = [i for i in dev_in_ind if phase_diff[i]<0]
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devp_in_ind = [i for i in dev_in_ind if phase_diff[i]>0]
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devm_out_ind = [i for i in dev_out_ind if phase_diff[i]<0]
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devp_out_ind = [i for i in dev_out_ind if phase_diff[i]>0]
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if extremes is None:
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extremes = min(len(dev_in_ind),len(dev_out_ind))//1000*100
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print(len(dev_out_ind), len(dev_in_ind))
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#print(all_res, av_diff)
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bot_in_ind = dev_in_ind[ :extremes]
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top_in_ind = dev_in_ind[-extremes: ]
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bot_out_ind = dev_out_ind[ :extremes]
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top_out_ind = dev_out_ind[-extremes: ]
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bot_ind = bot_in_ind + bot_out_ind
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top_ind = top_in_ind + top_out_ind
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bot_res = []
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top_res = []
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dev_res = []
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tot_res = []
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bot_ph = []
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top_ph = []
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dev_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_ind]
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top_pha = [abs(phase_diff[i+dt]) for i in top_ind]
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dev_pha = [abs(phase_diff[i+dt]) for i in dev_ind]
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tot_pha = np.abs(phase_diff[dt:dt-maxdt])
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bot_res.append( norm(resultant(bot_pha)) )
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top_res.append( norm(resultant(top_pha)) )
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dev_res.append( norm(resultant(dev_pha)) )
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tot_res.append( norm(resultant(tot_pha)) )
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bot_ph.append( phase(*resultant(bot_pha)) )
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top_ph.append( phase(*resultant(top_pha)) )
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dev_ph.append( phase(*resultant(dev_pha)) )
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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_ind]) )
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top_ei.append( np.average([ei[i+dt] for i in top_ind]) )
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dev_ei.append( np.average([ei[i+dt] for i in dev_ind]) )
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tot_ei.append( np.average(ei[dt:dt-maxdt]) )
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if dt%100==99:
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print(f'Done dt={dt}')
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qtplot(f'Diachronic resultant for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*4,
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[bot_res, top_res, dev_res, tot_res],
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['Resultant ev of bottom {extremes} ei', 'Resultant ev of top {extremes} ei',
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'Resultant ev of phase filtered 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=path+'plots/',
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filename=f'Diachronic Resultant Norm balanced eps={round(eps,3):.3f} dim={dim} extremes={extremes} roll{pha_dev:.3f}.png',
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close=True)
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qtplot(f'Diachronic resultant phase for dim={dim} with 4 layers',
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[np.array(range(maxdt))]*4,
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[bot_ph, top_ph, dev_ph, tot_ph],
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['bottom {extremes} ei', 'top {extremes} ei',
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'all filtered ei', 'Average'],
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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=path+'plots/',
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filename=f'Diachronic Resultant Phase balanced eps={round(eps,3):.3f} 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,
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# [max(av_ei)*diff_res, av_ei],
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# ['Resultant', 'avEI'],
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# export=True,
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# path=path,
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# filename=f'Resultant and EI for dim={dim}.png',
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# close=True)
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