111 lines
3.8 KiB
Python
111 lines
3.8 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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def H2(x):
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return -x*m.log2(x)-(1-x)*m.log2(1-x)
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extremes = 50000
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maxdt = 500
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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/steps=1000100/dim={dim:02}/'
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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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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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bot_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[ extremes]
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top_ei = sorted(enumerate(ei[:-maxdt]), key = lambda x: x[1])[-extremes]
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bot_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] <= bot_ei[1]]
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top_ei_pool = [ei for ei in enumerate(ei[:-maxdt]) if ei[1] >= top_ei[1]]
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bot_eis = choose(bot_ei_pool, extremes)
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top_eis = choose(top_ei_pool, extremes)
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bot_ind = [enum[0] for enum in bot_eis]
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top_ind = [enum[0] for enum in top_eis]
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bot_res = []
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top_res = []
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for dt in range(maxdt):
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bot_pha = [phase_diff[i+dt] for i in bot_ind]
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top_pha = [phase_diff[i+dt] for i in top_ind]
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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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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))]*3,
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[bot_res, top_res, [all_res]*maxdt],
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['Resultant ev of bottom 100 ei', 'Resultant ev of top 100 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,
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filename=f'Diachronic Resultant for eps={round(eps,3)} dim={dim} extremes={extremes}.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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