Add evaluations and examples
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timofej
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111
evaluation/4Layer Bootstrap.py
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111
evaluation/4Layer Bootstrap.py
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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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60
evaluation/4Layer Res+EI.py
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60
evaluation/4Layer Res+EI.py
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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 datetime import datetime
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from plot import qtplot
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eps_space = list(np.linspace(0.005,0.5,100))
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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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for dim in [7]:
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diff_res = [0]
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av_ei = [0]
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for eps in eps_space:
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path=f'/cloud/Public/_data/neuropercolation/4lay/steps=100000/dim={dim:02}/'
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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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res = np.linalg.norm(resultant(phase_diff))
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diff_res.append(res)
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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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av_ei.append(np.average(ei))
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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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[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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94
evaluation/4Layer Survey.py
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evaluation/4Layer Survey.py
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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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pha_divs = 8
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pha_sort = [0]*pha_divs
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pha_sort = [[pha for pha in list(enumerate(phase_diff))[:-maxdt] if i*m.pi/pha_divs <= abs(pha[1]) <= (i+1)*m.pi/pha_divs] for i in range(pha_divs)]
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dia_ei = [[np.average([ei[pha[0]+dt] for pha in pha_div]) for dt in range(maxdt)] for pha_div in pha_sort]
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qtplot(f'Diachronic EI for dim={dim} with 4 layers',
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[list(range(maxdt))]*pha_divs,
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dia_ei,
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[f'EI evolution for initial phase in [{div}pi,{div+1}pi]' for div in range(pha_divs)],
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x_tag = 'dt',
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y_tag = 'average EI',
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colors = 'rgb',
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export=True,
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path=path,
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filename=f'Diachronic EI for eps={round(eps,3)} dim={dim} pha_divs={pha_divs}.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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138
evaluation/plot.py
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evaluation/plot.py
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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 16:12:53 2023
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@author: astral
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"""
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import sys
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import os
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import pyqtgraph as qt
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import pyqtgraph.exporters
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from PyQt5.QtGui import QFont
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import math as m
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import numpy as np
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def __get_color(factor, gamma=0.8):
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frequency=380+factor*(750-380)
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lightfrequency = 0.4*(3*np.log10(frequency/2)-2)/4
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wavelength = 300/lightfrequency
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'''steps of 10Hz: 22 means 220Hz'''
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'''This converts a given wavelength of light to an
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approximate RGB color value. The wavelength must be given
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in values between 0 and 1 for 0=380nm through 1=750nm
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(789 THz through 400 THz).
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Based on code by Dan Bruton
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http://www.physics.sfasu.edu/astro/color/spectra.html
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'''
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wavelength = float(wavelength)
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if wavelength >= 380 and wavelength <= 440:
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attenuation = 0.3 + 0.7 * (wavelength - 380) / (440 - 380)
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R = ((-(wavelength - 440) / (440 - 380)) * attenuation) ** gamma
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G = 0.0
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B = (1.0 * attenuation) ** gamma
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elif wavelength >= 440 and wavelength <= 490:
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R = 0.0
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G = ((wavelength - 440) / (490 - 440)) ** gamma
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B = 1.0
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elif wavelength >= 490 and wavelength <= 510:
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R = 0.0
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G = 1.0
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B = (-(wavelength - 510) / (510 - 490)) ** gamma
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elif wavelength >= 510 and wavelength <= 580:
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R = ((wavelength - 510) / (580 - 510)) ** gamma
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G = 1.0
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B = 0.0
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elif wavelength >= 580 and wavelength <= 645:
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R = 1.0
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G = (-(wavelength - 645) / (645 - 580)) ** gamma
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B = 0.0
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elif wavelength >= 645 and wavelength <= 750:
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attenuation = 0.3 + 0.7 * (750 - wavelength) / (750 - 645)
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R = (1.0 * attenuation) ** gamma
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G = 0.0
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B = 0.0
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else:
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R = 0.0
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G = 0.0
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B = 0.0
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R *= 255
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G *= 255
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B *= 255
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return (int(R), int(G), int(B))
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def plot_execute():
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if sys.flags.interactive != 1 or not hasattr(qt.QtCore, 'PYQT_VERSION'):
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qt.QtGui.QApplication.exec_()
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def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_tag=None, colors=None, export=False, path=None, filename=None, lw=4, close=False):
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linewidth = lw
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#app = qt.mkQApp()
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ph = qt.plot()
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ph.showGrid(x=True,y=True)
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ph.setXRange(np.min(spaces), np.max(spaces))
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# ph.setYRange(0.0, 6)
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#ph.setTitle(title='Susceptibility density evolution for different automaton sizes', offset=(1000,500))#.format(dim))
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ph.setLabel('left', y_tag)
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ph.setLabel('bottom', x_tag)
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#ph.setXRange(0, 0.15)
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ph.addLegend(offset=(400, 30))
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#s = ph.plot(np.linspace(0.01,0.32,32), eps_max_freq0, title=sp_Title, pen='w')
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#s = ph.plot(np.linspace(0.01,0.32,32), eps_max_freq1, title=sp_Title, pen='w')
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if colors=='rgb':
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colors=[__get_color(fac/(len(vals)-1)) for fac in range(len(vals))]
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elif colors is None:
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colors=['r', 'g', 'b', 'y', 'c', 'm', 'w', (100,100,0), (0,100,100), (100,0,100)]
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for i in range(len(vals)):
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s = ph.plot(spaces[i], vals[i],
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name=names[i], pen=qt.mkPen(colors[i], width=linewidth))
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title_item = qt.TextItem(text=titletext, anchor=(0.5, 7), color='grey')
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ph.addItem(title_item)
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title_item.setPos(ph.getViewBox().viewRect().center())
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font = QFont()
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font.setPointSize(14) # Adjust the font size as needed
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title_item.setFont(font)
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if export:
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if not os.path.exists(path):
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os.makedirs(path)
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exporter = qt.exporters.ImageExporter(ph.plotItem)
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# set export parameters if needed
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exporter.parameters()['width'] = 1200 # (note this also affects height parameter)
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# save to file
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exporter.export(path+filename)
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def handleAppExit():
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# Add any cleanup tasks here
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print("closing")
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if close:
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ph.close()
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# app.aboutToQuit.connect(handleAppExit)
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# app.exec_()
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@ -5,7 +5,7 @@ Created on Fri Aug 18 19:05:04 2023
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@author: astral
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"""
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from datetime import datetime
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import numpy as np
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from neuropercolation import Simulate1Layer
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@ -23,4 +23,4 @@ for dim in [49,100]:
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).run(evolutions_per_second=30,
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last_evolution_step=100000,
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save_states=False)
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print(f'Done eps={eps:.3f} at dim={dim}')
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print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
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37
examples/Simulate2Layers.py
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37
examples/Simulate2Layers.py
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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 Fri Aug 18 19:05:04 2023
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@author: astral
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"""
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from datetime import datetime
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import numpy as np
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from neuropercolation import Simulate2Layers, Simulate4Layers
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eps_space = np.linspace(0.005,0.5,100)
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#eps_space = np.linspace(0.135,0.15,4)
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for dim in [8]:
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for eps in eps_space:
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eps = round(eps,3)
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sim = Simulate4Layers(dim,
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eps,
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||||
steps=100100,
|
||||
draw=None,
|
||||
save='all',
|
||||
path=f'/cloud/Public/_data/neuropercolation/4lay/steps=100100/dim={dim:02}/',
|
||||
)
|
||||
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
|
||||
|
||||
for eps in eps_space:
|
||||
eps = round(eps,3)
|
||||
sim = Simulate2Layers(dim,
|
||||
eps,
|
||||
steps=100100,
|
||||
draw=None,
|
||||
save='all',
|
||||
path=f'/cloud/Public/_data/neuropercolation/2lay/steps=100100/dim={dim:02}/',
|
||||
)
|
||||
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
|
||||
32
examples/Simulate4Layers.py
Normal file
32
examples/Simulate4Layers.py
Normal file
@ -0,0 +1,32 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Aug 18 19:05:04 2023
|
||||
|
||||
@author: astral
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
from neuropercolation import Simulate4Layers
|
||||
|
||||
eps_space = np.linspace(0.005,0.5,100)
|
||||
#eps_space = np.linspace(0.135,0.15,4)
|
||||
|
||||
stp = 1000100
|
||||
|
||||
for batch in range(1,5):
|
||||
for dim in [7]:
|
||||
for eps in eps_space[1:41:2]:
|
||||
eps = round(eps,3)
|
||||
cons = [(n,n) for n in range(dim)]+[(n,(n+3)%7) for n in range(dim)]
|
||||
sim = Simulate4Layers(dim,
|
||||
eps,
|
||||
coupling=cons,
|
||||
steps=stp,
|
||||
draw=None,
|
||||
res=2,
|
||||
save='simple',
|
||||
path=f'/cloud/Public/_data/neuropercolation/4lay/cons={len(cons)}_steps={stp}/dim={dim:02}/batch={batch}/',
|
||||
)
|
||||
print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
|
||||
Loading…
Reference in New Issue
Block a user