Develop evaluation codes
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timofej
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0f62707e33
@ -26,12 +26,12 @@ def resultant(sample):
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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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extremes = None
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maxdt = 300
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for dim in [7]:
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for dim in [9]:
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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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path=f'/cloud/Public/_data/neuropercolation/4lay/cons=27-3diag_steps=1000100/dim=09/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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@ -49,6 +49,7 @@ for dim in [7]:
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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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@ -62,39 +63,75 @@ for dim in [7]:
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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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pha_center = av_diff
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pha_dev = m.pi/8
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orth_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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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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if extremes is None:
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extremes = len(orth_ind)//4000*1000
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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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print(len(orth_ind))
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#print(all_res, av_diff)
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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_ind = orth_ind[ :extremes]
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top_ind = orth_ind[-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_ei]
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# top_ind = [enum[0] for enum in top_ei]
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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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orth_res = []
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tot_res = []
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bot_ei = []
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top_ei = []
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orth_ei = []
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tot_ei = []
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for dt in range(maxdt):
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bot_res.append( norm(resultant([phase_diff[i+dt] for i in bot_ind])) )
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top_res.append( norm( resultant([phase_diff[i+dt] for i in top_ind])) )
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orth_res.append( norm( resultant([phase_diff[i+dt] for i in orth_ind])) )
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tot_res.append( norm( resultant(phase_diff[dt:dt-maxdt])) )
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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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orth_ei.append( np.average([ei[i+dt] for i in orth_ind]) )
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tot_ei.append( np.average(ei[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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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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[np.array(range(maxdt))]*4,
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[bot_res, top_res, orth_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,
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filename=f'Diachronic Resultant for eps={round(eps,3)} dim={dim} extremes={extremes}.png',
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filename=f'Diachronic Resultant 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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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, orth_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,
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filename=f'Diachronic EI 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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92
evaluation/4Layer EI to Phase Survey.py
Normal file
92
evaluation/4Layer EI to Phase Survey.py
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@ -0,0 +1,92 @@
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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/cons=14_steps=1000100/dim={dim:02}/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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try:
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with open(path+f"eps={round(eps,3):.3f}_channelsum.txt", 'r', encoding='utf-8') as f:
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chasum = 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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chasum = [int(np.sum(cha)) for cha in channels]
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with open(path+f"eps={round(eps,3):.3f}_channelsum.txt", 'w', encoding='utf-8') as f:
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json.dump(chasum, f, indent=1)
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maxchas = max(chasum)
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res = [norm(resultant([pha for i, pha in enumerate(phase_diff) if chasum[i]==n])) for n in range(maxchas+1) if len([pha for i, pha in enumerate(phase_diff) if chasum[i]==n])>=20]
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#dist = [len([pha for i, pha in enumerate(phase_diff) if chasum[i]==n]) for n in range(maxchas+1)]
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qtplot(f'Channel-Resultant for dim={dim}',
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[list(range(len(res)))],
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[res],
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[f'Resultant against open channels'],
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x_tag = 'open channels',
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y_tag = 'resultant',
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export=True,
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path=path,
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filename=f'Channel-Resultant eps={round(eps,3):.3f} dim={dim}.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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@ -29,14 +29,16 @@ def H2(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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for dim in [9]:
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for eps in eps_space[:1]:
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path=f'/cloud/Public/_data/neuropercolation/4lay/cons=9-3dist_steps=1000100/dim=09/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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# None
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# if True:
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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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@ -62,22 +64,34 @@ for dim in [7]:
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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_divs = 32
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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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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]
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for i in range(pha_divs)]
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dia_ei = [np.average([ei[i] for i, pha in pha_div]) for pha_div in pha_sort]
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phase_dist = [len(pha)/len(phase[0][:-maxdt]) for pha 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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qtplot(f'EI Distribution against phase diff for dim={dim}',
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[[(div+.5)*m.pi/pha_divs for div in range(pha_divs)]],
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[dia_ei],
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[f'EI distribution against phase diff'],
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x_tag = 'abs phase',
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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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filename=f'Phase-EI eps={round(eps,3):.3f} dim={dim} pha_divs={pha_divs}.png',
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close=True)
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qtplot(f'Phasediff Distribution for dim={dim}',
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[[(div+.5)*m.pi/pha_divs for div in range(pha_divs)]],
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[phase_dist],
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[f'Phase distribution'],
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x_tag = 'abs phase',
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y_tag = 'frequency',
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export=True,
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path=path,
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filename=f'Phasediff dist eps={round(eps,3):.3f} 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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@ -126,6 +126,8 @@ def qtplot(titletext, spaces, vals, names, x_tag=f'noise level {chr(949)}', y_ta
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# save to file
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exporter.export(path+filename)
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print(f'Saving to {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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@ -15,18 +15,20 @@ eps_space = np.linspace(0.005,0.5,100)
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stp = 1000100
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for batch in range(1,5):
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for dim in [7]:
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for batch in range(5):
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for dim in [8]:
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for eps in eps_space[1:41:2]:
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eps = round(eps,3)
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cons = [(n,n) for n in range(dim)]+[(n,(n+3)%7) for n in range(dim)]
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cons = [(n,(n+m)%dim) for n in range(dim) for m in [0,int(dim/2)]]
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initstate = [[0,0],[0,0]]
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sim = Simulate4Layers(dim,
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eps,
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coupling=cons,
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init=initstate,
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steps=stp,
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draw=None,
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res=2,
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save='simple',
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path=f'/cloud/Public/_data/neuropercolation/4lay/cons={len(cons)}_steps={stp}/dim={dim:02}/batch={batch}/',
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path=f'/cloud/Public/_data/neuropercolation/4lay/cons={len(cons)}-2diag_steps={stp}/dim={dim:02}/batch={batch}/',
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)
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print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
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