405 lines
17 KiB
Python
405 lines
17 KiB
Python
#!/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: timofej
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"""
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import os
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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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def new_folder(path):
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if not os.path.exists(path):
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os.makedirs(path)
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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 = 200
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stp = 1000100
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batch = 0
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print(f'Started at {datetime.now()}')
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for dim in [9]:
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for eps in eps_space[14:]:
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eps = round(eps,3)
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path='/cloud/Public/_data/neuropercolation/4lay/cons=27-knight_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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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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phase_diff = [round(pha,6) for pha in phase_diff]
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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 = [round(np.sum(cha)*(1-H2(eps)),6) 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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extremes = 10000 #[l//2 for l in lens]
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ext_rat = extremes/(stp-100)
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circparts = 32
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pha_dev = m.pi/circparts
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pha_max = np.max(np.abs(phase_diff))
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phase_in_part = lambda ph, i: abs(ph)<=pha_max/circparts if i==0 else i*pha_max/circparts<abs(ph)<=(i+1)*pha_max/circparts
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dev_parts = [sorted([i for i,val in enumerate(ei[:-maxdt]) if phase_in_part(phase_diff[i],j)],
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key = lambda i: ei[i]) for j in range(circparts)]
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ext_parts = [int(np.ceil(len(dev_parts[i])*ext_rat)) for i in range(circparts)]
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top_parts = [dev_parts[i][-ext_parts[i]:] if ext_parts[i]>0 else [] for i in range(circparts)]
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bot_parts = [dev_parts[i][:ext_parts[i]] if ext_parts[i]>0 else [] for i in range(circparts)]
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top = []
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for part in top_parts:
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top.extend(part)
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bot = []
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for part in bot_parts:
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bot.extend(part)
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print(len(top), len(bot), extremes, 'equal?')
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sampling = 'samedist_varmaxpha'
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# pha_center = av_diff
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# pha_dev = m.pi/8
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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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# infra_phase = lambda ph: (pha_center-pha_dev)<=abs(ph)<=(pha_center )
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# supra_phase = lambda ph: (pha_center )< abs(ph)<=(pha_center+pha_dev)
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# dev_inf = sorted([i for i,val in enumerate(ei[:-maxdt]) if infra_phase(phase_diff[i])], key = lambda i: ei[i])
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# dev_sup = sorted([i for i,val in enumerate(ei[:-maxdt]) if supra_phase(phase_diff[i])], key = lambda i: ei[i])
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# ext_inf = round(extremes*len(dev_inf)/(len(dev_inf)+len(dev_sup)))
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# ext_sup = round(extremes*len(dev_sup)/(len(dev_inf)+len(dev_sup)))
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# top_inf = dev_inf[-ext_inf:]
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# top_sup = dev_sup[-ext_sup:]
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# top = top_inf + top_sup
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# print(len(top), extremes, 'equal?')
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# sampling = 'biequal'
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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 i in top:
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causal_maxdt=0
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for file in os.listdir(path):
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f = os.path.join(path, file)
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if not os.path.isfile(f):
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c_maxdt = file.replace('causal_maxdt=','')
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if c_maxdt != file:
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causal_maxdt = int(c_maxdt)
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if causal_maxdt>=maxdt:
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path_c = path+f'causal_maxdt={causal_maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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else:
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path_c = path+f'causal_maxdt={maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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causal_init = states[i].translate(str.maketrans('', '', '.-='))
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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='simple',
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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 i in []:#top:
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original_maxdt=0
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for file in os.listdir(path):
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f = os.path.join(path, file)
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if not os.path.isfile(f):
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o_maxdt = file.replace('original_maxdt=','')
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if c_maxdt != file:
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original_maxdt = int(o_maxdt)
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if original_maxdt>=maxdt:
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path_c = path+f'original_maxdt={original_maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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else:
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path_c = path+f'original_maxdt={maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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causal_init = states[i].translate(str.maketrans('', '', '.-='))
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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='simple',
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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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phasediff = [round(pha,6) for pha in phasediff]
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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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dis_ei = []
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tot_ei = []
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present_dt=0
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for file in os.listdir(path):
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f = os.path.join(path, file)
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if not os.path.isfile(f):
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p_maxdt = file.replace(f'{sampling}_causal_roll{pha_dev:.3f}_maxdt=','')
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if p_maxdt != file:
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present_dt = int(p_maxdt)
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if present_dt>0:
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try:
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datapath = path + f"{sampling}_causal_roll{pha_dev:.3f}_maxdt={present_dt}/data/"
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with open(datapath+f"eps={round(eps,3):.3f}_bot_dia_res.txt", 'r', encoding='utf-8') as f:
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bot_res = json.load(f)
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with open(datapath+f"eps={round(eps,3):.3f}_top_dia_res.txt", 'r', encoding='utf-8') as f:
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top_res = json.load(f)
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with open(datapath+f"eps={round(eps,3):.3f}_dis_dia_res.txt", 'r', encoding='utf-8') as f:
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dis_res = json.load(f)
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with open(datapath+f"eps={round(eps,3):.3f}_bot_dia_ei.txt", 'r', encoding='utf-8') as f:
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bot_ei = json.load(f)
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with open(datapath+f"eps={round(eps,3):.3f}_top_dia_ei.txt", 'r', encoding='utf-8') as f:
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top_ei = json.load(f)
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with open(datapath+f"eps={round(eps,3):.3f}_dis_dia_ei.txt", 'r', encoding='utf-8') as f:
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dis_ei = json.load(f)
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except:
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present_dt=0
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bot_res = []
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top_res = []
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dis_res = []
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for dt in range(present_dt,maxdt):
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top_pha = [(phase_diff[i+dt]) for i in top]
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bot_pha = [(phase_diff[i+dt]) for i in bot]
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dis = []
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for i in top:
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causal_maxdt=0
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for file in os.listdir(path):
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f = os.path.join(path, file)
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if not os.path.isfile(f):
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c_maxdt = file.replace('causal_maxdt=','')
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if c_maxdt != file:
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causal_maxdt = int(c_maxdt)
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if causal_maxdt>=maxdt:
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path_c = path+f'causal_maxdt={causal_maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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else:
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path_c = path+f'causal_maxdt={maxdt}/eps={round(eps,3):.3f}/{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.append((json.load(f)[dt]))
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dei = []
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for i in top:
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causal_maxdt=0
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for file in os.listdir(path):
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f = os.path.join(path, file)
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if not os.path.isfile(f):
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c_maxdt = file.replace('causal_maxdt=','')
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if c_maxdt != file:
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causal_maxdt = int(c_maxdt)
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if causal_maxdt>=maxdt:
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path_c = path+f'causal_maxdt={causal_maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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else:
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path_c = path+f'causal_maxdt={maxdt}/eps={round(eps,3):.3f}/{i:0{len(str(stp))}}/'
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with open(path_c+f'eps={round(eps,3):.3f}_channels.txt', 'r', encoding='utf-8') as f:
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dei.append(np.sum(json.load(f)[dt])*(1-H2(eps)))
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dis_pha = dis
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tot_pha = (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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dis_res.append( [norm(resultant(dis_pha))] )
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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(1)] )
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# top_ph.append( [phase(*resultant(top_pha[i])) for i in range(1)] )
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# dis_ph.append( [phase(*resultant(dis_pha[i])) for i in range(1)] )
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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])] )
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top_ei.append( [np.average([ei[i+dt] for i in top])] )
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dis_ei.append( [np.average(dei)] )
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tot_ei.append( np.average(ei[dt:dt-maxdt]) )
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if dt%10==9:
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print(f'Done dt={dt:{len(str(maxdt))}d}')
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plotpath = path+f'{sampling}_causal_roll{pha_dev:.3f}_maxdt={maxdt}/'
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new_folder(plotpath+'data/')
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with open(plotpath+f"data/eps={round(eps,3):.3f}_bot_dia_res.txt", 'w', encoding='utf-8') as f:
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json.dump(list(bot_res), f, indent=1)
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with open(plotpath+f"data/eps={round(eps,3):.3f}_top_dia_res.txt", 'w', encoding='utf-8') as f:
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json.dump(list(top_res), f, indent=1)
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with open(plotpath+f"data/eps={round(eps,3):.3f}_dis_dia_res.txt", 'w', encoding='utf-8') as f:
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json.dump(list(dis_res), f, indent=1)
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with open(plotpath+f"data/eps={round(eps,3):.3f}_bot_dia_ei.txt", 'w', encoding='utf-8') as f:
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json.dump(list(bot_ei), f, indent=1)
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with open(plotpath+f"data/eps={round(eps,3):.3f}_top_dia_ei.txt", 'w', encoding='utf-8') as f:
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json.dump(list(top_ei), f, indent=1)
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with open(plotpath+f"data/eps={round(eps,3):.3f}_dis_dia_ei.txt", 'w', encoding='utf-8') as f:
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json.dump(list(dis_ei), f, indent=1)
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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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dis_ei = list(zip(*dis_ei))
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qtplot(f'Diachronic resultant for dim={dim} eps={eps:.3f} with 4 layers',
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[np.array(range(maxdt))]*4,
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[tot_res, bot_res[0], dis_res[0], top_res[0]],
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['Average Resultant', f'bottom {extremes} ei'
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f'dis {extremes} ei', f'top {extremes} ei'],
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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'All Diachronic Resultant Norm eps={round(eps,3):.3f} dim={dim} extremes={extremes}.png',
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close=True)
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# qtplot(f'Diachronic resultant phase for dim={dim} eps={eps:.3f} 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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# ['top {extremes} ei', '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'All Diachronic Resultant Phase 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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