SwitchingFCalongAtask/metaAfterTask.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 23 10:09:40 2021

@author: Oscar Portoles
"""

import numpy as np
import sys
import visArchis_seq as v
# import optimProbs as oudp
import modelStages as udp
import scipy as sp
from itertools import combinations


tSimu  = 400
saveTo = './dataSave/metaFC1000afterPulse_P040_G0p15L0p6.npz'


# Load data
d = v.LoadData()
d.nameModel = 'stgSequence_Lopt_allPu1500_wE_P040_optIni_v5_fixG0p15L0p6_Stg'
d.nameEnding =''
d.stages = [1,2,3,4]
m = d.getBestModel()
pairs = np.array(list(combinations(range(m.nroi),2)), dtype=np.int32)

# get puses and best fitness
pulses = np.zeros((m.nroi,m.nStg))
fits   = np.zeros(m.nStg)
for i in range(m.nStg):
    pulses[:,i] = m.logs[i][0][0]
    fits[i] = m.logs[i][0][3]

metaG, metaL,sim2eFC = [None]*4, [None]*4, [None]*4
metaGtot, metaLtot = np.zeros(4), np.zeros(4)
matchFCs = [None]*4
for iX in range(4):
    stages = list(range(iX+1))
    kao = udp.KAOmodel(stageIx=stages)
    # use model with pulses
    kao.iniCond = 2
    kao.tMax = tSimu
    kao._setHistDelayAndContainers()
    kao._edgesImpulse()  
    kao._getEmpiReativEnv()
    kao.doNoise(variance=0)
    kao.pulse = pulses[:,stages]
    # kao.pulse = np.zeros((68,1))
    kao.runModel_Pulsein_sequence()
    
    zds = kao.downsampleModel(kao.z)
    # metastability
    metaG_ = np.abs(np.mean(np.exp(1j*np.angle(zds)),axis=0))
    metaGtot[iX] = np.mean(np.std(metaG_,axis=0))
    metaG[iX] = np.mean(metaG_, axis=1)
    zdsA  = udp.KAOmodel._absZ(zds)
    metaL[iX] = zdsA
    metaLtot[iX] = np.mean( np.mean(np.std(zdsA,axis=1),axis=0) ) 
    # match to last FC state
    csi     = np.sign( np.imag(zds[pairs[:,0],:,:] * np.conjugate(zds[pairs[:,1],:,:])) )
    mcsi    = np.sign( np.mean(csi, axis=2) ) # mean over models
    sim2eFC_= np.ones((len(stages), mcsi.shape[1]),dtype=np.float32)
    for i in range(len(stages)):
        sim2eFC_[i,:] = np.float32(np.sum( kao.eFCs[i,:] * mcsi.T, axis=1) / kao.neFCs[i])
    sim2eFC[iX] = sim2eFC_
        
np.savez(saveTo, sim2eFC=sim2eFC, metaG=metaG,metaL=metaL,metaGtot=metaGtot,metaLtot=metaLtot,pulses=pulses,fitSt=fits)