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testChannelBG.py
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testChannelBG.py
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#!/apps/free/python/2.7.10/bin/python
# -*- coding: utf-8 -*-
from LGneurons import *
from modelParams import *
from gurneyParams import *
import nest.raster_plot
#import time
import sys
import os
# end if
# params possible keys:
# - nb{MSN,FSI,STN,GPi,GPe,CSN,PTN,CMPf} : number of simulated neurons for each population
# - Ie{GPe,GPi} : constant input current to GPe and GPi
# - G{MSN,FSI,STN,GPi,GPe} : gain to be applied on LG14 input synaptic weights for each population
#------------------------------------------
# Creates the populations of neurons necessary to simulate a BG circuit
#------------------------------------------
def createBG_MC():
#==========================
# Creation of neurons
#-------------------------
print '\nCreating neurons\n================'
nbSim['MSN'] = params['nbMSN']
createMC('MSN',params['nbCh'])
nbSim['FSI'] = params['nbFSI']
createMC('FSI',params['nbCh'])
nbSim['STN'] = params['nbSTN']
createMC('STN',params['nbCh'])
nbSim['GPe'] = params['nbGPe']
createMC('GPe',params['nbCh'])
for i in range(len(Pop['GPe'])):
nest.SetStatus(Pop['GPe'][i],{"I_e":params['IeGPe']})
nbSim['GPi'] = params['nbGPi']
createMC('GPi',params['nbCh'])
for i in range(len(Pop['GPi'])):
nest.SetStatus(Pop['GPi'][i],{"I_e":params['IeGPi']})
parrot = True # switch to False at your risks & perils...
nbSim['CSN'] = params['nbCSN']
createMC('CSN',params['nbCh'], fake=True, parrot=parrot)
nbSim['PTN'] = params['nbPTN']
createMC('PTN',params['nbCh'], fake=True, parrot=parrot)
nbSim['CMPf'] = params['nbCMPf']
createMC('CMPf',params['nbCh'], fake=True, parrot=False)
print "Number of simulated neurons:", nbSim
#------------------------------------------
# Connects the populations of a previously created multi-channel BG circuit
#------------------------------------------
def connectBG_MC(antagInjectionSite,antag,prms=params):
# there were a problem for changing the params dynamically
# so we added a local variable
params = prms
G = {'MSN': params['GMSN'],
'FSI': params['GFSI'],
'STN': params['GSTN'],
'GPe': params['GGPe'],
'GPi': params['GGPi'],
}
print "Gains on LG14 syn. strength:", G
#-------------------------
# connection of populations
#-------------------------
print '\nConnecting neurons\n================'
print "**",antag,"antagonist injection in",antagInjectionSite,"**"
print '* MSN Inputs'
connectMC('ex','CSN','MSN', 'focused', inDegree= min(params['inDegCSNMSN'],nbSim['CSN']),gain=G['MSN'])
connectMC('ex','PTN','MSN', 'focused', inDegree= min(params['inDegPTNMSN'],nbSim['PTN']),gain=G['MSN'])
connectMC('ex','CMPf','MSN','focused', inDegree= min(params['inDegCMPfMSN'],nbSim['CMPf']),gain=G['MSN'])
connectMC('in','MSN','MSN', 'focused', inDegree= min(params['inDegMSNMSN'],nbSim['MSN']),gain=G['MSN'])
connectMC('in','FSI','MSN', 'diffuse', inDegree= min(params['inDegFSIMSN'],nbSim['FSI']),gain=G['MSN']) # diffuse ? focused ?
# some parameterizations from LG14 have no STN->MSN or GPe->MSN synaptic contacts
if alpha['STN->MSN'] != 0:
print "alpha['STN->MSN']",alpha['STN->MSN']
connectMC('ex','STN','MSN', 'diffuse', inDegree= min(params['inDegSTNMSN'],nbSim['STN']),gain=G['MSN'])
if alpha['GPe->MSN'] != 0:
print "alpha['GPe->MSN']",alpha['GPe->MSN']
connectMC('in','GPe','MSN', 'diffuse', inDegree= min(params['inDegGPeMSN'],nbSim['GPe']),gain=G['MSN']) # diffuse ? focused ?
print '* FSI Inputs'
connectMC('ex','CSN','FSI', 'focused', inDegree= min(params['inDegCSNFSI'],nbSim['CSN']),gain=G['FSI'])
connectMC('ex','PTN','FSI', 'focused', inDegree= min(params['inDegPTNFSI'],nbSim['PTN']),gain=G['FSI'])
if alpha['STN->FSI'] != 0:
connectMC('ex','STN','FSI', 'diffuse', inDegree= min(params['inDegSTNFSI'],nbSim['STN']),gain=G['FSI'])
connectMC('in','GPe','FSI', 'focused', inDegree= min(params['inDegGPeFSI'],nbSim['GPe']),gain=G['FSI'])
connectMC('ex','CMPf','FSI','focused', inDegree= min(params['inDegCMPfFSI'],nbSim['CMPf']),gain=G['FSI'])
connectMC('in','FSI','FSI', 'diffuse',inDegree= min(params['inDegFSIFSI'],nbSim['FSI']),gain=G['FSI'])
print '* STN Inputs'
connectMC('ex','PTN','STN', 'focused',inDegree= min(params['inDegPTNSTN'],nbSim['PTN']), gain=G['STN'])
connectMC('ex','CMPf','STN','focused',inDegree= min(params['inDegCMPfSTN'],nbSim['CMPf']), gain=G['STN'])
connectMC('in','GPe','STN', 'focused',inDegree= min(params['inDegGPeSTN'],nbSim['GPe']), gain=G['STN']) # or diffuse, to be in line with the 2008 model?
print '* GPe Inputs'
if antagInjectionSite == 'GPe':
if antag == 'AMPA':
connectMC('NMDA','CMPf','GPe','focused',inDegree= min(params['inDegCMPfGPe'],nbSim['CMPf']), gain=G['GPe'])
connectMC('NMDA','STN','GPe','diffuse', inDegree= min(params['inDegSTNGPe'],nbSim['STN']), gain=G['GPe'])
connectMC('in','MSN','GPe','focused', inDegree= min(params['inDegMSNGPe'],nbSim['MSN']), gain=G['GPe'])
connectMC('in','GPe','GPe','diffuse', inDegree= min(params['inDegGPeGPe'],nbSim['GPe']), gain=G['GPe']) # diffuse or focused?
elif antag == 'NMDA':
connectMC('AMPA','CMPf','GPe','focused',inDegree= min(params['inDegCMPfGPe'],nbSim['CMPf']), gain=G['GPe'])
connectMC('AMPA','STN','GPe','diffuse', inDegree= min(params['inDegSTNGPe'],nbSim['STN']), gain=G['GPe'])
connectMC('in','MSN','GPe','focused', inDegree= min(params['inDegMSNGPe'],nbSim['MSN']), gain=G['GPe'])
connectMC('in','GPe','GPe','diffuse', inDegree= min(params['inDegGPeGPe'],nbSim['GPe']), gain=G['GPe'])
elif antag == 'AMPA+GABAA':
connectMC('NMDA','CMPf','GPe','focused',inDegree= min(params['inDegCMPfGPe'],nbSim['CMPf']), gain=G['GPe'])
connectMC('NMDA','STN','GPe','diffuse',inDegree= min(params['inDegSTNGPe'],nbSim['STN']), gain=G['GPe'])
elif antag == 'GABAA':
connectMC('ex','CMPf','GPe','focused',inDegree= min(params['inDegCMPfGPe'],nbSim['CMPf']), gain=G['GPe'])
connectMC('ex','STN','GPe', 'diffuse',inDegree= min(params['inDegSTNGPe'],nbSim['STN']), gain=G['GPe'])
else:
print antagInjectionSite,": unknown antagonist experiment:",antag
else:
connectMC('ex','CMPf','GPe','focused',inDegree= min(params['inDegCMPfGPe'],nbSim['CMPf']), gain=G['GPe'])
connectMC('ex','STN','GPe', 'diffuse',inDegree= min(params['inDegSTNGPe'],nbSim['STN']), gain=G['GPe'])
connectMC('in','MSN','GPe', 'focused',inDegree= min(params['inDegMSNGPe'],nbSim['MSN']), gain=G['GPe'])
connectMC('in','GPe','GPe', 'diffuse',inDegree= min(params['inDegGPeGPe'],nbSim['GPe']), gain=G['GPe'])
print '* GPi Inputs'
if antagInjectionSite =='GPi':
if antag == 'All':
pass
elif antag == 'NMDA':
connectMC('in','MSN','GPi', 'focused',inDegree= min(params['inDegMSNGPi'],nbSim['MSN']),gain=G['GPi'])
connectMC('AMPA','STN','GPi', 'diffuse', inDegree= min(params['inDegSTNGPi'],nbSim['STN']),gain=G['GPi'])
connectMC('in','GPe','GPi', 'diffuse',inDegree= min(params['inDegGPeGPi'],nbSim['GPe']),gain=G['GPi'])
connectMC('AMPA','CMPf','GPi','focused',inDegree= min(params['inDegCMPfGPi'],nbSim['CMPf']),gain=G['GPi'])
elif antag == 'NMDA+AMPA':
connectMC('in','MSN','GPi', 'focused',inDegree= min(params['inDegMSNGPi'],nbSim['MSN']),gain=G['GPi'])
connectMC('in','GPe','GPi', 'diffuse',inDegree= min(params['inDegGPeGPi'],nbSim['GPe']),gain=G['GPi'])
elif antag == 'AMPA':
connectMC('in','MSN','GPi', 'focused',inDegree= min(params['inDegMSNGPi'],nbSim['MSN']),gain=G['GPi'])
connectMC('NMDA','STN','GPi', 'diffuse',inDegree= min(params['inDegSTNGPi'],nbSim['STN']),gain=G['GPi'])
connectMC('in','GPe','GPi', 'diffuse',inDegree= min(params['inDegGPeGPi'],nbSim['GPe']),gain=G['GPi'])
connectMC('NMDA','CMPf','GPi','focused',inDegree= min(params['inDegCMPfGPi'],nbSim['CMPf']),gain=G['GPi'])
elif antag == 'GABAA':
connectMC('ex','STN','GPi', 'diffuse',inDegree= min(params['inDegSTNGPi'],nbSim['STN']),gain=G['GPi'])
connectMC('ex','CMPf','GPi','focused',inDegree= min(params['inDegCMPfGPi'],nbSim['CMPf']),gain=G['GPi'])
else:
print antagInjectionSite,": unknown antagonist experiment:",antag
else:
connectMC('in','MSN','GPi', 'focused',inDegree= min(params['inDegMSNGPi'],nbSim['MSN']),gain=G['GPi'])
connectMC('ex','STN','GPi', 'diffuse',inDegree= min(params['inDegSTNGPi'],nbSim['STN']),gain=G['GPi'])
connectMC('in','GPe','GPi', 'diffuse',inDegree= min(params['inDegGPeGPi'],nbSim['GPe']),gain=G['GPi'])
connectMC('ex','CMPf','GPi','focused',inDegree= min(params['inDegCMPfGPi'],nbSim['CMPf']),gain=G['GPi'])
def on_se_la_gere() :
print " on va se la gerer celle la !"
je_genere()
#------------------------------------------
# Checks that the BG model parameterization defined by the "params" dictionary can respect the electrophysiological constaints (firing rate at rest).
# If testing for a given antagonist injection experiment, specifiy the injection site in antagInjectionSite, and the type of antagonists used in antag.
# Returns [score obtained, maximal score]
# params possible keys:
# - nb{MSN,FSI,STN,GPi,GPe,CSN,PTN,CMPf} : number of simulated neurons for each population
# - Ie{GPe,GPi} : constant input current to GPe and GPi
# - G{MSN,FSI,STN,GPi,GPe} : gain to be applied on LG14 input synaptic weights for each population
#------------------------------------------
def checkAvgFR_MC(out=dict(),NbTrials=1,ctx_activity=None,showRasters=False,params={},antagInjectionSite='none',antag='',logFileName='',CSNFR=[2.,10.], PActiveCSN=1., PTNFR=[15.,35], PActivePTN=1.,shutOffset=False,offTime=1000,simuTime=5000):
nest.ResetKernel()
dataPath='log/'
nest.SetKernelStatus({'local_num_threads': params['nbcpu'] if ('nbcpu' in params) else 2, "data_path": dataPath})
initNeurons()
offsetDuration = float(offTime)
simDuration = float(simuTime) # ms
# nest.SetKernelStatus({"overwrite_files":True}) # Thanks to use of timestamps, file names should now
# be different as long as they are not created during the same second
print '/!\ Using the following LG14 parameterization',params['LG14modelID']
loadLG14params(params['LG14modelID'])
# We check that all the necessary parameters have been defined. They should be in the modelParams.py file.
# If one of them misses, we exit the program.
necessaryParams=['nbCh','nbMSN','nbFSI','nbSTN','nbGPe','nbGPi','nbCSN','nbPTN','nbCMPf','IeGPe','IeGPi','GMSN','GFSI','GSTN','GGPe','GGPi','inDegCSNMSN','inDegPTNMSN','inDegCMPfMSN','inDegMSNMSN','inDegFSIMSN','inDegSTNMSN','inDegGPeMSN','inDegCSNFSI','inDegPTNFSI','inDegSTNFSI','inDegGPeFSI','inDegCMPfFSI','inDegFSIFSI','inDegPTNSTN','inDegCMPfSTN','inDegGPeSTN','inDegCMPfGPe','inDegSTNGPe','inDegMSNGPe','inDegGPeGPe','inDegMSNGPi','inDegSTNGPi','inDegGPeGPi','inDegCMPfGPi',]
for nprm in necessaryParams:
if nprm not in params:
print "Missing parameter:",np
exit()
#-------------------------
# creation and connection of the neural populations
#-------------------------
createBG_MC()
connectBG_MC(antagInjectionSite,antag)
# lets draw the connectivity matrix
if not ctx_activity is None :
print "Multi-trial test"
# ctx input activities
#-------------------------
# prepare the firing rates of the inputs for the 5 steps of the experiment
#-------------------------
gCSN = CSNFR[1]-CSNFR[0]
gPTN = PTNFR[1]-PTNFR[0]
actLevels = np.array([ctx_activity])
NbTrials = len(ctx_activity)
CSNrate= gCSN * actLevels + np.ones((NbTrials)) * CSNFR[0]
PTNrate= gPTN * actLevels + np.ones((NbTrials)) * PTNFR[0]
#-------------------------
# and prepare the lists of neurons that will be affected by these activity changes
#-------------------------
ActPop = {'CSN':[()],'PTN':[()]}
if 'Fake' in globals():
if 'CSN' in Fake:
if PActiveCSN==1.:
ActPop['CSN']=Fake['CSN']
else:
for i in chanRange:
ActPop['CSN'][0] = tuple(rnd.choice(a=np.array(Fake['CSN'][0]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in chanRange:
ActPop['CSN'][0] = tuple(rnd.choice(a=np.array(Pop['CSN'][0]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if 'PTN' in Fake:
if PActivePTN==1.:
ActPop['PTN']=Fake['PTN']
else:
for i in chanRange:
ActPop['PTN'][0] = tuple(rnd.choice(a=np.array(Fake['PTN'][0]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in chanRange :
ActPop['PTN'][0] = tuple(rnd.choice(a=np.array(Pop['PTN'][0]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in chanRange:
ActPop['CSN'][0] = tuple(rnd.choice(a=np.array(Pop['CSN'][0]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in chanRange :
ActPop['PTN'][0] = tuple(rnd.choice(a=np.array(Pop['PTN'][0]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else :
NbTrials = 1
score = 0
text=[]
frstr = "#" + str(params['LG14modelID'])+ " , " + antagInjectionSite + ','
for trial in range(NbTrials) :
#-------------------------
# measures
#-------------------------
spkDetect={} # spike detectors used to record the experiment
expeRate={}
antagStr = ''
if antagInjectionSite != 'none':
antagStr = antagInjectionSite+'_'+antag+'_'
for N in NUCLEI:
# 1000ms offset period for network stabilization
if shutOffset and timeStep!=0:
start = trial*simDuration
stop = (trial+1)*simDuration
else :
start = offsetDuration + trial*(offsetDuration+simDuration)
stop = (trial+1)*(offsetDuration+simDuration)
spkDetect[N] = nest.Create("spike_detector", params={"withgid": True, "withtime": True, "label": str(trial)+'_'+antagStr+N, "to_file": False, 'start': start ,'stop':stop})
for i in range(len(Pop[N])):
nest.Connect(Pop[N][i], spkDetect[N])
#-------------------------
# Simulation
#-------------------------
if not ctx_activity is None :
print '====== Step',trial,'======'
print 'Channel 0:',CSNrate[0,trial],PTNrate[0,trial]
nest.SetStatus(ActPop['CSN'][0],{'rate':CSNrate[0,trial]})
nest.SetStatus(ActPop['PTN'][0],{'rate':PTNrate[0,trial]})
if shutOffset and timeStep!=0 :
nest.Simulate(simDuration)
else :
nest.Simulate(simDuration+offsetDuration)
s = '----- RESULTS -----'
print s
text.append(s+'\n')
if antagInjectionSite == 'none':
validationStr = "\n#" + str(params['LG14modelID']) + " , "
frstr += "none , "
for N in NUCLEI:
strTestPassed = 'NO!'
expeRate[N] = nest.GetStatus(spkDetect[N], 'n_events')[0] / float(nbSim[N]*simDuration*params['nbCh']) * 1000
if expeRate[N] <= FRRNormal[N][1] and expeRate[N] >= FRRNormal[N][0]:
# if the measured rate is within acceptable values
strTestPassed = 'OK'
score += 1
validationStr += N + "=OK , "
else :
# out of the ranges
if expeRate[N] > FRRNormal[N][1] :
difference = expeRate[N] - FRRNormal[N][1]
validationStr += N + "=+%.2f , " % difference
else :
difference = expeRate[N] - FRRNormal[N][0]
validationStr += N + "=%.2f , " % difference
frstr += '%f , ' %(expeRate[N])
s = '* '+N+' - Rate: '+str(expeRate[N])+' Hz -> '+strTestPassed+' ('+str(FRRNormal[N][0])+' , '+str(FRRNormal[N][1])+')'
print s
text.append(s+'\n')
else:
validationStr = ""
frstr += str(antag) + " , "
for N in NUCLEI:
expeRate[N] = nest.GetStatus(spkDetect[N], 'n_events')[0] / float(nbSim[N]*simDuration*params['nbCh']) * 1000
if N == antagInjectionSite:
strTestPassed = 'NO!'
if expeRate[N] <= FRRAnt[N][antag][1] and expeRate[N] >= FRRAnt[N][antag][0]:
# if the measured rate is within acceptable values
strTestPassed = 'OK'
score += 1
validationStr += N + "_" + antag + "=OK , "
else :
# out of the ranges
if expeRate[N] > FRRNormal[N][1] :
difference = expeRate[N] - FRRNormal[N][1]
validationStr += N + "_" + antag + "=+%.2f , " % difference
else :
difference = expeRate[N] - FRRNormal[N][0]
validationStr += N + "_" + antag + "=%.2f , " % difference
s = '* '+N+' with '+antag+' antagonist(s): '+str(expeRate[N])+' Hz -> '+strTestPassed+' ('+str(FRRAnt[N][antag][0])+' , '+str(FRRAnt[N][antag][1])+')'
print s
text.append(s+'\n')
else:
s = '* '+N+' - Rate: '+str(expeRate[N])+' Hz'
print s
text.append(s+'\n')
frstr += '%f , ' %(expeRate[N])
out[trial] = expeRate['GPi']
s = '-------------------'
print s
text.append(s+'\n')
os.system("rm -f log/*.gdf")
frstr+='\n'
firingRatesFile=open(dataPath+'firingRates.csv','a')
firingRatesFile.writelines(frstr)
firingRatesFile.close()
#print "************************************** file writing",text
#res = open(dataPath+'OutSummary_'+logFileName+'.txt','a')
res = open(dataPath+'OutSummary.txt','a')
res.writelines(text)
res.close()
validationFile = open("validationArray.csv",'a')
validationFile.write(validationStr)
validationFile.close()
#-------------------------
# Displays
#-------------------------
if showRasters and interactive:
displayStr = ' ('+antagStr[:-1]+')' if (antagInjectionSite != 'none') else ''
for N in NUCLEI:
nest.raster_plot.from_device(spkDetect[N],hist=True,title=N+displayStr)
nest.raster_plot.show()
return (score, 5 if antagInjectionSite == 'none' else 1),out
#-----------------------------------------------------------------------
# PActiveCNS/PTN : proportion of "active" neurons in the CSN/PTN populations (in [0.,1.])
#
#-----------------------------------------------------------------------
def checkGurneyTest(showRasters=False,params={},CSNFR=[2.,10.], PActiveCSN=1., PTNFR=[15.,35], PActivePTN=1., antagInjectionSite='none',antag=''):
nest.ResetKernel()
dataPath='log/'
nest.SetKernelStatus({'local_num_threads': params['nbcpu'] if ('nbcpu' in params) else 2, "data_path": dataPath})
initNeurons()
offsetDuration = 200.
simDuration = 800. # ms
loadLG14params(params['LG14modelID'])
# We check that all the necessary parameters have been defined. They should be in the modelParams.py file.
# If one of them misses, we exit the program.
necessaryParams=['nbCh','nbMSN','nbFSI','nbSTN','nbGPe','nbGPi','nbCSN','nbPTN','nbCMPf','IeGPe','IeGPi','GMSN','GFSI','GSTN','GGPe','GGPi','inDegCSNMSN','inDegPTNMSN','inDegCMPfMSN','inDegMSNMSN','inDegFSIMSN','inDegSTNMSN','inDegGPeMSN','inDegCSNFSI','inDegPTNFSI','inDegSTNFSI','inDegGPeFSI','inDegCMPfFSI','inDegFSIFSI','inDegPTNSTN','inDegCMPfSTN','inDegGPeSTN','inDegCMPfGPe','inDegSTNGPe','inDegMSNGPe','inDegGPeGPe','inDegMSNGPi','inDegSTNGPi','inDegGPeGPi','inDegCMPfGPi',]
for nepa in necessaryParams:
if nepa not in params:
print "Missing parameter:",nepa
exit()
nbRecord = 2 # number of channels whose activity will be recorded
if params['nbCh']<2:
print 'need at least 2 channels to perform Gurney test'
exit()
elif params['nbCh']>2:
nbRecord = 3 # if we have more than 2 channels, we will also record one of the neutral channels
#-------------------------
# creation and connection of the neural populations
#-------------------------
createBG_MC()
connectBG_MC(antagInjectionSite,antag)
#-------------------------
# prepare the firing rates of the inputs for the 5 steps of the experiment
#-------------------------
gCSN = CSNFR[1]-CSNFR[0]
gPTN = PTNFR[1]-PTNFR[0]
activityLevels = np.array([[0,0.4,0.4,0.6,0.4], [0.,0.,0.6,0.6,0.6]])
CSNrate= gCSN * activityLevels + np.ones((5)) * CSNFR[0]
PTNrate= gPTN * activityLevels + np.ones((5)) * PTNFR[0]
#-------------------------
# and prepare the lists of neurons that will be affected by these activity changes
#-------------------------
ActPop = {'CSN':[(),()],'PTN':[(),()]}
if 'Fake' in globals():
if 'CSN' in Fake:
if PActiveCSN==1.:
ActPop['CSN']=Fake['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Fake['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Pop['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if 'PTN' in Fake:
if PActivePTN==1.:
ActPop['PTN']=Fake['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Fake['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Pop['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Pop['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Pop['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
#-------------------------
# log-related variables
#-------------------------
score = 0
expeRate={}
for N in NUCLEI:
expeRate[N]=-1. * np.ones((nbRecord,5))
inspector = {}
for N in NUCLEI:
inspector[N] = nest.Create("spike_detector", params={"withgid": True, "withtime": True, "label": N, "to_file": False})
for i in range(nbRecord):
nest.Connect(Pop[N][i],inspector[N])
#-------------------------
# write header in firingRate summary file
#-------------------------
frstr = 'step , '
for i in range(nbRecord):
for N in NUCLEI:
frstr += N+' ('+str(i)+') , '
frstr+='\n'
firingRatesFile=open(dataPath+'firingRates.csv','w')
firingRatesFile.writelines(frstr)
#----------------------------------
# Loop over the 5 steps of the test
#----------------------------------
for timeStep in range(5):
#-------------------------
# measures
#-------------------------
spkDetect=[{},{},{}] # list of spike detector dictionaries used to record the experiment in the first 3 channels
antagStr = ''
if antagInjectionSite != 'none':
antagStr = antagInjectionSite+'_'+antag+'_'
for i in range(nbRecord):
for N in NUCLEI:
spkDetect[i][N] = nest.Create("spike_detector", params={"withgid": True, "withtime": True, "label": str(timeStep)+'_'+antagStr+N, "to_file": True, 'start':offsetDuration + timeStep*(offsetDuration+simDuration),'stop':(timeStep+1)*(offsetDuration+simDuration)})
nest.Connect(Pop[N][i], spkDetect[i][N])
frstr = str(timeStep) + ', '
#-------------------------
# Simulation
#-------------------------
print '====== Step',timeStep,'======'
print 'Channel 0:',CSNrate[0,timeStep],PTNrate[0,timeStep]
print 'Channel 1:',CSNrate[1,timeStep],PTNrate[1,timeStep]
'''
if 'Fake' in globals():
if 'CSN' in Fake:
nest.SetStatus(Fake['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Fake['CSN'][1],{'rate':CSNrate[1,timeStep]})
else:
nest.SetStatus(Pop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Pop['CSN'][1],{'rate':CSNrate[1,timeStep]})
if 'PTN' in Fake:
nest.SetStatus(Fake['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Fake['PTN'][1],{'rate':PTNrate[1,timeStep]})
else:
nest.SetStatus(Pop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Pop['PTN'][1],{'rate':PTNrate[1,timeStep]})
else:
nest.SetStatus(Pop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Pop['CSN'][1],{'rate':CSNrate[1,timeStep]})
nest.SetStatus(Pop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Pop['PTN'][1],{'rate':PTNrate[1,timeStep]})
'''
nest.SetStatus(ActPop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(ActPop['CSN'][1],{'rate':CSNrate[1,timeStep]})
nest.SetStatus(ActPop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(ActPop['PTN'][1],{'rate':PTNrate[1,timeStep]})
nest.Simulate(simDuration+offsetDuration)
for i in range(nbRecord):
print '------ Channel',i,'-------'
for N in NUCLEI:
#strTestPassed = 'NO!'
expeRate[N][i,timeStep] = nest.GetStatus(spkDetect[i][N], 'n_events')[0] / float(nbSim[N]*simDuration) * 1000
print 't('+str(timeStep)+')',N,':',expeRate[N][i,timeStep],'Hz'
frstr += '%f , ' %(expeRate[N][i,timeStep])
strTestPassed = 'YES!'
if timeStep == 0:
for i in range(params['nbCh']):
if expeRate['GPi'][0,timeStep]<FRRNormal['GPi'][0]:
strTestPassed = 'NO!'
meanRestGPi = expeRate['GPi'][:,timeStep].mean()
elif timeStep == 1:
if expeRate['GPi'][0,timeStep] > meanRestGPi*0.9:
strTestPassed = 'NO!'
elif timeStep == 2:
if expeRate['GPi'][1,timeStep] > meanRestGPi*0.9 or expeRate['GPi'][0,timeStep] < expeRate['GPi'][1,timeStep]:
strTestPassed = 'NO!'
elif timeStep == 3:
if expeRate['GPi'][0,timeStep] > meanRestGPi*0.9 or expeRate['GPi'][1,timeStep] > meanRestGPi*0.9 :
strTestPassed = 'NO!'
elif timeStep == 4:
if expeRate['GPi'][1,timeStep] > meanRestGPi*0.9 or expeRate['GPi'][0,timeStep] < expeRate['GPi'][1,timeStep]:
strTestPassed = 'NO!'
if strTestPassed == 'YES!':
score +=1
print '------ Result ------'
print expeRate['GPi'][0,timeStep],'Hz',expeRate['GPi'][1,timeStep],'Hz',strTestPassed
# write measured firing rates in csv file
frstr+='\n'
firingRatesFile.writelines(frstr)
#-------------------------
# Displays
#-------------------------
'''
if showRasters and interactive:
for i in range(nbRecord):
displayStr = ' Channel '+str(i)
displayStr+=' ('+antagStr[:-1]+')' if (antagInjectionSite != 'none') else ''
#for N in NUCLEI:
for N in ['MSN','STN']:
#nest.raster_plot.from_device(spkDetect[i][N],hist=True,title=N+displayStr)
nest.raster_plot.from_device(spkDetect[i][N],hist=False,title=N+displayStr)
nest.raster_plot.show()
'''
if showRasters and interactive:
for N in NUCLEI:
nest.raster_plot.from_device(inspector[N],hist=True,title=N)
nest.raster_plot.show()
firingRatesFile.close()
return score,5
#-----------------------------------------------------------------------
# PActiveCNS/PTN : proportion of "active" neurons in the CSN/PTN populations (in [0.,1.])
#
#-----------------------------------------------------------------------
def checkGurneyTestGeneric(trials_dico,ratio=1.5,offsetTime=200,simuTime=800,shuffled=True,xytab=np.arange(0.,1.,0.1),showRasters=False,params={},CSNFR=[2.,10.], PActiveCSN=1., PTNFR=[15.,35], PActivePTN=1., antagInjectionSite='none',antag=''):
nest.ResetKernel()
dataPath='log/'
nest.SetKernelStatus({'local_num_threads': params['nbcpu'] if ('nbcpu' in params) else 2, "data_path": dataPath})
initNeurons()
offsetDuration = float(offsetTime)
simDuration = float(simuTime) # ms
print '/!\ Using the following LG14 parameterization',params['LG14modelID']
loadLG14params(params['LG14modelID'])
# We check that all the necessary parameters have been defined. They should be in the modelParams.py file.
# If one of them misses, we exit the program.
necessaryParams=['nbCh','nbMSN','nbFSI','nbSTN','nbGPe','nbGPi','nbCSN','nbPTN','nbCMPf','IeGPe','IeGPi','GMSN','GFSI','GSTN','GGPe','GGPi','inDegCSNMSN','inDegPTNMSN','inDegCMPfMSN','inDegMSNMSN','inDegFSIMSN','inDegSTNMSN','inDegGPeMSN','inDegCSNFSI','inDegPTNFSI','inDegSTNFSI','inDegGPeFSI','inDegCMPfFSI','inDegFSIFSI','inDegPTNSTN','inDegCMPfSTN','inDegGPeSTN','inDegCMPfGPe','inDegSTNGPe','inDegMSNGPe','inDegGPeGPe','inDegMSNGPi','inDegSTNGPi','inDegGPeGPi','inDegCMPfGPi',]
for nepa in necessaryParams:
if nepa not in params:
print "Missing parameter:",nepa
exit()
nbRecord = 2 # number of channels whose activity will be recorded
if params['nbCh']<2:
print 'need at least 2 channels to perform Gurney test'
exit()
elif params['nbCh']>2:
nbRecord = 3 # if we have more than 2 channels, we will also record one of the neutral channels
#-------------------------
# creation and connection of the neural populations
#-------------------------
createBG_MC()
connectBG_MC(antagInjectionSite,antag,prms=params)
#-------------------------
# prepare the firing rates of the inputs for the 5 steps of the experiment
#-------------------------
gCSN = CSNFR[1]-CSNFR[0]
gPTN = PTNFR[1]-PTNFR[0]
activityLevels1 = list()
activityLevels2 = list()
if xytab[0] != 0. :
xytab.insert(0,0.)
repet = len(xytab)
xytab = map(lambda x : round(x,1),list(xytab))
for x in xytab :
activityLevels1 += [x] * repet
activityLevels2 += xytab
if shuffled :
# keeping 0,0 as the first test and shuffling the rest
zipper = zip(activityLevels1[1:],activityLevels2[1:])
rnd.shuffle(zipper) # to have it with a random seed : rnd.shuffle(zipper,rnd.random)
zipper = zip(*zipper)
activityLevels1[1:] = zipper[0]
activityLevels2[1:] = zipper[1]
activityLevels = np.array([activityLevels1,activityLevels2]) # for both channels
nbTimes = len(activityLevels[0])
CSNrate = gCSN * activityLevels + np.ones((nbTimes)) * CSNFR[0]
PTNrate= gPTN * activityLevels + np.ones((nbTimes)) * PTNFR[0]
#-------------------------
# and prepare the lists of neurons that will be affected by these activity changes
#-------------------------
ActPop = {'CSN':[(),()],'PTN':[(),()]}
if 'Fake' in globals():
if 'CSN' in Fake:
if PActiveCSN==1.:
ActPop['CSN']=Fake['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Fake['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Pop['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if 'PTN' in Fake:
if PActivePTN==1.:
ActPop['PTN']=Fake['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Fake['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Pop['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
else:
if PActiveCSN==1.:
ActPop['CSN']=Pop['CSN']
else:
for i in range(2):
ActPop['CSN'][i] = tuple(rnd.choice(a=np.array(Pop['CSN'][i]),size=int(nbSim['CSN']*PActiveCSN),replace=False))
if PActivePTN==1.:
ActPop['PTN']=Pop['PTN']
else:
for i in range(2):
ActPop['PTN'][i] = tuple(rnd.choice(a=np.array(Pop['PTN'][i]),size=int(nbSim['PTN']*PActivePTN),replace=False))
#-------------------------
# log-related variables
#-------------------------
score = 0
expeRate={}
for N in NUCLEI:
expeRate[N]=-1. * np.ones((nbRecord,nbTimes)) # for the 100 tests
inspector = {}
for N in NUCLEI:
inspector[N] = nest.Create("spike_detector", params={"withgid": True, "withtime": True, "label": N, "to_file": False})
for i in range(nbRecord):
nest.Connect(Pop[N][i],inspector[N])
#-------------------------
# write header in firingRate summary file
#-------------------------
frstr = 'step , '
for i in range(nbRecord):
for N in NUCLEI:
frstr += N+' ('+str(i)+') , '
frstr+='\n'
firingRatesFile=open(dataPath+'firingRates.csv','w')
firingRatesFile.writelines(frstr)
#----------------------------------
# Loop over all the steps of the test
#----------------------------------
for timeStep in range(nbTimes):
print "\n\tTime step %d\n" % timeStep
key = (round(activityLevels[0][timeStep],1),round(activityLevels[1][timeStep],1))
#-------------------------
# measures
#-------------------------
spkDetect=[{},{},{}] # list of spike detector dictionaries used to record the experiment in the first 3 channels
antagStr = ''
if antagInjectionSite != 'none':
antagStr = antagInjectionSite+'_'+antag+'_'
for i in range(nbRecord):
for N in NUCLEI:
spkDetect[i][N] = nest.Create("spike_detector", params={"withgid": True, "withtime": True, "label": str(timeStep)+'_'+antagStr+N, "to_file": False, 'start':offsetDuration + timeStep*(offsetDuration+simDuration),'stop':(timeStep+1)*(offsetDuration+simDuration)})
nest.Connect(Pop[N][i], spkDetect[i][N])
frstr = str(timeStep) + ', '
#-------------------------
# Simulation
#-------------------------
print '====== Step',timeStep,'======'
print 'Channel 0:',CSNrate[0,timeStep],PTNrate[0,timeStep]
print 'Channel 1:',CSNrate[1,timeStep],PTNrate[1,timeStep]
'''
if 'Fake' in globals():
if 'CSN' in Fake:
nest.SetStatus(Fake['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Fake['CSN'][1],{'rate':CSNrate[1,timeStep]})
else:
nest.SetStatus(Pop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Pop['CSN'][1],{'rate':CSNrate[1,timeStep]})
if 'PTN' in Fake:
nest.SetStatus(Fake['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Fake['PTN'][1],{'rate':PTNrate[1,timeStep]})
else:
nest.SetStatus(Pop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Pop['PTN'][1],{'rate':PTNrate[1,timeStep]})
else:
nest.SetStatus(Pop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(Pop['CSN'][1],{'rate':CSNrate[1,timeStep]})
nest.SetStatus(Pop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(Pop['PTN'][1],{'rate':PTNrate[1,timeStep]})
'''
nest.SetStatus(ActPop['CSN'][0],{'rate':CSNrate[0,timeStep]})
nest.SetStatus(ActPop['CSN'][1],{'rate':CSNrate[1,timeStep]})
nest.SetStatus(ActPop['PTN'][0],{'rate':PTNrate[0,timeStep]})
nest.SetStatus(ActPop['PTN'][1],{'rate':PTNrate[1,timeStep]})
nest.Simulate(simDuration+offsetDuration)
for i in range(nbRecord):
print '------ Channel',i,'-------'
for N in NUCLEI:
#strTestPassed = 'NO!'
expeRate[N][i,timeStep] = nest.GetStatus(spkDetect[i][N], 'n_events')[0] / float(nbSim[N]*simDuration) * 1000
print 't('+str(timeStep)+')',N,':',expeRate[N][i,timeStep],'Hz'
frstr += '%f , ' %(expeRate[N][i,timeStep])
strTestPassed = 'YES!'
choosenChannel = "0"
# for the no input activity in both channels
if activityLevels[0][timeStep]==activityLevels[1][timeStep]==0 :
for i in range(params['nbCh']):
if expeRate['GPi'][i,timeStep]<FRRNormal['GPi'][0] or expeRate['GPi'][i,timeStep]>FRRNormal['GPi'][1]:
strTestPassed = 'NO!'
choosenChannel = "3"
else :
choosenChannel = "0"
meanRestGPi = expeRate['GPi'][:,timeStep].mean()
# for equal & non zero input activity
elif activityLevels[0][timeStep] == activityLevels[1][timeStep] :
if expeRate['GPi'][0,timeStep] > meanRestGPi*0.9 or expeRate['GPi'][1,timeStep] > meanRestGPi*0.9 :
strTestPassed = 'NO!'
choosenChannel = "3"
# both ok checking the chosen one
elif expeRate['GPi'][0,timeStep] > expeRate['GPi'][1,timeStep] * ratio:
choosenChannel = "2"
elif expeRate['GPi'][1,timeStep] > expeRate['GPi'][0,timeStep] * ratio:
choosenChannel = "1"
else :
choosenChannel = "0"
# for channel 1 input activity higher then channel 2 input activity
elif activityLevels[0][timeStep] > activityLevels[1][timeStep] :
if expeRate['GPi'][0,timeStep] > meanRestGPi*0.9 :
strTestPassed = 'NO!'
choosenChannel = "3"
# both ok checking the chosen one
elif expeRate['GPi'][0,timeStep] > expeRate['GPi'][1,timeStep] * ratio:
choosenChannel = "2"
elif expeRate['GPi'][1,timeStep] > expeRate['GPi'][0,timeStep] * ratio:
choosenChannel = "1"
else :
choosenChannel = "0"
# for channel 1 input activity lower then channel 2 input activity
elif activityLevels[0][timeStep] < activityLevels[1][timeStep] :
if expeRate['GPi'][1,timeStep] > meanRestGPi*0.9 :
strTestPassed = 'NO!'
choosenChannel = "3"
# both ok checking the chosen one
elif expeRate['GPi'][0,timeStep] > expeRate['GPi'][1,timeStep] * ratio:
choosenChannel = "2"
elif expeRate['GPi'][1,timeStep] > expeRate['GPi'][0,timeStep] * ratio:
choosenChannel = "1"
else :
choosenChannel = "0"
print '------ Result ------'
print expeRate['GPi'][0,timeStep],'Hz',expeRate['GPi'][1,timeStep],'Hz',strTestPassed
chan1Val = activityLevels[0][timeStep]
chan2Val = activityLevels[1][timeStep]
print "channel1 : ",chan1Val
print "channel2 : ",chan2Val
print "gagnant = ", choosenChannel
print "--------------------"
if trials_dico.has_key(key) :
trials_dico[key].append(choosenChannel)
else :
trials_dico[key] = [choosenChannel]
# write measured firing rates in csv file
#frstr+='\n'
#firingRatesFile.writelines(frstr)
os.system("rm -f log/*.gdf")
#-------------------------
# Displays
#-------------------------
'''
if showRasters and interactive:
for i in range(nbRecord):
displayStr = ' Channel '+str(i)
displayStr+=' ('+antagStr[:-1]+')' if (antagInjectionSite != 'none') else ''
#for N in NUCLEI:
for N in ['MSN','STN']:
#nest.raster_plot.from_device(spkDetect[i][N],hist=True,title=N+displayStr)
nest.raster_plot.from_device(spkDetect[i][N],hist=False,title=N+displayStr)
nest.raster_plot.show()
'''
if showRasters and interactive:
for N in NUCLEI:
nest.raster_plot.from_device(inspector[N],hist=True,title=N)
nest.raster_plot.show()
firingRatesFile.close()
return xytab,trials_dico
#-----------------------------------------------------------------------
# PActiveCNS/PTN : proportion of "active" neurons in the CSN/PTN populations (in [0.,1.])
#
#-----------------------------------------------------------------------
def checkGurneyTestGenericReZero(frtrials_dico,ratio=1.5,shuffled=True,xytab=np.arange(0.,1.,0.1),showRasters=False,params={},CSNFR=[2.,10.], PActiveCSN=1., PTNFR=[15.,35], PActivePTN=1., antagInjectionSite='none',antag='',reversing=False,shutOffset=False,offset=200,simuTime=800,sameVal=None,constantChan=None,rezero=False):
nest.ResetKernel()
dataPath='log/'
nest.SetKernelStatus({'local_num_threads': params['nbcpu'] if ('nbcpu' in params) else 2, "data_path": dataPath})
initNeurons()
offsetDuration = float(offset)
simDuration = float(simuTime) # ms
print '/!\ Using the following LG14 parameterization',params['LG14modelID']
loadLG14params(params['LG14modelID'])
# VARIABLES :
selected = []
steps = []
# We check that all the necessary parameters have been defined. They should be in the modelParams.py file.
# If one of them misses, we exit the program.
necessaryParams=['nbCh','nbMSN','nbFSI','nbSTN','nbGPe','nbGPi','nbCSN','nbPTN','nbCMPf','IeGPe','IeGPi','GMSN','GFSI','GSTN','GGPe','GGPi','inDegCSNMSN','inDegPTNMSN','inDegCMPfMSN','inDegMSNMSN','inDegFSIMSN','inDegSTNMSN','inDegGPeMSN','inDegCSNFSI','inDegPTNFSI','inDegSTNFSI','inDegGPeFSI','inDegCMPfFSI','inDegFSIFSI','inDegPTNSTN','inDegCMPfSTN','inDegGPeSTN','inDegCMPfGPe','inDegSTNGPe','inDegMSNGPe','inDegGPeGPe','inDegMSNGPi','inDegSTNGPi','inDegGPeGPi','inDegCMPfGPi',]
for nepa in necessaryParams:
if nepa not in params:
print "Missing parameter:",nepa
exit()
nbRecord = 2 # number of channels whose activity will be recorded
if params['nbCh']<2:
print 'need at least 2 channels to perform Gurney test'
exit()
elif params['nbCh']>2:
nbRecord = 3 # if we have more than 2 channels, we will also record one of the neutral channels
#-------------------------
# creation and connection of the neural populations
#-------------------------
createBG_MC()
connectBG_MC(antagInjectionSite,antag)
#-------------------------
# prepare the firing rates of the inputs for the 5 steps of the experiment
#-------------------------
gCSN = CSNFR[1]-CSNFR[0]
gPTN = PTNFR[1]-PTNFR[0]
activityLevels1 = list()
activityLevels2 = list()
if xytab[0] != 0. :
xytab.insert(0,0.)
xytab = map(lambda x : round(x,1),list(xytab))
for x in xytab :
# to associate every nb with every nb
activityLevels1 += [x] * len(xytab)
activityLevels2 += xytab
if shuffled :
# keeping 0,0 as the first test and shuffling the rest
zipper = zip(activityLevels1[1:],activityLevels2[1:])
rnd.shuffle(zipper) # to have it with a random seed : rnd.shuffle(zipper,rnd.random)
activityLevels = zip(*zipper)
else :
activityLevels = ([activityLevels1,activityLevels2]) # for both channels
# to generate always the same tuple of activity for chan1 and 2 during length time as : sameVal=None or (chan1 activity,chan2 activity,length)
if not sameVal is None :
activityLevels[0] = [sameVal[0]] * sameVal[2]
activityLevels[1] = [sameVal[1]] * sameVal[2]
# to generate always one channel constant as constantChan=None or (chan nb,chan activity)
if not constantChan is None :
if sameVal :
activityLevels[constantChan[0]] = [constantChan[1]] * sameVal[2]
else :
activityLevels[constantChan[0]] = [constantChan[1]] * len(xytab)
# the other chan is a simple xytab
activityLevels[((constantChan[0]+1)%2)] = xytab
if activityLevels[0][0]!=0. or activityLevels[1][0]!=0. :
activityLevels[0].insert(0,0.)
activityLevels[1].insert(0,0.)
nbTimes = len(activityLevels[0])
# for generating a rest state, a (0,0) Cortical input
if rezero :
nbTimes *= 2
# inserting 0.,0.
actLevs = []
for i in range(nbTimes) :
if i%2==1 :
actLevs.append((activityLevels[0][i/2],activityLevels[1][i/2]))
else :
actLevs.append((0.,0.))
activityLevels = zip(*actLevs)