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simulator.py
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# -*- coding: utf-8 -*-
"""
##
## Simulation of the electrophysiology of the beta-cell and hubs effects.
## Using package NEURON yale.
##
## Created by Chon Lei
## Last updated: August 2017
##
"""
try:
from neuron import h
except Exception:
raise Exception("Please properly install NEURON package: http://www.neuron.yale.edu/neuron/download")
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pylab as plt
import random as random
import os.path as path
import modelSetup
import sys
import pickle
######
## Define model and setup; for testing only
######
"""
model
gjmodel
morphology
species # 0: mouse; 1: human; 2: cubic lattice
pyseed
isImitateExp # if True, simulate whole islet but only analyse imaged cells
mode # 0: WT; 1: silent hubs; 2: silent non hubs
silenceStart # I clamp hubs to silence them, compare results from Johnston et al., 2016
silenceDur
silenceAmp #-100#mV #-0.005#uA
pHubs # percentage/fraction of hubs in islet (if <1) else number of hubs in islet (i.e. >1)
methodToPickHubs # 0: random; 1: top GJ links; 2: bottom GJ links
whichHub # indix of imaged hub/non-hub to silence
ggap # model 1,2: ~1/6.*5.1*0.385*1e-4; model 3: ~0.12 [nS]
ggaphub
pggaphubstd # fraction of ggaphub as std
pggapstd # fraction of ggap as std
gjtau
dthres # spatial cutoff distance to def GJ connection
isletsize # islet size of interest (None for whole islet)
hetVar # it is % of mean's standard deviation
tstop # usually in [ms]
dt # usually in [ms]
downSampling # down sample the output -> output_timestep = dt*downSampling
tbatch # split simulation into batches; same unit as tstop
"""
modelParam = {'model' : 5, \
'gjmodel' : 1, \
'morphology' : 1, \
'species' : 0, \
'pyseed' : 2, \
'isImitateExp' : 1, \
'mode' : 1, \
'silenceStart' : 600e3, \
'silenceDur' : 250e3, \
'silenceAmp' : -100.0, \
'pHubs' : 0.1, \
'methodToPickHubs' : 0 , \
'whichHub' : 0 , \
'ggap' : 0.01, \
'ggaphub' : 0.01, \
'pggaphubstd' : 0.7, \
'pggapstd' : 0.7, \
'gjtau' : 400.0, \
'p_connect': 1.0, \
'dthres' : 17.5, \
'isletsize' : 40 , \
'hetVar' : 0.2, \
'tstop' : 600e3, \
'dt' : 0.1 , \
'downSampling' : 1000, \
'tbatch' : 5e2}
#modelParam['model_kwargs'] = {'beta':{'gkatp':(6.,7.0) , 'useDistribution':'sq'} , 'betahub':{'hubgkatp':11.0 }}
#modelParam['model_kwargs'] = {'beta':{'gamma':(0.985,0.0) , 'useDistribution':None , 'applytime':0e3} , 'betahub':{'hubgamma':1.0 , 'applytime':0e3}}
#modelParam['model_kwargs'] = {'beta':{'gkatp':(6.,7.0) , 'useDistribution':'sq' , 'applytime':0e3} , 'betahub':{'hubgkatp':11.0 , 'applytime':0e3}}
modelParam['model_kwargs'] = {'beta':{'glu':(6.,7.) , 'useDistribution':'sq' , 'applytime':0e3} , 'betahub':{'hubglu':11.0 , 'applytime':0e3}}
def main(modelParam=modelParam, hubsList_temp=[]):
######
## Define model and setup
######
model = modelParam['model' ]
gjmodel = modelParam['gjmodel']
morphology = modelParam['morphology']
species = modelParam['species']
pyseed = modelParam['pyseed']
isImitateExp = modelParam['isImitateExp']
mode = modelParam['mode']
silenceStart = modelParam['silenceStart']
silenceDur = modelParam['silenceDur']
silenceAmp = modelParam['silenceAmp']
pHubs = modelParam['pHubs']
##TODO need to do methodToPickHubs
methodToPickHubs = modelParam['methodToPickHubs']
whichHub = modelParam['whichHub']
ggap = modelParam['ggap']
ggaphub = modelParam['ggaphub']
try:
pggaphubstd = modelParam['pggaphubstd']
except Exception:
pggaphubstd = 0.0
try:
pggapstd = modelParam['pggapstd']
except Exception:
pggapstd = 0.0
gjtau = modelParam['gjtau']
dthres = modelParam['dthres']
isletsize = modelParam['isletsize']
hetVar = modelParam['hetVar']
tstop = modelParam['tstop']
dt = modelParam['dt']
downSampling = modelParam['downSampling']
tbatch = modelParam['tbatch']
try:
# model 1 default: {'beta':{} , 'betahub':{'hubkatp':-5.8}}
# model 2 default: {'beta':{'gkatp':(6.5,0.0) , 'useDistribution':None} , 'betahub':{'hubgkatp':10}}
# model 3 default: {'beta':{'gkatp':(6.5,0.0) , 'useDistribution':None , 'applytime':5e3} , 'betahub':{'hubgkatp':10 , 'applytime':5e3}}
# model 4 default: {'beta':{'gamma':(0.5,0.0) , 'useDistribution':None , 'applytime':5e3} , 'betahub':{'hubgamma':1.0 , 'applytime':5e3}}
model_kwargs = modelParam['model_kwargs']
except:
model_kwargs = { 'beta':{} , 'betahub':{} }
try:
p_connect = modelParam['p_connect']
except:
p_connect = 0.7#1.0
if isImitateExp:
isletsize = None # force to use whole islet
# Create output directories and log file
try:
parentout = modelParam['parentout']
subidx = modelParam['subidx']
outputidx, outputdir = modelSetup.outputSetup_sub(model,morphology,pyseed,mode,parentout,subidx)
except Exception:
outputidx, outputdir = modelSetup.outputSetup(model,morphology,pyseed,mode)
outlog = path.join(outputdir, outputidx+'.log')
outCa = path.join(outputdir, 'Ca_'+outputidx)
outVm = path.join(outputdir, 'Vm_'+outputidx)
# save modelParam
with open(path.join(outputdir,'modelParam.pkl'), 'wb') as f:
pickle.dump(modelParam, f, pickle.HIGHEST_PROTOCOL)
# log model setup as txt file
with open(outlog, 'w') as f:
f.write('#model = %d \n#gjmodel = %d \n#morphology = %d \n#species = %d \n#pyseed = %d \n#isImitateExp = %d \n#mode = %d \n#silenceStart = %f \n#silenceDur = %f \n#silenceAmp = %f \n#pHubs = %f \n#methodToPickHubs = %d \n#ggap = %f \n#ggaphub = %f \n#gjtau = %f \n#dthres = %f \n#isletsize = '%(model,gjmodel,morphology,species,pyseed,isImitateExp,mode,silenceStart,silenceDur,silenceAmp,pHubs,methodToPickHubs,ggap,ggaphub,gjtau,dthres)+str(isletsize)+' \n')
f.write('#hetVar = %f \n#tstop = %f \n#dt = %f \n#downSampling = %d \n#tbatch = %f \n\n'%(hetVar,tstop,dt,downSampling,tbatch))
if model == 1:
## Created by Chon Lei
## The current version is based on the model from
## M. Meyer-Hermann 2007 Biophysical Journal Vol. 93
## Last updated: 18/02/2017
pathToModel = "../models/betacell_hermann2007_vFinal/"
modelSetup.setupHetDict(varp=hetVar)
loadHetero = modelSetup.loadHeteroHermann2007
setHetero = modelSetup.setHeteroHermann2007
HetDict = modelSetup.HetDictHermann2007
#h.load_file (pathToModel+"betahub.hoc")
def defineBeta(cellList,i,**kwargs):
# define beta cell
cellList.append(h.betacell())
if hetVar>0:
setHetero(cellList[i],HetMatrix,i)
def defineBetaHub(cellList,i,hubkatp=-5.8):
# define beta hub cell
cellList.append(h.betahub())
cellList[i].soma(0.5).nkatp_katp = hubskatp
elif model == 2:
## Created by Chon Lei
## The current version is based on the model from
## M. Meyer-Hermann 2007 Biophysical Journal Vol. 93
## Last updated: 18/02/2017
pathToModel = "../models/betacell_hermann2007_vMetabolic/"
modelSetup.setupHetDict(varp=hetVar)
loadHetero = modelSetup.loadHeteroHermann2007
setHetero = modelSetup.setHeteroHermann2007
HetDict = modelSetup.HetDictHermann2007
def defineBeta(cellList,i,gkatp=(6.5,0.0),useDistribution=None):
# define beta cell
cellList.append(h.betacell())
if hetVar>0:
setHetero(cellList[i],HetMatrix,i)
if useDistribution == None:
cellList[i].soma(0.5).gammatoset_katp = gkatp[0]
elif useDistribution == 'sq':
cellList[i].soma(0.5).gammatoset_katp = np.random.uniform(gkatp[0],gkatp[1])
elif useDistribution == 'normal':
cellList[i].soma(0.5).gammatoset_katp = gkatp[0] + np.random.normal(0.0,1.0)*np.sqrt(gkatp[1])
else:
cellList[i].soma(0.5).gammatoset_katp = gkatp[0]
def defineBetaHub(cellList,i,hubgkatp=10.0):
# define beta hub cell
cellList.append(h.betacell())
cellList[i].soma(0.5).gammatoset_katp = hubgkatp
elif model==3:
## Created by Chon Lei
## The current version is based on the model from
## Cha et al. 2011 The Journal of General Physiology
## Last updated: 18/08/2017
pathToModel = "../models/betacell_cha2011_vMetabolic/"
#TODO
modelSetup.setupHetDict(varp=hetVar)
#loadHetero = modelSetup.setHeteroCha2011
setHetero = modelSetup.setHeteroCha2011
setOrigin = modelSetup.setOriginCha2011
HetDict = modelSetup.HetDictCha2011
def defineBeta(cellList,i,gkatp=(6.0,0.0),useDistribution=None,applytime=5e3):
# define beta cell
cellList.append(h.betacell())
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
if hetVar>0:
setHetero(cellList[i],HetMatrix,i)
else:
setOrigin(cellList[i])
if useDistribution == None:
cellList[i].soma(0.5).gammatoset_bcellcha = gkatp[0]
elif useDistribution == 'sq':
cellList[i].soma(0.5).gammatoset_bcellcha = np.random.uniform(gkatp[0],gkatp[1])
elif useDistribution == 'normal':
cellList[i].soma(0.5).gammatoset_bcellcha = gkatp[0] + np.random.normal(0.0,1.0)*np.sqrt(gkatp[1])
else:
cellList[i].soma(0.5).gammatoset_bcellcha = gkatp[0]
def defineBetaHub(cellList,i,hubgkatp=10.0,applytime=5e3):
# define beta hub cell
cellList.append(h.betacell())
setOrigin(cellList[i])
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
cellList[i].soma(0.5).gammatoset_bcellcha = hubgkatp
elif model==4:
## Created by Chon Lei
## The current version is based on the model from
## Cha et al. 2011 The Journal of General Physiology and Hraha et al. 2014 PLOS Computational Biology
## Last updated: 21/08/2017
pathToModel = "../models/betacell_cha2011_hraha2014/"
#TODO
modelSetup.setupHetDict(varp=hetVar)
#loadHetero = modelSetup.setHeteroCha2011
setHetero = modelSetup.setHeteroCha2011
setOrigin = modelSetup.setOriginCha2011
HetDict = modelSetup.HetDictCha2011
def defineBeta(cellList,i,gamma=(0.5,0.0),useDistribution=None,applytime=5e3):
# define beta cell
cellList.append(h.betacell())
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
if hetVar>0:
setHetero(cellList[i],HetMatrix,i)
else:
setOrigin(cellList[i])
if useDistribution == None:
cellList[i].soma(0.5).gammamut_bcellcha = gamma[0]
elif useDistribution == 'sq':
cellList[i].soma(0.5).gammamut_bcellcha = np.random.uniform(gamma[0],gamma[1])
elif useDistribution == 'normal':
cellList[i].soma(0.5).gammamut_bcellcha = gamma[0] + np.random.normal(0.0,1.0)*np.sqrt(gamma[1])
else:
cellList[i].soma(0.5).gammamut_bcellcha = gamma[0]
def defineBetaHub(cellList,i,hubgamma=1.0,applytime=5e3):
# define beta hub cell
cellList.append(h.betacell())
setOrigin(cellList[i])
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
cellList[i].soma(0.5).gammamut_bcellcha = hubgamma
elif model==5:
## Created by Chon Lei
## The current version is based on the model from
## Cha et al. 2011 The Journal of General Physiology and Hraha et al. 2014 PLOS Computational Biology
## Last updated: 21/08/2017
pathToModel = "../models/betacell_cha2011_vMetabolic2/"
#TODO
modelSetup.setupHetDict(varp=hetVar)
#loadHetero = modelSetup.setHeteroCha2011
setHetero = modelSetup.setHeteroCha2011all
setOrigin = modelSetup.setOriginCha2011all
HetDict = modelSetup.HetDictCha2011all
def defineBeta(cellList,i,glu=(0.5,0.0),useDistribution=None,applytime=5e3):
# define beta cell
cellList.append(h.betacell())
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
if hetVar>0:
setHetero(cellList[i],HetMatrix,i)
else:
setOrigin(cellList[i])
if useDistribution == None:
cellList[i].soma(0.5).gammatoset_bcellcha = glu[0]
elif useDistribution == 'sq':
cellList[i].soma(0.5).gammatoset_bcellcha = np.random.uniform(glu[0],glu[1])
elif useDistribution == 'normal':
cellList[i].soma(0.5).gammatoset_bcellcha = glu[0] + np.random.normal(0.0,1.0)*np.sqrt(glu[1])
else:
cellList[i].soma(0.5).gammatoset_bcellcha = glu[0]
#cellList[i].soma(0.5).gKATP_bcellcha = 2.31*1.5 + np.random.normal(0.0,1.0)*2.31*hetVar
#cellList[i].soma(0.5).PCaL_bcellcha = 48.9*0.7 + np.random.normal(0.0,1.0)*48.9*hetVar
def defineBetaHub(cellList,i,hubglu=1.0,applytime=5e3):
# define beta hub cell
cellList.append(h.betacell())
setOrigin(cellList[i])
cellList[i].soma(0.5).gammaapplytime_bcellcha = applytime
cellList[i].soma(0.5).gammatoset_bcellcha = hubglu
#cellList[i].soma(0.5).gKATP_bcellcha = 2.31*0.5 + np.random.normal(0.0,1.0)*2.31*hetVar
#cellList[i].soma(0.5).PCaL_bcellcha = 48.9*1.5 + np.random.normal(0.0,1.0)*48.9*hetVar
if gjmodel==1:
pathToGJModel = "../models/gapjunction_pedersen2015/"
elif gjmodel==2:
pathToGJModel = "../models/gapjunction_hermann2010/"
if species==0:
pathToMorphology = "../morphologies/mouse/Mouse 40-%d.txt"%morphology
elif species==1:
pathToMorphology = ""
elif species==2:
pathToMorphology = "../morphologies/cubic_lattice/cubic%d.txt"%morphology
random.seed(pyseed)
np.random.seed(pyseed)
modelSetup.SetRandomSeed(pyseed)
######
## Import system setup files (.hoc files and system matrix)
######
CoorData = np.loadtxt(pathToMorphology)
# process CoorData to be acceptable format in modelSetup.genCoupleMatrix()
CoorData = CoorData[CoorData[:,0]==11][:,1:4]
CoupledMatrix = modelSetup.genCoupleMatrix(CoorData,dthres,isletsize,True)
tempCoupledMatrix = CoupledMatrix + CoupledMatrix.T
ncells = CoupledMatrix.shape[0]
nSpatialLinks = np.sum(CoupledMatrix+CoupledMatrix.T,1)
if ncells != CoupledMatrix.shape[1]:
raise Exception("CoupledMatrix invalid dimensions.")
Total = (ncells*ncells)/2 - ncells # maximum number of gapjunctions that could be formed
if isImitateExp==1:
imagedCells = modelSetup.getImagedCellIdx(CoorData,topDir=2,imageDepth=10,Ncells=100,method=0)
else:
imagedCells = []
with open(outlog, 'a') as f:
f.write('#imagedCells = ')
f.write(','.join(map(str, imagedCells)))
f.write('\n\n')
try:
# assumed using x64 bits; change if needed
h('nrn_load_dll("%sx86_64/.libs/libnrnmech.so")'%pathToModel)
h('nrn_load_dll("%sx86_64/.libs/libnrnmech.so")'%pathToGJModel)
h.load_file (pathToModel+"betahub.hoc")
h.load_file (pathToModel+"betacell.hoc")
h.load_file (pathToGJModel+"gapjunction.hoc")
except Exception:
raise Exception("Please make sure files has been compiled using \n$ nrnivmodl\n")
######
## System set-up
######
print("*************************")
print("Starting system set-up...")
# Define beta hubs cells
numHubs = int(pHubs*ncells) if pHubs<=1 else pHubs
temp = range(ncells)
random.shuffle(temp)
hubsList = temp[0:numHubs]
if hubsList_temp!=None and hubsList_temp!=[]:
hubsList = hubsList_temp
if False:
truehub = hubsList[0]
#truehub2 = hubsList[1]
hubsList = [truehub]
hubsList += list(np.arange(ncells)[tempCoupledMatrix[:,truehub]>0])
#hubsList += [truehub2]
#hubsList += list(np.arange(ncells)[tempCoupledMatrix[:,truehub2]>0])
if False:
# pick #link and dist
tmpList = []
print hubsList
for hubtmp in hubsList:
if np.sum(tempCoupledMatrix[:,hubtmp])==6:
tmpList.append(hubtmp)
hubsList = tmpList[:]
print hubsList
for iii in range(len(tmpList)-1):
for jjj in range(iii+1,len(tmpList)):
#print np.sqrt(np.sum((CoorData[tmpList[iii]]-CoorData[tmpList[jjj]])**2))
if np.sqrt(np.sum((CoorData[tmpList[iii]]-CoorData[tmpList[jjj]])**2))>(2.1*dthres) and (iii in hubsList):
try:
print jjj
hubsList.remove(tmpList[jjj])
except:
pass
hubsList = hubsList[0:numHubs]
if True:
# pick by number of links
print hubsList
tmpList = np.argsort(np.sum(tempCoupledMatrix, axis=0))
# hubsList = list(tmpList[:len(hubsList)]) # The smallest number of links
hubsList = list(tmpList[-len(hubsList):]) # The largest number of links
imagedHubs = list(set(hubsList).intersection(imagedCells))
numImHubs = int(pHubs*len(imagedCells)) if pHubs<=1 else len(imagedCells)/ncells*pHubs
if isImitateExp and len(imagedHubs) < max(numImHubs,1) and True:
nMorehubs = max(numImHubs,1) - len(imagedHubs)
tempCellsToPick = [x for x in imagedCells if x not in imagedHubs]
random.shuffle(tempCellsToPick)
hubsList += tempCellsToPick[0:nMorehubs]
imagedHubs += tempCellsToPick[0:nMorehubs]
# Use a previously generated indices
# Uncomment this to set specific hubs
#hubsList = [self_def_list]
#imagedHubs = hubsList[:]
print(hubsList)
with open(outlog, 'a') as f:
f.write('#hubsList = \n')
f.write(','.join(map(str, hubsList)))
f.write('\n\n')
f.write('#imagedHubs = ')
f.write(','.join(map(str, imagedHubs)))
f.write('\n\n')
# Randomly pick some non-hubs cells
numNonHubsToPick = numHubs
temp = [i for i in range(ncells) if i not in hubsList]
if True:
aroundHub = []
for hubhub in hubsList:
aroundHub += list(np.arange(ncells)[tempCoupledMatrix[:,hubhub]>0])
temp = [i for i in temp if i not in aroundHub]
random.shuffle(temp)
nonHubsToPickList = temp[0:numNonHubsToPick]
imagedNonHubs = []
if isImitateExp:
temp = [i for i in imagedCells if i not in imagedHubs]
random.shuffle(temp)
imagedNonHubs = temp[0:len(imagedHubs)]
print(nonHubsToPickList)
with open(outlog, 'a') as f:
f.write('#nonHubsToPickList = ')
f.write(','.join(map(str, nonHubsToPickList)))
f.write('\n\n')
f.write('#imagedNonHubs = ')
f.write(','.join(map(str, imagedNonHubs)))
f.write('\n\n')
# Declare heterogeneity matrix
HetMatrix = np.zeros((len(HetDict)+1,ncells))
# Use a previously generated heterogeneity matrix
# Comment this out and use setHeteroHermann2007() to run another simulation
#HetMatrix = np.loadtxt('HetMatrix-mouse40-3.txt')
##TODO: Add function to silent cells
def silenceCell(iclampList,cell,delay=250e3,dur=250e3,amp=-100.0):
# vclamp cell to imitate cell silencing in experiments
iclampList.append(h.SEClamp (0.5, sec = cell.soma) )
iclampList[-1].dur1 = silenceStart
iclampList[-1].rs = 1e9
iclampList[-1].dur2 = dur
iclampList[-1].amp2 = amp
def silenceCellI(iclampList,cell,delay=250e3,dur=250e3,amp=-0.005):
# iclamp cell to imitate cell silencing in experiments
# all spiking: -0.0005; all stay -120mV: -0.005; all stay -72mV: -0.001; all stay -90mV: -0.002;
iclampList.append(h.IClamp (0.5, sec = cell.soma) )
iclampList[-1].delay = delay
iclampList[-1].dur = dur
iclampList[-1].amp = amp
print "Defining cells..."
# Define as beta hub cell if in the hubsList
# Introduce heterogeneity to each defined cell
cell = []
iclamp_hubs = []
toPick = random.randint(0,len(hubsList))
for i in range(ncells):
if i not in hubsList:
defineBeta(cell,i,**(model_kwargs['beta']))
if isImitateExp:
#if i in list(np.arange(ncells)[tempCoupledMatrix[:,imagedHubs[whichHub]]>0]):
#if (i == imagedNonHubs[whichHub]) and (mode==2):
#if False and (i in imagedCells[:50]):
if mode==2 and i in nonHubsToPickList[:50]:
print "silencing cell ",i
with open(outlog, 'a') as f:
f.write('#silencedCell = %d\n'%i)
f.write('#cell%d_nSpatialLinks = %d\n'%(i,nSpatialLinks[i]))
silenceCell(iclamp_hubs,cell[i],silenceStart,silenceDur,silenceAmp)
else:
if (i in nonHubsToPickList[:]) and (mode==2):
print "silencing cell ",i
with open(outlog, 'a') as f:
f.write('#silencedCell = %d\n'%i)
f.write('#cell%d_nSpatialLinks = %d\n'%(i,nSpatialLinks[i]))
silenceCell(iclamp_hubs,cell[i],silenceStart,silenceDur,silenceAmp)
else:
defineBetaHub(cell,i,**(model_kwargs['betahub']))
if isImitateExp:
if mode==1 and (i in hubsList[:50]):#i==imagedHubs[whichHub]:
#or i in list(np.arange(ncells)[tempCoupledMatrix[:,imagedHubs[whichHub]]>0]):
print "silencing cell ",i
with open(outlog, 'a') as f:
f.write('#silencedCell = %d\n'%i)
f.write('#cell%d_nSpatialLinks = %d\n'%(i,nSpatialLinks[i]))
silenceCell(iclamp_hubs,cell[i],silenceStart,silenceDur,silenceAmp)
else:
if mode==1 and i==hubsList[toPick]:
print "silencing cell ",i
with open(outlog, 'a') as f:
f.write('#silencedCell = %d\n'%i)
f.write('#cell%d_nSpatialLinks = %d\n'%(i,nSpatialLinks[i]))
silenceCell(iclamp_hubs,cell[i],silenceStart,silenceDur,silenceAmp)
# Declare heterogeneity GJ matrix
HetGjMatrix = np.zeros(CoupledMatrix.shape)
# Use a previously generated heterogeneity GJ matrix
#HetMatrix = np.loadtxt('')
#TODO can put this in modelSetup.py too
print "Defining gap junction connections..."
gap = []
for i in range(ncells):
for j in range(ncells):
a = np.random.rand()
if CoupledMatrix[i,j] > 0 and ((i in hubsList) or (j in hubsList)) and a<p_connect:
if pggaphubstd > 0:
HetGjMatrix[i,j] = max(ggaphub * (1.0 + np.random.normal(0.0,1.0)*pggaphubstd), 0)
else:
HetGjMatrix[i,j] = ggaphub
if False:
if i in hubsList:
HetGjMatrix[i,j] = ggaphub*7.0/np.sum(tempCoupledMatrix[:,i]>0) * (1.0 + np.random.normal(0.0,1.0)*pggaphubstd)
else:
HetGjMatrix[i,j] = ggaphub*7.0/np.sum(tempCoupledMatrix[:,j]>0) * (1.0 + np.random.normal(0.0,1.0)*pggaphubstd)
gap.append(h.gapjunction(cell[i], cell[j], 0.5, 0.5, HetGjMatrix[i,j]*CoupledMatrix[i,j],gjtau))
elif CoupledMatrix[i,j] > 0 and a<p_connect: #and ((i not in nonHubsToPickList) or (j not in nonHubsToPickList)):
if pggapstd > 0:
HetGjMatrix[i,j] = max(ggap * (1.0 + np.random.normal(0.0,1.0)*pggapstd), 0)
else:
HetGjMatrix[i,j] = ggap
gap.append(h.gapjunction(cell[i], cell[j], 0.5, 0.5, HetGjMatrix[i,j]*CoupledMatrix[i,j],gjtau))
######
## External stimulation
######
"""
stimulus = h.IClamp (0.5, sec = cell[0].soma)
stimulus.delay = 100
stimulus.dur = 300
stimulus.amp = 0.5
"""
######
## System recorder initalisation
######
print("Creating recorder vectors...")
t = h.Vector()
t.record(h._ref_t)
vrec = []
carec = []
for i in range(ncells):
vrec.append(h.Vector())
carec.append(h.Vector())
vrec[i].record(cell[i].soma(0.5)._ref_v)
carec[i].record(cell[i].soma(0.5)._ref_cai)
######
## Main simulation
######
h.load_file("stdrun.hoc")
h.init()
h.dt = dt
h.steps_per_ms = 1./h.dt
print("Running main simulation...")
temptstop = 0 # tstop for current batch
nbatch = int(tstop/tbatch) # split simulation into nbatch
print("Dividing simulation into %d batches..."%nbatch)
tremain = tstop%tbatch # remaining simulation time after nbatch
for i in xrange(nbatch):
if temptstop >= silenceStart:
for iclamp in iclamp_hubs:
iclamp.rs = 0.001
#if temptstop > 150e3:
# for iclamp in iclamp_hubs:
# iclamp.amp = -10.0
#if temptstop > 200e3:
# for iclamp in iclamp_hubs:
# iclamp.amp = -20.0
#if temptstop > 250e3:
# for iclamp in iclamp_hubs:
# iclamp.amp = -30.0
#if temptstop > 300e3:
# for iclamp in iclamp_hubs:
# iclamp.amp = -40.0
temptstop += tbatch # tstop for current batch
h.frecord_init() # reuse all recording vectors
h.continuerun(temptstop)
# exporting Ca time series
tosave = modelSetup.convertSimOutput(carec,downSampling,reuse=True)
modelSetup.savedat(outCa,tosave,'Ca',outlog,idx=i)
# exporting Vm time series
tosave = modelSetup.convertSimOutput(vrec,downSampling,reuse=True)
modelSetup.savedat(outVm,tosave,'Vm',outlog,idx=i)
print("Finished section %d out of %d."%(i+1,nbatch))
if tremain > 0:
print("Running final section...")
h.frecord_init() # reuse all recording vectors
h.continuerun(tstop) # run until the end
# exporting Ca time series
tosave = modelSetup.convertSimOutput(carec,downSampling,reuse=True)
modelSetup.savedat(outCa,tosave,'Ca',outlog,idx=i+1)
# exporting Vm time series
tosave = modelSetup.convertSimOutput(vrec,downSampling,reuse=True)
modelSetup.savedat(outVm,tosave,'Vm',outlog,idx=i+1)
print("Simulation completed! :)")
print("*************************")
######
## Visualisation
######
toVisual = False
if toVisual:
t = np.array(t)
membranepotential = np.array(vrec[0])
plt.plot(t, membranepotential)
membranepotential2 = np.array(vrec[102])
plt.plot(t, membranepotential2)
plt.show()
##########
## End ##
##########
if __name__== '__main__':
try:
print("Loading external model parameters from %s"%sys.argv[1])
modelParam = pickle.load(open(sys.argv[1],'rb'))
except Exception:
print("Cannot read external model parameters...\nUsing default parameters...\n\n")
#modelParam['pHubs'] = 0.05
modelParam['whichHub'] = 3
#modelParam['ggaphub'] = 0.4
#modelParam['silenceAmp'] = -30.0
# just try to run main(), but not expecting to be used in this way
main(modelParam)
##