# $Id: run.py,v 1.53 2010/12/15 22:20:27 samn Exp $
from pyinit import *
from geom import *
from networkmsj import *
from params import *
import sys
# sets up external inputs
if net.noise:
net.set_noise_inputs(h.tstop) #h.tstop sets duration of inpus for make noise case
# handler for printing out time during simulation run
def fi():
for i in range(0,int(h.tstop),100):
h.cvode.event(i, "print " + str(i))
fih = h.FInitializeHandler(1, fi)
# initialize random # generators of NetStims - forces it at beginning of each sim
def myInitNetStims():
#for i in range(19):
# print i,net.pyr.cell[i].soma.v,net.pyr.cell[i].Adend3.v,net.pyr.cell[i].Bdend.v
#for i in range(19):
# print i,net.olm.cell[i].soma.v
#for i in range(19):
# print i,net.bas.cell[i].soma.v
net.init_NetStims()
fihns = h.FInitializeHandler(0, myInitNetStims)
# fihns = h.FInitializeHandler(1, myInitNetStims)
# handler for washin/washout
fiwash = None
olmWash = [0, 0] # olm NMDA value for washin/washout
basWash = [0, 0] # basket NMDA value for washin/washout
pyrWashA = [0, 0] # ...
pyrWashB = [0, 0] # ...
washinT = 0 # washin time
washoutT = 0 # washout time
def dowashin():
print("washIN at ", washinT, " = ", h.t , " ", olmWash[0], basWash[0], pyrWashB[0], pyrWashA[0])
net.olm.set_r("somaNMDA",olmWash[0])
net.bas.set_r("somaNMDA",basWash[0])
net.pyr.set_r("BdendNMDA",pyrWashB[0])
net.pyr.set_r("Adend3NMDA",pyrWashA[0])
def dowashout():
print("washOUT at ", washoutT, " = " , h.t, " ", olmWash[1], basWash[1], pyrWashB[1], pyrWashA[1])
net.olm.set_r("somaNMDA",olmWash[1])
net.bas.set_r("somaNMDA",basWash[1])
net.pyr.set_r("BdendNMDA",pyrWashB[1])
net.pyr.set_r("Adend3NMDA",pyrWashA[1])
def setwash():
print("washinT ", washinT, " washoutT ", washoutT)
h.cvode.event(washinT,"nrnpython(\"dowashin()\")")
h.cvode.event(washoutT,"nrnpython(\"dowashout()\")")
# example to do washin/washout, after loading sim:
# import run
# h.tstop=100
# run.olmWash = [0, 1]
# run.basWash = [1, 1]
# run.pyrWashA = [1, 1]
# run.pyrWashB = [1, 1]
# run.washinT = 30
# run.washoutT = 60
# fiwash = h.FInitializeHandler(1,setwash)
# h.run()
class Power():
pass
class Batch:
def __init__(self,net):
self.net = net #the network, cells, synapses, etc.
self.pow = Power() #the data
def copydata(self,obj):
self.pow.n = obj.n
self.pow.x = obj.x
self.pow.timer = obj.timer
self.pow.tp = obj.tp
self.pow.gp = obj.gp
self.pow.tf = obj.tf
self.pow.gf = obj.gf
self.pow.arch = obj.arch
def save(self):
file = open('filen.obj', 'w')
pickle.dump(self.pow,file)
def load(self):
file = open('filen.obj', 'r')
self.pow = pickle.load(file)
#this function is based on loop in r function, to get a string for the sim params
def getsimstr(self,r1,r2,r3,r4):
simstr = "olm_somaNMDA_" + str(r1) + "_"
simstr = simstr + "bas_somaNMDA_" + str(r2) + "_"
simstr = simstr + "pyr_BdendNMDA_" + str(r3) + "_"
simstr = simstr + "pyr_Adend3NMDA_" + str(r4) + "_"
return simstr
def r(self, n):
self.pow.n = n
self.pow.arch = Archive()
self.pow.x = numpy.linspace(0,1,self.pow.n)
self.pow.timer = h.startsw()
self.pow.tp = numpy.zeros((self.pow.n,self.pow.n,self.pow.n,self.pow.n))
self.pow.gp = numpy.zeros((self.pow.n,self.pow.n,self.pow.n,self.pow.n))
self.pow.tf = numpy.zeros((self.pow.n,self.pow.n,self.pow.n,self.pow.n))
self.pow.gf = numpy.zeros((self.pow.n,self.pow.n,self.pow.n,self.pow.n))
for i1,r1 in enumerate(self.pow.x):
self.net.olm.set_r("somaNMDA",r1)
for i2, r2 in enumerate(self.pow.x):
self.net.bas.set_r("somaNMDA",r2)
for i3, r3 in enumerate(self.pow.x):
self.net.pyr.set_r("BdendNMDA",r3)
for i4, r4 in enumerate(self.pow.x):
self.net.pyr.set_r("Adend3NMDA",r4)
simstr = self.getsimstr(r1,r2,r3,r4)
print("NMDA/AMPA: " + simstr)
h.run()
print("Time: ", h.startsw() - self.pow.timer)
self.pow.arch.reset_time_stamp()
self.net.calc_psd() #calculate lfp,psd and draw it, then save
self.pow.arch.save_fig(3,simstr+"fft")
self.net.rasterplot()#draw raster for all cells, then save
self.pow.arch.save_fig(1,simstr+"rasterogram")
self.pow.arch.save_vec(simstr+"lfp",self.net.vlfp) #save LFP Vector to file
self.net.setrastervecs() #setup raster Vectors for ALL cells & save
self.pow.arch.save_vec(simstr+"idvec",self.net.myidvec)
self.pow.arch.save_vec(simstr+"timevec",self.net.mytimevec)
self.pow.tp[i1,i2,i3,i4] = self.net.tp
self.pow.gp[i1,i2,i3,i4] = self.net.gp
self.pow.tf[i1,i2,i3,i4] = self.net.tf
self.pow.gf[i1,i2,i3,i4] = self.net.gf
def plot_r(self):
self.plot_fun(5, "tp","Theta_Power")
self.plot_fun(6, "gp","Gamma_Power")
self.plot_fun(7, "tf","Theta_Frequency")
self.plot_fun(8, "gf","Gamma_Frequency")
self.plot_fun(9, "tp","Theta_Power_Mean", 1)
self.plot_fun(10,"gp","Gamma_Power_Mean", 1)
self.plot_fun(11,"tf","Theta_Frequency_Mean",1)
self.plot_fun(12,"gf","Gamma_Frequency_Mean",1)
def plot_fun(self, fig, var, ylabel, mode=0):
cond = ["olm","bas","pyrB","pyrA3"]
f = pylab.figure(fig)
f.clf()
f.canvas.mpl_connect('pick_event', onpick)
pylab.subplot(2,2,1)
pylab.xlabel("NMDA/AMPA for " + cond[0])
pylab.ylabel(ylabel)
if mode==0:
for i1, r1 in enumerate(self.pow.x):
for i2, r2 in enumerate(self.pow.x):
for i3, r3 in enumerate(self.pow.x):
print("[:,"+str(i1)+","+str(i2)+","+str(i3)+"]")
pylab.plot(self.pow.x, self.pow.__dict__[var][:,i1,i2,i3],label="[:,"+str(i1)+","+str(i2)+","+str(i3)+"]", picker=1)
#pylab.label()
else:
pylab.plot(self.pow.x, self.pow.__dict__[var].mean(axis=1).mean(axis=1).mean(axis=1))
pylab.subplot(2,2,2)
pylab.xlabel("NMDA/AMPA for " + cond[1])
pylab.ylabel(ylabel)
if mode==0:
for i1, r1 in enumerate(self.pow.x):
for i2, r2 in enumerate(self.pow.x):
for i3, r3 in enumerate(self.pow.x):
pylab.plot(self.pow.x, self.pow.__dict__[var][i1,:,i2,i3],label="["+str(i1)+",:,"+str(i2)+","+str(i3)+"]", picker=1)
#pylab.label()
else:
pylab.plot(self.pow.x, self.pow.__dict__[var].mean(axis=0).mean(axis=1).mean(axis=1))
pylab.subplot(2,2,3)
pylab.xlabel("NMDA/AMPA for " + cond[2])
pylab.ylabel(ylabel)
if mode==0:
for i1, r1 in enumerate(self.pow.x):
for i2, r2 in enumerate(self.pow.x):
for i3, r3 in enumerate(self.pow.x):
pylab.plot(self.pow.x, self.pow.__dict__[var][i1,i2,:,i3],label="["+str(i1)+","+str(i2)+",:,"+str(i3)+"]", picker=1)
#pylab.label()
else:
pylab.plot(self.pow.x, self.pow.__dict__[var].mean(axis=0).mean(axis=0).mean(axis=1))
pylab.subplot(2,2,4)
pylab.xlabel("NMDA/AMPA for " + cond[3])
pylab.ylabel(ylabel)
if mode==0:
for i1, r1 in enumerate(self.pow.x):
for i2, r2 in enumerate(self.pow.x):
for i3, r3 in enumerate(self.pow.x):
pylab.plot(self.pow.x, self.pow.__dict__[var][i1,i2,i3,:],label="["+str(i1)+","+str(i2)+","+str(i3)+",:]", picker=1)
#pylab.label()
else:
pylab.plot(self.pow.x, self.pow.__dict__[var].mean(axis=0).mean(axis=0).mean(axis=0))
self.pow.arch.save_fig(fig,ylabel)
def onpick(event):
print("REWR")
print(str(event.artist.get_label())+" ("+str(event.mouseevent.xdata)+","+str(event.mouseevent.ydata)+")")
return True
#save vec to fn (fn is path)
def mysvvec(fn,vec):
fp = h.File()
fp.wopen(fn)
if fp.isopen():
vec.vwrite(fp)
fp.close()
else:
print("savevec ERR: couldn't open " + fn)
#this class is for saving output, i.e. figures and py files to backup
class Archive:
def __init__(self):
self.figprefix = "./gif" #prefix for saving figures
self.datprefix = "./data"
self.pyprefix = "./backup"
self.reset_time_stamp()
self.save_pyfile("par_sim.py")
self.save_pyfile("Cells.py")
def save_fig(self, fig, name):
fn = os.path.join(self.figprefix, self.time_stamp+name+".svg")
pylab.figure(fig)
pylab.savefig(fn, orientation='landscape', format='svg', dpi=72)
def reset_time_stamp(self):
ts = datetime.datetime.now().timetuple()
self.time_stamp = "_"+str(ts.tm_year)+"_"+str(ts.tm_mon)+"_"+str(ts.tm_mday)+"_"+str(ts.tm_hour)+"_"+str(ts.tm_min)+"_"+str(ts.tm_sec)
def save_pyfile(self, fn):
nfn = os.path.join(self.pyprefix,fn+self.time_stamp+".py")
shutil.copy(fn, nfn)
def save_vec(self, fn, vec):
nfn = os.path.join(self.datprefix,fn+".vec")
mysvvec(nfn,vec)
#run a sim and save data
def minrunsv(simstr,tstop=1200,dt=0.1):
h.tstop=tstop
h.dt=dt
h.run()
print("saving output data")
net.calc_lfp()
fn = "./data/"+simstr+"_lfp.vec"
mysvvec(fn,net.vlfp)
net.setsnq() # make NQS with spike times
fn = "./data/"+simstr+"_snq.nqs"
net.snq.sv(fn)
print("making and saving output figures")
#read a Vector from file, fn is file-path, vec is a Vector
def myrdvec(fn,vec):
fp=h.File()
fp.ropen(fn)
if not fp.isopen():
print("myrdvec ERRA: Couldn't open " + fn)
return False
vec.vread(fp)
fp.close()
return True
#load data from minrunsv into net.vlfp,net.snq
def loadminrundat(simstr):
fs = "./data/"+simstr+"_lfp.vec"
try:
net.vlfp.resize(0)
except:
net.vlfp = h.Vector()
myrdvec(fs,net.vlfp)
fs = "./data/"+simstr+"_snq.nqs"
try:
h.nqsdel(net.snq)
except:
pass
try:
net.snq=h.NQS(fs)
except:
print("loadminrundat ERRB: couldn't read snq from " + fs)
net.snq.verbose=0 # next, copy snq into vectors so can plot with net.rasterplot
for po in net.cells:
for i in range(len(po.lidvec)):
id = po.cell[i].id
po.lidvec[i].resize(0)
po.ltimevec[i].resize(0)
if net.snq.select("id",id):
po.lidvec[i].copy(net.snq.getcol("id"))
po.ltimevec[i].copy(net.snq.getcol("t"))
net.snq.verbose=1
def testrun():
net.olm.set_r("somaNMDA",0)
h.run()
arch = Archive()
net.rasterplot(1)
arch.save_fig(1,"tmp_rasterplot")
net.psr.cell[0].plot_volt("soma",2)
arch.save_fig(2,"tmp_psr_soma_volt")
net.calc_psd(3)
arch.save_fig(3,"tmp_fft")
print("\a")
def batchrun():
bat = Batch(net)
bat.r(3)
bat.plot_r()
def myrast(spikes,times,sz=12):
if h.g[0] == None:
h.gg()
spikes.mark(h.g[0],times,"O",sz,1,1)
h.g[0].exec_menu("View = plot")
# testsame - for debugging two runs to make sure output is the same
def testsame(ts,v1,v2):
h.tstop = ts
v1 = h.Vector()
h.run()
net.calc_lfp()
v1.copy(net.vlfp)
v2 = h.Vector()
h.run()
net.calc_lfp()
v2.copy(net.vlfp)
print("same = " , v1.eq(v2))
#gethilbnqs - make two NQS objects out of LFP with phase/amplitude/filered signals in theta and gamma bands
def gethilbnqs(vlfp,minth=3,maxth=12,ming=30,maxg=80,usemlab=True):
sampr = 1e3/h.dt # sampling rate in Hertz
if usemlab:
nqtheta=h.mathilbert(vlfp,sampr,minth,maxth)
nqgamma=h.mathilbert(vlfp,sampr,ming,maxg)
else:
nar = vlfp.to_python() # -> python -> numpy format
nar = numpy.array(nar)
nqtheta=filt.gethilbnq(nar,sampr,minth,maxth) # get an NQS with 'theta'
nqgamma=filt.gethilbnq(nar,sampr,ming,maxg)# get an NQS with 'gamma'
return [nqtheta,nqgamma]
#getampphnq - get an nqs with gamma amplitude vs theta phase - uses NQS objects created by gethilbnqs
def getampphnq(nqtheta,nqgamma,phbins=100,skipms=200):
colp = int(nqgamma.fi("phase")) # column index for phase
cola = int(nqgamma.fi("amp")) # column index for amp
phmin=nqgamma.v[colp].min() # minimum phase of gamma
phmax=nqgamma.v[colp].max() # maximum phase of gamma
phrng=phmax-phmin # range of gamma phase
nq = h.NQS("avgamp","phase","n","err","minamp","maxamp") # output nqs - amp is average amplitude for a phase, vn is # of samples @ the phase
#minamp is avgamp - stderr, maxamp is avgamp + stderr. those columns just for easier display of avg+/-error
vamp=nq.v[0] # average amplitude for a given phase
vph=nq.v[1] # theta phase
vn=nq.v[2] # number of samples at the given phase
ve=nq.v[3] # stderror
vmin=nq.v[4] # avg-stderror
vmax=nq.v[5] # avg+stderror
vph.indgen(phmin,phmax,phrng/phbins) # init range of phases
nq.pad()
vamp.fill(0)
vn.fill(0) # init counts to 0
lv = h.List() # list to keep amplitude samples
for i in range(int(vph.size())):
lv.append(h.Vector())
sz=int(nqgamma.v[0].size())
startx=int(skipms/h.dt)
for i in range(startx,sz,1):
bin=int(phbins*(nqtheta.v[colp][i]-phmin)/phrng)
if bin<0:
print("bin < 0!")
if bin>=phbins+1:
print("bin >= phbins+1")
lv.o(bin).append(nqgamma.v[cola][i])
for i in range(0,int(vamp.size()),1):
sz = lv.o(i).size()
if sz > 0: # if no samples, skip
av = lv.o(i).mean()
if sz > 1: # make sure can call stderr
er = lv.o(i).stderr()
else:
er = 0
vamp.x[i] = av
vn.x[i] = sz
ve.x[i] = er
vmin.x[i] = av - er
vmax.x[i] = av + er
return nq
# checkbase - compares baseline to OLM activity off
# returns results in a python list
def checkbase(endt=3e3,skipms=200,justone=False):
vlfp = []
vtmp = []
nqp = []
nqa = []
nqh = []
snq = []
fnq = []
h.tstop=endt
j = 0
dt = h.dt
for i in range(1,-1,-1):
print("set olm NMDA to ", float(i))
net.olm.set_r("somaNMDA",float(i))
print("running for " , endt , " ms ")
h.run()
net.calc_lfp()
vlfp.append(net.vlfp)
vtmp.append(h.Vector())
vtmp[j].copy(vlfp[j],skipms/dt,vlfp[j].size()-1)
vtmp[j].sub(vtmp[j].mean())
nqp.append( h.matpmtm(vtmp[j],1e3/dt) )
vtmp[j].copy(vlfp[j],skipms/dt,vlfp[j].size()-1)
nqh.append( gethilbnqs(vtmp[j],3,12,30,80) )
nqa.append( getampphnq(nqh[j][0],nqh[j][1]) )
net.setsnq()
snq.append( h.NQS() )
snq[j].cp(net.snq)
net.setfnq(skipms)
fnq.append( h.NQS() )
fnq[j].cp(net.fnq)
net.pravgrates()
j += 1
if justone:
break
return [vlfp,vtmp,nqp,nqa,nqh,snq,fnq]
############################
# setup multithreading #
pc = h.ParallelContext() #
pc.nthread(32) #
#h.load_file('parcom.hoc')
#pc = h.ParallelComputeTool()
#pc.nthread(8)
#p.multisplit(True)
############################
if 0:
testrun()
if 0:
h.tstop=200
net.pyr.cell[0].set_spikes([100],"BdendNMDA", 28*0.04e-3)
h.run()
net.pyr.cell[0].plot_volt("soma")
if 0:
batchrun()
####################################################################################################
