/
scanparam.py
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/
scanparam.py
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#Scan parameter module
#Copyright Andres Agudelo-Toro (https://sites.google.com/site/aagudelotoro/)
import pylab as pl
import simulationpanel
import nrnsim as ns
import smech
from scipy.optimize import leastsq
def panel():
global Ras,Rms,Ie,Ies
global tstart
global pan,t0,t1
#Pulse start time
tstart = 0.1
#Ras = [1.,500.,20.] #Ohm*cm
#Rms = [200.,10000.,200.] #Ohm*cm^2
Ras = [5.0, 500.0, 50.0] #Ohm*cm
Rms = [200.0, 10000.0, 1000.0] #Ohm*cm^2
Ie = 4. #nA
Ies = pl.c_[pl.ones_like(ns.t)]
Ies[:pl.ceil(tstart/ns.dt)] = 0.
t0 = 0.101
t1 = 0.6
pan = simulationpanel.SimulationPanel()
pan.Move((640,600))
pan.setdict(globals())
pan.addcommand('sim()')
pan.addcommand('plottao()')
pan.addvar('Ras')
pan.addvar('Rms')
pan.addvar('Ie')
pan.addvar('t0')
pan.addvar('t1')
def sim():
global V,Vlin,tao_e,Rar,Rmr
Rar = pl.arange(Ras[0],Ras[1],Ras[2])
Rmr = pl.arange(Rms[0],Rms[1],Rms[2])
ns.mech.setcurrent(Ie*Ies,ns.dt)
li = len(Rmr)
lj = len(Rar)
tao_e = pl.empty((li,lj))
tao_l = pl.empty((li,lj))
tao_n = pl.empty((li,lj))
for i in range(li):
for j in range(lj):
#Special conditions
if Rar[j] < 10.:
sec.L(15000.)
else:
sec.L(7000.)
if Rmr[i] > 5000.:
ns.h.tstop = 50.
else:
ns.h.tstop = 20.
sec.Rm(Rmr[i])
sec.Ra(Rar[j])
print Rmr[i],Rar[j]
ns.sim()
#Obtain voltage, steady state voltage, normalize and
#get logarithmic values
t = ns.t
Vinf = sec.nrnV0[-1]
V = 1 - pl.array(sec.nrnV0)[:-1]/Vinf
Vlin = pl.log(V)
print Vinf
#Estimate the time constant finding the
#point at witch the voltage reaches the
#value 1/e
nz, = pl.nonzero(V>(1/pl.e))
#The time where V ~ 1/e is the point
#right after the last nz
tao_e[i,j] = t[nz[-1]+1] - tstart
print 'tao_e',tao_e[i,j]
#Define least squares data interval and
#make the pulse starting time to be zero
i0 = int(t0/ns.dt)
i1 = int(t1/ns.dt)
t01 = t[:i1-i0]
V01 = V[i0:i1]
Vlin01 = Vlin[i0:i1]
#Linear least squares
A = pl.c_[t01,pl.ones_like(t01)]
m, c = pl.lstsq(A, Vlin01.copy())[0]
tao_l[i,j] = -1./m - tstart
print 'tao_l',tao_l[i,j],'(',m, c, pl.exp(c),')'
#Parametric function: v is the parameter vector and
#x the independent varible
fp = lambda p, t: p[0]*pl.exp(p[1]*t)
#fp = lambda p, t: p[0]*pl.exp(p[1]*t) + p[2]*pl.exp(p[3]*t)
#fp = lambda p, t: pl.exp(p[0]*t)
#Error function
e = lambda p, t, V: (fp(p,t)-V)
#Initial parameter guess
p0 = [1., -5.]
#p0 = [1., -5., 1., -1.]
#p0 = [-5.]
#Fitting
p, success = leastsq(e, p0, args=(t01,V01), maxfev=10000)
tao_n[i,j] = -1./p[1] - tstart
print 'tao_n',tao_n[i,j],'(',p,success,')'
"""
f = pl.figure(0)
pl.clf()
a = f.add_subplot(111)
a.plot(t-t0,Vlin)
a.plot([t01[0], t01[-1]],[Vlin01[0], Vlin01[-1]],'kx')
a.plot(t01,m*t01 + c)
pl.title('linear least squares')
a.set_xlabel('t [ms]')
a.set_ylabel('log(Vm)')
pl.legend(('data','lsq interv.','linear'))
f = pl.figure(1)
pl.clf()
a = f.add_subplot(111)
a.plot(t-t0,V)
a.plot([t01[0], t01[-1]],[V01[0], V01[-1]],'kx')
a.plot(t,pl.exp(m*t + c))
#a.plot(t,fp([p],t))
a.plot(t,fp(p,t))
pl.title('linear vs non-linear lsq')
a.set_xlabel('t [ms]')
a.set_ylabel('Vm [mV]')
pl.legend(('data','lsq interv.','linear','non-linear'))
"""
def plottao():
f = pl.figure(2)
pl.clf()
pl.pcolor(Rar,Rmr,tao_e)
#pl.contourf(Rar,Rmr,tao_e,10)
cb = pl.colorbar()
pl.contour(Rar,Rmr,tao_e,10,colors='r')
pl.ylim(Rms[0],Rms[1])
cb.set_label('tao_e at x = 0 [ms]')
pl.xlabel('Ra [Ohm*cm]')
pl.ylabel('Rm [Ohm*cm^2]')
pl.twinx()
#Show real tao equivalent values right
pl.ylim(Rms[0]*1e-3,Rms[1]*1e-3)
pl.ylabel('tao [ms]')
def nrninit():
global sec
geom = 'infaxon'
ns.nrninit(geom)
#Prepare stimulator
ns.mech = smech.CurrentClamp(
ns.h,ns.sections,[0.0])
sec = ns.sections.list[0]
ns.loadsession(geom)
def init():
ns.settime(0.005,50.) #milliseconds
panel()
nrninit()