def main(argv):
integrationFilename = 'results/integrationINapIKFig10_10_INapIKLowThreshold_2-1Sync.npz'
figFilename = 'figures/fig10_10c_INapIKLowThreshold_2-1Sync.eps'
fontsize = 20
marker = "o"
integrationRes = np.load(integrationFilename)
spikeIndices = getPeakIndices(v=integrationRes["ysDCTrainPulses"][0, :])
spikeTimes = integrationRes["timesDCTrainPulses"][spikeIndices]
pulseTimes = np.arange(start=integrationRes["tPulse"],
stop=spikeTimes[-1],
step=integrationRes["Ts"])
pulsePhases = np.empty(pulseTimes.shape)
for i in range(len(pulsePhases)):
precedingSpikeIndex = np.nonzero(spikeTimes < pulseTimes[i])[0][-1]
pulsePhases[i] = pulseTimes[i] - spikeTimes[precedingSpikeIndex]
plt.plot(np.arange(start=1, stop=len(pulseTimes) + 1, step=1),
pulsePhases,
marker=marker)
plt.xlabel("pulse number, n")
plt.ylabel(r'phase, $\theta_n$')
plt.grid()
# for i in range(len(pulsePhases)):
# annotation = r"$\theta_{%d}$"%(i+1)
# plt.annotate(annotation, xy=(i+1, pulsePhases[i]), fontsize=fontsize)
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
fontsize = 20
marker = "o"
integrationFilename = 'results/integrationINapIKFig10_10_INapIKLowThreshold_belowUnstable.npz'
prcFilename = 'results/prcINapIKFig10_4RightPanel_INapIKLowThreshold.npz'
figFilename = 'figures/fig10_10d_INapIKLowThreshold_belowUnstable.eps'
prcRes = np.load(prcFilename)
integrationRes = np.load(integrationFilename)
poincarePhaseMap = (prcRes["phases"]+prcRes["prc"]+integrationRes["Ts"])%prcRes["T"]
plt.plot(prcRes["phases"], poincarePhaseMap, marker="o")
plt.plot(prcRes["phases"], prcRes["phases"], marker="o")
plt.xlabel(r"$\theta_n$")
plt.ylabel(r"$\theta_{n+1}$")
# plt.xlim((0, .9))
# plt.ylim((0, .9))
spikeIndices = getPeakIndices(v=integrationRes["ysDCTrainPulses"][0,:])
spikeTimes = integrationRes["timesDCTrainPulses"][spikeIndices]
pulseTimes = np.arange(start=integrationRes["tPulse"], stop=spikeTimes[-1], step=integrationRes["Ts"])
pulsePhases = np.empty(pulseTimes.shape)
for i in range(len(pulsePhases)):
precedingSpikeIndex = np.nonzero(spikeTimes<pulseTimes[i])[0][-1]
pulsePhases[i] = pulseTimes[i]-spikeTimes[precedingSpikeIndex]
for i in range(len(pulsePhases)):
annotation = r"$\theta_{%d}$"%(i+1)
plt.annotate(annotation, xy=(pulsePhases[i], pulsePhases[i]), fontsize=fontsize)
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
resultsFilename = 'results/integrationINapIKFig10_10.npz'
figFilename = 'figures/fig10_10c.eps'
nPhases = 7
fontsize = 20
marker = "o"
results = np.load(resultsFilename)
pulseTimes = results["offset"]+\
np.arange(start=0, stop=nPhases, step=1)*results["Ts"]
spikeIndices = getPeakIndices(v=results["ys"][0, :])
spikeTimes = results["times"][spikeIndices]
pulsePhases = np.empty(pulseTimes.shape)
for i in range(len(pulsePhases)):
precedingSpikeIndex = np.nonzero(spikeTimes < pulseTimes[i])[0][-1]
pulsePhases[i] = pulseTimes[i] - spikeTimes[precedingSpikeIndex]
plt.plot(np.arange(start=1, stop=nPhases + 1, step=1),
pulsePhases,
marker=marker)
plt.xlabel("pulse number, n")
plt.ylabel(r'phase, $\theta_n$')
plt.grid()
for i in range(len(pulsePhases)):
annotation = r"$\theta_{%d}$" % (i + 1)
plt.annotate(annotation, xy=(i + 1, pulsePhases[i]), fontsize=fontsize)
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
resultsFilename = 'results/integrationINapIKFig10_1.npz'
aFigFilename = 'figures/fig10_1a.eps'
results = np.load(resultsFilename)
times = results['times']
ys = results['ys']
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
print("Spike times:")
print(spikeTimes)
times = np.delete(times, np.arange(0, spikeIndices[0]))
spikeTimes = spikeTimes - times[0]
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
plt.plot(times, ys[0, :])
for spikeTime in spikeTimes:
plt.axvline(x=spikeTime, color="r")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane Potential (mV)")
plt.savefig(aFigFilename)
plt.show()
pdb.set_trace()
def main(argv):
i0 = 35
numberOfPhaseMarks = 8
annotationGap = 0.02
initialTransientLength = 10
resultsFilename = 'results/integrationINapIKFig10_1_INapIKLowThreshold.npz'
aFigFilename = 'figures/fig10_1b_INapIKLowThreshold.eps'
results = np.load(resultsFilename)
times = results['times']
ys = results['ys']
transientIndices = np.nonzero(times < initialTransientLength)[0]
pdb.set_trace()
times = np.delete(arr=times, obj=transientIndices)
ys = np.delete(arr=ys, obj=transientIndices, axis=1)
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
times = np.delete(times, np.arange(0, spikeIndices[0]))
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
spikeIndices = spikeIndices - spikeIndices[0]
spikeTimes = spikeTimes - spikeTimes[0]
period = spikeTimes[1] - spikeTimes[0]
phases = times % period
phasesToPlot = np.arange(0, period, period / numberOfPhaseMarks)
indicesBtwFirstAndSecondSpike = np.arange(0, spikeIndices[1])
phasesToSearch = phases[indicesBtwFirstAndSecondSpike]
indicesPhasesToPlot = np.empty(len(phasesToPlot), dtype=np.int64)
for i in xrange(len(phasesToPlot)):
phaseToPlot = phasesToPlot[i]
indicesPhasesToPlot[i] = np.argmin(np.abs(phasesToSearch -
phaseToPlot))
plt.figure()
plotLowThresholdINapIKVectorField(i=i0)
plt.plot(ys[0, :], ys[1, :], label="limit cycle attractor")
plt.plot(ys[0, indicesPhasesToPlot], ys[1, indicesPhasesToPlot], 'ro')
plt.annotate("0,T",
xy=ys[:, 0],
xytext=ys[:, indicesPhasesToPlot[0]] + annotationGap,
color="red",
size=14)
for i in xrange(1, len(indicesPhasesToPlot)):
plt.annotate("%d/%dT" % (i, numberOfPhaseMarks),
xy=ys[:, indicesPhasesToPlot[i]],
xytext=ys[:, indicesPhasesToPlot[i]] + annotationGap,
color="red",
size=14)
plt.grid()
plt.legend(loc="upper left")
plt.xlabel('Voltage (mv)')
plt.ylabel('K activation variable, n')
plt.ylim((-0.1, 0.8))
plt.savefig(aFigFilename)
plt.show()
pdb.set_trace()
def main(argv):
if len(argv) != 3:
sys.exit("Usage: %s <phase index> <number of phases>" % argv[0])
indexPhaseX0 = int(argv[1])
numberOfPhasesForX0 = int(argv[2])
i0 = 10
factorsDeltaT = 1.0 / 32.0 * np.arange(1, 6)
# factorsDeltaT = 1.0/32.0*np.arange(5, 6)
nInitialConditions = 10
maxDeltaX0 = 1.0
maxDeltaX1 = 0.01
dt = 1e-5
integrationFilename = "results/integrationINapIKFig10_1.npz"
isochronFilename = \
"results/isochronINapIKFig10_1Phase%02dOver%d.npz"%(indexPhaseX0,
numberOfPhasesForX0)
results = np.load(integrationFilename)
times = results["times"]
ys = results["ys"]
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
times = np.delete(times, np.arange(0, spikeIndices[0]))
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
spikeIndices = spikeIndices - spikeIndices[0]
spikeTimes = spikeTimes - spikeTimes[0]
period = spikeTimes[1] - spikeTimes[0]
phases = times % period
phasesForX0 = np.arange(0, period, period / numberOfPhasesForX0)
indicesBtwFirstAndSecondSpike = np.arange(0, spikeIndices[1])
phasesToSearch = phases[indicesBtwFirstAndSecondSpike]
indicesPhasesForX0 = np.empty(len(phasesForX0), dtype=np.int64)
for i in xrange(len(phasesForX0)):
phaseForX0 = phasesForX0[i]
indicesPhasesForX0[i] = np.argmin(np.abs(phasesToSearch - phaseForX0))
x0 = ys[:, indicesPhasesForX0[indexPhaseX0]]
def i(t):
return (i0)
model = INapIKModel.getHighThresholdInstance(i=i)
isochron = computeIsochron(model,
x0=x0,
deltaTs=factorsDeltaT * period,
nInitialConditions=nInitialConditions,
maxDeltaX0=maxDeltaX0,
maxDeltaX1=maxDeltaX1,
dt=dt)
np.savez(isochronFilename, isochron=isochron)
def main(argv):
integrationFilename = "results/integrationINapIKFig10_10_matching1-1Sync.npz"
figFilename = "figures/fig10_10b_matching1-1Sync.eps"
nPhases = 6
ylim = (-.1, .7)
fontsize = 20
integrationRes = np.load(integrationFilename)
plt.plot(integrationRes["ysDCTrainPulses"][0, :],
integrationRes["ysDCTrainPulses"][1, :],
label="train pulses")
plt.plot(integrationRes["ysDC"][0, :],
integrationRes["ysDC"][1, :],
label="DC")
plotHighThresholdINapIKVectorField(i=integrationRes["i0"])
plt.xlabel("Membrane Potential (V)")
plt.ylabel("K activation variable, n")
plt.ylim(ylim)
spikeIndices = getPeakIndices(v=integrationRes["ysDCTrainPulses"][0, :])
spikeTimes = integrationRes["timesDCTrainPulses"][spikeIndices]
pulseTimes = np.arange(start=integrationRes["tPulse"],
stop=spikeTimes[-1],
step=integrationRes["Ts"])
pulseIndices = np.empty(pulseTimes.shape)
for i in range(len(pulseIndices)):
pulseIndices[i] = np.argmin(
np.abs(pulseTimes[i] - integrationRes["timesDCTrainPulses"]))
for i in range(len(pulseIndices)):
annotation = r"$\theta_{%d}$" % (i + 1)
plt.annotate(annotation,
xy=(integrationRes["ysDCTrainPulses"][0, pulseIndices[i]],
integrationRes["ysDCTrainPulses"][1,
pulseIndices[i]]),
fontsize=fontsize)
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
nPhases = 6
fontsize = 20
marker = "o"
integrationFilename = 'results/integrationINapIKFig10_10.npz'
prcFilename = 'results/prcINapIKFig10_4RightPanel.npz'
figFilename = 'figures/fig10_10d.eps'
prcRes = np.load(prcFilename)
integrationRes = np.load(integrationFilename)
poincarePhaseMap = (prcRes["phases"] + prcRes["prc"] +
integrationRes["Ts"]) % prcRes["T"]
plt.plot(prcRes["phases"], poincarePhaseMap)
plt.plot(prcRes["phases"], prcRes["phases"])
plt.xlabel(r"$\theta_n$")
plt.ylabel(r"$\theta_{n+1}$")
pulseTimes = integrationRes["offset"]+\
np.arange(start=0, stop=nPhases, step=1)*integrationRes["Ts"]
spikeIndices = getPeakIndices(v=integrationRes["ys"][0, :])
spikeTimes = integrationRes["times"][spikeIndices]
pulsePhases = np.empty(pulseTimes.shape)
for i in range(len(pulsePhases)):
precedingSpikeIndex = np.nonzero(spikeTimes < pulseTimes[i])[0][-1]
pulsePhases[i] = pulseTimes[i] - spikeTimes[precedingSpikeIndex]
for i in range(len(pulsePhases)):
annotation = r"$\theta_{%d}$" % (i + 1)
plt.annotate(annotation,
xy=(pulsePhases[i], pulsePhases[i]),
fontsize=fontsize)
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
indexPhaseX0 = 1
numberOfPhasesForX0 = 40
i0 = 10
nSPLUNew = 1000
vMin = -90
vMax = 15
nMin = -0.1
nMax = 0.8
integrationFilename = "results/integrationINapIKFig10_1.npz"
isochronFilename = \
"results/isochronINapIKFig10_1Phase%02dOver%d.npz"%(indexPhaseX0,
numberOfPhasesForX0)
figFilename = \
"figures/isochronINapIKFig10_1Phase%02dOver%d.eps"%(indexPhaseX0,
numberOfPhasesForX0)
results = np.load(integrationFilename)
times = results["times"]
ys = results["ys"]
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
times = np.delete(times, np.arange(0, spikeIndices[0]))
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
spikeIndices = spikeIndices - spikeIndices[0]
spikeTimes = spikeTimes - spikeTimes[0]
period = spikeTimes[1] - spikeTimes[0]
phases = times % period
phasesForX0 = np.arange(0, period, period / numberOfPhasesForX0)
indicesBtwFirstAndSecondSpike = np.arange(0, spikeIndices[1])
phasesToSearch = phases[indicesBtwFirstAndSecondSpike]
indicesPhasesForX0 = np.empty(len(phasesForX0), dtype=np.int64)
for i in xrange(len(phasesForX0)):
phaseForX0 = phasesForX0[i]
indicesPhasesForX0[i] = np.argmin(np.abs(phasesToSearch - phaseForX0))
x0 = ys[:, indicesPhasesForX0[indexPhaseX0]]
results = np.load(isochronFilename)
isochron = results["isochron"]
validIndices = np.logical_and(
np.logical_and(vMin <= isochron[0, :], isochron[0, :] <= vMax),
np.logical_and(nMin <= isochron[1, :],
isochron[1, :] <= nMax)).nonzero()[0]
isochron = isochron[:, validIndices]
sortedIsochron = sortIsochron(isochron=isochron)
splTck, splU = splprep(sortedIsochron, s=5.0)
splUNew = np.linspace(splU.min(), splU.max(), nSPLUNew)
splXInter, splYInter = splev(splUNew, splTck, der=0)
# plt.figure()
# plotHighThresholdINapIKVectorField(i=i0)
plt.plot(ys[0, :], ys[1, :], label="limit cycle attractor")
# pdb.set_trace()
plt.annotate("x0", xy=x0, color="red", size=14)
def i(t):
return (i0)
model = INapIKModel.getHighThresholdInstance(i=i)
plotINapIKNullclines(i=i0,
eL=model._eL,
gL=model._gL,
eNa=model._eNa,
gNa=model._gNa,
eK=model._eK,
gK=model._gK,
mVOneHalf=model._mVOneHalf,
mK=model._mK,
nVOneHalf=model._nVOneHalf,
nK=model._nK)
plt.plot(isochron[0, :],
isochron[1, :],
marker="o",
color="red",
linestyle="None")
plt.plot(splXInter, splYInter, color="gray", linestyle="solid")
plt.legend(loc="upper left")
plt.xlabel("Voltage (mv)")
plt.ylabel("K activation variable, n")
plt.xlim((-90, 15))
plt.ylim((-0.1, 0.8))
plt.savefig(figFilename)
plt.show()
pdb.set_trace()
def main(argv):
# if len(argv)!=3:
# sys.exit("Usage: %s <phase index> <number of phases>"%argv[0])
# indexPhaseX0 = int(argv[1])
# numberOfPhasesForX0 = int(argv[2])
indexPhaseX0 = 5
numberOfPhasesForX0 = 40
i0 = 10
# factorsDeltaT = 1.0/12.0*np.arange(1, 11)
factorsDeltaT = 1.0 / 36.0 * np.arange(5, 6)
# factorsDeltaT = 1.0/32.0*np.arange(1, 6)
# factorsDeltaT = 1.0/32.0*np.arange(5, 6)
nInitialConditions = 40
# maxDeltaX0 = 1.0
# maxDeltaX1 = 0.01
maxDistance = 0.02
dt = 1e-5
vMin = -50.0
vMax = -20.0
tol = 1e-3
addIntersectNullclines = False
integrationFilename = "results/integrationINapIKFig10_1.npz"
isochronFilename = \
"results/isochronINapIKFig10_1Phase%02dOver%d.npz"%(indexPhaseX0,
numberOfPhasesForX0)
results = np.load(integrationFilename)
times = results["times"]
ys = results["ys"]
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
times = np.delete(times, np.arange(0, spikeIndices[0]))
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
spikeIndices = spikeIndices - spikeIndices[0]
spikeTimes = spikeTimes - spikeTimes[0]
period = spikeTimes[1] - spikeTimes[0]
phases = times % period
phasesForX0 = np.arange(0, period, period / numberOfPhasesForX0)
indicesBtwFirstAndSecondSpike = np.arange(0, spikeIndices[1])
phasesToSearch = phases[indicesBtwFirstAndSecondSpike]
indicesPhasesForX0 = np.empty(len(phasesForX0), dtype=np.int64)
for i in xrange(len(phasesForX0)):
phaseForX0 = phasesForX0[i]
indicesPhasesForX0[i] = np.argmin(np.abs(phasesToSearch - phaseForX0))
x0 = ys[:, indicesPhasesForX0[indexPhaseX0]]
def i(t):
return (i0)
model = INapIKModel.getHighThresholdInstance(i=i)
print("Processing x0=(%.2f,%.4f)" % (x0[0], x0[1]))
plt.annotate("x0", xy=x0, color="red", size=14)
# isochron = computeIsochron(model, x0=x0, deltaTs=factorsDeltaT*period, nInitialConditions=nInitialConditions, maxDeltaX0=maxDeltaX0, maxDeltaX1=maxDeltaX1, dt=dt)
isochron = computeIsochron(model,
x0=x0,
deltaTs=factorsDeltaT * period,
nInitialConditions=nInitialConditions,
maxDistance=maxDistance,
dt=dt)
if addIntersectNullclines:
xIntersectionNullclines = model.getIntersectionOfNullclines(vMin=vMin,
vMax=vMax,
tol=tol)
isochron = np.append(xIntersectionNullclines, isochron, 1)
np.savez(isochronFilename, isochron=isochron)
def main(argv):
resultsFilename = "results/integrationINapIKFig10_10_INapIKLowThreshold_belowUnstable.npz"
v0 = -60.00
t0 = 0.0
tf = 800.0
dt = 1e-3
nTSteps = int(round((tf - t0) / dt))
times = np.empty(nTSteps + 1)
i0 = 50
nTransientSpikes = 6
pulsePhase = 0.44
pulseStrength = 10.0
pulseWidthFactor = 100
Ts = 3.403
pulseWidth = pulseWidthFactor * dt
def iDC(t):
return (i0)
iNapIKModelDC = INapIKModel.getLowThresholdInstance()
iNapIKModelDC.setI(i=iDC)
n0DC = iNapIKModelDC._nInf(v=v0)
y0DC = np.array([v0, n0DC])
resDC = integrateModelForward(model=iNapIKModelDC,
y0=y0DC,
dt=dt,
nTSteps=nTSteps)
spikeIndices = getPeakIndices(v=resDC["ys"][0, :])
if len(spikeIndices) <= nTransientSpikes:
raise ValueError("nTransientSpikes=%d is too large" %
(nTransientSpikes))
spikeTimes = resDC["times"][spikeIndices]
tPulse = spikeTimes[nTransientSpikes + 1] + pulsePhase
def iDCTrainPulses(t):
if t > tPulse:
r = (t - tPulse) % Ts
if r < pulseWidth / 2 or Ts - pulseWidth / 2 < r:
return (i0 + pulseStrength)
return (i0)
model = INapIKModel.getLowThresholdInstance()
model.setI(i=iDCTrainPulses)
n0 = model._nInf(v=v0)
y0 = np.array([v0, n0])
resDCTrainPulses = integrateModelForward(model=model,
y0=y0,
dt=dt,
nTSteps=nTSteps)
# peakIndices = getPeakIndices(v=resDCTrainPulses["ys"][0,:])
# resDCTrainPulses["ys"] = resDCTrainPulses["ys"][:,peakIndices[0]:]
# resDCTrainPulses["times"] = resDCTrainPulses["times"][peakIndices[0]:]-\
# resDCTrainPulses["times"][peakIndices[0]]
appliedIs = np.empty(resDCTrainPulses["times"].shape)
for i in range(len(resDCTrainPulses["times"])):
appliedIs[i] = iDCTrainPulses(t=resDCTrainPulses["times"][i])
np.savez(resultsFilename,
timesDC=resDC["times"],
ysDC=resDC["ys"],
timesDCTrainPulses=resDCTrainPulses["times"],
ysDCTrainPulses=resDCTrainPulses["ys"],
i0=i0,
i=appliedIs,
Ts=Ts,
tPulse=tPulse)
plt.plot(resDC["times"], resDC["ys"][0, :], color="blue", label="DC stim")
plt.plot(resDCTrainPulses["times"],
resDCTrainPulses["ys"][0, :],
color="magenta",
label="Pulse stim")
plt.xlabel("Time (sec)")
plt.ylabel("Membrane Potential (mV)")
plt.legend(loc="upper left")
ax = plt.gca()
ax2 = ax.twinx()
ax2.plot(resDCTrainPulses["times"], appliedIs, color="lightgray")
ax2.set_ylabel("Input Current (mA)")
plt.show()
pdb.set_trace()
def main(argv):
resultsFilename = "results/integrationINapIKFig10_10_2-1Sync.npz"
v0 = -60.00
t0 = 0.0
tf = 198.0
dt = 1e-3
nTSteps = int(round((tf - t0) / dt))
times = np.empty(nTSteps + 1)
# i0 = 4.7
i0 = 9.4
nTransientSpikes = 6
pulseStrength = 200.0
pulseWidth = 100 * dt
Ts = 18.37
offset = 9.0
# offset = 14.0
def iDC(t):
return (i0)
iNapIKModelDC = INapIKModel.getHighThresholdInstance()
iNapIKModelDC.setI(i=iDC)
n0DC = iNapIKModelDC._nInf(v=v0)
y0DC = np.array([v0, n0DC])
resDC = integrateForward(deriv=iNapIKModelDC.deriv,
t0=t0,
y0=y0DC,
dt=dt,
nTSteps=nTSteps)
def iDCTrainPulses(t):
r = (t - offset) % Ts
if r < pulseWidth / 2 or Ts - pulseWidth / 2 < r:
return (i0 + pulseStrength)
return (i0)
model = INapIKModel.getHighThresholdInstance()
model.setI(i=iDCTrainPulses)
n0 = model._nInf(v=v0)
y0 = np.array([v0, n0])
resDCTrainPulses = integrateForward(deriv=model.deriv,
t0=t0,
y0=y0,
dt=dt,
nTSteps=nTSteps)
# peakIndices = getPeakIndices(v=resDCTrainPulses["ys"][0,:])
# resDCTrainPulses["ys"] = resDCTrainPulses["ys"][:,peakIndices[0]:]
# resDCTrainPulses["times"] = resDCTrainPulses["times"][peakIndices[0]:]-\
# resDCTrainPulses["times"][peakIndices[0]]
appliedIs = np.empty(resDCTrainPulses["times"].shape)
for i in range(len(resDCTrainPulses["times"])):
appliedIs[i] = iDCTrainPulses(t=resDCTrainPulses["times"][i])
# np.savez(resultsFilename, times=resDCTrainPulses["times"],
# ys=resDCTrainPulses["ys"],
# i0=i0, i=appliedIs, Ts=Ts, offset=offset)
# np.savez(resultsFilename,
# timesDC=resDC["times"],
# ysDC=resDC["ys"],
# timesDCTrainPulses=resDCTrainPulses["times"],
# ysDCTrainPulses=resDCTrainPulses["ys"],
# i0=i0, i=appliedIs, Ts=Ts, tPulse=tPulse)
spikeIndices = getPeakIndices(v=resDC["ys"][0, :])
if len(spikeIndices) <= nTransientSpikes:
raise ValueError("nTransientSpikes=%d is too large" %
(nTransientSpikes))
spikeTimes = resDC["times"][spikeIndices]
ts = spikeTimes[1:] - spikeTimes[:-1]
T = np.mean(ts)
print("T=%.02f" % (T))
plt.plot(resDCTrainPulses["times"],
resDCTrainPulses["ys"][0, :],
color="blue")
plt.xlabel("Time (sec)")
plt.ylabel("Membrane Potential (mV)")
ax = plt.gca()
ax2 = ax.twinx()
ax2.plot(resDCTrainPulses["times"], appliedIs, color="red")
ax2.set_ylabel("Input Current (mA)")
plt.show()
def main(argv):
i0 = 10
numberOfPhasesForX0 = 40
indexPhaseX0 = 38
annotationGap = 0.02
factorsDeltaT = 1.0 / 32.0 * np.arange(1, 6)
# factorsDeltaT = 1.0/32.0*np.arange(5, 6)
nInitialConditions = 10
maxDeltaX0 = 1.0
maxDeltaX1 = 0.01
dt = 1e-5
nSPLUNew = 1000
vMin = -90
vMax = 15
nMin = -0.1
nMax = 0.8
resultsFilename = "results/integrationINapIKFig10_1.npz"
aFigFilename = "figures/fig10_1b.eps"
results = np.load(resultsFilename)
times = results["times"]
ys = results["ys"]
spikeIndices = getPeakIndices(v=ys[0, :])
spikeTimes = times[spikeIndices]
times = np.delete(times, np.arange(0, spikeIndices[0]))
times = times - times[0]
ys = np.delete(ys, np.arange(0, spikeIndices[0]), axis=1)
spikeIndices = spikeIndices - spikeIndices[0]
spikeTimes = spikeTimes - spikeTimes[0]
period = spikeTimes[1] - spikeTimes[0]
phases = times % period
phasesForX0 = np.arange(0, period, period / numberOfPhasesForX0)
indicesBtwFirstAndSecondSpike = np.arange(0, spikeIndices[1])
phasesToSearch = phases[indicesBtwFirstAndSecondSpike]
indicesPhasesForX0 = np.empty(len(phasesForX0), dtype=np.int64)
for i in xrange(len(phasesForX0)):
phaseForX0 = phasesForX0[i]
indicesPhasesForX0[i] = np.argmin(np.abs(phasesToSearch - phaseForX0))
x0 = ys[:, indicesPhasesForX0[indexPhaseX0]]
def i(t):
return (i0)
model = INapIKModel.getHighThresholdInstance(i=i)
isochron = computeIsochron(model,
x0=x0,
deltaTs=factorsDeltaT * period,
nInitialConditions=nInitialConditions,
maxDeltaX0=maxDeltaX0,
maxDeltaX1=maxDeltaX1,
dt=dt)
# begin tmp code
np.savez("~/tmp/isochron.npz", isochron=isochron)
# results = np.load("/tmp/isochron.npz")
# isochron = results["isochron"]
# end tmp code
validIndices = np.logical_and(
np.logical_and(vMin <= isochron[0, :], isochron[0, :] <= vMax),
np.logical_and(nMin <= isochron[1, :],
isochron[1, :] <= nMax)).nonzero()[0]
isochron = isochron[:, validIndices]
sortOrder = np.argsort(isochron[1, :])
isochron = isochron[:, sortOrder]
splTck, splU = splprep(isochron, s=5.0)
splUNew = np.linspace(splU.min(), splU.max(), nSPLUNew)
splXInter, splYInter = splev(splUNew, splTck, der=0)
# plt.figure()
# plotHighThresholdINapIKVectorField(i=i0)
plt.plot(ys[0, :], ys[1, :], label="limit cycle attractor")
# pdb.set_trace()
plt.annotate("x0", xy=x0, color="red", size=14)
plotINapIKNullclines(i=i0,
eL=model._eL,
gL=model._gL,
eNa=model._eNa,
gNa=model._gNa,
eK=model._eK,
gK=model._gK,
mVOneHalf=model._mVOneHalf,
mK=model._mK,
nVOneHalf=model._nVOneHalf,
nK=model._nK)
plt.plot(isochron[0, :],
isochron[1, :],
marker="o",
color="red",
linestyle="None")
plt.plot(splXInter, splYInter, color="gray", linestyle="solid")
plt.legend(loc="upper left")
plt.xlabel("Voltage (mv)")
plt.ylabel("K activation variable, n")
plt.xlim((-90, 15))
plt.ylim((-0.1, 0.8))
plt.show()
pdb.set_trace()
