def testShotNoiseFromSpikes(self):
stg = stgen.StGen()
from numpy import array
st = stg.poisson_generator(10.0,0.0,1000.0)
ge = stgen.shotnoise_fromspikes(st,2.0,10.0,dt=0.1)
assert ge.t_start==0.0
assert ge.t_stop==1000.0
st = stg.poisson_generator(10.0,0.0,1000.0)
ge = stgen.shotnoise_fromspikes(st,2.0,10.0,dt=0.1,t_start=500.0,t_stop=1500.0)
assert ge.t_start==500.0
assert ge.t_stop==1500.0
def testShotNoiseFromSpikes(self):
stg = stgen.StGen()
from numpy import array
st = stg.poisson_generator(10.0, 0.0, 1000.0)
ge = stgen.shotnoise_fromspikes(st, 2.0, 10.0, dt=0.1)
assert ge.t_start == 0.0
assert ge.t_stop == 1000.0
st = stg.poisson_generator(10.0, 0.0, 1000.0)
ge = stgen.shotnoise_fromspikes(st,
2.0,
10.0,
dt=0.1,
t_start=500.0,
t_stop=1500.0)
assert ge.t_start == 500.0
assert ge.t_stop == 1500.0
# generate shot noise from step poisson
from NeuroTools import stgen
clf()
stg = stgen.StGen()
rate = numpy.array([6.0,10.0,6.0,6.0])
t = numpy.array([0.0,100.0,200.0,300.0])
q = 2.0
tau = 10.0
trials = 1000
st = stg.inh_poisson_generator(rate, t, 300.0)
g,tsn = stgen.shotnoise_fromspikes(st,q,tau,dt=0.1,t_start=0.0,t_stop=300.0,array=True)
for i in range(trials-1):
st = stg.inh_poisson_generator(rate, t, 300.0)
g_tmp,tmp = stgen.shotnoise_fromspikes(st,q,tau,dt=0.1,t_start=0.0,t_stop=300.0,array=True)
g+=g_tmp
plot(t,rate,'b:',ls='steps-post',lw=2)
plot(tsn,g/q/tau/trials*1000.0,'r-',lw=2)
xlabel('time [ms]',size=20)
xticks(size=16)
ylabel(r'$\nu(t)$ [Hz]',size=20)
yticks(size=16)
clf()
stg = stgen.StGen()
rate = numpy.array([6.0, 10.0, 6.0, 6.0])
t = numpy.array([0.0, 100.0, 200.0, 300.0])
q = 2.0
tau = 10.0
trials = 1000
st = stg.inh_poisson_generator(rate, t, 300.0)
g, tsn = stgen.shotnoise_fromspikes(st,
q,
tau,
dt=0.1,
t_start=0.0,
t_stop=300.0,
array=True)
for i in range(trials - 1):
st = stg.inh_poisson_generator(rate, t, 300.0)
g_tmp, tmp = stgen.shotnoise_fromspikes(st,
q,
tau,
dt=0.1,
t_start=0.0,
t_stop=300.0,
array=True)
g += g_tmp
plot(t, rate, 'b:', ls='steps-post', lw=2)
pylab.plot(time_axis, cc, label = 'no correlation', alpha = 0.5)
# inject correlation into st1 and st2
st3 = sg.poisson_generator(rate = rate_shared, t_stop = duration)
st1.merge(st3)
st2.merge(st3)
cc2 = numpy.correlate(st1.time_histogram(time_bin = 1.),
st2.time_histogram(time_bin = 1.),
mode = 'same')
pylab.plot(time_axis, cc2, label = '10% correlation', alpha = 0.5)
pylab.legend()
pylab.show()
#generate shot noise from st1
st1_shot = stgen.shotnoise_fromspikes(st1,
q = 1.0,
tau = 10.,
t_start = st1.t_start,
t_stop = st1.t_stop)
f = pylab.figure()
pylab.plot(st1_shot.signal)
f.gca().set_title('shot noise')
f.gca().set_xlabel('time [ms]')
# spike triggered average reveals the original shot-noise kernel
sta = st1_shot.event_triggered_average(st1, display = True, average = True)