def make_spike_signal(ts, dur, tmax, loval=0, hival=1, dt=0.1):
"""Helper function: Square-pulse spike signal between two levels, loval
and hival. Pulses occur at times given by ts array for duration given by
dur scalar, from time 0 to tmax.
Returns a single-entry dictionary of Variable objects with key 'Istim'.
Default loval = 0, hival = 1.
To improve performance with adaptive time-step solvers, three (two before,
one after) points are added before and after each pulse, with a minimum
step time given by dt.
"""
assert len(ts) > 0, "No spike time events provided!"
assert isincreasing(ts), "This function expects strictly increasing times"
assert ts[0] != 0, "This function does not support initial step up at t=0"
assert dur > dt, "Duration must be larger than dt"
times = [0]
vals = [loval]
# check that ts are separated by at least dur+4*dt
assert all(np.diff(ts) > dur+4*dt), "Separate events by at least 4*dt"
assert tmax > ts[-1]+dur, "tmax must be larger than last event end time"
for t in ts:
times.extend([t-2.9*dt, t-dt, t, t+dur-dt, t+dur, t+dur+dt])
vals.extend([loval, loval, hival, hival, hival, loval])
if tmax > ts[-1]+dur+dt:
times.append(tmax)
vals.append(loval)
coorddict = {'Istim': vals}
vpts = Pointset(coorddict=coorddict, indepvararray=times)
return pointset_to_vars(vpts, discrete=False)
def make_noise_signal(dt, t_end, mean, stddev, num_cells, seed=None):
"""Helper function: Gaussian white noise at sample rate = dt for 1 or more cells,
for a duration of t_end."""
if seed is not None:
np.random.seed(seed)
N = ceil(t_end*1./dt)
t = np.linspace(0, t_end, N)
coorddict = {}
for cellnum in range(num_cells):
coorddict['noise%i' % (cellnum+1)] = np.random.normal(0, stddev, N)
vpts = Pointset(coorddict=coorddict, indepvararray=t)
return pointset_to_vars(vpts, discrete=False)