def run(self, signal):
(K,H,Mu) = (self.K,self.H,self.Mu)
trial = signal.trial
stims = signal.signal
self.Nx= signal.dims()
bst = run_bases(self.stim_basis, stims)
spikes = zeros((self.N,trial.length()))
lams = zeros((self.N,trial.length()))
raster = []
flipsp = fliplr(self.spike_basis)
for t in xrange(1,trial.length()):
bsp = self.rebase(flipsp,spikes[:,:t])
lams[:,t] = self.logI(K,H,Mu,bsp,bst[:,t,:])
spikes[:,t] = poisson(exp(lams[:,t]))
spiked = flatnonzero(spikes[:,t])
raster = raster + zip(spiked, [trial.bin_to_time(t)]*len(spiked))
return Result(self.params, signal, [], raster, {'lambda': (lams)})
def test_lnp_simulator():
trial = Trial(t_start=0,t_stop=4,dt=1)
stim = array([[1,3,5,7]])
sign = Signal(trial,stim)
spike_basis = array([[2,1]])
stim_basis = spike_basis
bst = run_bases(stim_basis, stim)
N = 2
K = array([[[1]],[[1]]]) # both neurons driven by input
H = array([[[0],[0]],[[1],[0]]]) # 0->1, no auto
M = array([1,1]) # unit mean firing rates
ps = s.LNPSimulator(spike_basis,stim_basis,N,K,H,M)
bst2 = ps.rebase(stim_basis, stim)
assert bst.ravel()[-1] == bst2
resu = ps.run(sign)
resu.write_to_file("results/lnp_sim_test.pickle")
