S = 2000
burn = 500
print('Running simulation...')
print('Parameters:\nN={:d}\nT={:.2f}\ndt_max={:.2f}\nS={:d}\n'.format(
N, T, dt_max, S))
# Construct network
model = NetworkPoisson(N=N, dt_max=dt_max)
stable = False
while not stable:
model.init_parameters()
stable = model.check_stability()
# Generate data
print('Generating data...', end='')
data = model.generate_data(T=T)
print('(size={})'.format(len(data[0])))
# Plot the data
model.plot_data(data)
# Sample the posterior (cython extension)
lambda0_, W_, mu_, tau_ = model.sample_ext(data, T=T, size=S, method='cython')
# Plot the sample
plot_sample(lambda0_, model.lamb, 'lambda0')
plot_sample(W_, model.W, 'W')
plot_sample(mu_, model.mu, 'mu')
plot_sample(tau_, model.tau, 'tau')
model.plot_impulse(mu=np.median(mu_[0, 0, :]), tau=np.median(tau_[0, 0, :]))
if len(events[0]) > max_events:
print('Event count exceeds max_events; skipping\n')
with open(
'/Users/colinswaney/Desktop/output_date={}_grp={}.txt'.
format(date, group), 'a') as fout:
fout.write('{}\n'.format(name))
events = None
# Generate MCMC sample
if (events is not None):
# Create a network
model = NetworkPoisson(N=N, dt_max=dt_max)
# Gibbs sampling
print("Beginning sampling... (M = {})".format(len(events[0])))
start = time.time()
lambda0, W, mu, tau = model.sample_ext(events, T=T, size=nsamples)
print("Finished MCMC sampling.")
# Save samples.
with h5.File(write_path, 'a') as hdf:
print('Writing sample to HDF5...')
hdf.create_dataset(name='{}/{}/{}'.format(
name, date, 'lambda0'),
data=lambda0)
hdf.create_dataset(name='{}/{}/{}'.format(name, date, 'W'),
data=W)
hdf.create_dataset(name='{}/{}/{}'.format(name, date, 'mu'),
data=mu)
hdf.create_dataset(name='{}/{}/{}'.format(name, date, 'tau'),
data=tau)
print('Done.\n')