def main(args):
log = prep_log(args)
log.info("Running {}.".format(args.name))
if args.verbose:
log.info(args)
prior_lims = np.array([[0, 1], [-10., 10.], [-120., 120.], [0., 2000],
[0., 0.5], [0, 0.05], [0., 0.5], [0, 0.05]])
if args.verbose:
log.info('prior')
log.info(prior_lims)
m = ChannelOmni(third_exp_model=False, seed=args.seed)
p = dd.Uniform(lower=prior_lims[:, 0], upper=prior_lims[:, 1])
s = ChannelStats()
g = Default(model=m, prior=p, summary=s)
tic = time.time()
dats = g.gen(args.n_samples)
toc = time.time()
log.info('Generation took {}s.'.format(toc - tic))
np.save('{}/theta.npy'.format(args.bp), dats[0])
np.save('{}/stats.npy'.format(args.bp), dats[1])
# Summary Statistics
S = syn_obs_stats(x_o['I'],
params=params,
dt=x_o['dt'],
t_on=t_on,
t_off=t_off,
n_summary=n_summary,
summary_stats=2,
data=x_o)
M = DAPSimulator(x_o['I'], x_o['dt'], -75)
s = DAPSummaryStatsMoments(t_on, t_off, n_summary=n_summary)
G = Default(model=M, prior=prior_unif, summary=s) # Generator
print('S', S)
print('G', G.gen(1))
S = np.array([S, S])
# Runing the simulation
inf_snpe = SNPE(generator=G,
n_components=n_components,
n_hiddens=n_hiddens,
obs=S,
reg_lambda=reg_lambda,
pilot_samples=0)
logs, tds, posteriors = inf_snpe.run(n_train=[n_samples],
n_rounds=n_rounds,
proposal=prior_unif,
monitor=observables)
