# Setup Priors
prior_min, prior_max, labels = load_prior_ranges(n_params)
prior_unif = Uniform(lower=prior_min, upper=prior_max)
# 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=1, data=x_o)
M = DAPSimulator(x_o['I'], x_o['dt'], -75)
s = DAPSummaryStatsStepMoments(t_on, t_off, n_summary=n_summary)
G = Default(model=M, prior=prior_unif, summary=s) # Generator
# 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, prior_norm=True)
reg_lambda=reg_lambda, pilot_samples=0)
logs, tds, posteriors = inf_snpe.run(n_train=[n_samples], n_rounds=n_rounds,
proposal=prior_unif)
# Analyse results
samples_prior = prior_unif.gen(n_samples=int(5e5))
samples_posterior = posteriors[-1].gen(n_samples=int(5e5))
print('posterior:', posteriors[-1].mean)
x_post = syn_obs_data(I, dt, posteriors[-1].mean)
idx = np.arange(0, len(x_o['data']))
s = DAPSummaryStats(t_on, t_off, n_summary=n_summary)
elif summary_stats_type == 1:
s = DAPSummaryStatsA(t_on, t_off, n_summary=n_summary)
elif summary_stats_type == 2:
s = DAPSummaryStatsNoAP(t_on, t_off, n_summary=n_summary)
else:
raise ValueError('Only 0, 1, 2 as an option for summary statistics.')
sum_stats = DAPSummaryStatsA(t_on, t_off, n_summary=8)
G = Default(model=M, prior=prior, summary=sum_stats) # Generator
# Runing the simulation
inf_snpe = SNPE(generator=G,
n_components=1,
n_hiddens=[2],
obs=S,
pilot_samples=10)
logs, tds, posteriors = inf_snpe.run(n_train=[n_samples],
n_rounds=n_rounds,
monitor=observables,
round_cl=1)
# Analyse results
print('expected mean, std', expected.mean, expected.std)
print('posterior mean, std', posteriors[-1].mean, posteriors[-1].std)
# Saving Data into pickle files
posterior_sampl = simulate_data_distr(posteriors[-1],
M,
prior_unif = Uniform(lower=prior_min, upper=prior_max)
samples_prior = prior_unif.gen(n_samples=int(5e5))
idx = np.arange(0, len(x_o['data']))
# 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=0, 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
# Runing the simulation
inf_snpe = SNPE(generator=G, n_components=n_components, n_hiddens=n_hiddens, obs=S,
reg_lambda=reg_lambda, prior_mixin=prior_mixin, pilot_samples=0)
inf_snpe.standardize_init()
#run
logs, tds, posteriors = inf_snpe.run(n_train=[n_samples], n_rounds=n_rounds,
proposal=prior_unif, monitor=observables,
epochs=epochs, round_cl=round_cl)
# Save parameters
posteriors_means, posteriors_std = [], []
for i, posterior in enumerate(posteriors):
posteriors_means.append(posterior.mean)
posteriors_std.append(posterior.std)
params=params,
dt=x_o['dt'],
t_on=t_on,
t_off=t_off,
n_summary=n_summary,
summary_stats=0,
data=x_o)
M = DAPSimulator(x_o['I'], x_o['dt'], -75)
s = DAPSummaryStats(t_on, t_off, n_summary=n_summary)
G = Default(model=M, prior=prior_unif, summary=s) # Generator
# Runing the simulation
inf_snpe = SNPE(generator=G,
n_components=n_components,
n_hiddens=n_hiddens,
obs=S,
pilot_samples=10,
prior_norm=True)
logs, tds, posteriors = inf_snpe.run(n_train=[n_samples],
n_rounds=n_rounds,
proposal=prior_unif,
monitor=observables)
# Analyse results
samples_prior = prior_unif.gen(n_samples=int(5e5))
samples_posterior = posteriors[-1].gen(n_samples=int(5e5))
# Plots
x_post = syn_obs_data(i_inj[0], dt, posteriors[-1].mean)
idx = np.arange(0, len(x_o['data']))
print(prior_min, prior_max, labels)
# Summary Statistics
# calcualte summary statistics
sum_stats_step = DAPSummaryStatsStepMoments(t_on_step, t_off_step, n_summary=17)
sum_stats_mom = DAPSummaryStatsMoments(t_on, t_off, n_summary=17)
s_step = sum_stats_step.calc([x_step])
s_ramp = sum_stats_mom.calc([x_o])
dap1 = DAPSimulatorMultiProtocol(I_all, dt_all, -75)
G = Default(model=dap1, prior=prior_unif, summary=sum_stats_step) # Generator
# Runing the simulation
inf_snpe = SNPE(generator=G, n_components=n_components, n_hiddens=n_hiddens, obs=s_step,
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)
# Analyse results
samples_prior = prior_unif.gen(n_samples=int(5e5))
samples_posterior = posteriors[-1].gen(n_samples=int(5e5))
print('posterior:', posteriors[-1].mean)
x_post = syn_obs_data(I, dt, posteriors[-1].mean)
x_post_step = syn_obs_data(I_step, dt, posteriors[-1].mean)
idx = np.arange(0, len(x_o['data']))
sum_stats = [sum_stats_step, sum_stats_mom]
s_step = sum_stats_step.calc([x_step])
s_ramp = sum_stats_mom.calc([x_ramp])
# S = [s_step, s_ramp]
S = s_step
print('summary starts observed:', S)
print('summary stats ramp:', sum_stats_mom.calc(data_list[0]))
print('summary stats step:', sum_stats_step.calc(data_list[1]))
G = DAPDefault(model=dap1, prior=prior_unif, summary=sum_stats) # Generator
inf_snpe = SNPE(generator=G,
n_components=1,
n_hiddens=[2],
obs=S,
reg_lambda=reg_lambda,
pilot_samples=0)
logs, tds, posteriors = inf_snpe.run(n_train=[10],
n_rounds=1,
proposal=prior_unif)
print('goal parameters:', params[:2])
print('posterior parameters:', posteriors[-1].mean)
# plot inputs
fig, ax = plt.subplots(ncols=1, nrows=2, figsize=(20, 10))
ax[0].plot(Ir)
ax[1].plot(Is)