print(params)
print(labels)
# Set up themodel
dap = DAPcython(-75, params)
U = dap.simulate(dt, t, I)
# generate data format for SNPE / OBSERVABLE
x_o = {'data': U.reshape(-1),
'time': t,
'dt': dt,
'I': I}
# 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)
dt = 0.01
percent_accept = 1
# Get current
I, t, t_on, t_off = syn_current(duration=70, dt=dt, t_on=15, t_off=20, amp=3.1)
params, labels = obs_params_gbar(reduced_model=True)
dap = DAPcython(-75, params * 10)
# Set up the model
sim = DAPSimulator(I, dt, -75, dim_param=2)
stats = DAPSummaryStatsMoments(t_on, t_off, n_summary=n_summary)
# Setup Priors
prior_min = np.array([0, 0])
prior_max = np.array([2, 2])
prior_unif = Uniform(lower=prior_min, upper=prior_max)
# generate desired data
U = dap.simulate(dt, t, I)
y_o = {'data': U.reshape(-1), 'time': t, 'dt': dt, 'I': I}
y = stats.calc([y_o])
# Sample Parameters
params = prior_unif.gen(n_samples=n_samples)
# calculate the distance for each parameters
norms = []
for p in tqdm(params):
x_o = sim.gen_single(p)
y_obs = stats.calc([x_o])
'time': tr,
'dt': dtr,
'I': Ir
}
x_step = {
'data': vs.reshape(-1),
# x_step = {'data': vs,
'time': ts,
'dt': dts,
'I': Is
}
# Setup Priors
prior_min, prior_max, labels = load_prior_ranges(2)
prior_unif = Uniform(lower=prior_min, upper=prior_max)
# define model
dap1 = DAPSimulatorMultiProtocol(I_all, dt_all, -75)
# run model
stats = dap1.gen_single(params, I_all[0], tr, dtr)
data_list = dap1.gen(params_list)
# calcualte summary statistics
sum_stats_step = DAPSummaryStatsStepMoments(t_ons, t_offs, n_summary=17)
sum_stats_mom = DAPSummaryStatsMoments(t_onr, t_offr, n_summary=17)
sum_stats = [sum_stats_step, sum_stats_mom]
s_step = sum_stats_step.calc([x_step])
s_ramp = sum_stats_mom.calc([x_ramp])
