# 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)
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))
from dap import DAPcython
from dap.dap_simulator import DAPSimulator
# General Settings Pick
n_samples = 100
n_summary = 13
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)
import matplotlib.pyplot as plt
from dap.dap_simulator import DAPSimulator
from dap.utils import obs_params_gbar, syn_current
params, labels = obs_params_gbar()
I, t, t_on, t_off = syn_current(duration=150, dt=0.01)
# define model
dap1 = DAPSimulator(I, 0.01, -75)
# run model
stats = dap1.gen_single(params)
# plot voltage trace
fig, ax = plt.subplots(ncols=1, nrows=2, figsize=(20, 10))
ax[0].grid()
ax[0].set_ylabel('V (mV)')
ax[0].set_xlabel('t (ms)')
ax[0].plot(stats['time'], stats['data'], label='DAP')
ax[0].legend()
ax[1].plot(t, I)
plt.show()