"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/best_comb/%i/stim.txt"
% item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/best_comb/%i" % item
# Do statistics for each parameter
stat_list = []
for _ in p_names:
stat_list.append(np.empty((n, 6), dtype=np.float))
# Load fixed parameters: list of parameters to be inferred
fixed_params = []
for name in p_names:
fixed_params.append(get_default_param(name))
# Generate deterministic trace and create synthetic data with noise model
t, v = model(stype='custom', custom_stim=stim)
data = white(noise, v)
pset = ParameterSet(*fixed_params)
modell = partial(model, stype='custom', custom_stim=stim)
inf = IndependentInference(model=modell,
noise_std=noise,
target_trace=data,
parameter_set=pset,
working_path=working_path,
speed=speed)
if __name__ == '__main__':
inf.run_sim()
startTime = time.time()
for i in range(num_of_iter):
print(str(i) + " is DONE out of " + str(num_of_iter))
# Sampling current parameter from normal distribution
current_Ra = np.random.normal(pRa.mean, 10)
current_gpas = np.random.normal(pgpas.mean, pgpas.sigma)
current_cm = np.random.normal(pcm.mean, pcm.sigma)
# Generate deterministic trace and create synthetic data with noise model
t, v = stick_and_ball(Ra=current_Ra,
gpas=current_gpas,
cm=current_cm,
stype='custom',
custom_stim=stim)
data = white(noise_sigma, v)
# if i == 0:
# plt.figure()
# plt.title("Neuron voltage response to stimuli")
# plt.xlabel('Time [ms]')
# plt.ylabel('Voltage [mV]')
# plt.plot(t, v, color='#2FA5A0')
# plt.show()
# Set up range in a way that the true parameter value will be in the middle
Ra_start = current_Ra - 50
Ra_end = current_Ra + 50
gpas_start = current_gpas - 0.00005
gpas_end = current_gpas + 0.00005
cm_start = current_cm - 0.5
value=1.)
gpas = RandomVariable(name='gpas',
range_min=0.00005,
range_max=0.00015,
resolution=60,
mean=0.00008,
sigma=0.00002,
value=0.0001)
# Ra = RandomVariable(name='Ra', range_min=50., range_max=150., resolution=60, mean=100., sigma=20.)
# 2.) Set up parameter set
cm_gpas = ParameterSet(cm, gpas)
# 3.) Sythetic data
t, v = stick_and_ball()
exp_v = white(noise, v)
# 4.) Set up inference
inf = IndependentInference(
model=stick_and_ball,
noise_std=noise,
target_trace=exp_v,
parameter_set=cm_gpas,
working_path=
"/home/terbed/PROJECTS/SPE/parameter-inference/module/examples/output",
speed='max',
save=False)
# 5.) Run inference
if __name__ == "__main__":
inf.run_sim()