fixed_p = np.ndarray(shape=(len(initial_params), len(prior_set.params)))
for idx, item in enumerate(initial_params):
for i, param in enumerate(prior_set.params):
fixed_p[idx, i] = item[param.name]
database.create_array(
database.root,
"fixed_params",
title="True value for each parameter in given simulation",
atom=tb.Atom.from_dtype(fixed_p.dtype),
shape=fixed_p.shape,
obj=fixed_p)
database.flush()
print("Parameter space initialization data saved to disk")
print(database)
database.close()
# RUN INFERENCE --------------------------------------------------------------------------------------------
working_path = "/home/szabolcs/parameter_inference/exp/inference_w"
if __name__ == '__main__':
run_protocol_simulations(model=exp_model,
target_traces=target_traces,
noise_std=noise_std,
param_set=prior_set,
working_path=working_path)
runningTime = (time.time() - startTime) / 60
print("\n\nThe script was running for %f minutes" % runningTime)
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/%i/stim.txt" %
item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/combining2/zaps/%i" % item
modell = partial(model, stype='custom', custom_stim=stim)
# Generate synthetic data for each fixed params and given repetition
target_traces = more_w_trace(sigma=noise_std,
model=modell,
params=fixed_params,
rep=noise_rep)
if __name__ == '__main__':
run_protocol_simulations(model=modell,
target_traces=target_traces,
noise_std=noise_std,
param_set=prior_set,
fixed_params=fixed_params,
working_path=working_path)
for item in duration:
print(
"\n\n---------------------------------------- Running %i ms impulse protocol"
% item)
# Stimulus path
stim = np.loadtxt(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/steps/%i/stim.txt" %
item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/combining2/steps/%i" % item
modell = partial(model, stype='custom', custom_stim=stim)