# Set up parameters using prior information about them (fix the range we are assuming the true parameter)
prior_params = []
for idx, item in enumerate(p_names):
prior_params.append(
RandomVariable(name=item,
range_min=p_range[idx][0],
range_max=p_range[idx][1],
resolution=p_res[idx],
sigma=p_std[idx],
mean=p_mean[idx]))
prior_set = ParameterSet(*prior_params)
prior_set.batch_len = batch_size
if batch_size is not None:
prior_set.isBatch = True
else:
prior_set.isBatch = False
prior_set.create_batch()
# Create fixed params sampled from prior
fixed_params = sampling_from_prior(prior_set, fixed_param_num)
# Save parameter informations
# Create database for data
database = tb.open_file(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/comb_colored_srsoma-rdend_gpas-dens/paramsetup.hdf5",
mode="w")
# Save param initialization
param_init = []
model = stick_and_ball
batch_size = 30000
# Set up random seed
np.random.seed(42)
# Set up parameters using prior information about them (fix the range we are assuming the true parameter)
prior_params = []
for idx, item in enumerate(p_names):
prior_params.append(RandomVariable(name=item, range_min=p_range[idx][0], range_max=p_range[idx][1],
resolution=p_res[idx], sigma=p_std[idx], mean=p_mean[idx]))
prior_set = ParameterSet(*prior_params)
prior_set.batch_len = batch_size
prior_set.isBatch = True
prior_set.create_batch()
# Create fixed params sampled from prior
fixed_params = sampling_from_prior(prior_set, fixed_param_num)
# Save parameter informations
# Create database for data
database = tb.open_file("/Users/Dani/TDK/parameter_estim/stim_protocol2/combining3/paramsetup.hdf5", mode="w")
# Save param initialization
param_init = []
for param in prior_set.params:
param_init.append(param.get_init())
param_init = np.array(param_init, dtype=str)