def parallel_step_code2(code, error_model, decoder, max_runs, perm_rates,
code_name, layout, error_probability,
realization_index):
result_one_realiz = app_defp.run_defp(code, error_model, decoder,
error_probability, perm_rates,
code_name, layout, max_runs)
return result_one_realiz
def parallel_step_code(code, error_model, decoder, max_runs, perm_rates,
code_name, layout, error_probability, run_id,
realization_index):
np.random.seed(1234 * (realization_index + 1) * (run_id + 1))
result_one_realiz = app_defp.run_defp(code, error_model, decoder,
error_probability, perm_rates,
code_name, layout, max_runs)
return result_one_realiz
def parallel_step_code(code, error_model, decoder, max_runs, perm_rates,
code_name, layout, error_probabilities,
realization_index):
result_one_realiz = []
for error_probability_index, error_probability in enumerate(
error_probabilities):
result_one_realiz.append(
app_defp.run_defp(code, error_model, decoder, error_probability,
perm_rates, code_name, layout, max_runs))
return result_one_realiz
def parallel_step(code, error_model, decoder, max_runs, perm_rates, code_name,
layout, error_probability, run_id, realization_index):
np.random.seed(1234 * (run_id + 1) * (realization_index + 1))
random_seed = 1234 * (run_id + 1) * (realization_index + 1)
result_one_realiz = app_defp.run_defp(code, error_model, decoder,
error_probability, perm_rates,
code_name, layout, max_runs, None,
random_seed)
# def run_defp(code,error_model,decoder,error_probability,perm_rates,code_name,layout,max_runs=None,max_failures=None,random_seed=None):
return result_one_realiz
def parallel_step_code(code, error_model, decoder, max_runs, perm_rates,
code_name, layout, error_probabilities,
realization_index):
pL_list = np.zeros((len(error_probabilities)))
std_list = np.zeros((len(error_probabilities)))
# perm_mat,perm_vec= deform_matsvecs(code,decoder,error_model)
for error_probability_index, error_probability in enumerate(
error_probabilities):
# perm_mat,perm_vec= deform_matsvecs(code,decoder,error_model)
[pL_list[error_probability_index], std_list[error_probability_index]
] = app_defp.run_defp(code, error_model, decoder, error_probability,
perm_rates, code_name, layout, max_runs)
return [pL_list, std_list]
def parallel_step_p(code,error_model,decoder,max_runs,perm_rates,code_name,layout,error_probability):
result_onep= app_defp.run_defp(code,error_model,decoder,error_probability,perm_rates,code_name,layout,max_runs)
return result_onep
def parallel_step_p(code, error_model, decoder, max_runs, perm_rates,
code_name, layout, error_probability):
# perm_mat,perm_vec= deform_matsvecs(code,decoder,error_model)
result = app_defp.run_defp(code, error_model, decoder, error_probability,
perm_rates, code_name, layout, max_runs)
return result
