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