def parallel_step_p(code, hadamard_mat, hadamard_vec, XYperm_mat, XYperm_vec,
ZYperm_mat, ZYperm_vec, error_model, decoder, max_runs,
error_probability):
result = app_def.run_def(code, hadamard_mat, hadamard_vec, XYperm_mat,
XYperm_vec, ZYperm_mat, ZYperm_vec, error_model,
decoder, error_probability, max_runs)
return result
def parallel_step_code(code,error_model,decoder,max_runs,perm_rates,code_name,error_probabilities,realization_index):
pL_list=np.zeros((len(error_probabilities)))
std_list=np.zeros((len(error_probabilities)))
for error_probability_index,error_probability in enumerate(error_probabilities):
[pL_list[error_probability_index],std_list[error_probability_index]]= app_def.run_def(code,error_model,decoder,error_probability,perm_rates,code_name,max_runs)
return [pL_list,std_list]
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_def.run_def(code, error_model, decoder, error_probability,
perm_rates, code_name, layout, max_runs)
return result
def parallel_step_p(code, hadamard_mat, error_model, decoder, max_runs,
n_errors_code):
result = app_def.run_def(code, hadamard_mat, error_model, decoder,
n_errors_code, max_runs)
return result
def parallel_step_p(code,error_model,decoder,max_runs,perm_rates,code_name,error_probability):
result= app_def.run_def(code,error_model,decoder,error_probability,perm_rates,code_name,max_runs)
return result
def parallel_step_p(code,perm_mat,perm_vec,perm_mat,perm_vec,perm_mat,perm_vec,error_model, decoder, max_runs, error_probability):
perm_mat,perm_vec,perm_mat,perm_vec,perm_mat,perm_vec= deform_matsvecs(code,decoder,error_model)
result= app_def.run_def(code,perm_mat,perm_vec,perm_mat,perm_vec,perm_mat,perm_vec, error_model, decoder, error_probability, max_runs)
return result
