def _execute(states, use_optimizations, output_mode, plot_results, output_graph, save_can=False): # Iterate over each state, load the data and perform the reduction out_scale_factors_list = [] out_shift_factors_list = [] for state in states: out_scale_factors, out_shift_factors = \ single_reduction_for_batch(state, use_optimizations, output_mode, plot_results, output_graph, save_can=save_can) out_shift_factors_list.append(out_shift_factors) out_scale_factors_list.append(out_scale_factors) return out_scale_factors_list, out_shift_factors_list
def _execute(states, use_optimizations, output_mode): # Iterate over each state, load the data and perform the reduction for state in states: single_reduction_for_batch(state, use_optimizations, output_mode)
def _execute(states, use_optimizations, output_mode, plot_results, output_graph): # Iterate over each state, load the data and perform the reduction for state in states: single_reduction_for_batch(state, use_optimizations, output_mode, plot_results, output_graph)