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,
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)