#!/usr/bin/env python3 import getopt import plotter import re import sys from dfatool import PTAModel, RawData, soft_cast_int, pta_trace_to_aggregate opts = {} def model_quality_table(result_lists, info_list): for state_or_tran in result_lists[0].keys(): for key in result_lists[0][state_or_tran].keys(): buf = '{:20s} {:15s}'.format(state_or_tran, key) for i, results in enumerate(result_lists): info = info_list[i] buf += ' ||| ' if info == None or info(state_or_tran, key): result = results[state_or_tran][key] if 'smape' in result: buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) else: buf += '{:6} {:9.0f}'.format('', result['mae']) else: buf += '{:6}----{:9}'.format('', '') print(buf) def combo_model_quality_table(result_lists, info_list): for state_or_tran in result_lists[0][0].keys(): for key in result_lists[0][0][state_or_tran].keys(): for sub_result_lists in result_lists: buf = '{:20s} {:15s}'.format(state_or_tran, key) for i, results in enumerate(sub_result_lists): info = info_list[i] buf += ' ||| ' if info == None or info(state_or_tran, key): result = results[state_or_tran][key] if 'smape' in result: buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) else: buf += '{:6} {:9.0f}'.format('', result['mae']) else: buf += '{:6}----{:9}'.format('', '') print(buf) if __name__ == '__main__': ignored_trace_indexes = [] discard_outliers = None try: raw_opts, args = getopt.getopt(sys.argv[1:], "", 'plot ignored-trace-indexes= discard-outliers='.split(' ')) for option, parameter in raw_opts: optname = re.sub(r'^--', '', option) opts[optname] = parameter if 'ignored-trace-indexes' in opts: ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(','))) if 0 in ignored_trace_indexes: print('[E] arguments to --ignored-trace-indexes start from 1') if 'discard-outliers' in opts: discard_outliers = float(opts['discard-outliers']) except getopt.GetoptError as err: print(err) sys.exit(2) raw_data = RawData(args) preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes) m1 = PTAModel(by_name, parameters, arg_count, traces = preprocessed_data, ignore_trace_indexes = ignored_trace_indexes) m2 = PTAModel(by_name, parameters, arg_count, traces = preprocessed_data, ignore_trace_indexes = ignored_trace_indexes, discard_outliers = discard_outliers) print('--- simple static model ---') static_m1 = m1.get_static() static_m2 = m2.get_static() #for state in model.states(): # print('{:10s}: {:.0f} µW ({:.2f})'.format( # state, # static_model(state, 'power'), # model.generic_param_dependence_ratio(state, 'power'))) # for param in model.parameters(): # print('{:10s} dependence on {:15s}: {:.2f}'.format( # '', # param, # model.param_dependence_ratio(state, 'power', param))) #for trans in model.transitions(): # print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ ({:.2f} / {:.2f} / {:.2f})'.format( # trans, static_model(trans, 'energy'), # static_model(trans, 'rel_energy_prev'), # static_model(trans, 'rel_energy_next'), # model.generic_param_dependence_ratio(trans, 'energy'), # model.generic_param_dependence_ratio(trans, 'rel_energy_prev'), # model.generic_param_dependence_ratio(trans, 'rel_energy_next'))) # print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration'))) static_q1 = m1.assess(static_m1) static_q2 = m2.assess(static_m2) static_q12 = m1.assess(static_m2) print('--- LUT ---') lut_m1 = m1.get_param_lut() lut_m2 = m2.get_param_lut() lut_q1 = m1.assess(lut_m1) lut_q2 = m2.assess(lut_m2) lut_q12 = m1.assess(lut_m2) print('--- param model ---') param_m1, param_i1 = m1.get_fitted() for state in m1.states(): for attribute in ['power']: if param_i1(state, attribute): print('{:10s}: {}'.format(state, param_i1(state, attribute)['function']._model_str)) print('{:10s} {}'.format('', param_i1(state, attribute)['function']._regression_args)) for trans in m1.transitions(): for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: if param_i1(trans, attribute): print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i1(trans, attribute)['function']._model_str)) print('{:10s} {:10s} {}'.format('', '', param_i1(trans, attribute)['function']._regression_args)) param_m2, param_i2 = m2.get_fitted() for state in m2.states(): for attribute in ['power']: if param_i2(state, attribute): print('{:10s}: {}'.format(state, param_i2(state, attribute)['function']._model_str)) print('{:10s} {}'.format('', param_i2(state, attribute)['function']._regression_args)) for trans in m2.transitions(): for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: if param_i2(trans, attribute): print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i2(trans, attribute)['function']._model_str)) print('{:10s} {:10s} {}'.format('', '', param_i2(trans, attribute)['function']._regression_args)) analytic_q1 = m1.assess(param_m1) analytic_q2 = m2.assess(param_m2) analytic_q12 = m1.assess(param_m2) model_quality_table([static_q1, analytic_q1, lut_q1], [None, param_i1, None]) model_quality_table([static_q2, analytic_q2, lut_q2], [None, param_i2, None]) model_quality_table([static_q12, analytic_q12, lut_q12], [None, param_i2, None]) combo_model_quality_table([ [static_q1, analytic_q1, lut_q1], [static_q2, analytic_q2, lut_q2], [static_q12, analytic_q12, lut_q12]], [None, param_i1, None]) sys.exit(0)