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#!/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]['by_name'].keys():
for key in result_lists[0]['by_name'][state_or_tran].keys():
buf = '{:20s} {:15s}'.format(state_or_tran, key)
for i, results in enumerate(result_lists):
results = results['by_name']
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]['by_name'].keys():
for key in result_lists[0][0]['by_name'][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):
results = results['by_name']
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)
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