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#!/usr/bin/env python3
import getopt
import plotter
import re
import sys
from dfatool import PTAModel, RawData, pta_trace_to_aggregate
from dfatool import soft_cast_int, is_numeric, gplearn_to_function
opts = {}
def print_model_quality(results):
for state_or_tran in results.keys():
print()
for key, result in results[state_or_tran].items():
if 'smape' in result:
print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
state_or_tran, key, result['smape'], result['mae']))
else:
print('{:20s} {:15s} {:.0f}'.format(
state_or_tran, key, result['mae']))
def format_quality_measures(result):
if 'smape' in result:
return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
else:
return '{:6} {:9.0f}'.format('', result['mae'])
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):
info = info_list[i]
buf += ' ||| '
if info == None or info(state_or_tran, key):
result = results['by_name'][state_or_tran][key]
buf += format_quality_measures(result)
else:
buf += '{:6}----{:9}'.format('', '')
print(buf)
def model_summary_table(result_list):
buf = 'transition duration'
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['duration_by_trace'])
print(buf)
buf = 'total energy '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['energy_by_trace'])
print(buf)
buf = 'transition timeout '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['timeout_by_trace'])
print(buf)
def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
print('')
print(r'key attribute $1 - \frac{\sigma_X}{...}$')
for state_or_tran in model.by_name.keys():
for attribute in model.by_name[state_or_tran]['attributes']:
print('{} {} {:.8f}'.format(state_or_tran, attribute, model.generic_param_dependence_ratio(state_or_tran, attribute)))
print('')
print(r'key attribute parameter $1 - \frac{...}{...}$')
for state_or_tran in model.by_name.keys():
for attribute in model.by_name[state_or_tran]['attributes']:
for param in model.parameters():
print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.param_dependence_ratio(state_or_tran, attribute, param)))
if state_or_tran in model._num_args:
for arg_index in range(model._num_args[state_or_tran]):
print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.arg_dependence_ratio(state_or_tran, attribute, arg_index)))
if __name__ == '__main__':
ignored_trace_indexes = None
discard_outliers = None
safe_functions_enabled = False
function_override = {}
show_models = []
show_quality = []
try:
optspec = (
'plot-unparam= plot-param= show-models= show-quality= '
'ignored-trace-indexes= discard-outliers= function-override= '
'with-safe-functions'
)
raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.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'])
if 'function-override' in opts:
for function_desc in opts['function-override'].split(';'):
state_or_tran, attribute, *function_str = function_desc.split(' ')
function_override[(state_or_tran, attribute)] = ' '.join(function_str)
if 'show-models' in opts:
show_models = opts['show-models'].split(',')
if 'show-quality' in opts:
show_quality = opts['show-quality'].split(',')
if 'with-safe-functions' in opts:
safe_functions_enabled = True
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)
ref_model = PTAModel(
by_name, parameters, arg_count,
traces = preprocessed_data,
ignore_trace_indexes = ignored_trace_indexes,
discard_outliers = discard_outliers,
function_override = function_override,
use_corrcoef = False)
model = PTAModel(
by_name, parameters, arg_count,
traces = preprocessed_data,
ignore_trace_indexes = ignored_trace_indexes,
discard_outliers = discard_outliers,
function_override = function_override,
use_corrcoef = True)
if 'plot-unparam' in opts:
for kv in opts['plot-unparam'].split(';'):
state_or_trans, attribute = kv.split(' ')
plotter.plot_y(model.by_name[state_or_trans][attribute])
if len(show_models):
print('--- simple static model ---')
static_model = model.get_static()
ref_static_model = ref_model.get_static()
if 'static' in show_models or 'all' in show_models:
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_quality = model.assess(static_model)
ref_static_quality = ref_model.assess(ref_static_model)
if len(show_models):
print('--- LUT ---')
lut_model = model.get_param_lut()
lut_quality = model.assess(lut_model)
ref_lut_model = ref_model.get_param_lut()
ref_lut_quality = ref_model.assess(ref_lut_model)
if len(show_models):
print('--- param model ---')
param_model, param_info = model.get_fitted(safe_functions_enabled = safe_functions_enabled)
ref_param_model, ref_param_info = ref_model.get_fitted(safe_functions_enabled = safe_functions_enabled)
print('')
print('')
print('state_or_trans attribute param stddev_ratio corrcoef')
for state in model.states():
for attribute in model.attributes(state):
for param in model.parameters():
print('{:10s} {:10s} {:10s} {:f} {:f}'.format(state, attribute, param,
ref_model.param_dependence_ratio(state, attribute, param),
model.param_dependence_ratio(state, attribute, param)))
for trans in model.transitions():
for attribute in model.attributes(trans):
for param in model.parameters():
print('{:10s} {:10s} {:10s} {:f} {:f}'.format(trans, attribute, param,
ref_model.param_dependence_ratio(trans, attribute, param),
model.param_dependence_ratio(trans, attribute, param)))
print('')
print('')
analytic_quality = model.assess(param_model)
ref_analytic_quality = ref_model.assess(ref_param_model)
if 'tex' in show_models or 'tex' in show_quality:
print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
if 'table' in show_quality or 'all' in show_quality:
print('corrcoef:')
model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
print('heuristic:')
model_quality_table([ref_static_quality, ref_analytic_quality, ref_lut_quality], [None, ref_param_info, None])
if 'summary' in show_quality or 'all' in show_quality:
print('corrcoef:')
model_summary_table([static_quality, analytic_quality, lut_quality])
print('heuristic:')
model_summary_table([ref_static_quality, ref_analytic_quality, ref_lut_quality])
if 'plot-param' in opts:
for kv in opts['plot-param'].split(';'):
state_or_trans, attribute, param_name, *function = kv.split(' ')
if len(function):
function = gplearn_to_function(' '.join(function))
else:
function = None
plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
sys.exit(0)
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