diff options
| -rwxr-xr-x | bin/analyze-timing.py | 25 |
1 files changed, 25 insertions, 0 deletions
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 2f60d1f..7e8174d 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -62,6 +62,12 @@ Options: --export-energymodel=<model.json> Export energy model. Requires --hwmodel. + +--filter-param=<parameter name>=<parameter value> + Only consider measurements where <parameter name> is <parameter value> + All other measurements (including those where it is None, that is, has + not been set yet) are discarded. Note that this may remove entire + function calls from the model. """ import getopt @@ -141,6 +147,7 @@ if __name__ == '__main__': optspec = ( 'plot-unparam= plot-param= show-models= show-quality= ' 'ignored-trace-indexes= discard-outliers= function-override= ' + 'filter-param= ' 'cross-validate= ' 'corrcoef ' 'with-safe-functions hwmodel= export-energymodel=' @@ -184,6 +191,9 @@ if __name__ == '__main__': if 'corrcoef' not in opts: opts['corrcoef'] = False + if 'filter-param' in opts: + opts['filter-param'] = opts['filter-param'].split('=') + except getopt.GetoptError as err: print(err) sys.exit(2) @@ -192,6 +202,21 @@ if __name__ == '__main__': preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes) + + if 'filter-param' in opts: + param_index = parameters.index(opts['filter-param'][0]) + param_value = soft_cast_int(opts['filter-param'][1]) + names_to_remove = set() + for name in by_name.keys(): + indices_to_keep = list(map(lambda x: x[param_index] == param_value, by_name[name]['param'])) + by_name[name]['param'] = list(map(lambda iv: iv[1], filter(lambda iv: indices_to_keep[iv[0]], enumerate(by_name[name]['param'])))) + for attribute in by_name[name]['attributes']: + by_name[name][attribute] = by_name[name][attribute][indices_to_keep] + if len(by_name[name][attribute]) == 0: + names_to_remove.add(name) + for name in names_to_remove: + by_name.pop(name) + model = AnalyticModel(by_name, parameters, arg_count, use_corrcoef = opts['corrcoef']) if xv_method: |