diff options
Diffstat (limited to 'bin/analyze-timing.py')
| -rwxr-xr-x | bin/analyze-timing.py | 23 |
1 files changed, 21 insertions, 2 deletions
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 659a3d7..6c84a67 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -21,6 +21,9 @@ Options: parameters. Also plots the corresponding measurements. If gplearn function is set, it is plotted using dashed lines. +--param-info + Show parameter names and values + --show-models=<static|paramdetection|param|all|tex> static: show static model values as well as parameter detection heuristic paramdetection: show stddev of static/lut/fitted model @@ -77,7 +80,8 @@ import re import sys from dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate from dfatool import soft_cast_int, is_numeric, gplearn_to_function -from dfatool import CrossValidator, filter_aggregate_by_param +from dfatool import CrossValidator +from utils import filter_aggregate_by_param from parameters import prune_dependent_parameters import utils @@ -151,7 +155,7 @@ if __name__ == '__main__': 'ignored-trace-indexes= discard-outliers= function-override= ' 'filter-param= ' 'cross-validate= ' - 'corrcoef ' + 'corrcoef param-info ' 'with-safe-functions hwmodel= export-energymodel=' ) raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' ')) @@ -216,6 +220,12 @@ if __name__ == '__main__': if xv_method: xv = CrossValidator(AnalyticModel, by_name, parameters, arg_count) + if 'param-info' in opts: + for state in model.names: + print('{}:'.format(state)) + for param in model.parameters: + print(' {} = {}'.format(param, model.stats.distinct_values[state][param])) + if 'plot-unparam' in opts: for kv in opts['plot-unparam'].split(';'): state_or_trans, attribute, ylabel = kv.split(':') @@ -228,6 +238,15 @@ if __name__ == '__main__': if 'static' in show_models or 'all' in show_models: for trans in model.names: print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration'))) + for param in model.parameters: + print('{:10s} dependence on {:15s}: {:.2f}'.format( + '', + param, + model.stats.param_dependence_ratio(trans, 'duration', param))) + if model.stats.has_codependent_parameters(trans, 'duration', param): + print('{:24s} co-dependencies: {:s}'.format('', ', '.join(model.stats.codependent_parameters(trans, 'duration', param)))) + for param_dict in model.stats.codependent_parameter_value_dicts(trans, 'duration', param): + print('{:24s} parameter-aware for {}'.format('', param_dict)) if xv_method == 'montecarlo': static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) |