diff options
| -rwxr-xr-x | bin/analyze-timing.py | 6 | ||||
| -rwxr-xr-x | lib/dfatool.py | 39 |
2 files changed, 41 insertions, 4 deletions
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 66a67a8..19940b5 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -188,7 +188,7 @@ if __name__ == '__main__': preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes) - model = AnalyticModel(by_name, parameters) + model = AnalyticModel(by_name, parameters, arg_count) if xv_method: xv = CrossValidator(AnalyticModel, by_name, parameters, arg_count) @@ -239,6 +239,10 @@ if __name__ == '__main__': print('{:10s} {:10s} {:10s} stddev {:f}'.format( transition, attribute, param, model.stats.stats[transition][attribute]['std_by_param'][param] )) + for i, arg_stddev in enumerate(model.stats.stats[transition][attribute]['std_by_arg']): + print('{:10s} {:10s} arg{:d} stddev {:f}'.format( + transition, attribute, i, arg_stddev + )) if info != None: for param_name in sorted(info['fit_result'].keys(), key=str): param_fit = info['fit_result'][param_name]['results'] diff --git a/lib/dfatool.py b/lib/dfatool.py index ffe5a3b..995508e 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -520,6 +520,8 @@ class TimingData: for log_entry in trace['trace']: paramkeys = sorted(log_entry['parameter'].keys()) paramvalues = [soft_cast_int(log_entry['parameter'][x]) for x in paramkeys] + if arg_support_enabled and 'args' in log_entry: + paramvalues.extend(map(soft_cast_int, log_entry['args'])) if not 'param' in log_entry['offline_aggregates']: log_entry['offline_aggregates']['param'] = list() if 'duration' in log_entry['offline_aggregates']: @@ -982,15 +984,46 @@ class AnalyticModel: assess -- calculate model quality """ - def __init__(self, by_name, parameters, verbose = True): - """Create a new AnalyticModel and compute parameter statistics.""" + def __init__(self, by_name, parameters, arg_count = None, verbose = True): + """ + Create a new AnalyticModel and compute parameter statistics. + + parameters: + `by_name`: measurements aggregated by (function/state/...) name. Layout: + dictionary with one key per name ('send', 'TX', ...) or + one key per name and parameter combination + (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...). + + Parameter values must be ordered corresponding to the lexically sorted parameter names. + + Each element is in turn a dict with the following elements: + - param: list of parameter values in each measurement (-> list of lists) + - attributes: list of keys that should be analyzed, + e.g. ['power', 'duration'] + - for each attribute mentioned in 'attributes': A list with measurements. + All list except for 'attributes' must have the same length. + + For example: + parameters = ['foo_count', 'irrelevant'] + by_name = { + 'foo' : [1, 1, 2], + 'duration' : [5, 6, 7], + 'attributes' : ['foo', 'duration'], + 'param' : [[1, 0], [1, 0], [2, 0]] + # foo_count-^ ^-irrelevant + } + `parameters`: List of parameter names + `verbose`: Print debug/info output while generating the model? + """ self.cache = dict() self.by_name = by_name self.by_param = by_name_to_by_param(by_name) self.names = sorted(by_name.keys()) self.parameters = sorted(parameters) self.verbose = verbose - self._num_args = _num_args_from_by_name(by_name) + self._num_args = arg_count + if self._num_args is None: + self._num_args = _num_args_from_by_name(by_name) self.stats = ParamStats(self.by_name, self.by_param, self.parameters, self._num_args, verbose = verbose) |