diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/dfatool.py | 40 |
1 files changed, 26 insertions, 14 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index ac0885b..17a5d43 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -472,7 +472,7 @@ class ParamStats: influencer_parameters = self._reduce_param_matrix(dep_by_value, other_param_list) return influencer_parameters - def _param_independence_ratio(self, state_or_trans, attribute, param): + def _param_independence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float: """ Return the heuristic ratio of parameter independence for state_or_trans, attribute, and param. @@ -488,20 +488,37 @@ class ParamStats: # This means that the variation of param does not affect the model quality -> no influence, return 1 return 1. - influencer_parameters = self._get_codependent_parameters(statistics, param) - if len(influencer_parameters): - print('{}/{} {} <-> {}'.format(state_or_trans, attribute, param, influencer_parameters)) - return statistics['std_param_lut'] / statistics['std_by_param'][param] - def param_dependence_ratio(self, state_or_trans, attribute, param): + def param_dependence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float: """ Return the heuristic ratio of parameter dependence for state_or_trans, attribute, and param. A value close to 0 means no influence, a value close to 1 means high probability of influence. + + :param state_or_trans: state or transition name + :param attribute: model attribute + :param param: parameter name + + :returns: parameter dependence (float between 0 == no influence and 1 == high probability of influence) """ return 1 - self._param_independence_ratio(state_or_trans, attribute, param) + def reverse_dependent_parameters(self, state_or_trans: str, attribute: str, param: str) -> list: + """ + Return parameters whose value influences whether `attribute` of `state_or_trans` depends on `param` or not. + + For example, a radio's TX POWER is only influenced by the packet length if dynamically sized payloads are enabled. + So reverse_dependent_parameters('TX', 'POWER', 'packet_length') == ['dynamic_payload_size']. + + :param state_or_trans: state or transition name + :param attribute: model attribute + :param param: parameter name + + :returns: list of parameters + """ + return self._get_codependent_parameters(self.stats[state_or_trans][attribute], param) + def _arg_independence_ratio(self, state_or_trans, attribute, arg_index): statistics = self.stats[state_or_trans][attribute] if self.use_corrcoef: @@ -514,7 +531,7 @@ class ParamStats: return 1 return statistics['std_param_lut'] / statistics['std_by_arg'][arg_index] - def arg_dependence_ratio(self, state_or_trans, attribute, arg_index): + def arg_dependence_ratio(self, state_or_trans: str, attribute: str, arg_index: int) -> float: return 1 - self._arg_independence_ratio(state_or_trans, attribute, arg_index) # This heuristic is very similar to the "function is not much better than @@ -1570,14 +1587,14 @@ def _add_trace_data_to_aggregate(aggregate, key, element): def filter_aggregate_by_param(aggregate, parameters, parameter_filter): """ - Remove entries with certain parameter values from `aggregate`. + Remove entries which do not have certain parameter values from `aggregate`. :param aggregate: aggregated measurement data, must be a dict conforming to aggregate[state or transition name]['param'] = (first parameter value, second parameter value, ...) and aggregate[state or transition name]['attributes'] = [list of keys with measurement data, e.g. 'power' or 'duration'] :param parameters: list of parameters, used to map parameter index to parameter name. parameters=['foo', ...] means 'foo' is the first parameter - :param parameter_filter: [[name, value], [name, value], ...] list of parameter values to remove. Values refer to normalizad parameter data. + :param parameter_filter: [[name, value], [name, value], ...] list of parameter values to keep, all others are removed. Values refer to normalizad parameter data. """ for param_name_and_value in parameter_filter: param_index = parameters.index(param_name_and_value[0]) @@ -1741,11 +1758,6 @@ class PTAModel: self.ignore_trace_indexes = ignore_trace_indexes self._aggregate_to_ndarray(self.by_name) - def distinct_param_values(self, state_or_tran, param_index = None, arg_index = None): - if param_index != None: - param_values = map(lambda x: x[param_index], self.by_name[state_or_tran]['param']) - return sorted(set(param_values)) - def _aggregate_to_ndarray(self, aggregate): for elem in aggregate.values(): for key in elem['attributes']: |