diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/dfatool.py | 35 |
1 files changed, 25 insertions, 10 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 8990aed..95e76e7 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -1139,13 +1139,12 @@ class AnalyticModel: assess -- calculate model quality """ - def __init__(self, by_name, parameters, arg_count = None, verbose = True, use_corrcoef = False): + def __init__(self, by_name, parameters, arg_count = None, function_override = dict(), verbose = True, use_corrcoef = False): """ 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 + :param 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)), ...). @@ -1167,16 +1166,23 @@ class AnalyticModel: 'param' : [[1, 0], [1, 0], [2, 0]] # foo_count-^ ^-irrelevant } - `parameters`: List of parameter names - `verbose`: Print debug/info output while generating the model? - use_corrcoef -- use correlation coefficient instead of stddev comparison - to detect whether a model attribute depends on a parameter + :param parameters: List of parameter names + :param function_override: dict of overrides for automatic parameter function generation. + If (state or transition name, model attribute) is present in function_override, + the corresponding text string is the function used for analytic (parameter-aware/fitted) + modeling of this attribute. It is passed to AnalyticFunction, see + there for the required format. Note that this happens regardless of + parameter dependency detection: The provided analytic function will be assigned + even if it seems like the model attribute is static / parameter-independent. + :param verbose: Print debug/info output while generating the model? + :param use_corrcoef: use correlation coefficient instead of stddev comparison to detect whether a model attribute depends on a parameter """ 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.function_override = function_override.copy() self.verbose = verbose self._use_corrcoef = use_corrcoef self._num_args = arg_count @@ -1292,7 +1298,16 @@ class AnalyticModel: for attribute in self.by_name[name]['attributes']: fit_result = get_fit_result(paramfit.results, name, attribute, self.verbose) - if len(fit_result.keys()): + if (name, attribute) in self.function_override: + function_str = self.function_override[(name, attribute)] + x = AnalyticFunction(function_str, self.parameters, num_args) + x.fit(self.by_param, name, attribute) + if x.fit_success: + param_model[name][attribute] = { + 'fit_result': fit_result, + 'function' : x + } + elif len(fit_result.keys()): x = analytic.function_powerset(fit_result, self.parameters, num_args) x.fit(self.by_param, name, attribute) @@ -1516,7 +1531,7 @@ class PTAModel: self.cache = {} np.seterr('raise') self._outlier_threshold = discard_outliers - self.function_override = function_override + self.function_override = function_override.copy() self.verbose = verbose self.hwmodel = hwmodel self.ignore_trace_indexes = ignore_trace_indexes |