diff options
| -rw-r--r-- | .gitlab-ci.yml | 1 | ||||
| -rwxr-xr-x | bin/analyze-timing.py | 2 | ||||
| -rwxr-xr-x | lib/dfatool.py | 35 | ||||
| -rwxr-xr-x | test/test_timingharness.py | 29 |
4 files changed, 56 insertions, 11 deletions
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 5d19d51..8e3ef97 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -19,6 +19,7 @@ run_tests: - wget -qO test-data/20170220_164723_RF24_int_A.tar https://lib.finalrewind.org/energy-models/20170220_164723_RF24_int_A.tar - wget -qO test-data/20190815_103347_nRF24_no-rx.json https://lib.finalrewind.org/energy-models/20190815_103347_nRF24_no-rx.json - wget -qO test-data/20190815_111745_nRF24_no-rx.json https://lib.finalrewind.org/energy-models/20190815_111745_nRF24_no-rx.json + - wget -qO test-data/20190815_122531_nRF24_no-rx.json https://lib.finalrewind.org/energy-models/20190815_122531_nRF24_no-rx.json - PYTHONPATH=lib pytest-3 --cov=lib - python3-coverage html artifacts: diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 1c27533..39a915f 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -222,7 +222,7 @@ if __name__ == '__main__': for name in names_to_remove: by_name.pop(name) - model = AnalyticModel(by_name, parameters, arg_count, use_corrcoef = opts['corrcoef']) + model = AnalyticModel(by_name, parameters, arg_count, use_corrcoef = opts['corrcoef'], function_override = function_override) if xv_method: xv = CrossValidator(AnalyticModel, by_name, parameters, arg_count) 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 diff --git a/test/test_timingharness.py b/test/test_timingharness.py index b5937ad..5fb5fb1 100755 --- a/test/test_timingharness.py +++ b/test/test_timingharness.py @@ -49,6 +49,7 @@ class TestModels(unittest.TestCase): self.assertAlmostEqual(model.stats.param_dependence_ratio(transition, 'duration', 'channel'), 0, places=2) param_model, param_info = model.get_fitted() + self.assertEqual(param_info('getObserveTx', 'duration'), None) self.assertEqual(param_info('setPALevel', 'duration'), None) self.assertEqual(param_info('setRetries', 'duration'), None) self.assertEqual(param_info('setup', 'duration'), None) @@ -59,6 +60,34 @@ class TestModels(unittest.TestCase): self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[2], 1, places=0) self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[3], 1, places=0) + def test_function_override(self): + raw_data = TimingData(['test-data/20190815_122531_nRF24_no-rx.json']) + preprocessed_data = raw_data.get_preprocessed_data(verbose = False) + by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) + model = AnalyticModel(by_name, parameters, arg_count, verbose = False, function_override={('write', 'duration'): '(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))'}) + self.assertEqual(model.names, 'setAutoAck setPALevel setRetries setup write'.split(' ')) + static_model = model.get_static() + self.assertAlmostEqual(static_model('setAutoAck', 'duration'), 72, places=0) + self.assertAlmostEqual(static_model('setPALevel', 'duration'), 146, places=0) + self.assertAlmostEqual(static_model('setRetries', 'duration'), 73, places=0) + self.assertAlmostEqual(static_model('setup', 'duration'), 6533, places=0) + self.assertAlmostEqual(static_model('write', 'duration'), 1181, places=0) + + for transition in 'setAutoAck setPALevel setRetries setup write'.split(' '): + self.assertAlmostEqual(model.stats.param_dependence_ratio(transition, 'duration', 'channel'), 0, places=2) + + param_model, param_info = model.get_fitted() + self.assertEqual(param_info('setAutoAck', 'duration'), None) + self.assertEqual(param_info('setPALevel', 'duration'), None) + self.assertEqual(param_info('setRetries', 'duration'), None) + self.assertEqual(param_info('setup', 'duration'), None) + self.assertEqual(param_info('write', 'duration')['function']._model_str, '(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))') + + self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[0], 1162, places=0) + self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[1], 464, places=0) + self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[2], 1, places=0) + self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[3], 1, places=0) + self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[4], 1086, places=0) if __name__ == '__main__': unittest.main() |