Add function override support to AnalyticModel, analyze-timing.py

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()