eval: remove unused function_args argument

3 files changed, 20 insertions, 14 deletions

diff --git a/lib/automata.py b/lib/automata.py
index 5849daa..c59f47a 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -1108,6 +1108,7 @@ class PTA:
                     for i in range(len(self.parameters))
                 ]
             )
+        param_list = dict_to_list(param_dict)
         for function in trace:
             if isinstance(function[0], Transition):
                 function_name = function[0].name
@@ -1120,7 +1121,7 @@ class PTA:
                 total_energy += state.get_energy(duration, param_dict)
                 if state.power.value_error is not None:
                     total_energy_error += (
-                        duration * state.power.eval_mae(param_dict, function_args)
+                        duration * state.power.eval_mae(param_list + function_args)
                     ) ** 2
                 total_duration += duration
                 # assumption: sleep is near-exact and does not contribute to the duration error
@@ -1128,17 +1129,15 @@ class PTA:
                     accounting.sleep(duration)
             else:
                 transition = state.get_transition(function_name)
-                total_duration += transition.duration.eval(
-                    dict_to_list(param_dict) + function_args
-                )
+                total_duration += transition.duration.eval(param_list + function_args)
                 if transition.duration.value_error is not None:
                     total_duration_mae += (
-                        transition.duration.eval_mae(param_dict, function_args) ** 2
+                        transition.duration.eval_mae(param_list + function_args) ** 2
                     )
                 total_energy += transition.get_energy(param_dict, function_args)
                 if transition.energy.value_error is not None:
                     total_energy_error += (
-                        transition.energy.eval_mae(param_dict, function_args) ** 2
+                        transition.energy.eval_mae(param_list + function_args) ** 2
                     )
                 param_dict = transition.get_params_after_transition(
                     param_dict, function_args
diff --git a/lib/functions.py b/lib/functions.py
index df74903..da9ad48 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -168,10 +168,10 @@ class ModelFunction:
     def is_predictable(self, param_list):
         raise NotImplementedError
 
-    def eval(self, param_list, arg_list):
+    def eval(self, param_list):
         raise NotImplementedError
 
-    def eval_mae(self, param_list, arg_list):
+    def eval_mae(self, param_list):
         if self.is_predictable(param_list):
             return self.function_error["mae"]
         return self.value_error["mae"]
@@ -231,7 +231,7 @@ class StaticFunction(ModelFunction):
         """
         return True
 
-    def eval(self, param_list=None, arg_list=None):
+    def eval(self, param_list=None):
         """
         Evaluate model function with specified param/arg values.
 
@@ -275,9 +275,9 @@ class SplitFunction(ModelFunction):
             return False
         return self.child[param_value].is_predictable(param_list)
 
-    def eval(self, param_list, arg_list=list()):
+    def eval(self, param_list):
         param_value = param_list[self.param_index]
-        return self.child[param_value].eval(param_list, arg_list)
+        return self.child[param_value].eval(param_list)
 
     def to_json(self):
         ret = super().to_json()
@@ -485,7 +485,7 @@ class AnalyticFunction(ModelFunction):
                 return False
         return True
 
-    def eval(self, param_list, arg_list=[]):
+    def eval(self, param_list):
         """
         Evaluate model function with specified param/arg values.
 
@@ -493,8 +493,6 @@ class AnalyticFunction(ModelFunction):
             corresponds to lexically first parameter, etc.
         :param arg_list: argument values (list of float), if arguments are used.
         """
-        if len(self.model_args) == 0:
-            return self._function(param_list, arg_list)
         return self._function(self.model_args, param_list)
 
     def to_json(self):
diff --git a/lib/parameters.py b/lib/parameters.py
index aedb6cd..5fd44c3 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -567,6 +567,15 @@ class ParamStats:
 
 
 class ModelAttribute:
+    """
+    A ModelAttribute instance handles a single model attribute, e.g. TX state power or something() function call duration, and corresponding benchmark data.
+
+    It provides three models:
+    - a static model (`mean`, `median`) as lower bound of model accuracy
+    - a LUT model (`by_param`) as upper bound of model accuracy
+    - a fitted model (`model_function`, a `ModelFunction` instance)
+    """
+
     def __init__(self, name, attr, data, param_values, param_names, arg_count=0):
 
         # Data for model generation