AnalyticFunction: Remove _ prefix from public attributes

2 files changed, 13 insertions, 13 deletions

diff --git a/lib/automata.py b/lib/automata.py
index 69b3969..ebe1871 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -103,7 +103,7 @@ class PTAAttribute:
     def __repr__(self):
         if self.function is not None:
             return "PTAATtribute<{:.0f}, {}>".format(
-                self.value, self.function._model_str
+                self.value, self.function.model_function
             )
         return "PTAATtribute<{:.0f}, None>".format(self.value)
 
@@ -137,8 +137,8 @@ class PTAAttribute:
         }
         if self.function:
             ret["function"] = {
-                "raw": self.function._model_str,
-                "regression_args": list(self.function._regression_args),
+                "raw": self.function.model_function,
+                "regression_args": list(self.function.model_args),
             }
             ret["function_error"] = self.function_error
         return ret
diff --git a/lib/functions.py b/lib/functions.py
index 0b849bd..99ba17d 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -141,7 +141,7 @@ class AnalyticFunction:
         """
         self._parameter_names = parameters
         self._num_args = num_args
-        self._model_str = function_str
+        self.model_function = function_str
         rawfunction = function_str
         self._dependson = [False] * (len(parameters) + num_args)
         self.fit_success = False
@@ -174,12 +174,12 @@ class AnalyticFunction:
             self._function = function_str
 
         if regression_args:
-            self._regression_args = regression_args.copy()
+            self.model_args = regression_args.copy()
             self._fit_success = True
         elif type(function_str) == str:
-            self._regression_args = list(np.ones((num_vars)))
+            self.model_args = list(np.ones((num_vars)))
         else:
-            self._regression_args = []
+            self.model_args = []
 
     def get_fit_data(self, by_param, state_or_tran, model_attribute):
         """
@@ -260,22 +260,22 @@ class AnalyticFunction:
             error_function = lambda P, X, y: self._function(P, X) - y
             try:
                 res = optimize.least_squares(
-                    error_function, self._regression_args, args=(X, Y), xtol=2e-15
+                    error_function, self.model_args, args=(X, Y), xtol=2e-15
                 )
             except ValueError as err:
                 logger.warning(
                     "Fit failed for {}/{}: {} (function: {})".format(
-                        state_or_tran, model_attribute, err, self._model_str
+                        state_or_tran, model_attribute, err, self.model_function
                     ),
                 )
                 return
             if res.status > 0:
-                self._regression_args = res.x
+                self.model_args = res.x
                 self.fit_success = True
             else:
                 logger.warning(
                     "Fit failed for {}/{}: {} (function: {})".format(
-                        state_or_tran, model_attribute, res.message, self._model_str
+                        state_or_tran, model_attribute, res.message, self.model_function
                     ),
                 )
         else:
@@ -308,9 +308,9 @@ class AnalyticFunction:
             corresponds to lexically first parameter, etc.
         :param arg_list: argument values (list of float), if arguments are used.
         """
-        if len(self._regression_args) == 0:
+        if len(self.model_args) == 0:
             return self._function(param_list, arg_list)
-        return self._function(self._regression_args, param_list)
+        return self._function(self.model_args, param_list)
 
 
 class analytic: