diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/automata.py | 6 | ||||
| -rw-r--r-- | lib/functions.py | 20 |
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: |