slightly reworked API for accessing info about minimizers

src/easyscience/fitting/minimizers/minimizer_base.py

@@ -40,7 +40,7 @@ def __init__(
         fit_function: Callable,
         method: Optional[str] = None,
     ):  # todo after constraint changes, add type hint: obj: BaseObj  # noqa: E501
-        if method not in self.available_methods():
+        if method not in self.supported_methods():
             raise FitError(f'Method {method} not available in {self.__class__}')
         self._object = obj
         self._original_fit_function = fit_function
@@ -132,12 +132,23 @@ def convert_to_pars_obj(self, par_list: Optional[Union[list]] = None):
         :return: engine Parameters compatible object
         """
 
+    @staticmethod
+    @abstractmethod
+    def supported_methods() -> List[str]:
+        """
+        Return a list of supported methods for the minimizer.
+
+        :return: List of supported methods
+        :rtype: List[str]
+        """
+
+    @staticmethod
     @abstractmethod
-    def available_methods(self) -> List[str]:
+    def all_methods() -> List[str]:
         """
-        Return a list of available methods for the engine.
+        Return a list of all available methods for the minimizer.
 
-        :return: List of available methods
+        :return: List of all available methods
         :rtype: List[str]
         """
 

src/easyscience/fitting/minimizers/minimizer_bumps.py

@@ -55,9 +55,16 @@ def __init__(
         super().__init__(obj=obj, fit_function=fit_function, method=method)
         self._p_0 = {}
 
-    def available_methods(self) -> List[str]:
+    @staticmethod
+    def all_methods() -> List[str]:
         return FIT_AVAILABLE_IDS_FILTERED
 
+    @staticmethod
+    def supported_methods() -> List[str]:
+        # only a small subset
+        methods = ['scipy.leastsq','amoeba', 'newton', 'lm']
+        return methods
+
     def fit(
         self,
         x: np.ndarray,
@@ -92,7 +99,7 @@ def fit(
         default_method = {}
         if self._method is not None:
             default_method = {'method': self._method}
-        if method is not None and method in self.available_methods():
+        if method is not None and method in self.supported_methods():
             default_method['method'] = method
 
         if weights is None:

src/easyscience/fitting/minimizers/minimizer_dfo.py

@@ -46,9 +46,16 @@ def __init__(
         super().__init__(obj=obj, fit_function=fit_function, method=method)
         self._p_0 = {}
 
-    def available_methods(self) -> List[str]:
+    @staticmethod
+    def supported_methods() -> List[str]:
         return ['leastsq']
 
+    @staticmethod
+    def all_methods() -> List[str]:
+        return [
+            'leastsq',
+        ]
+
     def fit(
         self,
         x: np.ndarray,
@@ -83,7 +90,7 @@ def fit(
         default_method = {}
         if self._method is not None:
             default_method = {'method': self._method}
-        if method is not None and method in self.available_methods():
+        if method is not None and method in self.supported_methods():
             default_method['method'] = method
 
         if weights is None:

src/easyscience/fitting/minimizers/minimizer_lmfit.py

@@ -49,7 +49,8 @@ def __init__(
         """
         super().__init__(obj=obj, fit_function=fit_function, method=method)
 
-    def available_methods(self) -> List[str]:
+    @staticmethod
+    def all_methods() -> List[str]:
         return [
             'least_squares',
             'leastsq',
@@ -65,6 +66,15 @@ def available_methods(self) -> List[str]:
             'bfgs',
         ]
 
+    @staticmethod
+    def supported_methods() -> List[str]:
+        return [
+            'least_squares',
+            'leastsq',
+            'powell',
+            'cobyla',
+        ]
+
     def fit(
         self,
         x: np.ndarray,
@@ -102,7 +112,7 @@ def fit(
         if self._method is not None:
             default_method = {'method': self._method}
         if method is not None:
-            if method in self.available_methods():
+            if method in self.supported_methods():
                 default_method['method'] = method
             else:
                 raise FitError(f'Method {method} not available in {self.__class__}')

tests/integration_tests/Fitting/test_fitter.py

@@ -152,7 +152,7 @@ def test_lmfit_methods(fit_method):
     sp_sin.phase.fixed = False
 
     f = Fitter(sp_sin, sp_sin)
-    assert fit_method in f._minimizer.available_methods()
+    assert fit_method in f._minimizer.supported_methods()
     result = f.fit(x, y, method=fit_method)
     check_fit_results(result, sp_sin, ref_sin, x)
 
@@ -171,7 +171,7 @@ def test_bumps_methods(fit_method):
 
     f = Fitter(sp_sin, sp_sin)
     f.switch_minimizer("Bumps")
-    assert fit_method in f._minimizer.available_methods()
+    assert fit_method in f._minimizer.supported_methods()
     result = f.fit(x, y, method=fit_method)
     check_fit_results(result, sp_sin, ref_sin, x)
 

tests/integration_tests/Fitting/test_fitter_legacy_parameter.py

@@ -152,7 +152,7 @@ def test_lmfit_methods(fit_method):
     sp_sin.phase.fixed = False
 
     f = Fitter(sp_sin, sp_sin)
-    assert fit_method in f._minimizer.available_methods()
+    assert fit_method in f._minimizer.supported_methods()
     result = f.fit(x, y, method=fit_method)
     check_fit_results(result, sp_sin, ref_sin, x)
 
@@ -171,7 +171,7 @@ def test_bumps_methods(fit_method):
 
     f = Fitter(sp_sin, sp_sin)
     f.switch_minimizer("Bumps")
-    assert fit_method in f._minimizer.available_methods()
+    assert fit_method in f._minimizer.supported_methods()
     result = f.fit(x, y, method=fit_method)
     check_fit_results(result, sp_sin, ref_sin, x)
 

tests/unit_tests/Fitting/minimizers/test_minimizer_base.py

@@ -15,7 +15,7 @@ class TestMinimizerBase():
     def minimizer(self):
         # This avoids the error: TypeError: Can't instantiate abstract class with abstract methods __init__
         MinimizerBase.__abstractmethods__ = set()
-        MinimizerBase.available_methods = MagicMock(return_value=['method'])
+        MinimizerBase.supported_methods = MagicMock(return_value=['method'])
 
         minimizer = MinimizerBase(
             obj='obj',
@@ -27,7 +27,7 @@ def minimizer(self):
     def test_init_exception(self):
         # When Then
         MinimizerBase.__abstractmethods__ = set()
-        MinimizerBase.available_methods = MagicMock(return_value=['method'])
+        MinimizerBase.supported_methods = MagicMock(return_value=['method'])
 
         # Expect
         with pytest.raises(FitError):

tests/unit_tests/Fitting/minimizers/test_minimizer_bumps.py

@@ -32,9 +32,13 @@ def test_init_exception(self) -> None:
                 method='not_leastsq'
             )
 
-    def test_available_methods(self, minimizer: Bumps) -> None:
+    def test_all_methods(self, minimizer: Bumps) -> None:
         # When Then Expect
-        assert minimizer.available_methods() == ['amoeba', 'de', 'dream', 'newton', 'scipy.leastsq', 'lm']
+        assert minimizer.all_methods() == ['amoeba', 'de', 'dream', 'newton', 'scipy.leastsq', 'lm']
+
+    def test_supported_methods(self, minimizer: Bumps) -> None:
+        # When Then Expect
+        assert set(minimizer.supported_methods()) == set(['scipy.leastsq','newton', 'lm', 'amoeba'])
 
     def test_fit(self, minimizer: Bumps, monkeypatch) -> None:
         # When

tests/unit_tests/Fitting/minimizers/test_minimizer_dfo.py

@@ -32,9 +32,13 @@ def test_init_exception(self) -> None:
                 method='not_leastsq'
             )
 
-    def test_available_methods(self, minimizer: DFO) -> None:
+    def test_supported_methods(self, minimizer: DFO) -> None:
         # When Then Expect
-        assert minimizer.available_methods() == ['leastsq']
+        assert minimizer.supported_methods() == ['leastsq']
+
+    def test_supported_methods(self, minimizer: DFO) -> None:
+        # When Then Expect
+        assert minimizer.supported_methods() == ['leastsq']
 
     def test_fit(self, minimizer: DFO) -> None:
         # When

tests/unit_tests/Fitting/minimizers/test_minimizer_lmfit.py

@@ -127,14 +127,15 @@ def test_fit_method(self, minimizer: LMFit) -> None:
         minimizer._make_model = MagicMock(return_value=mock_model)
         minimizer._set_parameter_fit_result = MagicMock()
         minimizer._gen_fit_results = MagicMock(return_value='gen_fit_results')
-        minimizer.available_methods = MagicMock(return_value=['method_passed'])
+        minimizer.supported_methods = MagicMock(return_value=['method_passed'])
+        minimizer.all_methods = MagicMock(return_value=['method_passed'])
 
         # Then
         minimizer.fit(x=1.0, y=2.0, method='method_passed')
 
         # Expect
         mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='method_passed')
-        minimizer.available_methods.assert_called_once_with()
+        minimizer.supported_methods.assert_called_once_with()
 
     def test_fit_kwargs(self, minimizer: LMFit) -> None:
         # When