Merge pull request #982 from lmfit/modelfunc_barestare

Modelfunc barestar

lmfit/model.py

@@ -529,16 +529,20 @@ def _parse_params(self):
             for fnam, fpar in sig.parameters.items():
                 if fpar.kind == fpar.VAR_KEYWORD:
                     keywords_ = fnam
-                elif fpar.kind in (fpar.POSITIONAL_ONLY, fpar.POSITIONAL_OR_KEYWORD):
+                elif fpar.kind in (fpar.KEYWORD_ONLY,
+                                   fpar.POSITIONAL_OR_KEYWORD):
                     default_vals[fnam] = fpar.default
                     if (isinstance(fpar.default, (float, int, complex))
                        and not isinstance(fpar.default, bool)):
                         kw_args[fnam] = fpar.default
+                        pos_args.append(fnam)
                     elif fpar.default == fpar.empty:
                         pos_args.append(fnam)
                     else:
                         kw_args[fnam] = fpar.default
                         indep_vars.append(fnam)
+                elif fpar.kind == fpar.POSITIONAL_ONLY:
+                    raise ValueError("positional only arguments with '/' are not supported")
                 elif fpar.kind == fpar.VAR_POSITIONAL:
                     raise ValueError(f"varargs '*{fnam}' is not supported")
         # inspection done

tests/test_model.py

@@ -1697,3 +1697,32 @@ def test_model_refitting():
     assert result.init_values['peak_amplitude'] < 21
     assert result.init_values['peak_sigma'] > 2
     assert result.init_values['peak_sigma'] < 4
+
+
+def test_model_barestar():
+    """Github #976 (and discussion #975)"""
+    def foo(x, *, a=1, b=1, kind='linear'):
+        if kind == 'linear':
+            return a + b * x
+        elif kind == 'exponential':
+            return a * np.exp(b * x)
+
+    modl = lmfit.Model(foo)
+    assert modl.independent_vars == ['x', 'kind']
+    assert modl._param_names == ['a', 'b']
+
+
+def test_model_bareslash():
+    """Github #976 (and discussion #975)"""
+    def foo(x, /, a=1, b=1, kind='linear'):
+        if kind == 'linear':
+            return a + b * x
+        elif kind == 'exponential':
+            return a * np.exp(b * x)
+    try:
+        lmfit.Model(foo)
+        that_failed = False
+    except ValueError:
+        that_failed = True
+
+    assert that_failed