Cherry picks minor fixes for v1.4

src/gstools/__init__.py

@@ -180,7 +180,7 @@
 
 try:
     from gstools._version import __version__
-except ModuleNotFoundError:  # pragma: nocover
+except ModuleNotFoundError:  # pragma: no cover
     # package is not installed
     __version__ = "0.0.0.dev0"
 

src/gstools/covmodel/base.py

@@ -1138,6 +1138,13 @@ def __eq__(self, other):
             return False
         return compare(self, other)
 
+    def __setattr__(self, name, value):
+        """Set an attribute."""
+        super().__setattr__(name, value)
+        # if an optional variogram argument was given, check bounds
+        if hasattr(self, "_opt_arg") and name in self._opt_arg:
+            self.check_arg_bounds()
+
     def __repr__(self):
         """Return String representation."""
         return model_repr(self)

src/gstools/variogram/estimator.pyx

@@ -156,23 +156,23 @@ cdef _estimator_func choose_estimator_func(str estimator_type):
     cdef _estimator_func estimator_func
     if estimator_type == 'm':
         estimator_func = estimator_matheron
-    elif estimator_type == 'c':
+    else: # estimator_type == 'c'
         estimator_func = estimator_cressie
     return estimator_func
 
 cdef _normalization_func choose_estimator_normalization(str estimator_type):
     cdef _normalization_func normalization_func
     if estimator_type == 'm':
         normalization_func = normalization_matheron
-    elif estimator_type == 'c':
+    else: # estimator_type == 'c'
         normalization_func = normalization_cressie
     return normalization_func
 
 cdef _normalization_func_vec choose_estimator_normalization_vec(str estimator_type):
     cdef _normalization_func_vec normalization_func_vec
     if estimator_type == 'm':
         normalization_func_vec = normalization_matheron_vec
-    elif estimator_type == 'c':
+    else: # estimator_type == 'c'
         normalization_func_vec = normalization_cressie_vec
     return normalization_func_vec
 

tests/test_covmodel.py

@@ -279,11 +279,20 @@ def test_fitting(self):
         # init guess
         with self.assertRaises(ValueError):
             model.fit_variogram(self.gamma_x, self.gamma_y, init_guess="wrong")
+        with self.assertRaises(ValueError):
+            model.fit_variogram(
+                self.gamma_x, self.gamma_y, init_guess={"wrong": 1}
+            )
+        # sill fixing
         model.var_bounds = [0, np.inf]
         model.fit_variogram(
             self.gamma_x, np.array(self.gamma_y) + 1, sill=2, alpha=False
         )
         self.assertAlmostEqual(model.var + model.nugget, 2)
+        # check isotropicity for latlon models
+        model = Stable(latlon=True)
+        with self.assertRaises(ValueError):
+            model.fit_variogram(self.gamma_x, 3 * [self.gamma_y])
 
     def test_covmodel_class(self):
         model_std = Gaussian(rescale=3, var=1.1, nugget=1.2, len_scale=1.3)