Field: add 'dim' property that's identical to model.field_dim

gstools/field/base.py

@@ -154,19 +154,18 @@ def pre_pos(self, pos, mesh_type="unstructured"):
         """
         # save mesh-type
         self.mesh_type = mesh_type
-        dim = self.model.field_dim
         # save pos tuple
         if mesh_type != "unstructured":
-            pos, shape = format_struct_pos_dim(pos, dim)
+            pos, shape = format_struct_pos_dim(pos, self.dim)
             self.pos = pos
             pos = gen_mesh(pos)
         else:
-            pos = np.array(pos, dtype=np.double).reshape(dim, -1)
+            pos = np.array(pos, dtype=np.double).reshape(self.dim, -1)
             self.pos = pos
             shape = np.shape(pos[0])
         # prepend dimension if we have a vector field
         if self.value_type == "vector":
-            shape = (self.model.dim,) + shape
+            shape = (self.dim,) + shape
             if self.model.latlon:
                 raise ValueError("Field: Vector fields not allowed for latlon")
         # return isometrized pos tuple and resulting field shape
@@ -352,6 +351,11 @@ def value_type(self, value_type):
             raise ValueError("Field: value type not in {}".format(VALUE_TYPES))
         self._value_type = value_type
 
+    @property
+    def dim(self):
+        """:class:`int`: Dimension of the field."""
+        return self.model.field_dim
+
     @property
     def name(self):
         """:class:`str`: The name of the class."""

gstools/field/plot.py

@@ -42,13 +42,13 @@ def plot_field(fld, field="field", fig=None, ax=None):  # pragma: no cover
     """
     plot_field = getattr(fld, field)
     assert not (fld.pos is None or plot_field is None)
-    if fld.model.field_dim == 1:
+    if fld.dim == 1:
         ax = _plot_1d(fld.pos, plot_field, fig, ax)
-    elif fld.model.field_dim == 2:
+    elif fld.dim == 2:
         ax = _plot_2d(
             fld.pos, plot_field, fld.mesh_type, fig, ax, fld.model.latlon
         )
-    elif fld.model.field_dim == 3:
+    elif fld.dim == 3:
         ax = _plot_3d(fld.pos, plot_field, fld.mesh_type, fig, ax)
     else:
         raise ValueError("Field.plot: only possible for dim=1,2,3!")

gstools/field/tools.py

@@ -154,15 +154,15 @@ def mesh_call(
         pass
 
     if isinstance(direction, str) and direction == "all":
-        select = list(range(f_cls.model.field_dim))
+        select = list(range(f_cls.dim))
     elif isinstance(direction, str):
-        select = _get_select(direction)[: f_cls.model.field_dim]
+        select = _get_select(direction)[: f_cls.dim]
     else:
-        select = direction[: f_cls.model.field_dim]
-    if len(select) < f_cls.model.field_dim:
+        select = direction[: f_cls.dim]
+    if len(select) < f_cls.dim:
         raise ValueError(
             "Field.mesh: need at least {} direction(s), got '{}'".format(
-                f_cls.model.field_dim, direction
+                f_cls.dim, direction
             )
         )
     # convert pyvista mesh
@@ -190,7 +190,7 @@ def mesh_call(
             offset = []
             length = []
             mesh_dim = mesh.points.shape[1]
-            if mesh_dim < f_cls.model.field_dim:
+            if mesh_dim < f_cls.dim:
                 raise ValueError("Field.mesh: mesh dimension too low!")
             pnts = np.empty((0, mesh_dim), dtype=np.double)
             for cell in mesh.cells:

gstools/krige/base.py

@@ -397,13 +397,16 @@ def _get_dists(self, pos1, pos2=None, pos2_slice=(0, None)):
     def get_mean(self, post_process=True):
         """Calculate the estimated mean of the detrended field.
 
+        Parameters
+        ----------
+        post_process : :class:`bool`, optional
+            Whether to apply field-mean and normalizer.
+            Default: `True`
+
         Returns
         -------
         mean : :class:`float` or :any:`None`
             Mean of the Kriging System.
-        post_process : :class:`bool`, optional
-            Whether to apply field-mean and normalizer.
-            Default: `True`
 
         Notes
         -----
@@ -491,7 +494,7 @@ def set_condition(
             raise ValueError("Krige.set_condition: missing cond_pos/cond_val.")
         # correctly format cond_pos and cond_val
         self._cond_pos, self._cond_val = set_condition(
-            cond_pos, cond_val, self.model.field_dim
+            cond_pos, cond_val, self.dim
         )
         if fit_normalizer:  # fit normalizer to detrended data
             self.normalizer.fit(self.cond_val - self.cond_trend)
@@ -544,7 +547,7 @@ def set_drift_functions(self, drift_functions=None):
             self._drift_functions = []
         elif isinstance(drift_functions, (str, int)):
             self._drift_functions = get_drift_functions(
-                self.model.field_dim, drift_functions
+                self.dim, drift_functions
             )
         else:
             if isinstance(drift_functions, collections.abc.Iterator):
@@ -623,16 +626,12 @@ def cond_ext_drift(self):
     @property
     def cond_mean(self):
         """:class:`numpy.ndarray`: Trend at the conditions."""
-        return eval_func(
-            self.mean, self.cond_pos, self.model.field_dim, broadcast=True
-        )
+        return eval_func(self.mean, self.cond_pos, self.dim, broadcast=True)
 
     @property
     def cond_trend(self):
         """:class:`numpy.ndarray`: Trend at the conditions."""
-        return eval_func(
-            self.trend, self.cond_pos, self.model.field_dim, broadcast=True
-        )
+        return eval_func(self.trend, self.cond_pos, self.dim, broadcast=True)
 
     @property
     def unbiased(self):