CovModel: Doc update; changed calculation of anisotropy in 3D

gstools/covmodel/base.py

@@ -61,15 +61,22 @@ class CovModel(metaclass=InitSubclassMeta):
         If a single value is given, the same length-scale will be used for
         every direction. If multiple values (for main and transversal
         directions) are given, `anis` will be
-        recalculated accordingly.
+        recalculated accordingly. If only two values are given in 3D,
+        the latter one will be used for both transversal directions.
         Default: ``1.0``
     nugget : :class:`float`, optional
         nugget of the model. Default: ``0.0``
     anis : :class:`float` or :class:`list`, optional
-        anisotropy ratios in the transversal directions [y, z].
+        anisotropy ratios in the transversal directions [e_y, e_z].
+
+            * e_y = l_y / l_x
+            * e_z = l_z / l_x
+
+        If only one value is given in 3D, e_y will be set to 1.
+        This value will be ignored, if multiple len_scales are given.
         Default: ``1.0``
     angles : :class:`float` or :class:`list`, optional
-        angles of rotation:
+        angles of rotation (given in rad):
 
             * in 2D: given as rotation around z-axis
             * in 3D: given by yaw, pitch, and roll (known as Tait–Bryan angles)

gstools/covmodel/tools.py

@@ -110,7 +110,15 @@ def set_len_anis(dim, len_scale, anis):
 
     Notes
     -----
-    If ``len_scale`` is given as list, ``anis`` will be recalculated.
+    If ``len_scale`` is given by at least two values,
+    ``anis`` will be recalculated.
+
+    If ``len_scale`` is given as list with to few values, the latter value will
+    be used for the remaining dimensions. (e.g. [l_1, l_2] in 3D is equal to
+    [l_1, l_2, l_2])
+
+    If to few ``anis`` values are given, the first dimensions will be filled
+    up with 1. (eg. anis=[e] in 3D is equal to anis=[1, e])
     """
     ls_tmp = np.atleast_1d(len_scale)[:dim]
     # use just one length scale (x-direction)
@@ -122,22 +130,14 @@ def set_len_anis(dim, len_scale, anis):
             # fill up the anisotropies with ones, such that len()==dim-1
             out_anis = np.pad(
                 out_anis,
-                (0, dim - len(out_anis) - 1),
+                (dim - len(out_anis) - 1, 0),
                 "constant",
                 constant_values=1.0,
             )
-    elif dim == 1:
-        # there is no anisotropy in 1 dimension
-        out_anis = np.empty(0)
     else:
         # fill up length-scales with main len_scale, such that len()==dim
         if len(ls_tmp) < dim:
-            ls_tmp = np.pad(
-                ls_tmp,
-                (0, dim - len(ls_tmp)),
-                "constant",
-                constant_values=out_len_scale,
-            )
+            ls_tmp = np.pad(ls_tmp, (0, dim - len(ls_tmp)), "edge")
         # if multiple length-scales are given, calculate the anisotropies
         out_anis = np.zeros(dim - 1, dtype=np.double)
         for i in range(1, dim):