remove variogram_normed from cov-model

gstools/covmodel/base.py

@@ -196,16 +196,14 @@ def __init_subclass__(cls):
             * ``model.variogram(r)``
                 :math:`\gamma\left(r\right)=
                 \sigma^2\cdot\left(1-\mathrm{cor}\left(r\right)\right)+n`
-            * ``model.variogram_normed(r)``
-                :math:`\tilde{\gamma}\left(r\right)=
-                1-\mathrm{cor}\left(r\right)`
             * ``model.covariance(r)``
                 :math:`C\left(r\right)=
                 \sigma^2\cdot\mathrm{cor}\left(r\right)`
             * ``model.correlation(r)``
                 :math:`\mathrm{cor}\left(r\right)`
 
-        Best practice is to use the ``correlation`` function!
+        Best practice is to use the ``correlation`` function, or the ``cor``
+        function. The latter one takes the dimensionles distance h=r/l.
         """
         # overrid one of these ################################################
 
@@ -237,7 +235,7 @@ def correlation(self, r):
             It has to be a monotonic decreasing function with
             :math:`\mathrm{cor}(0)=1` and :math:`\mathrm{cor}(\infty)=0`.
             """
-            return 1.0 - self.variogram_normed(r)
+            return 1.0 - (self.variogram(r) - self.nugget) / self.var
 
         def cor_from_cor(self, r):
             r"""Correlation function (or normalized covariance) of the model.
@@ -250,16 +248,6 @@ def cor_from_cor(self, r):
             r = np.array(np.abs(r), dtype=np.double)
             return self.cor(r / self.len_scale)
 
-        def variogram_normed(self, r):
-            r"""Normalized-variogram of the model.
-
-            Given by: :math:`\tilde{\gamma}\left(r\right)=
-            1-\mathrm{cor}\left(r\right)`
-
-            Where :math:`\mathrm{cor}(r)` is the correlation function.
-            """
-            return (self.variogram(r) - self.nugget) / self.var
-
         #######################################################################
 
         abstract = True
@@ -278,18 +266,13 @@ def variogram_normed(self, r):
             cls.correlation = correlation
         else:
             abstract = False
-        if not hasattr(cls, "variogram_normed"):
-            cls.variogram_normed = variogram_normed
-        else:
-            abstract = False
         if abstract:
             raise TypeError(
                 "Can't instantiate class '"
                 + cls.__name__
                 + "', "
                 + "without overriding at least on of the methods "
-                + "'variogram', 'covariance', "
-                + "'correlation', or 'variogram_normed'."
+                + "'variogram', 'covariance' or 'correlation'."
             )
 
         # modify the docstrings ###############################################
@@ -514,7 +497,7 @@ def percentile_scale(self, per=0.9):
 
         # define a curve, that has its root at the wanted point
         def curve(x):
-            return self.variogram_normed(x) - per
+            return 1.0 - self.correlation(x) - per
 
         # take 'per * len_scale' as initial guess
         return root(curve, per * self.len_scale)["x"][0]

gstools/covmodel/plot.py

@@ -8,7 +8,6 @@
 
 .. autosummary::
    plot_variogram
-   plot_variogram_normed
    plot_covariance
    plot_correlation
    plot_spectrum
@@ -18,11 +17,9 @@
 # pylint: disable=C0103
 from __future__ import print_function, division, absolute_import
 import numpy as np
-from matplotlib import pyplot as plt
 
 __all__ = [
     "plot_variogram",
-    "plot_variogram_normed",
     "plot_covariance",
     "plot_correlation",
     "plot_spectrum",
@@ -35,6 +32,8 @@
 
 
 def _get_fig_ax(fig, ax, ax_name="rectilinear"):  # pragma: no cover
+    from matplotlib import pyplot as plt
+
     if fig is None and ax is None:
         fig = plt.figure()
         ax = fig.add_subplot(111, projection=ax_name)
@@ -91,24 +90,6 @@ def plot_correlation(
     return ax
 
 
-def plot_variogram_normed(
-    model, x_min=0.0, x_max=None, fig=None, ax=None
-):  # pragma: no cover
-    """Plot normalized variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
-    if x_max is None:
-        x_max = 3 * model.integral_scale
-    x_s = np.linspace(x_min, x_max)
-    ax.plot(
-        x_s,
-        model.variogram_normed(x_s),
-        label=model.name + " normed variogram",
-    )
-    ax.legend()
-    fig.show()
-    return ax
-
-
 def plot_spectrum(
     model, x_min=0.0, x_max=None, fig=None, ax=None
 ):  # pragma: no cover