CovModel: Update and Refactoring

[MuellerSeb]

This PR updates and refactors the covariance models:

• Fitting routines now support input of directional empirical variograms to estimate anisotropy
• add new rescale argument and attribute to the CovModel class to be able to rescale the len_scale (usefull for unit conversion or rescaling len_scale to coincide with the integral_scale like it's the case with the Gaussian model)
  See: [Refactor] prefere "cor" to specify userdefined CovModel #90, Strange number in a variogram function  PyKrige#119
• added new len_rescaled attribute to the CovModel class, which is the rescaled len_scale: len_rescaled = len_scale / rescale
• new method default_rescale to provide default rescale factor (can be overridden)
• remove doctest calls
• docstring updates in CovModel and derived models
• updated all models to use the cor routine and make use of the rescale argument (See: [Refactor] prefere "cor" to specify userdefined CovModel #90)
• TPL models got a separate base class to not repeat code
• added new models (See: [Enhancement] more covariance models #88):
  • HyperSpherical: (Replaces the old Intersection model) Derived from the intersection of hyper-spheres in arbitrary dimensions. Coincides with the linear model in 1D, the circular model in 2D and the classical spherical model in 3D
  • SuperSpherical: like the HyperSpherical, but the shape parameter derived from dimension can be set by the user. Coincides with the HyperSpherical model by default
  • JBessel a hole model valid in all dimensions. The shape parameter controls the dimension it was derived from. For nu=0.5 this model coincides with the well known wave hole model.
  • TPLSimple a simple truncated power law controlled by a shape parameter nu. Coincides with the truncated linear model for nu=1
• string representation of the CovModel class is new using a float precision (CovModel._prec=3) to truncate longish output
• dimension validity check: raise a warning, if given model is not valid in the desired dimension (See: [Enhancement] dimensionality check for CovModel #86)
• more comprehensive testing

[MuellerSeb]

@LSchueler thanks 😉
So I will add a test for rescale. Fitting should be tested better as well.

[MuellerSeb]

Coverage at 92% now! 🎉