Add sum model

[MuellerSeb]

• added SumModel class
  • represents sum of covariance models
  • behaves just as a normal covariance model with kriging and field generation
  • covariance models can be added with overloaded + operator: model = m1 + m2
  • class is subscriptable to access sub-models by index: m1 = model[0]
  • included models will get a nugget of 0 and the nugget is stored separately in the sum-model
  • model variance is the sum of the sub-model variances
  • model length-scale is weighted sum of sub-model len-scales, where the weights are the ratios of the sub-models variance to the sum variance
  • anisotropy and rotation need to be the same for all included sub-models
  • parameters of the sub-models can be accessed by name with added index suffix
• added Nugget class (empty SumModel)
  • allow len scale of 0 in CovModel to enable a pure nugget model
  • added zero_var attribute to Field generators to shortcut field generation for pure nugget models

import gstools as gs
m1 = gs.Gaussian(dim=2, var=1.0, len_scale=2.0)
m2 = gs.Gaussian(dim=2, var=2.0, len_scale=20.0)
model = m1 + m2
model.plot()

Other changes

• removed var_raw attribute from CovModel (was rarely used and only relevant for the truncated power law models)
  • BREAKING CHANGE (but not to many should be affected)
  • TPLCovModel now has a intensity attribute which calculates was
• simplified variogram fitting (var_raw was a bad idea in the first place)
• variogram plotting now handles a len-scale of 0 (to properly plot nugget models)

TODOs

• enable fitting of summed models (harder than I though, since sub-arguments may have restrictions from the sum model like a fixed length-scale that is affected by the variance ratios and the sub length scales)
• testing the sum-model