Release Notes

This great release brings not only one but two impressive new features to GSTools: Plurigaussian Fields and Sum-Models. Plurigaussian Fields provide a smart way to introduce structure to random fields and with Sum-Models you are finally able to add two or more covariance models to better capture spatial patterns. In addition, we outsourced the cython code of GSTools into a separate package GSTools-Cython, which makes GSTools itself a lightweight pure python package.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Enhancements

• new feature: Plurigaussian simulations (PGS) (#370)
  • they simulate distributions of categorical data, e.g. lithofacies, hydrofacies, soil types, or cementitious materials
  • they naturally extend truncated Gaussian fields, which are already a part of GSTools through the field transformations
• new feature: support for Sum-Models (#364)
  • 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 (motivated by the integral scale, which satisfies this relation)
    • 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: model[0].nu == model.nu_0
    • fitting: if len_scale is fixed, none of the len_scale_<i> can be fixed since len_scale is calculated from variance ratios
  • added Nugget class (empty SumModel)
    • allow len scale of 0 in CovModel to enable a pure nugget model
    • added zero_var and model attributes to Generator ABC to shortcut field generation for pure nugget models

Changes

• outsourced cython code to a separate package GSTools-Cython (#376)
• 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 what var_raw was before
• 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)
• fitting: when sill is given and var and nugget are deselected from fitting, an error is raised if given var+nugget is not equal to sill (before, they were reset under the hood in a strange way)

Bugfixes

• pnt_cnt was not recalculated in vario_estimate when a mask was applied, together with a given sample size this resulted in an IndexError most of the times (#378)