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_ 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)