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v1.0.0 'Bouncy Blue'
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@MuellerSeb MuellerSeb released this 16 Jan 15:02
v1.0.0
4d99d03

Release Notes

After a lot of hard work and some brilliant ideas, we are finally ready to release the first stable version of GSTools!

This release is mainly characterized by the CovModel, which lets you define arbitrary covariance models, including fractal power law models, simply by defining the variogram, or the correlation function. It's up to you.

But also the usability has become a major boost. And some workflows have become very intuitive, like estimating a variogram model and its parameters from data and creating new spatial random fields with these parameters and exporting them for different programs to use.

The tutorials will help new users get familiar with GSTools in no time.

For more details, see the following lists.

Enhancements

  • added a new covariance class, which allows the easy usage of arbitrary covariance models
  • added many predefined covariance models, including truncated power law models
  • added tutorials and examples, showing and explaining the main features of GSTools
  • variogram models can be fitted to data
  • prebuilt binaries for many Linux distributions, Mac OS and Windows, making the installation, especially of the Cython code, much easier
  • the generated fields can now easily be exported to vtk files
  • variance scaling is supported for coarser grids
  • added pure Python versions of the variogram estimators, in case somebody has problems compiling Cython code
  • the documentation is now a lot cleaner and easier to use
  • the code is a lot cleaner and more consistent now
  • unit tests are now automatically tested when new code is pushed
  • test coverage of code is shown
  • GeoStat Framework now has a website, visit us: https://geostat-framework.github.io/

Changes

One word of caution: This release is not downwards compatible with release v0.4.0.

  • SRF creation has been adapted for the CovModel
  • a tuple pos is now used instead of x, y, and z for the axes
  • renamed estimate_unstructured and estimate_structured to vario_estimate_unstructured and vario_estimate_structured for less ambiguity
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