v1.1.0.rc2 'Reverberating Red' 2. Release candidate

Release Notes

Woah! GSTools went parallel. And at the same time the humongous memory consumption of the field generation became very modest.

The second big news is that GSTools can now finally generate conditioned random fields and provides kriging.

Installation

Since this is a pre-release you have to install it with:

pip install --pre -U gstools

For parallel compilation try:

pip install --pre --global-option="--openmp" -U gstools

Enhancements

• by using Cython for all the heavy computations, we could achieve quite some speed ups and reduce the memory consumption significantly #16
• parallel computation in Cython is now supported with the help of OpenMP and the performance increase is nearly linear with increasing cores #16
• new submodule krige providing simple (known mean) and ordinary (estimated mean) kriging working analogous to the srf class
• interface to pykrige to use the gstools CovModel with the pykrige routines (GeoStat-Framework/PyKrige#124)
• the srf class now provides a plot and a vtk_export routine
• incompressible flow fields can now be generated #14
• new submodule providing several field transformations like: Zinn&Harvey, log-normal, bimodal, ... #13
• Python 3.4 and 3.7 wheel support #19
• field can now be generated directly on meshes from meshio and ogs5py f4a3439
• the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible 29f7f1b
• tutorials on all important features of GSTools have been written for you guys #20
• a new interface to pyvista is provided to export fields to python vtk representation, which can be used for plotting, exploring and exporting fields #29

Changes

• the license was changed from GPL to LGPL in order to promote the use of this library #25
• the rotation angles are now interpreted in positive direction (counter clock wise)
• the force_moments keyword was removed from the SRF call method, it is now in provided as a field transformation #13
• drop support of python implementations of the variogram estimators #18
• the variogram_normed method was removed from the CovModel class due to redundance 25b1647
• the position vector of 1D fields does not have to be provided in a list-like object with length 1 a6f5be8
• we now require emcee version >= 3.0.0

Bugfixes

• several minor bugfixes

v1.1.0.rc1 'Reverberating Red' 1. Release candidate

Release Notes

Woah! GSTools went parallel. And at the same time the humongous memory consumption of the field generation became very modest.

The second big news is that GSTools can now finally generate conditioned random fields and provides krging.

Installation

Since this is a pre-release you have to install it with:

pip install --pre -U gstools

For parallel compilation try:

pip install --pre --global-option="--openmp" -U gstools

Enhancements

• by using Cython for all the heavy computations, we could achieve quite some speed ups and reduce the memory consumption significantly (#16)
• parallel computation in Cython is now supported with the help of OpenMP and the performance increase is nearly linear with increasing cores (#16)
• new submodule krige providing simple (known mean) and ordinary (estimated mean) kriging working analogous to the srf class
• interface to pykrige to use the gstools CovModel with the pykrige routines (GeoStat-Framework/PyKrige#124)
• the srf class now provides a plot and a vtk_export routine
• in-compressible flow fields are now creatable (#14)
• new submodule providing several field transformations like: Zinn&Harvey, log-normal, bimodal, ... (#13)
• Python 3.4 and 3.7 wheel support (#19)
• field can now be generated directly on meshes from meshio and ogs5py (f4a3439)
• the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible (29f7f1b)

Changes

• the rotation angles are now interpreted in positive direction (counter clock wise)
• the force_moments keyword was removed from the SRF call method, it is now in provided as a field transformation (#13)
• drop support of python implementations of the variogram estimators (#18)
• the variogram_normed method was removed from the CovModel class due to redundance (25b1647)

Bugfixes

• several minor bugfixes

v1.0.1 'Bouncy Blue'

Release Notes

Bugfix release. Fixed Numpy and Cython version during build process.

v1.0.0 'Bouncy Blue'

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

1.0rc12 'Bouncy Blue' 1. Release Candidate

Notes

First public release candidate (rc12) for version 1.0.

Enhancements

• cleaner code
• new CovModel to create covariance models
• a lot of predefined covariance models (including truncated power law models)
• interface change for SRF creation
• use of pos instead of x, y and z for simplification
• variogram fitting
• vtk export
• variance-scaling support
• new documentation

0.4.0 'Glorious Green'

Removed unused line in sphinx conf

0.3.6 'Original Orange'

Merge branch 'MuellerSeb-master'