Scikit-gstat

SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes Variogram and OrdinaryKriging. Additionally, various variogram classes inheriting from Variogram are available for solving directional or space-time related tasks. The module makes use of a rich selection of semi-variance estimators and variogram model functions while being extensible at the same time.

This version changed the `DirectionalVariogram` class quite substantially. The circular search area was removed, therefore `shapely` is not a dependency anymore and the variogram estimation for directional variograms got a performance gain of **several magnitudes**.

SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes Variogram and OrdinaryKriging. Additionally, various variogram classes inheriting from Variogram are available for solving directional or space-time related tasks. The module makes use of a rich selection of semi-variance estimators and variogram model functions while being extensible at the same time.

This version changed some of the internal parameter settings and removed old, not working code. An interface to gstools CovModel was added, which is still experimental and untested.

v.0.2.7
SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes Variogram and OrdinaryKriging. Additionally, various variogram classes inheriting from Variogram are available for solving directional or space-time related tasks. The module makes use of a rich selection of semi-variance estimators and variogram model functions while being extensible at the same time.

This version increases the test coverage a bit and the documentation made progress. Besides some minor bug fixes, the main new feature of this version is the module `skgstat.interfaces` that collects interfaces to other packages. PyKrige and scikit-learn are available. GsTools will follow with next release.

SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes Variogram and OrdinaryKriging. Additionally, various variogram classes inheriting from Variogram are available for solving
directional or space-time related tasks. The module makes use of a rich selection of semi-variance estimators and variogram model functions while being extensible at the same time.

Note that there are no unit tests for Kriging so far, and they are not documented. Kriging got some new keywords in this version and there are some strategies to increase performance or gain better results. The main bottleneck for performance is not handled yet (on purpose).
The Variogram.compiled_model function is deprecated and was replaced by the much faster Variogram.fitted_model.

SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes
two base classes `Variogram` and `OrdinaryKriging`. Additionally, various
variogram classes inheriting from ``Variogram`` are available for solving
directional or space-time related tasks. The module makes use of a rich selection of semi-variance
estimators and variogram model functions, while being extensible at the same
time.

Note that there are no unit tests for Kriging so far and they are not documented. At the current stage, the Kriging is also not optimized for performance. It may change significantly in a future version.

**[severe bug] A severe bug in Variogram.__vdiff_indexer was found and fixed**. The iterator was indexing the Variogram._diff array different from Variogram.distance. This lead to wrong semivariance values for all versions > 0.1.8!. Fixed now.

Beside this major bug fix unit tests for parameter setting were added and fit_sigma setting of 'exp' was fixed.
The formula from e^(1 / x) to 1. - e^(1 / x) in order to increase with distance and, thus, give less weight to distant lag classes during fitting.

v.0.2.2
With Version 0.2.2 a new class is introduced: DirectionalVariograms. This class inherits from the base Variogram class but changes the selection and grouping of point pairs, when lag classes are derived. It has several pre-defined models for specifying a directional dependency between observation point pairs. The variogram will only rely on point pairs that fulfill such a directional relationship.