Software Open Access
SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes
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.
0.5 brings two major improvements: Instead of passing a
numpy.ndarray, you can now use the new class
skgstat.MetricSpace, which can pre-calculate distances in case they are used all over the place. Secondly, the new interface functions
Variogram.to_empirical can be used to export a
Variogram to gstools and use their field generation, kriging and all the other fancy stuff there.
Changes since 0.4
MetricSpace <skgstat.MetricSpace>was introduced. This class can be passed to any class that accepted coordinates so far. This wrapper can be used to pre-calculate large distance matrices and pass it to a lot of Variograms.
MetricSpacePair <skgstat.MetricSpacePair>was introduced. This is a pair of two :class:
MetricSpaces <skgstat.MetricSpace>and pre-calculates all distances between the two spaces. This is i.e. used in Kriging to pre-calcualte all distance between the input coordinates and the interpolation grid only once.
matern <skgstat.models.matern>introduced in
0.3.2are reversed. The Matérn model does not adapt the smoothness scaling to effective range anymore, as the behavior was too inconsistent.
matern(0, ...) <skgstat.models.matern>now returns the nugget instead of
stable(0, ...) <skgstat.models.stable>now returns the nugget instead of
dim <skgstat.Variogram.dim>now returns the spatial dimensionality of the input data.
bins <skgstat.Variogram.bins>now cases manual setted bin edges automatically to a :func:
get_empirical <skgstat.Variogram.get_empirical>returns the empirical variogram. That is a tuple of the current :func:
bins <skgstat.Variogram.bins>and :func:
experimental <skgstat.Variogram.experimental>arrays, with the option to move the bin to the lag classes centers.
'stable_entropy'option that will optimize the lag class edges to be of comparable Shannon Entropy.