mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox

Description

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.

Version 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_gstools and 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.

Documentation

• Full Documentation https://mmaelicke.github.io/scikit-gstat
• User Guide https://mmaelicke.github.io/scikit-gstat/userguide/userguide.html
• Tutorials https://mmaelicke.github.io/scikit-gstat/tutorials/tutorials.html

Changes since 0.4

• [MetricSpace] A new class :class: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] A new class :class: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.

Version 0.4.4

• [models] the changes to :func:matern <skgstat.models.matern> introduced in 0.3.2 are reversed. The Matérn model does not adapt the smoothness scaling to effective range anymore, as the behavior was too inconsistent.
• [interface] minor bugfix of circular import in variogram_estimator interface
• [models] :func:matern(0, ...) <skgstat.models.matern> now returns the nugget instead of numpy.NaN
• [models] :func:stable(0, ...) <skgstat.models.stable> now returns the nugget instead of numpy.NaN or a ZeroDivisionError.

Version 0.4.3

• [Variogram] :func:dim <skgstat.Variogram.dim> now returns the spatial dimensionality of the input data.
• [Variogram] fixed a numpy depreaction warning in _calc_distances

Version 0.4.2

• [Variogram] :func:bins <skgstat.Variogram.bins> now cases manual setted bin edges automatically to a :func:numpy.array.
• [Variogram] :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.

Version 0.4.1

• [Variogram] moved the bin function setting into a wrapper instance method, which was an anonymous lambda before. This makes the Variogram serializable again.
• [Variogram] a list of pylint errors were solved. Still enough left.
• [binning] added 'stable_entropy' option that will optimize the lag class edges to be of comparable Shannon Entropy.