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 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 was introduced. This is a pair of two :class:MetricSpaces 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 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, ...) now returns the nugget instead of numpy.NaN [models] :func:stable(0, ...) now returns the nugget instead of numpy.NaN or a ZeroDivisionError. Version 0.4.3 [Variogram] :func: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 now cases manual setted bin edges automatically to a :func:numpy.array. [Variogram] :func:get_empirical returns the empirical variogram. That is a tuple of the current :func:bins and :func: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