Software Open Access

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

Mirko Mälicke; Romain Hugonnet; Helge David Schneider; Sebastian Müller; Egil Möller; Johan Van de Wauw


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Mirko Mälicke</dc:creator>
  <dc:creator>Romain Hugonnet</dc:creator>
  <dc:creator>Helge David Schneider</dc:creator>
  <dc:creator>Sebastian Müller</dc:creator>
  <dc:creator>Egil Möller</dc:creator>
  <dc:creator>Johan Van de Wauw</dc:creator>
  <dc:date>2022-02-04</dc:date>
  <dc:description>Here we present SciKit-GStat, an open source Python package for variogram estimation, that fits well into established frameworks for scientific computing like SciPy, numpy, gstools or pandas. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of usage and interactivity and it is therefore usable with only a little or even no knowledge in Python.

SciKit-GStat ships with a large number of predefined procedures, algorithms, and models, such as variogram estimators, theoretical spatial models, or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well.

SciKit-GStat can easily interface to GSTools.


	Find the documentation here
	Tutorials: https://mmaelicke.github.io/scikit-gstat/auto_examples/index.html
	DockerHub: https://hub.docker.com/r/mmaelicke/scikit-gstat


If you use SciKit-GStat, pleace cite this publication:


Mälicke, M.: SciKit-GStat 1.0: A SciPy flavoured geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-174, in review, 2021.


The code itself can also be cited:


Mirko Mälicke, Romain Hugonnet, Helge David Schneider, Sebastian Müller, Egil Möller, &amp; Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5970098
</dc:description>
  <dc:identifier>https://zenodo.org/record/5970098</dc:identifier>
  <dc:identifier>10.5281/zenodo.5970098</dc:identifier>
  <dc:identifier>oai:zenodo.org:5970098</dc:identifier>
  <dc:relation>url:https://github.com/mmaelicke/scikit-gstat/tree/v1.0.0</dc:relation>
  <dc:relation>doi:10.5194/gmd-2021-174</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1345584</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:title>mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
2,248
153
views
downloads
All versions This version
Views 2,24883
Downloads 1535
Data volume 429.4 MB10.8 MB
Unique views 1,90170
Unique downloads 1325

Share

Cite as