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https://github.com/mmaelicke/scikit-gstat

Geostatistical variogram estimation expansion in the scipy style
https://github.com/mmaelicke/scikit-gstat

geostatistics scikit scipy

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Geostatistical variogram estimation expansion in the scipy style

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README 

        
SciKit-GStat

============



.. image:: https://img.shields.io/pypi/v/scikit-gstat?color=green&logo=pypi&logoColor=yellow&style=flat-square   :alt: PyPI

    :target: https://pypi.org/project/scikit-gstat



.. image:: https://img.shields.io/github/v/release/mmaelicke/scikit-gstat?color=green&logo=github&style=flat-square   :alt: GitHub release (latest by date)

    :target: https://github.com/mmaelicke/scikit-gstat



.. image:: https://github.com/mmaelicke/scikit-gstat/workflows/Test%20and%20build%20docs/badge.svg

    :target: https://github.com/mmaelicke/scikit-gstat/actions



.. image:: https://codecov.io/gh/mmaelicke/scikit-gstat/branch/master/graph/badge.svg

    :target: https://codecov.io/gh/mmaelicke/scikit-gstat

    :alt: Codecov



.. image:: https://zenodo.org/badge/98853365.svg

   :target: https://zenodo.org/badge/latestdoi/98853365



How to cite

-----------



In case you use SciKit-GStat in other software or scientific publications,

please reference this module. There is a `GMD `_  publication. Please cite it like:



  Mälicke, M.: SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, 2022.



The code itself is published and has a DOI. It can be cited as:



  Mirko Mälicke, Romain Hugonnet, Helge David Schneider, Sebastian Müller, Egil Möller, & Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5970098



Full Documentation

------------------



The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat



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.

The estimators include:



- matheron

- cressie

- dowd

- genton

- entropy

- two experimental ones: quantiles, minmax



The models include:



- sperical

- exponential

- gaussian

- cubic

- stable

- matérn



with all of them in a nugget and no-nugget variation. All the estimator are

implemented using numba's jit decorator. The usage of numba might be subject

to change in future versions.



Installation

~~~~~~~~~~~~



PyPI

^^^^

.. code-block:: bash



  pip install scikit-gstat



**Note:** It can happen that the installation of numba or numpy is failing using pip. Especially on Windows systems.

Usually, a missing Dll (see eg. `#31 `_) or visual c++ redistributable is the reason.



GIT:

^^^^



.. code-block:: bash



  git clone https://github.com/mmaelicke/scikit-gstat.git

  cd scikit-gstat

  pip install -r requirements.txt

  pip install -e .



Conda-Forge:

^^^^^^^^^^^^



From Version `0.5.5` on `scikit-gstat` is also available on conda-forge.

Note that for versions `< 1.0` conda-forge will not always be up to date, but

from `1.0` on, each minor release will be available.



.. code-block:: bash



  conda install -c conda-forge scikit-gstat



Quickstart

----------



The `Variogram` class needs at least a list of coordiantes and values.

All other attributes are set by default.

You can easily set up an example by using the `skgstat.data` sub-module,

that includes a growing list of sample data.



.. code-block:: python



  import skgstat as skg



  # the data functions return a dict of 'sample' and 'description'

  coordinates, values = skg.data.pancake(N=300).get('sample')



  V = skg.Variogram(coordinates=coordinates, values=values)

  print(V)



.. code-block:: bash



  spherical Variogram

  -------------------

  Estimator:         matheron

  Effective Range:   353.64

  Sill:              1512.24

  Nugget:            0.00



All variogram parameters can be changed in place and the class will automatically

invalidate and update dependent results and parameters.



.. code-block:: python



  V.model = 'exponential'

  V.n_lags = 15

  V.maxlag = 500



  # plot - matplotlib and plotly are available backends

  fig = V.plot()



.. image:: ./example.png