scikit-gstat

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 <https://www.geoscientific-model-development.net>_ 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 <https://github.com/mmaelicke/scikit-gstat/issues/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