Metadata-Version: 2.1
Name: scikit-gstat
Version: 0.5.4
Summary: Geostatistical expansion in the scipy style
Home-page: UNKNOWN
Author: Mirko Maelicke
Author-email: mirko.maelicke@kit.edu
License: MIT License
Description: SciKit-GStat
        ============
        
        Info: scikit-gstat needs Python >= 3.5!
        
        .. 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
        
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            :target: https://github.com/mmaelicke/scikit-gstat
        
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            :target: https://github.com/mmaelicke/scikit-gstat/actions
        
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        .. 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. It is published and has a DOI. It can be cited
        as:
        
          Mirko Mälicke, Helge David Schneider, Sebastian Müller, & Egil Möller. (2021, April 20). 
            mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox 
            (Version v0.5.0). Zenodo. http://doi.org/10.5281/zenodo.4704356
        
        
        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
        
        GIT:
        
        .. code-block:: bash
        
          git clone https://github.com/mmaelicke/scikit-gstat.git
          cd scikit-gstat
          pip install -r requirements.txt
          pip install -e .
        
        **Note:** It can happen that the installation of shapely, 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. These errors are not caused by pip, scikit-gstat or the respective packages and there are a lot of issues in the shapely and numpy repo concerning these problems. Usually, the best workaround is to install especially shapely independent from scikit-gstat. As far as I know, these problems do not apply if anaconda is used like:
        
        .. code-block:: bash
          
          conda install shapely numpy
        
        Usage
        ~~~~~
        
        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 generating some random data:
        
        .. code-block:: python
        
          import numpy as np
          import skgstat as skg
        
          coordinates = np.random.gamma(0.7, 2, (30,2))
          values = np.random.gamma(2, 2, 30)
        
          V = skg.Variogram(coordinates=coordinates, values=values)
          print(V)
        
        .. code-block:: bash
        
          spherical Variogram
          -------------------
          Estimator:    matheron
          Range:        1.64
          Sill:         5.35
          Nugget:       0.00
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/x-rst
Provides-Extra: gstools
