Metadata-Version: 1.1
Name: scikit-gstat
Version: 0.2.2
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 >= 3.4!
        
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        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:
        
          Mälicke, Mirko, & Schneider, Helge David. (2018). mmaelicke/scikit-gstat:
          Geostatistical variogram toolbox (Version v0.2.2). Zenodo.
          http://doi.org/10.5281/zenodo.1345584
        
        Full Documentation
        ------------------
        
        The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat
        
        
        New Version 0.2
        ---------------
        
        Scikit-gstat was rewritten in major parts. Most of the changes are internal,
        but the attributes and behaviour of the `Variogram` has also changed
        substantially.
        A detailed description of of the new versions usage will follow. The last
        version of the old Variogram class, 0.1.8, is kept in the `version-0.1.8`
        branch on GitHub, but not developed any further. Those two versions are not
        compatible.
        
        Description
        -----------
        
        SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes
        two base classes :class:`Variogram <skgstat.Variogram>` and
        :class:`DirectionalVariogram <skgstat.DirectionalVariogram>`. Both have a
        very similar interface and can compute experimental variograms and model
        variograms. 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.
        At the current stage, the package does not include any kriging. This is planned for a future release.
        
        
        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 .
        
        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 :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
