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        "author": "Mirko Maelicke",
        "author_email": "mirko.maelicke@kit.edu",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "SciKit-GStat\n============\n\nInfo: scikit-gstat needs Python >= 3.4!\n\n.. image:: https://img.shields.io/badge/pypi%20package-0.2.5-green.svg\n    :target: https://pypi.org/project/scikit-gstat/0.2.5/\n\n.. image:: https://img.shields.io/badge/version-0.2.5-green.svg\n    :target: https://github.com/mmaelicke/scikit-gstat\n\n.. image:: https://travis-ci.org/mmaelicke/scikit-gstat.svg?branch=master\n    :target: https://travis-ci.org/mmaelicke/scikit-gstat\n    :alt: Build Status\n\n.. image:: https://api.codacy.com/project/badge/Grade/34022fb8b795435b8eeb5431159fa7c6\n   :alt: Codacy Badge\n   :target: https://app.codacy.com/app/mmaelicke/scikit-gstat?utm_source=github.com&utm_medium=referral&utm_content=mmaelicke/scikit-gstat&utm_campaign=Badge_Grade_Dashboard\n\n.. image:: https://readthedocs.org/projects/scikit-gstat/badge/?version=latest\n    :target: http://scikit-gstat.readthedocs.io/en/latest?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://codecov.io/gh/mmaelicke/scikit-gstat/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/mmaelicke/scikit-gstat\n    :alt: Codecov\n\n.. image:: https://zenodo.org/badge/98853365.svg\n   :target: https://zenodo.org/badge/latestdoi/98853365\n\nHow to cite\n-----------\n\nIn case you use SciKit-GStat in other software or scientific publications,\nplease reference this module. It is published and has a DOI. It can be cited\nas:\n\n  M\u00e4licke, Mirko, & Schneider, Helge David. (2018). mmaelicke/scikit-gstat:\n  Geostatistical variogram toolbox (Version v0.2.2). Zenodo.\n  http://doi.org/10.5281/zenodo.1345584\n\nFull Documentation\n------------------\n\nThe full documentation can be found at: https://mmaelicke.github.io/scikit-gstat\n\n\nNew Version 0.2\n---------------\n\nScikit-gstat was rewritten in major parts. Most of the changes are internal,\nbut the attributes and behaviour of the `Variogram` has also changed\nsubstantially.\nA detailed description of of the new versions usage will follow. The last\nversion of the old Variogram class, 0.1.8, is kept in the `version-0.1.8`\nbranch on GitHub, but not developed any further. Those two versions are not\ncompatible.\n\nDescription\n-----------\n\nSciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes\ntwo base classes ``Variogram`` and ``DirectionalVariogram``. Both have a\nvery similar interface and can compute experimental variograms and model\nvariograms. The module makes use of a rich selection of semi-variance\nestimators and variogram model functions, while being extensible at the same\ntime.\nThe estimators include:\n\n- matheron\n- cressie\n- dowd\n- genton\n- entropy\n- two experimental ones: quantiles, minmax\n\nThe models include:\n\n- sperical\n- exponential\n- gaussian\n- cubic\n- stable\n- mat\u00e9rn\n\nwith all of them in a nugget and no-nugget variation. All the estimator are\nimplemented using numba's jit decorator. The usage of numba might be subject\nto change in future versions.\nAt the current stage, the package does not include any kriging.\nThis is planned for a future release.\n\n\nInstallation\n~~~~~~~~~~~~\n\nPyPI:\n\n.. code-block:: bash\n\n  pip install scikit-gstat\n\nGIT:\n\n.. code-block:: bash\n\n  git clone https://github.com/mmaelicke/scikit-gstat.git\n  cd scikit-gstat\n  pip install -r requirements.txt\n  pip install -e .\n\nUsage\n~~~~~\n\nThe `Variogram` class needs at least a list of coordiantes and values.\nAll other attributes are set by default.\nYou can easily set up an example by generating some random data:\n\n.. code-block:: python\n\n  import numpy as np\n  import skgstat as skg\n\n  coordinates = np.random.gamma(0.7, 2, (30,2))\n  values = np.random.gamma(2, 2, 30)\n\n  V = skg.Variogram(coordinates=coordinates, values=values)\n  print(V)\n\n.. code-block:: bash\n\n  spherical Variogram\n  -------------------\n  Estimator:    matheron\n  Range:        1.64\n  Sill:         5.35\n  Nugget:       0.00",
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