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        "author": "Reece Stevens",
        "author_email": "rstevens@innolitics.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": ".. image:: https://travis-ci.org/innolitics/natural-neighbor-interpolation.svg?branch=master\n   :target: https://travis-ci.org/innolitics/natural-neighbor-interpolation\n\nDiscrete Sibson (Natural Neighbor) Interpolation\n================================================\n\nNatural neighbor interpolation is a method for interpolating scattered data\n(i.e. you know the values of a function at scattered locations).  It is often superior to linear barycentric interpolation, which is a commonly used method of interpolation provided by Scipy's `griddata` function.\n\nThere are several implementations of 2D natural neighbor interpolation in Python.  We needed a fast 3D implementation that could run without a GPU, so we wrote an implementation of Discrete Sibson Interpolation (a version of natural neighbor interpolation that is fast but introduces slight errors as compared to \"geometric\" natural neighbor interpolation).\n\nSee https://doi.org/10.1109/TVCG.2006.27 for details.\n\nDependencies\n------------\n\n- Python 2.7 or 3.4+\n- Numpy (has been tested with 1.13+)\n\nDemonstration\n-------------\n\nNatural neighbor interpolation can be more accurate than linear barycentric interpolation (Scipy's default) for smoothly varying functions.\n\nAlso, the final result looks better.\n\n.. image:: https://raw.githubusercontent.com/innolitics/natural-neighbor-interpolation/master/demo/linear_comparison.png\n   :target: https://raw.githubusercontent.com/innolitics/natural-neighbor-interpolation/master/demo/linear_comparison.png\n\n\n.. image:: https://raw.githubusercontent.com/innolitics/natural-neighbor-interpolation/master/demo/sin_sin_comparison.png\n   :target: https://raw.githubusercontent.com/innolitics/natural-neighbor-interpolation/master/demo/sin_sin_comparison.png\n\nNote that the natural neighbor values usually are extrapolated; they were cut off in the demo to fairly compare with Scipy's linear barycentric method, which does not extrapolate.\n\nUsage\n-----\n\nThis module exposes a single function, :code:`griddata`.\n\nThe API for :code:`naturalneighbor.griddata` is similar to\n:code:`scipy.interpolate.griddata`.  Unlike Scipy, the third argument is not a\ndense mgrid, but instead is just the ranges that would have been passed to :code:`mgrid`.  This is because the discrete Sibson approach requires the interpolated points to lie on an evenly spaced grid.\n\n.. code-block:: python\n\n    import scipy.interpolate\n    import numpy as np\n\n    import naturalneighbor\n\n    num_points = 10\n    num_dimensions = 3\n    points = np.random.rand(num_points, num_dimensions)\n    values = np.random.rand(num_points)\n\n    grids = tuple(np.mgrid[0:100:1, 0:50:100j, 0:100:2])\n    scipy_interpolated_values = scipy.interpolate.griddata(points, values, grids)\n\n    grid_ranges = [[0, 100, 1], [0, 50, 100j], [0, 100, 2]]\n    nn_interpolated_values = naturalneighbor.griddata(points, values, grid_ranges)\n\nFuture Work\n-----------\n\n- Provide options for extrapolation handling\n- Support floats and complex numbers (only support doubles at the moment)\n- Support 2D (only support 3D)\n- Add documentation with discussion on limitations of discrete sibson's method\n- Uncomment cpplint from tox.ini and cleanup C++ code\n- Generalize the threading model (currently it uses 8 threads---one for each quadrant)",
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