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        "author": "Florian Brucker",
        "author_email": "mail@florianbrucker.de",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "Traditional approaches for inter- and extrapolation of scattered data\nwork on a filled rectangular area surrounding the data points or in\ntheir filled convex hull. However, scattered data often consists of\ndifferent clusters of irregular shapes and usually contains areas where\nthere simply is no data. Forcing such data into a traditional inter-\nor extrapolation scheme often does not lead to the desired results.\n\nHeatmaps, on the other hand, deal well with scattered data but often do\nnot provide real interpolation: Instead they usually use raw sums of\nkernel functions which overestimate the target value in densely\npopulated areas.\n\nClusterpolation is a hybrid inter- and extrapolation scheme to fix this.\nIt uses kernel functions for a weighted inter- and extrapolation of\nlocal values, as well as for a density estimation of the data. The\nlatter is used to assign a membership degree to clusterpolated points:\nPoints with a low membership degree lie in an area where there's just\nnot enough data.",
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