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        "author": "Fran\u00e7ois Orieux",
        "author_email": "orieux@iap.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "=========================\nEdwin, bayesian inversion\n=========================\n\nThe Bayesian inversion (``edwin``) package provides algorithm\ndeveloped during my scientific research work in numerical computation\nfor inverse problems (signal, image processing). Feel free to use them\nas you want. Any comments and contributions are welcome.\n\nThe name ``edwin`` is in reference to Edwin T. Jaynes, a great\nBayesian Analysis scientific.\n\nAcknowledgements\n================\n\nThe use of ``edwin`` software package should be explicitly\nacknowledged in publications in the following form:\n\n1. an acknowledgment statement: \"Some of the results in this paper\n   have been derived using some of the ``edwin`` package algorithms\n   From F. Orieux et al. published in *citations*.\n\n2. at the first reference, a footnote placed in the main body of the\n   paper referring to the ``edwin`` web site, currently\n   http://bitbucket.org/forieux/edwin\n\nThe citations are mentioned in documentation, *References* section of\nthis file and are available in bibtex file.\n\nInfo\n====\n\n* Author: Fran\u00e7ois Orieux\n* Contact: orieux at iap dot fr\n* Project homepage: http://bitbucket.org/forieux/edwin\n* Downloads page: https://bitbucket.org/forieux/edwin/downloads\n\nContents\n========\n\nimprocessing.py\n    A module that implement the algorithm described in [2] for\n    unsupervised myopic image deconvolution. However the myopic part\n    is not actually available.\n\ninversion.py\n    A module that implement the algorithm described in [1] and use in\n    [3-4] and other papers. It's implement an unsupervised general\n    inverse problem algorithm estimation, based on MCMC algorithm.\n\nsampling.py\n    Implementation of stochastic sampling algorithm, specially [1].\n\noptim.py\n    A module that implement classical optimisation algorithm for use\n    of other module. They are design for very large system resolution\n    (dim > 1e6).\n\n\nRequirements\n============\n\nThis package depends on my free otb package (utility functions).\n\n* Numpy version >= 1.4.1\n* `otb <https://bitbucket.org/forieux/otb>`_ version >= 0.2.1\n\nInstallation\n============\n\nThe ``pip`` version::\n\n    pip install edwin\n\nIf you have not ``pip``, download the archive, decompress it and to\ninstall in your user path, run in a command line::\n\n    python setup.py install --user\n\nor for the system path, run as root::\n\n    python setup.py install\n\nDevelopment\n===========\n\nThis package follow the Semantic Versionning convention\nhttp://semver.org/. To get the development version you can clone the\nmercurial repository available here\nhttp://bitbucket.org/forieux/edwin\n\nThe ongoing development depends on my research activity but is open. I\ntry to fix bugs.\n\nLicense\n=======\n\n``edwin`` is free software distributed under the MIT license, see\nLICENSE.txt\n\nReferences\n==========\n\nA bibtex file is provided in the archive.\n\n.. [1] F. Orieux, O. F\u00e9ron and J.-F. Giovannelli, \"Sampling\n   high-dimensional Gaussian distributions for general linear inverse\n   problems\", IEEE Signal Processing Letters, 2012\n\n.. [2] Fran\u00e7ois Orieux, Jean-Fran\u00e7ois Giovannelli, and Thomas\n   Rodet, \"Bayesian estimation of regularization and point spread\n   function parameters for Wiener-Hunt deconvolution\",\n   J. Opt. Soc. Am. A 27, 1593-1607 (2010)\n\n.. [3] F. Orieux, E. Sepulveda, V. Loriette, B. Dubertret and\n   J.-C. Olivo-Marin, \"Bayesian Estimation for Optimized Structured\n   Illumination Microscopy\", IEEE trans. on Image Processing. 2012\n\n.. [4] F. Orieux, J.-F. Giovannelli, T. Rodet, and A. Abergel,\n   \"Estimating hyperparameters and instrument parameters in\n   regularized inversion Illustration for Herschel/SPIRE map\n   making\", Astronomy & Astrohpysics, 2013",
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