{
    "info": {
        "author": "Chris Beaumont",
        "author_email": "cbeaumont@cfa.harvard.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "ds9norm\n=======\n\nThis file implements a matplotlib Normalize object which mimics the\nfunctionality of image scaling functions in\n`ds9 <http://ds9.si.edu/site/Home.html>`__. It provides 5 ways of\nconfiguring image scaling:\n\n-  Clipping the low and high intensities at specified percentiles\n   (``clip_lo``, ``clip_hi``)\n-  Specifying an intensity transfer function for ramping from black to\n   white (``stretch``)\n-  Setting the mid-point of this transfer function (``bias``, akin to\n   dragging the right mouse button left/right in ds9)\n-  Setting how sharp the transition from black-white is (``contrast``,\n   akin to dragging the right mouse button up/down in ds9)\n\nExamples\n--------\n\nSee the `example\nnotebook <http://nbviewer.ipython.org/github/glue-viz/ds9norm/blob/master/Examples.ipynb>`__\n\n.. code:: python\n\n\n    data = fits.getdata('M51.fits')\n    norm = DS9Normalize(bias=0.2)\n    figure, axs = plt.subplots(ncols=3, nrows=3, squeeze=False, tight_layout=True)\n\n    for ax, bias in zip(axs[2], [.2, .5, .8]):\n        ax.imshow(data, norm=DS9Normalize(bias=bias))\n        ax.set_title('Bias = %0.1f' % bias)\n\n    for ax, contrast in zip(axs[1], [0.5, 1, 2]):\n        ax.imshow(data, norm=DS9Normalize(contrast=contrast))\n        ax.set_title('Contrast = %0.1f' % contrast)\n\n    for ax, stretch in zip(axs[0], ['linear', 'sqrt', 'arcsinh']):\n        ax.imshow(data, norm=DS9Normalize(stretch=stretch))\n        ax.set_title(stretch)\n\n.. figure:: gallery.png\n   :alt: ds9norm demo\n\n   ds9norm demo\nBuild Status\n------------\n\n[|Build Status|\\ ]\n(https://travis-ci.org/glue-viz/ds9norm?branch=master) [|Coverage\nStatus|\\ ] (https://coveralls.io/r/glue-viz/ds9norm)\n\n.. |Build Status| image:: https://travis-ci.org/glue-viz/ds9norm.png\n.. |Coverage Status| image:: https://coveralls.io/repos/glue-viz/ds9norm/badge.png",
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        "keywords": null,
        "license": "MIT",
        "maintainer": null,
        "maintainer_email": null,
        "name": "ds9norm",
        "package_url": "https://pypi.org/project/ds9norm/",
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        "requires_dist": null,
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        "summary": "A Matplotlib normalize object that replicates DS9 image stretching",
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