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        "author": "Keiron J. Pizzey",
        "author_email": "kjpizzey@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering"
        ],
        "description": "Gwydion\r\n=======\r\n\r\nGwydion allows users to generate pseudo-random scientific data easily.\r\n\r\nIn the spirit of `Faker`_, Gwydion allows you to generate pseudo-random\r\ndata using a simple, clean, and customisable API.\r\n\r\nGwydion is named after a `trickster from Welsh mythology`_.\r\n\r\nInstallation\r\n============\r\n\r\nYou can install from `PyPI`_ with\r\n\r\n::\r\n\r\n    pip install gwydion\r\n\r\n.. _Faker: https://github.com/joke2k/faker\r\n.. _trickster from Welsh mythology: http://en.wikipedia.org/wiki/Gwydion\r\n.. _PyPI: https://pypi.python.org/pypi\r\n\r\nExamples\r\n========\r\n\r\nSome basic examples of Gwydion objects are given below.\r\n\r\nIn the first example, we create a simple ``Linear`` object, given by the mathematical relationship ``y = mx + c``.\r\nWhen parameters are not set by the user, Gwydion objects will default to suitable random values. Objects will also, by default, add some random noise to the y-data.\r\nIn the example below we allow the ``Linear`` object to generate all of the parameters, but set the number of data points ``N`` manually.\r\n\r\n::\r\n\r\n    from gwydion import Linear\r\n\r\n    lin = Linear(N=6)\r\n\r\n    x, y = lin.data\r\n    print(x, y, sep='\\n')\r\n    # [  0.   2.   4.   6.   8.  10.]\r\n    # [ -0.17387604   5.59216341  11.77162695  17.70041889  23.55609025  28.67617757]\r\n\r\n\r\nIn this second example, an ``Exponential`` function is created with various manually selected parameters.\r\nExponential functions are given by ``y = I * base**(k*x)``.\r\nIn the example below we have set:\r\n\r\n- The number of data points ``N = 3``,\r\n- The intensity ``I = 10``,\r\n- The exponent multiplier ``k = -1``,\r\n- The x-limits ``xlim = (0, 10)``,\r\n- And chosen to not add any random noise to the data ``add_rand = False``.\r\n\r\nFor the ``Exponential`` object the default ``base`` is not random, but is instead to Euler's number ``e = 2.71828...``.\r\nThis fact, combined with ``k = -1``, means that our object below is effectively giving us exponential decay.\r\n\r\n::\r\n\r\n    from gwydion import Exponential\r\n\r\n    exp = Exponential(N=3, I=10, k=-1, xlim=(0,10), add_rand=False)\r\n\r\n    x, y = exp.data\r\n    print(x, y, sep='\\n')\r\n    # [  0.   5.  10.]\r\n    # [  1.00000000e+01   6.73794700e-02   4.53999298e-04]\r\n\r\n\r\nFinally, let's look at how ``Gwydion`` objects work with ``matplotlib``. In the example below, we generate\r\n5 ``Sine`` objects using a list comprehension. We can then use the ``plot`` function to plot each data set easily.\r\n\r\n\r\n::\r\n\r\n    from gwydion import Sine\r\n    import matplotlib.pyplot as plt\r\n\r\n    sines = [Sine(xlim=(0,5)) for _ in range(5)]\r\n\r\n    fig, ax = plt.subplots()\r\n\r\n    for sine in sines:\r\n        sine.plot(ax=ax)\r\n\r\n    ax.set_xlabel('Time')\r\n    ax.set_ylabel('Intensity')\r\n\r\n    plt.show()\r\n\r\n\r\n.. image:: http://i.imgur.com/oG6zDBC.png",
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