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    "info": {
        "author": "Pierre Chanial",
        "author_email": "pierre.chanial@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: Public Domain",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development"
        ],
        "description": "============\nPyBenchmarks\n============\n\nAutomate the creation of benchmark tables.\n\nThe benchmark utility times one or more code snippets or functions by iterating\nthrough input arguments or keyed variables. It returns a dictionary\ncontaining the elapsed time (all platforms) and memory usage (linux only)\nfor each combination of the input variables. An argument or keyed variable\nis iterated if and only if it is a list, a tuple, a generator or a range.\n\n\n========\nExamples\n========\n\n>>> import numpy as np\n>>> from pybenchmarks import benchmark\n>>> b = benchmark((np.empty, np.ones), (100, 10000, 1000000),\n...               dtype=(int, complex))\nempty: 100     dtype=int       1.62 us\nones : 100     dtype=int       3.61 us\nempty: 100     dtype=complex   1.70 us\nones : 100     dtype=complex   5.42 us\nempty: 10000   dtype=int       1.53 us\nones : 10000   dtype=int       7.53 us\nempty: 10000   dtype=complex   2.33 us\nones : 10000   dtype=complex  16.29 us\nempty: 1000000 dtype=int       1.87 us\nones : 1000000 dtype=int       1.84 ms\nempty: 1000000 dtype=complex   2.19 us\nones : 1000000 dtype=complex   4.20 ms\n\n>>> b = benchmark(['np.empty(n, dtype=dtype)', 'np.ones(n, dtype=dtype)'],\n...               dtype=(int, complex), n=(100, 10000, 1000000),\n...               setup='from __main__ import np')\n'np.empty(n, dt...: dtype=int     n=100       1.36 us\n'np.ones(n, dty...: dtype=int     n=100       2.83 us\n'np.empty(n, dt...: dtype=complex n=100       1.44 us\n'np.ones(n, dty...: dtype=complex n=100       3.50 us\n'np.empty(n, dt...: dtype=int     n=10000     1.22 us\n'np.ones(n, dty...: dtype=int     n=10000     7.05 us\n'np.empty(n, dt...: dtype=complex n=10000     1.35 us\n'np.ones(n, dty...: dtype=complex n=10000    23.78 us\n'np.empty(n, dt...: dtype=int     n=1000000   1.47 us\n'np.ones(n, dty...: dtype=int     n=1000000   2.04 ms\n'np.empty(n, dt...: dtype=complex n=1000000   2.91 us\n'np.ones(n, dty...: dtype=complex n=1000000   4.26 ms\n\n>>> import time\n>>> benchmark(time.sleep, (1, 2, 3))\n1   1.00 s\n2   2.00 s\n3   3.00 s\n\n>>> shapes = (100, 10000, 1000000)\n>>> setup = \"\"\"\n... import numpy as np\n... a = np.random.random_sample(shape)\n... \"\"\"\n>>> b = benchmark('np.dot(a, a)', shape=shapes, setup=setup)\nshape=100       1.38 us\nshape=10000     6.33 us\nshape=1000000 855.44 us\n\n>>> shapes = (10, 100, 1000)\n>>> setup=\"\"\"\n... import numpy as np\n... a = np.random.random_sample((m, n))\n... b = np.random.random_sample(n)\n... \"\"\"\n>>> b = benchmark('np.dot(a, b)', m=shapes, n=shapes, setup=setup)\nm=10   n=10     1.08 us\nm=100  n=10     1.61 us\nm=1000 n=10     6.91 us\nm=10   n=100    1.48 us\nm=100  n=100    4.16 us\nm=1000 n=100   20.69 us\nm=10   n=1000   4.42 us\nm=100  n=1000  39.23 us\nm=1000 n=1000 931.04 us\n\n>>> def f(x, n, start=1):\n...     out = start\n...     for i in range(n):\n...         out *= x\n...     return out\n>>> b = benchmark(f, np.full(10, 2), xrange(10), start=2.)\n0   1.09 us\n1   4.15 us\n2   5.25 us\n3   5.53 us\n4  13.10 us\n5   9.23 us\n6   9.69 us\n7  10.46 us\n8  13.03 us\n9  10.77 us",
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