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        "author_email": "Hasenpfote36@gmail.com",
        "bugtrack_url": null,
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            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development",
            "Topic :: Utilities"
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separately.**\n\nCompatibility\n-------------\n\nperfbench works with Python 3.4 or higher.\n\nDependencies\n------------\n\n-  `tqdm <https://github.com/tqdm/tqdm>`__\\ (4.6.1 or higher.)\n-  `cerberus <https://github.com/pyeve/cerberus>`__\\ (1.1 or higher.)\n-  `plotly <https://github.com/plotly/plotly.py>`__\\ (3.0.0 or higher)\n-  `notebook <https://github.com/jupyter/notebook>`__\\ (5.3 or higher.)\n-  `ipywidgets <https://github.com/jupyter-widgets/ipywidgets>`__\\ (7.2\n   or higher.)\n\nInstallation\n------------\n\n::\n\n   pip install perfbench\n\nUsage\n-----\n\n| **Plotting a single figure.**\n| `Here <https://plot.ly/~Hasenpfote/8/perfbench-demo1/>`__ is the\n  demonstration.\n\n.. code:: python\n\n   import numpy as np\n   from perfbench import *\n\n\n   bm = Benchmark(\n       datasets=[\n           Dataset(\n               factories=[\n                   lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),\n               ],\n               title='float64'\n           )\n       ],\n       dataset_sizes=[2 ** n for n in range(26)],\n       kernels=[\n           Kernel(\n               stmt='np.around(DATASET)',\n               setup='import numpy as np',\n               label='around'\n           ),\n           Kernel(\n               stmt='np.rint(DATASET)',\n               setup='import numpy as np',\n               label='rint'\n           )\n       ],\n       xlabel='dataset sizes',\n       title='around vs rint',\n   )\n   bm.run()\n   bm.plot()\n\n.. figure:: https://raw.githubusercontent.com/Hasenpfote/perfbench/master/docs/plotting_a_single_figure.png\n   :alt: plot1\n\n   plot1\n\n| **Plotting multiple plots on a single figure.**\n| `Here <https://plot.ly/~Hasenpfote/9/perfbench-demo2/>`__ is the\n  demonstration.\n\n.. code:: python\n\n   import numpy as np\n   from perfbench import *\n\n\n   bm = Benchmark(\n       datasets=[\n           Dataset(\n               factories=[\n                   lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float16),\n               ],\n               title='float16'\n           ),\n           Dataset(\n               factories=[\n                   lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float32),\n               ],\n               title='float32'\n           ),\n           Dataset(\n               factories=[\n                   lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),\n               ],\n               title='float64'\n           )\n       ],\n       dataset_sizes=[2 ** n for n in range(26)],\n       kernels=[\n           Kernel(\n               stmt='np.around(DATASET)',\n               setup='import numpy as np',\n               label='around'\n           ),\n           Kernel(\n               stmt='np.rint(DATASET)',\n               setup='import numpy as np',\n               label='rint'\n           ),\n       ],\n       xlabel='dataset sizes',\n       title='around vs rint',\n   )\n   bm.run()\n   bm.plot()\n\n.. figure:: https://raw.githubusercontent.com/Hasenpfote/perfbench/master/docs/plotting_multiple_plots_on_a_single_figure.png\n   :alt: plot2\n\n   plot2\n\n.. figure:: https://raw.githubusercontent.com/Hasenpfote/perfbench/master/docs/switching_between_subplots.png\n   :alt: plot2\n\n   plot2\n\n**Switching between layout sizes.**\n\n.. code:: python\n\n   import numpy as np\n   from perfbench import *\n\n\n   bm = Benchmark(\n       datasets=[\n           Dataset(\n               factories=[\n                   lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),\n               ],\n               title='float64'\n           )\n       ],\n       dataset_sizes=[2 ** n for n in range(26)],\n       kernels=[\n           Kernel(\n               stmt='np.around(DATASET)',\n               setup='import numpy as np',\n               label='around'\n           ),\n           Kernel(\n               stmt='np.rint(DATASET)',\n     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