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    "info": {
        "author": "Utkarsh Upadhyay",
        "author_email": "mail@musicallyut.in",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Utilities"
        ],
        "description": "========\nOverview\n========\n\n\n\nFunction decorator to make argument passing saner.\n\nI often have to write a function which runs a simulation/learning task which I\nneed to run for several different parameters. This initially is manageable, but\nthen slowly configuration creep starts to happen: I keep adding more and more\nparameters to the functions which run the simulations and keep making my old\ncode more and more fragile.\n\nI wrote ``decorated_options`` to decouple the arguments for different set of experiments.\n\nIn brief,  ``decorated_options`` converts this:\n\n::\n\n    def run(max_num_followers, num_segments, is_hawkes):\n        # ...\n        # ...\n\n    # tmp = run_multiple_followers(max_num_followers=10, num_segments=10, is_hawkes=True)\n    # tmp = run_multiple_followers(max_num_followers=100, num_segments=10, is_hawkes=False)\n    # tmp = run_multiple_followers(max_num_followers=10, num_segments=50, is_hawkes=True)\n    tmp = run_multiple_followers(max_num_followers=1000, num_segments=100, is_hawkes=False)\n\n\nto:\n\n::\n\n    from decorated_options import Options, optioned\n\n    @optioned('opts')\n    def run(max_num_followers, num_segments, is_hawkes):\n        # ...\n        # ...\n\n\n    opts = Options(max_num_followers=10, num_segments=10, is_hawkes=True)\n    # tmp = run_multiple_followers(opts=opts)\n    # tmp = run_multiple_followers(max_num_followers=100, is_hawkes=False, opts=opts)\n    # tmp = run_multiple_followers(num_segments=50, is_hawkes=False, opts=opts)\n    tmp = run_multiple_followers(max_num_followers=1000, num_segments=100, is_hawkes=False)\n\n\n\n* Benefits over ``**kwargs`` in receiving function:\n\n  1. Early reporting of errors at call-time.\n  2. No need to unpack the values.\n  3. Default values do not have to be hard-coded.\n  4. Allows progressive improvement, no need to change old code which uses positional arguments.\n\n* Benefits over ``**dict`` while calling:\n\n  1. Easier updating/overriding of values\n  2. Positional arguments also work\n  3. Guaranteed immutability (throws Exceptions on attempted violations.)\n\n* Benefits over default values in receiving function:\n\n  1. ``Options`` objects can save defaults for multiple settings.\n  2. De-couples default values from the functions themselves.\n\n\n\nInstallation\n============\n\n::\n\n    pip install git+https://github.com/musically-ut/decorated_options.git@master#egg=decorated_options\n\nDevelopment\n===========\n\nTo run the all tests run::\n\n    tox\n\nNote, to combine the coverage data from all the tox environments run:\n\n.. list-table::\n    :widths: 10 90\n    :stub-columns: 1\n\n    - - Windows\n      - ::\n\n            set PYTEST_ADDOPTS=--cov-append\n            tox\n\n    - - Other\n      - ::\n\n            PYTEST_ADDOPTS=--cov-append tox\n\n\nChangelog\n=========\n\n0.1.0 (2016-07-24)\n-----------------------------------------\n\n* First release on PyPI.",
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        "home_page": "https://github.com/musically-ut/decorated_options",
        "keywords": "decorators,data-science,configuration,arguments",
        "license": "BSD",
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        "maintainer_email": null,
        "name": "decorated_options",
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        "summary": "Function decorator to make argument passing saner.",
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