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        "author": "Albert Zeyer",
        "author_email": "albzey@gmail.com",
        "bugtrack_url": null,
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            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "===========================\nextpickle - extended pickle\n===========================\n\nExtends the original `Pickler class <https://docs.python.org/library/pickle.html>`_\nto be able to pickle some otherwise non-supported types.\nThe emphasis is to be fast and for communication via pipes/sockets\nwith the same Python version on the other end - thus we don't care that much for compatibility with other versions.\n\nA similar project is `dill <https://pypi.python.org/pypi/dill>`_, which is much bigger though.\n\nThis project is registered `on PyPI <https://pypi.python.org/pypi/extpickle>`_ and can be installed via\n\n.. code-block::\n  \n  pip install extpickle\n  \nFor some usage examples, see `the test code <https://github.com/albertz/extpickle/blob/master/tests/test_extpickle.py>`_.\n\nOver the base ``Pickler`` class, it adds pickling support for:\n\n* ``types.FunctionType``, ``types.CodeType`` and cell-types.\n  I.e. you can pickle lamdas and functions.\n  Note this will use the ``marshal`` module for the byte-code, so this is not portable across different Python versions.\n* Modules. This is done by referencing it via its name.\n  Note that is has some extra handling for ``__main__``, which is allowed to be a different module.\n* ``mod.__dict__`` where ``mod`` is a module is also stored just as a reference to the module, not a copy of the dict.\n* (Python 2) ``buffer``.\n* ``numpy.ndarray``.\n  This is also supported by the base class but our implementation is much faster.\n  Actually this is a bit weird because the default implementation should also be just as fast but it isn't.\n  Our implementation basically just uses ``fromstring``/``tostring``.\n* New-style classes.\n  The base class would try to look them up in a module. This will fail whenever it cannot be find in such namespace.\n  Our implementation, if that fails, will actually store the information to construct a new class,\n  i.e. the name, the bases and its dict.\n* (Python 2) Old-style classes. This is the same behavior as for the new-style classes.\n\nThis was used in the `TaskSystem project <https://github.com/albertz/TaskSystem>`_.",
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        "license": "2-clause BSD license",
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