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        "author": "Sergio Oller",
        "author_email": "sergioller@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "parmap\n======\n\n.. image:: https://travis-ci.org/zeehio/parmap.svg?branch=master\n    :target: https://travis-ci.org/zeehio/parmap\n\n.. image:: https://readthedocs.org/projects/parmap/badge/?version=latest\n    :target: https://readthedocs.org/projects/parmap/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://codecov.io/github/zeehio/parmap/coverage.svg?branch=master\n    :target: https://codecov.io/github/zeehio/parmap?branch=master\n\n.. image:: https://codeclimate.com/github/zeehio/parmap/badges/gpa.svg\n   :target: https://codeclimate.com/github/zeehio/parmap\n   :alt: Code Climate\n\n\nThis small python module implements four functions: ``map`` and\n``starmap``, and their async versions ``map_async`` and ``starmap_async``.\n\nWhat does parmap offer?\n-----------------------\n\n-  Provide an easy to use syntax for both ``map`` and ``starmap``.\n-  Parallelize transparently whenever possible.\n-  Pass additional positional and keyword arguments to parallelized functions.\n-  Show a progress bar (requires `tqdm` as optional package)\n\nInstallation:\n-------------\n\n::\n\n \u00a0pip install tqdm # for progress bar support\n  pip install parmap\n\n\nUsage:\n------\n\nHere are some examples with some unparallelized code parallelized with\nparmap:\n\nSimple parallelization example:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n  import parmap\n  # You want to do:\n  mylist = [1,2,3]\n  argument1 = 3.14\n  argument2 = True\n  y = [myfunction(x, argument1, mykeyword=argument2) for x in mylist]\n  # In parallel:\n  y = parmap.map(myfunction, mylist, argument1, mykeyword=argument2)\n\n\nShow a progress bar:\n~~~~~~~~~~~~~~~~~~~~~\n\nRequires ``pip install tqdm``\n\n::\n\n  # You want to do:\n  y = [myfunction(x) for x in mylist]\n  # In parallel, with a progress bar\n  y = parmap.map(myfunction, mylist, pm_pbar=True)\n\n\nPassing multiple arguments:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n  # You want to do:\n  z = [myfunction(x, y, argument1, argument2, mykey=argument3) for (x,y) in mylist]\n  # In parallel:\n  z = parmap.starmap(myfunction, mylist, argument1, argument2, mykey=argument3)\n\n  # You want to do:\n  listx = [1, 2, 3, 4, 5, 6]\n  listy = [2, 3, 4, 5, 6, 7]\n  param = 3.14\n  param2 = 42\n  listz = []\n  for (x, y) in zip(listx, listy):\n      listz.append(myfunction(x, y, param1, param2))\n  # In parallel:\n  listz = parmap.starmap(myfunction, zip(listx, listy), param1, param2)\n\n\nAdvanced: Multiple parallel tasks running in parallel\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIn this example, Task1 uses 5 cores, while Task2 uses 3 cores. Both tasks start\nto compute simultaneously, and we print a message as soon as any of the tasks\nfinishes, retreiving the result.\n\n::\n\n    import parmap\n    def task1(item):\n        return 2*item\n\n    def task2(item):\n        return 2*item + 1\n\n    items1 = range(500000)\n    items2 = range(500)\n\n    with parmap.map_async(task1, items1, pm_processes=5) as result1:\n        with parmap.map_async(task2, items2, pm_processes=3) as result2:\n            data_task1 = None\n            data_task2 = None\n            task1_working = True\n            task2_working = True\n            while task1_working or task2_working:\n                result1.wait(0.1)\n                if task1_working and result1.ready():\n                    print(\"Task 1 has finished!\")\n                    data_task1 = result1.get()\n                    task1_working = False\n                result2.wait(0.1)\n                if task2_working and result2.ready():\n                    print(\"Task 2 has finished!\")\n                    data_task2 = result2.get()\n                    task2_working = False\n    #Further work with data_task1 or data_task2\n\n\nmap and starmap already exist. Why reinvent the wheel?\n---------------------------------------------------------\n\nThe existing functions have some usability limitations:\n\n-  The built-in python function ``map`` [#builtin-map]_\n   is not able to parallelize.\n-  ``multiprocessing.Pool().starmap`` [#multiproc-starmap]_\n   is only available in python-3.3 and later versions.\n-  ``multiprocessing.Pool().map`` [#multiproc-map]_\n   does not allow any additional argument to the mapped function.\n-  ``multiprocessing.Pool().starmap`` allows passing multiple arguments,\n   but in order to pass a constant argument to the mapped function you\n   will need to convert it to an iterator using\n   ``itertools.repeat(your_parameter)`` [#itertools-repeat]_\n\n``parmap`` aims to overcome this limitations in the simplest possible way.\n\nAdditional features in parmap:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n-  Create a pool for parallel computation automatically if possible.\n-  ``parmap.map(..., ..., pm_parallel=False)`` # disables parallelization\n-  ``parmap.map(..., ..., pm_processes=4)`` # use 4 parallel processes\n-  ``parmap.map(..., ..., pm_pbar=True)`` # show a progress bar (requires tqdm)\n-  ``parmap.map(..., ..., pm_pool=multiprocessing.Pool())`` # use an existing\n   pool, in this case parmap will not close the pool.\n-  ``parmap.map(..., ..., pm_chunksize=3)`` # size of chunks (see\n   multiprocessing.Pool().map)\n\nLimitations:\n-------------\n\n``parmap.map()`` and ``parmap.starmap()`` (and their async versions) have their own \narguments (``pm_parallel``, ``pm_pbar``...). Those arguments are never passed\nto the underlying function. In the following example, ``myfun`` will receive \n``myargument``, but not ``pm_parallel``. Do not write functions that require\nkeyword arguments starting with ``pm_``, as ``parmap`` may need them in the future.\n\n::\n\n    parmap.map(myfun, mylist, pm_parallel=True, myargument=False)\n\nAdditionally, there are other keyword arguments that should be avoided in the\nfunctions you write, because of parmap backwards compatibility reasons. The list\nof conflicting arguments is: ``parallel``, ``chunksize``, ``pool``,\n``processes``, ``callback``, ``error_callback`` and ``parmap_progress``.\n\n\n\nAcknowledgments:\n----------------\n\nThis package started after `this question <https://stackoverflow.com/q/5442910/446149>`__, \nwhen I offered this `answer <http://stackoverflow.com/a/21292849/446149>`__, \ntaking the suggestions of J.F. Sebastian for his `answer <http://stackoverflow.com/a/5443941/446149>`__\n\nKnown works using parmap\n---------------------------\n\n- Davide Gerosa, Michael Kesden, \"PRECESSION. Dynamics of spinning black-hole\n  binaries with python.\" `arXiv:1605.01067 <https://arxiv.org/abs/1605.01067>`__, 2016\n- Thibault de Boissiere, `Implementation of Deep learning papers <https://github.com/tdeboissiere/DeepLearningImplementations>`__, 2017\n    - Wasserstein Generative Adversarial Networks `arXiv:1701.07875 <https://arxiv.org/abs/1701.07875>`__\n    - pix2pix `arXiv:1611.07004 <https://arxiv.org/abs/1611.07004>`__\n    - Improved Techniques for Training Generative Adversarial Networks `arXiv:1606.03498 <https://arxiv.org/abs/1606.03498>`__\n    - Colorful Image Colorization `arXiv:1603.08511 <https://arxiv.org/abs/1603.08511>`__\n    - Deep Feature Interpolation for Image Content Changes `arXiv:1611.05507 <https://arxiv.org/abs/1611.05507>`__\n    - InfoGAN `arXiv:1606.03657 <https://arxiv.org/abs/1606.03657>`__\n- Geoscience Australia, `SIFRA, a System for Infrastructure Facility Resilience Analysis <https://github.com/GeoscienceAustralia/sifra>`__, 2017\n- Andr\u00e9 F. Rendeiro, Christian Schmidl, Jonathan C. Strefford, Renata Walewska, Zadie Davis, Matthias Farlik, David Oscier, Christoph Bock \"Chromatin accessibility maps of chronic lymphocytic leukemia identify subtype-specific epigenome signatures and transcription regulatory networks\" Nat. Commun. 7:11938 doi: 10.1038/ncomms11938 (2016). `Paper <https://doi.org/10.5281/zenodo.231352>`__, `Code <https://github.com/epigen/cll-chromatin>`__\n\n\nReferences\n-----------\n\n.. [#builtin-map] http://docs.python.org/dev/library/functions.html#map\n.. [#multiproc-starmap] http://docs.python.org/dev/library/multiprocessing.html#multiprocessing.pool.Pool.starmap\n.. [#multiproc-map] http://docs.python.org/dev/library/multiprocessing.html#multiprocessing.pool.Pool.map\n.. [#itertools-repeat] http://docs.python.org/2/library/itertools.html#itertools.repeat\n\n\n\n",
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