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        "author": "Andrew Grigorev",
        "author_email": "andrew@ei-grad.ru",
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        "description": "bincount\n========\n\nNo-copy parallelized bincount returning dict.\n\nMotivation\n----------\n\nAs of Nov 2018, ``np.bincount`` is unusable with large memmaps:\n\n.. code-block::\n\n   >>> import numpy as np\n   >>> np.bincount(np.memmap('some-5gb-file.txt', mode='r'))\n   Traceback (most recent call last):\n     File \"<stdin>\", line 1, in <module>\n   MemoryError\n\nThe most effective pure-python solution for ``wc -l`` is a bit slow:\n\n.. code-block::\n\n   In [6]: %%time\n      ...: sum(1 for i in open('some-5gb-file.txt', mode='rb'))\n      ...:\n   CPU times: user 3.5 s, sys: 878 ms, total: 4.38 s\n   Wall time: 4.38 s\n   Out[6]: 58941384\n\nIt is 3x times slower than ``wc -l``:\n\n.. code-block::\n\n   In [1]: %%time\n      ...: !wc -l some-5gb-file.txt\n      ...:\n   58941384 some-5gb-file.txt\n   CPU times: user 1.48 ms, sys: 3.48 ms, total: 4.96 ms\n   Wall time: 1.24 s\n\nWhile it should be faster on modern multicore SMP systems:\n\n.. code-block::\n\n   In [1]: import numpy as np\n\n   In [2]: from bincount import bincount\n\n   In [3]: %%time\n      ...: bincount(np.memmap('some-5gb-file.txt', mode='r'))[10]\n      ...:\n   CPU times: user 6.83 s, sys: 354 ms, total: 7.19 s\n   Wall time: 705 ms\n   Out[4]: 58941384\n\nInstall\n-------\n\nPrequirements: C-compiler with OpenMP support.\n\nInstall with pip:\n\n.. code-block::\n\n   pip install bincount\n\nUsage\n-----\n\nThere is a ``bincount`` (a parallel version) and a ``bincount_single`` (which don't\nparallelize the calculation) functions, both returning the dict containing the\nnumber of occurrences of each byte value in the passed bytes-like object:\n\n.. code-block::\n\n   >>> from bincount import bincount\n   >>> bincount(open('a-tiny-file.txt', 'rb').read())\n   {59: 2, 65: 5, 66: 1, 67: 3, 68: 2, 69: 3, 73: 4, 76: 7, 84: 3, 86: 1, 95: 4}",
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