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        "author": "Mathieu Turcotte",
        "author_email": "turcotte.mat@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Other Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: General"
        ],
        "description": "Massif Parser\r\n=============\r\n\r\nA parser for Valgrind massif.out files.\r\n\r\nThe msparser module offers a simple interface to parse the Valgrind massif.out\r\nfile format, i.e. data files produced by the valgrind heap profiler and should be \r\ncompatible with Python 2.5 and up (including 3.x and pypy).\r\n\r\nHow do I use it?\r\n----------------\r\n\r\nImport the module\r\n`````````````````\r\nAs usual, import the module::\r\n\r\n    >>> import msparser\r\n\r\nParse a massif.out file\r\n```````````````````````\r\nTo extract the data from a massif.out file, you simply have to give its path to\r\nthe ``parse_file`` function::\r\n\r\n    >>> data = msparser.parse_file('massif.out')\r\n\r\nYou could also use the ``msparser.parse`` function directly with a file\r\ndescriptor.\r\n\r\nUnderstand the data\r\n```````````````````\r\n\r\nThe parsed data is returned as a dictionary which follow closely the massif.out\r\nformat. It looks like this::\r\n\r\n    >>> from pprint import pprint\r\n    >>> pprint(data, depth=1)\r\n    {'cmd': './a.out',\r\n     'desc': '--time-unit=ms',\r\n     'detailed_snapshots_index': [...],\r\n     'peak_snapshot_index': 16,\r\n     'snapshots': [...],\r\n     'time_unit': 'ms'}\r\n\r\nThe ``detailed_snapshots_index`` and ``peak_snapshot_index`` fields allow\r\nefficient localisation of the detailled and peak snapshots in the ``snapshots``\r\nlist. For example, to retrieve the peak snapshot from the ``snapshots`` list,\r\nwe could do::\r\n\r\n    >>> peak_index = data['peak_snapshot_index']\r\n    >>> peak_snapshot = data['snapshots'][peak_index]\r\n\r\nThe ``snapshots`` list stores dictionaries representing each snapshot data::\r\n\r\n    >>> second_snapshot = data['snapshots'][1]\r\n    >>> pprint(second_snapshot)\r\n    {'heap_tree': None,\r\n     'id': 1,\r\n     'mem_heap': 1000,\r\n     'mem_heap_extra': 8,\r\n     'mem_stack': 0,\r\n     'time': 183}\r\n\r\nIf the snapshot is detailled, the ``heap_tree`` field, instead of being None,\r\nwill store a heap tree::\r\n\r\n    >>> peak_heap_tree = peak_snapshot['heap_tree']\r\n    >>> pprint(peak_heap_tree, depth=3)\r\n    {'children': [{'children': [...], 'details': {...}, 'nbytes': 12000},\r\n                  {'children': [], 'details': {...}, 'nbytes': 10000},\r\n                  {'children': [...], 'details': {...}, 'nbytes': 8000},\r\n                  {'children': [...], 'details': {...}, 'nbytes': 2000}],\r\n     'details': None,\r\n     'nbytes': 32000}\r\n\r\nOn the root node, the ``details`` field is always None, but on the children\r\nnodes it's a dictionary which looks like this::\r\n\r\n    >>> first_child = peak_snapshot['heap_tree']['children'][0]\r\n    >>> pprint(first_child['details'], width=1)\r\n    {'address': '0x8048404',\r\n     'file': 'prog.c',\r\n     'function': 'h',\r\n     'line': 4}\r\n\r\nObviously, if the node is below the massif threshold, the ``details`` field\r\nwill be None.\r\n\r\nPutting It All Together\r\n```````````````````````\r\nFrom this data structure, it's very easy to write a procedure that produce a\r\ndata table ready for Gnuplot consumption::\r\n\r\n    print(\"# valgrind --tool=massif\", data['desc'], data['cmd'])\r\n    print(\"# id\", \"time\", \"heap\", \"extra\", \"total\", \"stack\", sep='\\t')\r\n    for snapshot in data['snapshots']:\r\n        id = snapshot['id']\r\n        time = snapshot['time']\r\n        heap = snapshot['mem_heap']\r\n        extra = snapshot['mem_heap_extra']\r\n        total = heap + extra\r\n        stack = snapshot['mem_stack']\r\n        print('  '+str(id), time, heap, extra, total, stack, sep='\\t')\r\n\r\nThe output should looks like this::\r\n\r\n    # valgrind --tool=massif --time-unit=ms ./a.out\r\n    # id    time    heap    extra   total   stack\r\n      0     0       0       0       0       0\r\n      1     183     1000    8       1008    0\r\n      2     184     2000    16      2016    0\r\n      3     184     3000    24      3024    0\r\n      4     184     4000    32      4032    0\r\n      5     184     5000    40      5040    0\r\n      6     184     6000    48      6048    0\r\n      7     184     7000    56      7056    0\r\n      8     184     8000    64      8064    0\r\n      9     184     9000    72      9072    0\r\n\r\nTests\r\n-----\r\n\r\nTo run msparser's test suite::\r\n\r\n    $ python msparser_test.py --verbose\r\n\r\nThe current build status on travis: http://travis-ci.org/#!/MathieuTurcotte/msparser\r\n\r\nLicense\r\n-------\r\n\r\nThis code is free to use under the terms of the `MIT license <http://mturcotte.mit-license.org/>`_.",
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