{ "info": { "author": "Jose M. Dana", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: MacOS", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.1", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Topic :: Software Development", "Topic :: Utilities" ], "description": "memprof\n=======\n\n``memprof`` is a memory profiler for Python.\n\nIt logs and plots the memory usage of all the variables during the\nexecution of the decorated methods.\n\nInstallation\n------------\n\nStable\n~~~~~~\n\n::\n\n sudo pip install --upgrade memprof\n\nor ::\n\n sudo easy_install --upgrade memprof\n\nor (Debian testing/unstable) ::\n\n sudo apt-get install python-memprof\n \n\nDevelopment\n~~~~~~~~~~~\n\n::\n\n git clone git://github.com/jmdana/memprof.git\n cd memprof\n sudo python setup.py install\n\nor ::\n\n sudo pip install git+https://github.com/jmdana/memprof\n\nUsage\n-----\n\nUsing ``memprof`` is as easy as adding a decorator to the methods that\nyou want to profile: ::\n\n @memprof\n def foo():\n\nAnd importing the module just by including the line below at the\nbeginning of your Python file: ::\n\n from memprof import memprof\n\nNow you can run as usual and logfiles with the names of your methods\nwill be created (e.g. ``foo.log``).\n\nGenerating plots\n~~~~~~~~~~~~~~~~\n\nThe logfiles are not very interesting so you might prefer to use the\n``-p``/``--plot`` flag: ::\n\n python -m memprof --plot \n python -m memprof -p \n\nWhich, in addition to the logfile, will generate a plot (``foo.png``):\n\n.. figure:: examples/foo.png\n :alt: Example plot\n\nThe grey bar indicates that the ``foo`` method wasn't running at that\npoint.\n\nThe flag may also be passed as an argument to the decorator: ::\n\n @memprof(plot = True)\n\nPlease keep in mind that the former takes precedence over the latter.\n\nAdjusting the threshold\n~~~~~~~~~~~~~~~~~~~~~~~\n\nYou may also want to specify a ``threshold``. The value will be the\nminimum size for a variable to appear in the plot (but it will always\nappear in the logfile!). The default value is 1048576 (1 MB) but you can\nspecify a different ``threshold`` (in bytes) with the\n``-t``/``--threshold`` flag: ::\n\n python -m memprof --threshold 1024 \n python -m memprof -t 1024 \n\nThe ``threshold`` may also be passed as an argument to the decorator: ::\n\n @memprof(threshold = 1024)\n\nPlease keep in mind that the former takes precedence over the latter.\n\nmp\\_plot\n~~~~~~~~\n\nIf, after running ``memprof``, you want to change the threshold and\ngenerate a new plot (or you forgot to use the ``-p``/``--plot`` flag\nwith ``memprof``), you don't have to re-run! Just call the command: ::\n\n mp_plot [-h] [-t THRESHOLD] logfiles [logfiles ...]\n\nand generate the plots again doing something like: ::\n\n mp_plot -t 128 logfile1.log logfile2.log\n\nor: ::\n\n mp_plot -t 1024 *.log\n\netc.\n\nContact\n-------\n\nMailing list\n~~~~~~~~~~~~\n\n- Subscribe by sending a message to memprof+subscribe@googlegroups.com\n- Once subscribed, you can send emails to memprof@googlegroups.com\n- List archives at http://groups.google.com/group/memprof\n\n--------------\n\nCopyright 2013-2019, Jose M. 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