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        "author": "Chris Ward",
        "author_email": "cward@redhat.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 2 :: Only",
            "Topic :: Database",
            "Topic :: Office/Business",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Utilities"
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        "description": ".. image:: metrique/server/static/img/metrique_logo.png\n\nMetrique\n========\n\nPython/MongoDB Information Platform and Data Warehouse\n\n*Metrique help bring data into an intuitive, indexable \ndata object collection that supports quick snapshotting, \nadvanced ad-hoc querying, including (mongodb) aggregations\nand mapreduce, along with python, ipython, pandas,\nnumpy, matplotlib, and so on, is fully integrated \nwith the scientific python computing stack. I hope\nso anyway. :)*\n\n**Author:** \"Chris Ward\" <cward@redhat.com>\n\n**Sources:** https://github.com/drpoovilleorg/metrique\n\n\nInstallation\n------------\n\nYou must first install MongoDB. Then, to continue, \nmake sure it's started.\n\n\n**Metrique**\n(suggested) Install virtualenv and create a new virtual \nenvironment for metrique. Activate it. \n\nInstall metrique::\n\n    python-pip install metrique -r requirements.txt\n\n.. note::\n     Make sure you have gcc and python-devel libraries installed\n\n.. note::\n     If you see 'Connection reset by peer' error, try option: --use-mirrors\n\n.. note::\n     If you see any other error, Google.\n\nYou should now be ready to go. \n\nRun metrique-server-config.py if you changed any defaults.\n\nTo start metrique, run::\n    \n    $[/metrique/server/bin] metrique-server start [2|1|0] [1|0]\n\nWhere argv are debug on+/on/off and async on/off respectively.\n\nIt's suggested to run :mod:metrique-server-setup after install\nas well, especially if you changed any default values of your\nmongo or metrique servers, they're hosted on a different\nip than `localhost`. \n\n\n**Client**\nIf the metrique server is running on anything other than \n`http://127.0.0.1`, run `metrique-client-setup`.\n\nThen,  launch a python shell. We suggest ipython notebook. \n\nAs of this time, :mod:cubes can be found in global\nmetrique namespace or local to the running user. \n\nDefault: `~/.metrique/cubes`\n\nTo quickly make those cubes available in sys.path::\n\n    IN  [] from metrique.client.cubes import set_cube_path\n    IN  [] set_cube_path()  # defaults to '~/.metrique/cubes'\n\nThen, to load a cube for extraction, query or administration,\nimport::\n\n    IN  [] from git_repo.gitrepo import Commit\n    IN  [] g = Commit(config_file=None, uri=None)\n\nPing the server to ensure your connected. If all \nis well, metriqe server should pong your ping!::\n\n    IN  [] g.ping()\n    OUT [] pong!\n\nTry running an example ::mod:git_commit etl job, for example::\n\n    IN  [4] g.extract(\"git_commit\")\n\nThen, analyse away::\n\n    IN  [5] q = c.query.fetch('git_commit', 'author, committer_ts') \n    IN  [6] q.groupby(['author']).size().plot(kind='barh')\n    OUT [6] <matplotlib.axes.AxesSubplot at 0x6f77ad0>",
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        "keywords": "data,mining,information,mongo,etl,analysis,search,query",
        "license": "GPLv3",
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