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        "author": "Charlie Taylor",
        "author_email": "cet@appliedpython.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "\r\n\r\n.. image:: https://travis-ci.org/sonofeft/M_Pool.svg?branch=master\r\n    :target: https://travis-ci.org/sonofeft/M_Pool\r\n\r\n.. image:: https://img.shields.io/pypi/v/M_Pool.svg\r\n    :target: https://pypi.python.org/pypi/m_pool\r\n        \r\n.. image:: https://img.shields.io/pypi/pyversions/M_Pool.svg\r\n    :target: https://wiki.python.org/moin/Python2orPython3\r\n\r\n.. image:: https://img.shields.io/pypi/l/M_Pool.svg\r\n    :target: https://pypi.python.org/pypi/m_pool\r\n\r\n\r\nA Wrapper For Numpy Arrays\r\n==========================\r\n\r\nProvides Named Axes, Interpolation, Iteration, Disk Persistence And Numerical Calcs\r\n\r\n\r\nSee the Code at: `<https://github.com/sonofeft/M_Pool>`_\r\n\r\nSee the Docs at: `<http://m_pool.readthedocs.org/en/latest/>`_\r\n\r\nSee PyPI page at:`<https://pypi.python.org/pypi/m_pool>`_\r\n\r\n\r\n\r\nM_Pool wraps multidimensional numpy arrays to provide the following features::\r\n\r\n    #. MatrixPool objects contain related Axis and Matrix objects\r\n        - MP = MatrixPool(name='N2O4_MMH')\r\n        \r\n    #. Axis objects are added by name and interpolation transform (used to linearize interpolation)\r\n        - epsAxis = Axis({'name':'eps', 'valueL':[10., 20., 30., 40., 50.], 'units':'', 'transform':'log10'})\r\n        - pcAxis = Axis({'name':'pc', 'valueL':[100.,200.,300,400], 'units':'psia', 'transform':'log10'})\r\n        - mrAxis = Axis({'name':'mr', 'valueL':[1,2,3], 'units':'', 'transform':''})\r\n        \r\n    #. Matrix objects added by name \r\n        - M = MP.add_matrix( name='cea_isp', units='sec', axisNameL=['eps','pc','mr'] )\r\n        \r\n    #. Find interpolated minimum or maximum\r\n        - interpD, max_val = M.solve_interp_max( order=3, method='TNC', tol=1.0E-8)\r\n            - where interpD and max_val look something like:\r\n            - interpD = {'pc': 225.00641803120988, 'eps': 34.991495018803455, 'mr': 1.7499612975876655}\r\n            - max_val = -0.000155216246295\r\n    \r\n    #. Disk-based persistence\r\n        - Save to pickle or hdf5 file\r\n            - MP.save_to_pickle() # saves MP to \"N2O4_MMH_matrix.pool\"\r\n            \r\n    #. Built-in statistics (standard deviation, median, mean/average, sum, minimum, maximum\r\n        - M.get_range()\r\n        - M.get_ave()\r\n        - M.get_mean()\r\n        - M.get_std()\r\n        - M.get_median()\r\n    \r\n    #. Interpolation on axes with named values\r\n        - interp_val = M.interp(order=2, pc=100, eps=20, mr=2.0)\r\n        - Uses transformed axes to help linearize interpolation\r\n        \r\n    #. Iterate over matrix\r\n        - for indeces,D,val in M.full_iter_items():\r\n            - gives something like:\r\n            - (0, 0, 0) {'pc': 100.0, 'eps': 10.0, 'mr': 1.0} 111.0\r\n            - (0, 0, 1) {'pc': 100.0, 'eps': 10.0, 'mr': 2.0} 112.0\r\n            - (0, 0, 2) {'pc': 100.0, 'eps': 10.0, 'mr': 3.0} 113.0\r\n            - ...\r\n\r\n",
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