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    "info": {
        "author": "Keith Goodman",
        "author_email": "labeled-array@googlegroups.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
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        "description": "The main class of the la package is a labeled array, larry. A larry consists\r\nof data and labels. The data is stored as a NumPy array and the labels as a\r\nlist of lists (one list per dimension).\r\n\r\nHere's larry in schematic form::\r\n\r\n                         date1    date2    date3\r\n                'AAPL'   209.19   207.87   210.11\r\n            y = 'IBM'    129.03   130.39   130.55\r\n                'DELL'    14.82    15.11    14.94\r\n                \r\nThe larry above is stored internally as a `Numpy <http://scipy.org>`_\r\narray and a list of lists::\r\n\r\n        y.label = [['AAPL', 'IBM', 'DELL'], [date1, date2, date3]]\r\n        y.x = np.array([[209.19, 207.87, 210.11],\r\n                        [129.03, 130.39, 130.55],\r\n                        [ 14.82,  15.11,  14.94]])               \r\n    \r\nA larry can have any number of dimensions except zero. Here, for example, is\r\none way to create a one-dimensional larry::\r\n\r\n    >>> import la\r\n    >>> y = la.larry([1, 2, 3])\r\n    \r\nIn the statement above the list is converted to a Numpy array and the labels\r\ndefault to ``range(n)``, where *n* in this case is 3.\r\n    \r\nlarry has built-in methods such as **ranking, merge, shuffle, move_sum,\r\nzscore, demean, lag** as well as typical Numpy methods like **sum, max, std,\r\nsign, clip**. NaNs are treated as missing data.\r\n    \r\nAlignment by label is automatic when you add (or subtract, multiply, divide)\r\ntwo larrys. You can also specify the join method (inner, outer, left, right)\r\nfor binary operations on two larrys with unaligned labels.\r\n    \r\nYou can archive larrys in HDF5 format using **save** and **load** or using a\r\ndictionary-like interface::\r\n\r\n    >>> io = la.IO('/tmp/dataset.hdf5')\r\n    >>> io['y'] = y   # <--- save\r\n    >>> z = io['y']   # <--- load\r\n    >>> del io['y']   # <--- delete from archive\r\n       \r\nFor the most part larry acts like a Numpy array. And, whenever you want,\r\nyou have direct access to the Numpy array that holds your data. For\r\nexample if you have a function, *myfunc*, that works on Numpy arrays and\r\ndoesn't change the shape or ordering of the array, then you can use it on a\r\nlarry, *y*, like this::\r\n\r\n                           y.x = myfunc(y.x)\r\n    \r\nlarry adds the convenience of labels, provides many built-in methods, and\r\nlet's you use your existing array functions.       \r\n\r\nLicense\r\n=======\r\n\r\nThe ``la`` package is distributed under a Simplified BSD license. Parts of\r\nNumPy, Scipy, and numpydoc, which all have BSD licenses, are included in\r\n``la``. Parts of matplotlib are also included. See the LICENSE file, which\r\nis distributed with the ``la`` package, for details.\r\n\r\nInstall\r\n=======\r\n\r\nRequirements:\r\n\r\n======================== ====================================================\r\nla                       Python, NumPy 1.5.1-1.6.1, Bottleneck 0.5.0\r\nUnit tests               nose\r\n======================== ====================================================\r\n\r\nOptional:\r\n\r\n============================= ================================================\r\nArchive larrys in HDF5        h5py, HDF 1.8\r\nCompile for speed boost       gcc or MinGW\r\nlar.ranking(norm='gaussian')  SciPy 0.8.0, 0.9.0\r\n============================= ================================================\r\n\r\nYou can download the `latest version of la <http://pypi.python.org/pypi/la>`_\r\nand `Bottleneck <http://pypi.python.org/pypi/Bottleneck>`_ from the Python\r\nPackage Index.\r\n            \r\n**GNU/Linux, Mac OS X et al.**\r\n\r\nTo install ``la``::\r\n\r\n    $ python setup.py build\r\n    $ sudo python setup.py install\r\n    \r\nOr, if you wish to specify where ``la`` is installed, for example inside\r\n``/usr/local``::\r\n\r\n    $ python setup.py build\r\n    $ sudo python setup.py install --prefix=/usr/local\r\n\r\nAlternatively, you can use the makefile to install ``la`` inplace::\r\n\r\n    $ make all\r\n\r\n**Windows**\r\n\r\nIn order to (optionally) compile the C code in the ``la`` package you need a\r\nWindows version of the gcc compiler. MinGW (Minimalist GNU for Windows)\r\ncontains gcc and has been used to successfully compile ``la`` on Windows.\r\n\r\nInstall MinGW and add it to your system path. Then install ``la`` with the\r\ncommands::\r\n\r\n    python setup.py build --compiler=mingw32\r\n    python setup.py install\r\n\r\n**Post install**\r\n\r\nAfter you have installed ``la``, run the suite of unit tests::\r\n\r\n    >>> import la\r\n    >>> la.test()\r\n    <snip>\r\n    Ran 3063 tests in 1.408s\r\n    OK\r\n    <nose.result.TextTestResult run=3063 errors=0 failures=0>\r\n    \r\nThe ``la`` package contains C extensions that speed up common alignment\r\noperations such as adding two unaligned larrys. If the C extensions don't\r\ncompile when you build ``la`` then there's an automatic fallback to python\r\nversions of the functions. To see whether you are using the C functions or the\r\nPython functions::\r\n\r\n    >>> la.info()\r\n    la version      0.6.0           \r\n    la file         /usr/local/lib/python2.6/dist-packages/la/__init__.pyc\r\n    NumPy           1.6.0  \r\n    Bottleneck      0.5.0\r\n    HDF5 archiving  Available (h5py 2.0.0)      \r\n    listmap         Faster C version\r\n    listmap_fill    Faster C version    \r\n    \r\nSince ``la`` can run in a pure python mode, you can use ``la`` by just saving\r\nit and making sure that python can find it.    \r\n    \r\nURLs\r\n====\r\n\r\n===============   ========================================================\r\n download          http://pypi.python.org/pypi/la\r\n docs              http://berkeleyanalytics.com/la\r\n code              https://github.com/kwgoodman/la\r\n mailing list      http://groups.google.com/group/labeled-array\r\n issue tracker     https://github.com/kwgoodman/la/issues\r\n===============   ========================================================",
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