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    "info": {
        "author": "Luca Sbardella",
        "author_email": "luca@quantmind.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Plugins",
            "Intended Audience :: Developers",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: JavaScript",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.1",
            "Programming Language :: Python :: 3.2",
            "Topic :: Office/Business :: Financial",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "A statistic package for python with enphasis on timeseries analysis.\nBuilt around numpy_, it provides several back-end timeseries classes including R-based objects via rpy2_.\nIt is shipped with a domain specific language for timeseries analysis\nand manipulation built on to of ply_.\nIt requires Python 2.6 and up, including Python 3 versions.\n\n--\n\n:Documentation: http://packages.python.org/dynts/\n:Dowloads: http://pypi.python.org/pypi/dynts/\n:Source: http://github.com/quantmind/dynts\n:Keywords: timeseries, quantitative, finance, statistics, numpy, R, web\n\n--\n\n\n.. contents::\n    :local:\n\n\nTimeserie Object\n========================\n\nTo create a timeseries object directly::\n\n\t>>> from dynts import timeseries\n\t>>> ts = timeseries('test')\n\t>>> ts.type\n\t'zoo'\n\t>>> ts.name\n\t'test'\n\t>>> ts\n\tTimeSeries:zoo:test\n\t>>> str(ts)\n\t'test'\n\n\nDSL\n=======\nAt the core of the library there is a Domain-Specific-Language (DSL_) dedicated\nto timeserie analysis and manipulation. DynTS makes timeserie manipulation easy and fun.\nThis is a simple multiplication::\n\t\n\t>>> import dynts\n\t>>> e = dynts.parse('2*GOOG')\n\t>>> e\n\t2.0 * goog\n\t>>> len(e)\n\t2\n\t>>> list(e)\n\t[2.0, goog]\n\t>>> ts = dynts.evaluate(e).unwind()\n\t>>> ts\n\tTimeSeries:zoo:2.0 * goog\n\t>>> len(ts)\n\t251\n\n\nRequirements\n=====================\nThere are few requirements that must be met:\n\n* python_ 2.6 up to python 3.2.\n* numpy_ version 1.5.1 or higher for arrays and matrices.\n* ply_ version 3.3 or higher, the building block of the DSL_.\n* ccy_ for date and currency manipulation.\n\nR backend\n===============================\nDepending on the back-end used, additional dependencies need to be met.\nFor example, there are back-ends depending on the following R packages:\n\n* rpy2_ if an R_ TimeSeries back-end is used (default).\n* zoo_ and PerformanceAnlytics_ for the ``zoo`` back-end (currently the default one)\n* timeSeries_ for the ``rmetrics`` back-end \n\nInstalling rpy2_ on Linux is straightforward, on windows it requires the\n`python for windows`__ extension library.\n\nOptional Requirements\n===============================\n\n* cython_ for performance. The library is not strictly dependent on cython, however its usage\n  is highly recommended. If available several python modules will be replaced by more efficient compiled C code.\n* xlwt_ to create spreadsheet from timeseries.\n* matplotlib_ for plotting.\n* djpcms_ for the ``web.views`` module.\n\n__ http://sourceforge.net/projects/pywin32/files/\n\n\n.. _running-tests:\n\nRunning Tests\n=================\nThere are three types of tests available:\n\n* ``regression`` for unit and regression tests.\n* ``profile`` for analysing performance of different backends and impact of cython_.\n* ``bench`` same as ``profile`` but geared towards speed rather than profiling.\n  \nFrom the distribution directory type::\n\t\n\tpython runtests.py\n\t\nThis will run by default the regression tests. To run a profile test\ntype::\n\n\tpython runtests.py -t profile <test-name>\n\t\nwhere ``<test-name>`` is the name of a profile test.\nTo obtain a list of available tests for each test type, run::\n\n\tpython runtests.py --list\n\nfor regression, or:: \n\n\tpython runtests.py -t profile --list\n\t\nfor profile, or::\n\n\tpython runtests.py -t bench --list\n\t\nfrom benchmarks.\n\t\nIf you access the internet behind a proxy server, pass the ``-p`` option, for example::\n\n\tpython runtests.py -p http://myproxy.com:80\n\nIt is needed since during tests some data is fetched from google finance.\n\nTo access coverage of tests you need to install the coverage_ package and run the tests using::\n\n\tcoverage run runtests.py\n\t\nand to check out the coverage report::\n\n\tcoverage report -m\n\t\n\nKudos\n===========\n* numpy_ developers.\n\n\nCommunity\n=================\nTrying to use an IRC channel **#dynts** on ``irc.freenode.net``\n(you can use the webchat at http://webchat.freenode.net/).\n\nIf you find a bug or would like to request a feature, please `submit an issue`__.\n\n__ http://github.com/quantmind/dynts/issues\n    \n.. _numpy: http://numpy.scipy.org/\n.. _ply: http://www.dabeaz.com/ply/\n.. _rpy2: http://rpy.sourceforge.net/rpy2.html\n.. _DSL: http://en.wikipedia.org/wiki/Domain-specific_language\n.. _R: http://www.r-project.org/\n.. _ccy: http://code.google.com/p/ccy/\n.. _zoo: http://cran.r-project.org/web/packages/zoo/index.html\n.. _PerformanceAnlytics: http://cran.r-project.org/web/packages/PerformanceAnalytics/index.html\n.. _timeSeries: http://cran.r-project.org/web/packages/timeSeries/index.html\n.. _Python: http://www.python.org/\n.. _xlwt: http://pypi.python.org/pypi/xlwt\n.. _matplotlib: http://matplotlib.sourceforge.net/\n.. _djpcms: http://djpcms.com\n.. _coverage: http://nedbatchelder.com/code/coverage/\n.. _cython: http://www.cython.org/",
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