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        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "Time Series Peristence\n======================\nThis Python package provides Time Series storage in flat files according to the **TeaFile** file format.\n\n\nIn Use\n======\n\n>>> tf = TeaFile.create(\"acme.tea\", \"Time Price Volume\", \"qdq\", \"ACME at NYSE\", {\"decimals\": 2, \"url\": \"www.acme.com\" })\n>>> tf.write(DateTime(2011, 3, 4,  9, 0), 45.11, 4500)\n>>> tf.write(DateTime(2011, 3, 4, 10, 0), 46.33, 1100)\n>>> tf.close()\n\n>>> tf = TeaFile.openread(\"acme.tea\")\n>>> tf.read()\nTPV(Time=2011-03-04 09:00:00:000, Price=45.11, Volume=4500)\n>>> tf.read()\nTPV(Time=2011-03-04 10:00:00:000, Price=46.33, Volume=1100)\n>>> tf.read()\n>>> tf.close()\n\n\nExchange Time Series between Apps / OS\n======================================\nYou can create, read and write TeaFiles with\n\n- R,\n- C++,\n- C# or\n- other applications\n\non\n\n- Linux, Unix,\n- Mac OS\n- Windows\n\n\nPython API Examples\n===================\n- programs        http://discretelogics.com/PythonAPI/examples.html\n- interactive     http://discretelogics.com/PythonAPI/interactive.html\n- examples.py (available in the package source)\n\n\nTeaFiles\n========\nTeaFiles are a very **simple**, yet highly **efficient**, way to store time series data \nproviding data exchange between programs written in C++, C# or applications like R, Octave, \nMatlab, running on Linux, Unix, Mac OS X or Windows.\n\n- **Binary** data composed from elementary data types **signed and unsigned integers, double and float** in IEEE 754 format is prefixed by a **header** holding a description of the item structure and the content.\n- Data can be directly accessed via **memory mapping**. \n- TeaFiles are **self describing**: Containing a description of the item structure they relieve opaqueness of straight binary files. Knowing that a file is a TeaFile is enough to access its data.\n\nlink to spec http://tbd\n\n\nScope of the Python API\n=======================\nThe Python API makes TeaFiles accessible everywhere. It just needs a python installation on any OS to inspect the description and data \nof a TeaFile:\n\n\n>>> # Show the decimals and displayname for all files in a folder:\n...\n>>> def showdecimals():\n    ...     for filename in os.listdir('.'):\n    ...         with TeaFile.openread(filename) as tf:\n    ...             nvs = tf.description.namevalues\n    ...             print('{} {} {}'.format(filename, nvs.get('Decimals'), nvs.get('DisplayName')))\n    ... \n    >>> showdecimals()\n    AA.day.tea 2 Alcoa, Inc.\n    AA.tick.tea 2 Alcoa, Inc.\n    AXP.day.tea 2 American Express Co.\n    ...\n\nData download from web services is also a good fit. See the examples.py file in the package source for a Yahoo finance download function in about 30 lines.\n\n\nLimitations\n===========\nWhen it comes to high performance processing of very large time series files, this API is currently not as fast as the C++ and C# APIs (Numbers coming soon on http://tbd). There are numerous ways to improve this if necessary, but no current plans at discretelogics to do so. Using the other languages/APIs is recommended. If you wish the Python API to be faster or want to work on that contact us.\n\n\nInstallation\n============\n\n**$ pip install teafiles**\n\npackage source with examples.py at http://bitbucket.org/discretelogics/teafiles.py\n\nTests\n=====\nRun the unit tests from the package root by\n\n$ python -m pytest .\\test\n\n\nPython 2.7 / 3.2\n================\nPackage tested under CPython 2.7.\nPython 3.2 planned\n\n\nAuthor\n======\nThis API brought to you by discretelogics, company specialicing in time series analysis and event processing.\nhttp://tbd\n\n\nVersion 0.7\n===========\nThe current version is reasonably tested by doctests and some pytests. Better test coverage with unit tests (currently pytest is used) \nis desirable.\n\ntbd towards version 1.0\n    - enhance pytest coverage\n    - consider api feedack\n    - cleaner test runs, cleanup test files\n\noptional\n    - enhance performance after measuring it in python 3 (maybe struct module plays a minor role there)\n\n\nLicense\n=======\nThis package is released under the GNU GENERAL PUBLIC LICENSE, see License.txt.\n\n\nFeedback\n========\nWelcome at: pythonapi@discretelogics.com",
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