Metadata-Version: 1.0
Name: teafiles
Version: 0.71 dev
Summary: Time Series storage in flat files.
Home-page: http://discretelogics.com
Author: discretelogics
Author-email: pythonapi@discretelogics.com
License: Creative Commons Attribution-Noncommercial-Share Alike license
Download-URL: http://pypi.python.org/packages/source/t/teafiles/teafiles-0.7dev.zip
Description: 
        
        Time Series Peristence
        ======================
        This Python package provides Time Series storage in flat files according to the **TeaFile** file format.
        
        
        In Use
        ======
        
        >>> tf = TeaFile.create("acme.tea", "Time Price Volume", "qdq", "ACME at NYSE", {"decimals": 2, "url": "www.acme.com" })
        >>> tf.write(DateTime(2011, 3, 4,  9, 0), 45.11, 4500)
        >>> tf.write(DateTime(2011, 3, 4, 10, 0), 46.33, 1100)
        >>> tf.close()
        
        >>> tf = TeaFile.openread("acme.tea")
        >>> tf.read()
        TPV(Time=2011-03-04 09:00:00:000, Price=45.11, Volume=4500)
        >>> tf.read()
        TPV(Time=2011-03-04 10:00:00:000, Price=46.33, Volume=1100)
        >>> tf.read()
        >>> tf.close()
        
        
        Exchange Time Series between Apps / OS
        ======================================
        You can create, read and write TeaFiles with
        
        - R,
        - C++,
        - C# or
        - other applications
        
        on
        
        - Linux, Unix,
        - Mac OS
        - Windows
        
        
        Python API Examples
        ===================
        - programs        http://discretelogics.com/PythonAPI/examples.html
        - interactive     http://discretelogics.com/PythonAPI/interactive.html
        - examples.py (available in the package source)
        
        
        TeaFiles
        ========
        TeaFiles are a very **simple**, yet highly **efficient**, way to store time series data 
        providing data exchange between programs written in C++, C# or applications like R, Octave, 
        Matlab, running on Linux, Unix, Mac OS X or Windows.
        
        - **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.
        - Data can be directly accessed via **memory mapping**. 
        - 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.
        
        link to spec http://tbd
        
        
        Scope of the Python API
        =======================
        The Python API makes TeaFiles accessible everywhere. It just needs a python installation on any OS to inspect the description and data 
        of a TeaFile:
        
        
        >>> # Show the decimals and displayname for all files in a folder:
        ...
        >>> def showdecimals():
            ...     for filename in os.listdir('.'):
            ...         with TeaFile.openread(filename) as tf:
            ...             nvs = tf.description.namevalues
            ...             print('{} {} {}'.format(filename, nvs.get('Decimals'), nvs.get('DisplayName')))
            ... 
            >>> showdecimals()
            AA.day.tea 2 Alcoa, Inc.
            AA.tick.tea 2 Alcoa, Inc.
            AXP.day.tea 2 American Express Co.
            ...
        
        Data 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.
        
        
        Limitations
        ===========
        When 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.
        
        
        Installation
        ============
        
        **$ pip install teafiles**
        
        package source with examples.py at http://bitbucket.org/discretelogics/teafiles.py
        
        Tests
        =====
        Run the unit tests from the package root by
        
        $ python -m pytest .\test
        
        
        Python 2.7 / 3.2
        ================
        Package tested under CPython 2.7.
        Python 3.2 planned
        
        
        Author
        ======
        This API brought to you by discretelogics, company specialicing in time series analysis and event processing.
        http://tbd
        
        
        Version 0.7
        ===========
        The current version is reasonably tested by doctests and some pytests. Better test coverage with unit tests (currently pytest is used) 
        is desirable.
        
        tbd towards version 1.0
            - enhance pytest coverage
            - consider api feedack
            - cleaner test runs, cleanup test files
        
        optional
            - enhance performance after measuring it in python 3 (maybe struct module plays a minor role there)
        
        
        License
        =======
        This package is released under the GNU GENERAL PUBLIC LICENSE, see License.txt.
        
        
        Feedback
        ========
        Welcome at: pythonapi@discretelogics.com
        
Keywords: timeseries analysis event processing teatime simulation finance
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
