Use TeaFiles.Py to create, read and write files holding time series data.

>>> tf = TeaFile.create("acme.tea", "Time Price Volume", "qdq", "ACME at NYSE", {"decimals": 2, "url": "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()

TeaFiles have a simple file layout, they contain raw binary data after a header that optionally holds metadata. APis abstract the header layout and make file creation as simple as

>>> tf = TeaFile.create("acme.tea", "Time Price Volume", "qdq", "ACME at NYSE", {"decimals": 2, "url": "acme.com" })

At the moment, APIs exist for

• C++,
• C#,
• Python (this one)
• other APis are planned and in draft phase (R, Octave/Matlab)

TeaFiles can be access from any operating system, like

• Linux / Unix
• Mac OS
• Windows

• programs http://www.discretelogics.com/doc/teafiles.py/examples.html
• interactive http://www.discretelogics.com/doc/teafiles.py/interactive.html
• examples.py (in the package source)

Find more about TeaFiles at http://www.discretelogics.com/teafiles

http://www.discretelogics.com/doc/teafiles.py/

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 for instance is a good fit. See the examples.py file in the package source for a Yahoo finance download function in about 30 lines.

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. There are numerous ways to improve this if necessary, but no current plans at discretelogics to do so. Use of other languages/APIs is recommended. If you intend to make this Python API faster contact us we should be able to identify points of potential speed enhancements.

The package is hosted on PyPi, so installation goes by

$ pip install teafiles

package source with examples.py at https://github.com/discretelogics/TeaFiles.Py

Run the unit tests from the package root by

$ python -m pytest .test

Package tested under CPython 2.7.

A Python 3.7 version is here: https://github.com/nikhgupta/TeaFiles.Py

This API brought to you by discretelogics, company specialicing in time series analysis and event processing. http://www.discretelogics.com

The current version is reasonably tested by doctests and some pytests. Better test coverage with unit tests (currently pytest is used) is desirable.

open points towards version 1.0

• pytest coverage
• cleaner test runs, cleanup test files

optional

• enhance performance after measuring it in python 3 (struct module could play a crucial role, so results might differ considerably)

This package is released under the MIT LICENSE.

Welcome at: [email protected]