December 2013
Volume 13, Issue 15
Free
OSA Fall Vision Meeting Abstract  |   October 2013
pyarbus: a Python library for eye tracking data analysis
Journal of Vision October 2013, Vol.13, P19. doi:10.1167/13.15.54
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Paul Ivanov; pyarbus: a Python library for eye tracking data analysis. Journal of Vision 2013;13(15):P19. doi: 10.1167/13.15.54.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

pyarbus is a library of tools for analysis and visualization of time-series data from eye tracking experiments. pyarbus aims to be:

  • - a Rosetta stone for different eyetracker data formats: one objective for pyarbus is to create a set of abstractions that holds the data, regardless of its original format, and to provide a unified interface to the data across different manufacturers. File formats from SR Research (EyeLink) and SMI (iViewX and RED) are currently supported, or the user can create new data containers directly by passing in arrays.

  • - temporally aware: data containers in pyarbus can be indexed with time points or time slices. Thus, indexing two different traces, perhaps collected at different rates, can be done in a unified manner, without having to remember to look up and verify what the appropriate sampling rate is, or having to hardcode assumptions into one's code.

  • - collaboration-oriented: pyarbus integrate well with the IPython Notebook (http://ipython.org/notebook.html), a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document. The lead developer of pyarbus is also a core developer of IPython, and new interactive widgets for IPython will be developed with pyarbus as a testbed.

  • - extensible: pyarbus itself is based on the nitime project (http://nipy.org/nitime/), which contains a core of numerical algorithms for time-series analysis both in the time and spectral domains.

  • - test-driven: all new features proposed for inclusion in pyarbus must come with tests that verify their functional correctness.

  • https://github.com/ivanov/pyarbus

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×