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