Roadmap

This page describes some of the limitations of the current implementation, and items in need of discussion.

• Support for all sample fields:

  The current implementation only supports reading xpos, ypos, and pupil size sample fields. We should add support for reading other sample fields, such as head position, velocity, etc. This shouldn’t be too difficult, but we will want to add a way to specify which fields to read in the API.

• Transfer this library to an organization:

  I created this library under my personal GitHub account, but if it gains traction we can create an organization for it, so that other developers can have push access to the repository, access to the CI’s etc.

• EDF Message string representation:

  The current implementation represents the messages as byte strings (i.e. b'stimulus_presentation'). This was inherited from the original implementation in pyeparse. I’m not sure whether this was to stay compatible with python 2, or if memory was a concern. We could just represent them as regular Python strings, but I will wait to see if anyone provides feedback on this before changing it.

• Use of structured Arrays:

  The current implementation stores data in structured Numpy arrays, so that for example you can do edf["discrete"]["messages"]["msg"] to get an array of the message strings. This was inherited from the original pyeparse implementation. Structured arrays are nice but add some complexity (and most users probably aren’t familiar with them?). I’m not sure if we should keep them, or just use dictionaries instead. I will wait to see if anyone provides feedback on this.

• Support for other file formats:

  If this library gains traction, we could add support for reading ASCII format files. To do this we should port the read_raw_eyelink code from MNE-Python, (and remove it from MNE-Python, such that MNE calls our function to read ASCII data).