During my first week in GSoC, I started to build a simple script that utilized the tools I've used to measure functional connectivity in my research. The challenge in building this script is that its goal is to utilize example data that MNE-Python uses in their examples and tutorials. In my first attempts, it was clear that the structure of the sample data is different from the data I've used for my research (in terms of the number of sensors, how bad channels are disregarded, and the use of data in both left and right hemispheres). I left off last week deciding that I needed to use a different, more visual based tool to understand the sample data so that I can manipulate the structure to fit into my connectivity model.
I've received lots of feedback in the first draft of this script and I see that over the next week, in addition to changing the example data structure to fit my model, I will need to change variable and function names to be aligned with MNE-Python's coding format. Outside of preferred naming format, I am learning that my scripts that will serve as tutorials for MNE-Connectivity will need to give the minimal amount of information to the users for them to navigate the tools, and that functions not important to the use should be hidden in the backend as to not overstimulate the user.