That was actually fantastic. The professor is quite goofy, but he really goes over everything from first principles and goes through a real example - constructing a solution without any cheating :))

I was a bit bummed out there weren't a lot of Compressed Sensing libraries around, but it seems you just need a "convex optimization" routine (aka linear programming). And these seem to exist in every language

I'll try to play around with this! Thank you so much

It's a fascinating topic and i'm still trying to get my head around some of the concepts.

Have fun on your discovery journey.

Are there any gotchas you've come across?

From the video tutorial is seems relatively straightforward. I guess the basis selection is a fundamental issue that will be problem-specific.

I will have to try it with some concrete examples. The first question I have is, will it still work if you have a lot of high frequency noise? In the cases I'm thinking either there is measurement noise or just other jitter. So while the lower frequencies are sparse but I guess the higher frequencies not so much. I can't bandpass the data b/c it's got lots of holes or it's irregularly spaced.

Maybe it'll still work though!