The data is readings from two oscilloscope channels, one color coded blue, the other color coded orange. I would like to filter out the supply noise (all the junk scattered around 0) in both channels to be better able to detect spikes that might occur at and after t=0.
The data files consist of ~25k points. I'm using scipy. The initial peak is always centered roughly on t=0.
My first thought is that the noise is generally symmetric, so I could add the bottom half to the top half to flatten things out. However, the points are discrete, so I think I need to smooth/shift the data somehow to get cancellation. Since the pulses are not symmetric, they should be attenuated only slightly.
I also thought about taking a bunch of small averages the width of a typical peak, but I'm not sure which method will be the most robust.
I don't have much experience, and would like to see if there's an altogether better method I should implement.