If the precise values of these floats is important in your domain (which it very well may be), a combination of approaches would probably be good! Would love to hear how well this approach works for you guys. Keep me updated :)
If the precise values of these floats is important in your domain (which it very well may be), a combination of approaches would probably be good! Would love to hear how well this approach works for you guys. Keep me updated :)
Obviously any time you're working with floating-point sample data the precise values of floats will almost always not be bit-accurate against what your model predicts (sometimes even if that model is a previous run of the same system with the same inputs as in this case); it's about defining an acceptable deviation. I guess what I'm saying is that for audio software, a peak-peak error of 0.1 equates to a signal at -20 dBFS (ref DBFS@1.0) (which of course is quite a large amount of error for an audio signal), so perhaps using higher-resolution graphs would be a good idea.
(Has anyone made a tool to diff sixels yet? /s)
For this, just thinking about sound, I wonder if you could invert the reference wave form and add it to the test to see how well it cancels? Then instead of just knowing there was a diff, you could get measurements of the degree of the diff.