Hi, one of the other devs here. As the poster below pointed out what you're missing is that in this case we know that an eigendecomposition or PCA will be useful. However if you're working on matrix decomposition algorithms like us, or if you're trying to design new forms of summary matrices because a covariance matrix isn't informative for your type of data then these types of visualizations are useful. We broadly work on designing new forms of matrix decomposition algorithms so it's very useful to look at the matrices and then try to determine what types of decompositions we want to do.
ok, different libraries have different use cases, the type of data we work with absolutely necessitates dynamic visualization. You wouldn't view a video with imshow would you?
Every time I've needed to scrub through something in time like that, dumping a ton of frames to disk using imshow has been good enough. Usually, the limiting factor is how quickly I can generate a single frame.
It's hard for me to imagine what you're doing that necessitates such fancy tools, but I'm definitely interested to learn! My failure of imagination is just that.
The example from the article with the subtitle "Large-scale calcium imaging dataset with corresponding behavior and down-stream analysis" is a good example. We have brain imaging video that is acquired simultaneously with behavioral video data. It is absolutely essential to view the raw video at 30-60Hz.
