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.
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?
We love imgui! Big thanks to the imgui devs, and Pascal Thomet who maintains the python bindings for imgui-bundle, and https://github.com/panxinmiao who made an Imgui Renderer for wgpu-py!
> Using the strengths of the human vision system to get a rough idea of what the typical data looks like and the frequency and character of outliers isn't dumping the job of exploratory data analysis onto the reader, it's how the job actually gets done in the first place.
Yup this is a good summary of the intent, we also have to remember that the eigenfaces dataset is a very clean/toy data example. Real datasets never look this good, and just going straight to an eigendecomp or PCA isn't informative without first taking a look at things. Often you may want to do something other than an eigendecomp or PCA, get an idea of your data first and then think about what to do to it.
Edit: the point of that example was to show that visually we can judge what the covariance matrix is producing in the "image space". Sometimes a covariance matrix isn't even the right type of statistic to compute from your data and interactively looking at your data in different ways can help.
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.