Do you have any numbers for the rough number of datapoints that can be handled? I'm curious if this enables plotting many millions of datapoints in a scatterplot for example.
Fastplotlib: GPU-accelerated, fast, and interactive plotting library
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Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#12Every two weeks or so I peruse github looking for something like this and I have to say this looks really promising. In statistical genetics we make really big scatterplots called Manhattan plots https://en.wikipedia.org/wiki/Manhattan_plot and we have to use all this highly specialized software to visualize at different scales (for a sense of what this looks like: https://my.locuszoom.org/gwas/236887/). Excited to try this out
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#13Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
I have to agree with your point about EDA. The library is neat, but even the example of covariance matrix animation is a bit contrived.
Every pixel has a covariance with every other pixel, so sliding though the rows of the covariance matrix generates as many faces on the right as there are pixels in a photograph of a face. However the pixels that strongly co-vary will produce very similar right side "face" pictures. To get a sense of how many different behaviours there are one would look for eigenvectors of this covariance matrix. And then 10 or so static eigenvectors of the covariance matrix (eigenfaces [1]) would be much more informative than thousands of animated faces displayed in the example.
Some times a big interactive visualisation can be a sign of not having a concrete goal or not knowing how to properly summarise. After all that's the purpose of a figure - to highlight insights, not to look for ways to display the entire dataset. And pictures that try to display the whole dataset end up shifting the job of exploratory analysis to a visual space and leave it for somebody else.
Thou of course there are exceptions.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#14Is it possible to put the interactive plots on your website? Or is this a Jupyter notebook only tool.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#15One of the big bottlenecks of plotting libraries is simply the time it takes to import the library. I’ve seen matplotlib being slow to import, and in Julia they even have a “time to first plot” metric. I’d be curious to see how this library compares.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#16"Fast" is a bold claim, given the complete lack of benchmarks and the fact that it's written entirely in Python...
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#17[flagged]
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#18Very cool to see imgui empowering so many different things.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#19[flagged]
Please stop.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#20Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
I'd be curious to hear more about your EDA workflow. What I want for EDA is a tool that let's me quickly toggle between common views of the dataset. I run through the same analysis over and over again, I don't want to type the same commands repeatedly. I have my own heuristics for which views I want, and I want a platform that lets me write functions that express those heuristics. I want to build the inteligence into…
At least I usually do prefer to do the EDA plotting by writing and editing code. This is a lot more flexible. It's relatively rare to need other interactivity than zooming and panning.
The differing approaches probably can be seen in some API choices, although the fastplotlib API is a lot more ergonomic than many others. Having to index the figure or prefixing plots with add_ are minor things, and probably preferable for application development, but for fast-iteration EDA they will start to irritate fast. The "mlab" API of matplotlib violates all sorts of software development principles, but it's very convenient for exploratory use.
Matplotlib's performance, especially with interaction and animation, and clunky interaction APIs are definite pain points, and a faster and better interaction supporting library for EDA would be very welcome. Something like a mlab-type wrapper would probably be easy to implement for fastplotlib.
And to bikeshed a bit, I don't love the default black background. It's against usual conventions, difficult for publication and a bit harder to read when used to white.