I constantly have to work around the slowness of matplotlib when creating animated sequences for my scientific work (even with the Agg back end)

I think meaningful is in the eye of the beholder. The library is designed such that the trace buffers are directly used as inputs to the WebGL2 drawing contexts to avoid unnecessary copying throughout the stack, which does make a difference when rendering on mobile and embedded devices with limited CPU but often having GPU resources available.

It depends on how much data you are planning on showing, but as you can see from the benchmarks its also more performant than other python charting libs for small data.

We also built this library for extreme customization with CSS/Tailwind support so rendering large amounts of data is an important but not the only advantage.

The good reason for worry about it is the same for data grid, list, scrolls and any other UI component that loads arbitrary data.

All UI, honestly, is only meaningful in what the screen size and our vision permit. END.

UNFORTUNATELY, you can't avoid that a user is writing "a___" and the source data has millions of things that start with `a` and all the others are dozens.

So, you can end with a massive influx of data, and sure the user see that big mess and wanna dial in, but in the meantime is nice if the UI not die in the process.

I can give you a prime example where you want to render all the data and quickly, oscilloscopes. It's quite common that you fetch traces with millions of samples, but then want to zoom into specific regions. There are lots of similar applications in experimental signal processing, where you want to have large data sets, but sampling easily will hide details that you want to see (unless you already know what exactly your data looks like).

Managing sampling is itself not totally trivial. Much easier from a DevX perspective to just have a library that can render all the datapoints.

Why settle for sampling when you can have the whole dataset?

The spiral pattern is an excellent example. "Sure it looks like this when you zoom out, but when you zoom in, you can see the finer structure of the points..."

don't you just love having a point cloud so dense that it's completely unreadable?

it seems a lot of people don't know about histograms...

There are some niche charting applications which are offloaded to FPGAs and even ASICs.

Ok, we won't convince you and we can move on.

Feel free to not use it, then.

You don't have to justify your decision to people here, literally just move on with your life and forget about it.

Yeah lots of things need to be improved this is the first release. Thanks for the tip

Thanks! XY already uses a similar pixel-aware approach

long line and area traces are reduced in Rust using M4 to produce viewportsized extrema, that is then refined as you zoom. Dense scatter plots use a fixed-size density grid plus a representative sample

See also Datoviz for 3D support in addition to fast, scalable, GPU-based 2D visualization: https://datoviz.org/ (I'm the main developer)

Yes, it’s significantly faster than existing Python charting libraries for real-time updates.

Instead of serializing and sending the full dataset as JSON, it sends compact typed binary buffers and only the screen-relevant data reducing payload size and browser-side work.

More detail here https://github.com/reflex-dev/xy#how-it-works

Thanks! Yes we are working on a Matplotlib compatible Api its very early but we hope to polish it up soon.

I have adjusted the title

Python libraries can have compiled artifacts that were written in any language. This is such a library.

Its a Python library with some rust in it, to speed up some of the calculations

its a python charting library

What do you mean by this?