I proudly present plotly-resampler, a wrapper around the plotly Figure object, which is able to visualize large sequences of time-series data.
I wrote a custom dash-component to optimize the callbacks (i.e. only send the to-be updated trace data to the client). I am rather sure that this component & the source code can still be improved!
Any feedback/contributions are welcome!
Main features:
Convenient to use:
just add the FigureResampler decorator around a plotly Figure constructor and call .show_dash()
allows all other plotly figure construction flexibility to be used!
Environment-independent
can be used in Jupyter, vscode-notebooks, Pycharm-notebooks, as application (on a server)
Interface for various downsampling algorithms:
ability to define your preferred sequence aggregation method
Could this be used with large amount of data that’s not strictly time-series? In my case I have data about frames in a video, so consecutive indices from 0 to a large number
plotly-resampler supports numeric-indices (i.e., not-time based indices (see the noisy-sine on the right-top subplot of the example gif)) so I suppose it will work!
This is really amazing work! I recently inherited an app the integrates Dash into a Flask app and then generates timeseries figures dynamically that could really benefit from something like this. @jonvdrdo I know you have an example of using this with dynamically generated figures, but do you think it could work with dash running on top of a custom Flask app?
Thank you for your kind words, means a lot!
Regarding the Flask app example; I think this should be possible, and it would also benefit others if we would also incorporate this into the examples folder of the repository.
I would suggest that you create a GitHub issue in which you post a (minimal) working flask example which you would like to have extended with plotly-resampler functionality!
It’s very generous of you to be willing to spend time working on all these examples! I really appreciate it.
The app I inherited is a bit odd compared with other example Flask Dash apps I’ve seen so I’ll try to come up with a minimal example using a more standard approach. Full disclosure I’m very new to the Flask and Dash libraries (and coding in general) so it may take me a bit to get an example ready, but I will do my best.
I’m really new to this community, but its really awesome to see what people like you are doing
This most likely means that our more efficient C-implementation of the LTTB data aggregation algorithm (i.e., lttbc) was not compiled successfully during installation of plotly-resampler.
(I suggest you take a closer look at the output during (re)installation)
As a result, this lttbc cannot be used at runtime, and we will fall back to (our somewhat) slower python implementation. So everything will work just fine, but a little slower.
what platform are you using, how do you install the package?
Can you compile other c-code on your platform (e.g., do you have gcc installed)?
Can you output the install output when you try to install it via pip? (i.e. pip install plotly-resampler==0.8.2)
If you can give me this infromation, I might be able to help you!
Yes, I can compile other C code and have gcc installed on my system.
I have configured my Pycharm to install any python package using pip. I get his output
I like to mention that I updated my plotly-resampler to 0.9.1 and I don’t have issues mentioned in the original post with LTTB package. Previously, I was using 0.8.2 version.
Thank you for building this and sharing with the community, 
