Plotly resampler is an amazing tool for handler larger data plots (potential with millions of data points. However upon configuring plots, it fails to trigger in Streamlit and doesn’t compute/update the plot and simply zooms in on the existing rendered graph. Any idea how this might be achieved?
Though it’s a hacky method (running dash in another process), this does seem to work plotly-resampler/streamlit_app.py at main · predict-idlab/plotly-resampler · GitHub
I needed to wrap some code in
if __name__ == __main__ to get it to work:
# 0. Create a noisy sine wave
import numpy as np
import plotly.graph_objects as go
from plotly_resampler import FigureResampler
x = np.arange(1_000_000)
noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000
### 1. Use FigureResampler
fig = FigureResampler(default_n_shown_samples=2_000)
fig.add_trace(go.Scattergl(name="noisy sine", showlegend=True), hf_x=x, hf_y=noisy_sin)
if __name__ == "__main__":
### 2. Run the visualization (which is a dash app) in a (sub)process on a certain port
# Note: starting a process allows executing code after `.show_dash` is called
from multiprocessing import Process
port = 9022
proc = Process(
target=fig.show_dash, kwargs=dict(mode="external", port=port)
# Deleting the lines below this and running this file will result in a classic running dash app
# Note: for just a dash app it is not even necessary to execute .show_dash in a (sub)process
### 3. Add as iframe component to streamlit
import streamlit.components.v1 as components
This would be a great component if someone wanted to build a component to support this natively!
Wow! Cool, thanks for sharing Will try to impliment this!
This looks super useful! Actually I think this should be an integrated feature of the plotly library. Yet, if someone could make plotly-resampler fully work with streamlit it would be great! The hacky solution proposed here only gives me black lines and is also a bit laggy.