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Comment by radarsat1 | Hacker News Reader

Does anyone know of a good non-blocking plotting library for Python?

I find that for some applications matplotlib really slows down my loops, but there are times that I really need to visualize the in-progress computations. While some other libraries might be faster, what I really want is simply to send instructions and data to a separate process that is performing the plotting. I'd like it to completely render a frame and simply skip instructions for following frames until it's done rendering, avoid buffering up every single frame that is sent to it, and have it plot the next available frame when it's done. I have yet to see a library that functions this way. Basically I want to be able to easily visualize an on-going computation without blocking it just for the rendering of temporary results.

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You should check out https://github.com/IGITUGraz/live-plotter (I am the author of the package)

It is designed exactly for this use-case -- visualizing results of an ongoing simulation. It does best effort plotting, but doesn't block computation for plotting. I would be happy to receive feedback on the package.

Cool, thanks.

What I did when I had a similar problem was to have a completely separate visualization program and pass data messages to it (using Celery and RabbitMQ). But that was in a setting where I was already using Celery for other tasks. Setting up a message queue for just visualization feels a bit over-engineered. Maybe you can just write to a text file, or a Sqlite db, and have the visualization program poll for changes.

Rabbitmq or redis will already be in apt or yum so a simple

  sudo apt-get install rabbitmq-server # or redis
  pip install celery

You are ready to use their example[0]

  from celery import Celery

  app = Celery('tasks', broker='pyamqp://guest@localhost//')

  @app.task
  def add(x, y):
      return x + y

The idea of a mq might feel over-engineered. But the deployment and use is actually straight forward.

[0]: http://docs.celeryproject.org/en/latest/getting-started/firs...

Bokeh supports streaming data to existing graphs. This lets you define a graph, display it in a browser, and then push updates from your code. This is a lot lighter than doing a full draw in matplotlib, plus works over the web.

Here's an intro blog post from 2013 [0] and a current tutorial [1]. This functionality depends on Bokeh Server, docs here [2]

[0] https://www.anaconda.com/developer-blog/painless-streaming-p...

[1] http://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blo...

[2] https://bokeh.pydata.org/en/latest/docs/user_guide/server.ht...

Oh that sounds excellent, I'll have to check it out.

coincidentally: https://news.ycombinator.com/item?id=15483264

Veusz (https://veusz.github.io/) does this. Data are sent to the plotting process using pickle over a socket.

This is a cool idea. I would be open to working with you on this if you'd like to collaborate! (my contact in profile)

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