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Using PyQt with QtAgg in Jupyterlab – I – a first simple example

As my readers know I presently study how to work with background jobs for plot and information updates in Jupyterlab. The reason for this endeavor is that I want to organize my work with ML- training and long evaluation runs a bit differently in the future. In particular I want to have the freedom of working in other regions (other cells) of a Python3 notebook while the long running jobs do their work in the background. This includes that these other cells may also start major background jobs or run code in parallel to the background tasks.

After some experiments I think that for my particular purposes a combination of QTAgg, Matplotlib and PyQt provides the best solution.

What I like about PyQt is its very clear structure. And you can use QTextEdit-widgets as a kind of canvas for print output. And e special Figure-widget for Matplotlib. Using PyQt opens up for many new options to build nice application windows around your ML-algorithms on your Linux desktop. Which will be extremely valuable for presentations or customer end applications.

In this post series I will present a few test applications that cover a lot of topics around Qt-widgets, widget- communication, threads, signals and events. We will start with a simple foreground application. Then we turn to threads and signals between threads. Afterward we integrate Matplotlib and deepen our understanding of handling threads in PyQt applications for Matplotlib and print output.

Objectives of this post

This first post translates a question posed at Stackoverflow [1] into a more complex example. With it I want to show

  • how to set up your own PyQt application in a Jupyterlab (Python) environment and display it directly as a Qt-window on your desktop,
  • how to create a nested PyQt “sub-window”-structure with the help of a PyQt-window layout,
  • how to react to events,
  • how to give a central widget its own paintEvent and a painter for drawing graphical primitives,
  • how to “hide” a widget element,
  • how you control which of your graphical primitives is drawn and which not.

This application does not yet use background jobs. So, due to its present structure it will block running any Python code in other notebook cells while we have some action going on in the application’s PyQt-widgets.

We are going to change this during the course of this post series. For the time being my main objective is only to show that PyQt can be used at all with QtAgg in Jupyterlab notebooks.

My “application” is a bit stupid in so far as 2 main button only trigger a continuous dynamic painting of a red line from a sub-window’s center to the mouse pointer or to the corners of their surrounding sub-window areas. It is simple and trivial, but it nevertheless gave me some fun constructing it.

If you like, you may think of the red line a “laser beam” shooting at the mouse pointer or the frame corners. I will use this term below when I discuss the functionality. There is also a video link at the end of the post.

All of this has obviously nothing to do with ML, yet, but it gives you a feeling how we can compose PyQt applications in a Jupyterlab environment. In other posts of this blog we will use our acquired PyQt knowledge to put Keras based ML-machinery into our own graphical interfaces on the Linux desktop. I.e. outside the browser tab for Jupyterlab.

Level of this post: In parts advanced. Not because the example is so complicated. Nevertheless some background information on Qt-Window layouts and Widgets is required. Good introductions are given here. Details for all Qt-widgets that I use below can be found at https://wiki.qt.io/.

But note: For a Jupyterlab Python notebook with an QtAgg-backend you do not need the whole app.exec() stuff described in the tutorial examples.

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