Interactive figures

When working with data, interactivity can be invaluable. The pan/zoom and mouse-location tools built into the Matplotlib GUI windows are often sufficient, but you can also use the event system to build customized data exploration tools.

Matplotlib ships with backends binding to several GUI toolkits (Qt, Tk, Wx, GTK, macOS, JavaScript) and third party packages provide bindings to kivy and Jupyter Lab. For the figures to be responsive to mouse, keyboard, and paint events, the GUI event loop needs to be integrated with an interactive prompt. We recommend using IPython (see below).

The pyplot module provides functions for explicitly creating figures that include interactive tools, a toolbar, a tool-tip, and key bindings:

pyplot.figure

Creates a new empty Figure or selects an existing figure

pyplot.subplots

Creates a new Figure and fills it with a grid of Axes

pyplot has a notion of "The Current Figure" which can be accessed through pyplot.gcf and a notion of "The Current Axes" accessed through pyplot.gca. Almost all of the functions in pyplot pass through the current Figure / Axes (or create one) as appropriate.

Matplotlib keeps a reference to all of the open figures created via pyplot.figure or pyplot.subplots so that the figures will not be garbage collected. Figures can be closed and deregistered from pyplot individually via pyplot.close; all open Figures can be closed via plt.close('all').

For more discussion of Matplotlib's event system and integrated event loops, please read:

IPython integration

We recommend using IPython for an interactive shell. In addition to all of its features (improved tab-completion, magics, multiline editing, etc), it also ensures that the GUI toolkit event loop is properly integrated with the command line (see Command prompt integration).

In this example, we create and modify a figure via an IPython prompt. The figure displays in a QtAgg GUI window. To configure the integration and enable interactive mode use the %matplotlib magic:

In [1]: %matplotlib
Using matplotlib backend: QtAgg

In [2]: import matplotlib.pyplot as plt

Create a new figure window:

In [3]: fig, ax = plt.subplots()

Add a line plot of the data to the window:

In [4]: ln, = ax.plot(range(5))

Change the color of the line from blue to orange:

In [5]: ln.set_color('orange')

If you wish to disable automatic redrawing of the plot:

In [6]: plt.ioff()

If you wish to re-enable automatic redrawing of the plot:

In [7]: plt.ion()

In recent versions of Matplotlib and IPython, it is sufficient to import matplotlib.pyplot and call pyplot.ion. Using the % magic is guaranteed to work in all versions of Matplotlib and IPython.

Interactive mode

pyplot.ion

Enable interactive mode.

pyplot.ioff

Disable interactive mode.

pyplot.isinteractive

Return whether plots are updated after every plotting command.

pyplot.show

Display all open figures.

pyplot.pause

Run the GUI event loop for interval seconds.

Interactive mode controls:

  • whether created figures are automatically shown

  • whether changes to artists automatically trigger re-drawing existing figures

  • when pyplot.show() returns if given no arguments: immediately, or after all of the figures have been closed

If in interactive mode:

  • newly created figures will be displayed immediately

  • figures will automatically redraw when elements are changed

  • pyplot.show() displays the figures and immediately returns

If not in interactive mode:

If you are in non-interactive mode (or created figures while in non-interactive mode) you may need to explicitly call pyplot.show to display the windows on your screen. If you only want to run the GUI event loop for a fixed amount of time, you can use pyplot.pause. This will block the progress of your code as if you had called time.sleep, ensure the current window is shown and re-drawn if needed, and run the GUI event loop for the specified period of time.

The GUI event loop being integrated with your command prompt and the figures being in interactive mode are independent of each other. If you use pyplot.ion but have not arranged for the event loop integration, your figures will appear but will not be interactive while the prompt is waiting for input. You will not be able to pan/zoom and the figure may not even render (the window might appear black, transparent, or as a snapshot of the desktop under it). Conversely, if you configure the event loop integration, displayed figures will be responsive while waiting for input at the prompt, regardless of pyplot's "interactive mode".

No matter what combination of interactive mode setting and event loop integration, figures will be responsive if you use pyplot.show(block=True), pyplot.pause, or run the GUI main loop in some other way.

Warning

Using Figure.show it is possible to display a figure on the screen without starting the event loop and without being in interactive mode. This may work (depending on the GUI toolkit) but will likely result in a non-responsive figure.

Default UI

The windows created by pyplot have an interactive toolbar with navigation buttons and a readout of the data values the cursor is pointing at. A number of helpful keybindings are registered by default.

Other Python prompts

Interactive mode works in the default Python prompt:

>>> import matplotlib.pyplot as plt
>>> plt.ion()
>>>

however this does not ensure that the event hook is properly installed and your figures may not be responsive. Please consult the documentation of your GUI toolkit for details.

Jupyter Notebooks / JupyterLab

Note

To get the interactive functionality described here, you must be using an interactive backend. The default backend in notebooks, the inline backend, is not. backend_inline renders the figure once and inserts a static image into the notebook when the cell is executed. Because the images are static, they can not be panned / zoomed, take user input, or be updated from other cells.

To get interactive figures in the 'classic' notebook or Jupyter lab, use the ipympl backend (must be installed separately) which uses the ipywidget framework. If ipympl is installed use the magic:

%matplotlib widget

to select and enable it.

If you only need to use the classic notebook, you can use

%matplotlib notebook

which uses the backend_nbagg backend provided by Matplotlib; however, nbagg does not work in Jupyter Lab.

GUIs + Jupyter

You can also use one of the non-ipympl GUI backends in a Jupyter Notebook. If you are running your Jupyter kernel locally, the GUI window will spawn on your desktop adjacent to your web browser. If you run your notebook on a remote server, the kernel will try to open the GUI window on the remote computer. Unless you have arranged to forward the xserver back to your desktop, you will not be able to see or interact with the window. It may also raise an exception.

PyCharm, Spyder, and VSCode

Many IDEs have built-in integration with Matplotlib, please consult their documentation for configuration details.