Hooking up your own events

If you’re coming from a background of scripting or working with python in an interactive console, thinking in terms of the “event loop” can feel a bit strange at times. Often we write code in a very procedural way: “do this … then do that, etc…”. With napari and other GUI programs however, usually you hook up a bunch of conditions to callback functions (e.g. “If this event happens, then call this function”) and then start the loop and hope you hooked everything up correctly! Indeed, much of the napari source code is dedicated to creating and handling events: search the codebase for .emit( and .connect( to find examples of creating and handling internal events, respectively.

If you would like to set up a custom event listener then you need to hook into the napari event loop. We offer a couple of convenience decorators to easily connect functions to key and mouse events.

Listening for keypress events

One option is to use keybindings, that will listen for keypresses and then call some callback whenever pressed, with the viewer instance passed as an argument to that function. As a basic example, to add a random image to the viewer every time the i key is pressed, and delete the last layer when the k key is pressed:

import numpy as np
import napari

viewer = napari.Viewer()

def add_layer(viewer):
    viewer.add_image(np.random.random((512, 512)))

def delete_layer(viewer):
    except IndexError:


See also this custom key bindings example.

Listening for mouse events

You can also listen for and react to mouse events, like a click or drag event, as shown here where we update the image with random data every time it is clicked.

import numpy as np
import napari

viewer = napari.Viewer()
layer = viewer.add_image(np.random.random((512, 512)))

def update_layer(layer, event):
    layer.data = np.random.random((512, 512))


As of this writing MouseProviders have 4 list of callbacks that can be registered:

  • mouse_move_callbacks

  • mouse_wheel_callbacks

  • mouse_drag_callbacks

  • mouse_double_click_callbacks

Please look at the documentation of MouseProvider for a more in depth discussion of when each callback is triggered. In particular single click can be registered with mouse_drag_callbacks, and mouse_double_click_callbacks is triggered in addition to mouse mouse_drag_callbacks.

See also the custom mouse functions and mouse drag callback examples.

Connection functions to native napari events

If you want something to happen following some event that happens within napari, the trick becomes knowing which native signals any given napari object provides for you to “connect” to. Until we have centralized documentation for all of the events offered by napari objects, the best way to find these is to browse the source code. Take for instance, the base Layer class: you’ll find in the __init__ method a self.events section that looks like this:

self.events = EmitterGroup(

That tells you that all layers are capable of emitting events called data, and name (among many others) that will (presumably) be emitted when that property changes. To provide your own response to that change, you can hook up a callback function that accepts the event object:

def print_layer_name(event):
    print(f"{event.source.name} changed its data!")


Long-running, blocking functions

An important detail here is that the napari event loop is running in a single thread. This works just fine if the handling of each event is very short, as is usually the case with moving sliders, and pressing buttons. However, if one of the events in the queue takes a long time to process, then every other event must wait!

Take this example in napari:

viewer = napari.Viewer()
# everything is fine so far... but if we trigger a long computation
image = np.random.rand(512, 1024, 1024).mean(0)
# the entire interface freezes!

Here we have a long computation (np.random.rand(512, 1024, 1024).mean(0)) that “blocks” the main thread, meaning no button press, key press, or any other event can be processed until it’s done. In this scenario, it’s best to put your long-running function into another thread or process. napari provides a convenience for that, described in Multithreading in napari.