napari Plugins

Plugins are Python packages that allow developers to extend napari functionality with new features. Currently, plugins can:

• Add file format support with readers and writers

• Add custom widgets and user interface elements for Python functions, etc.

• Provide sample data

• Change the look of napari with a theme

The napari hub is a repository for searching/discovering plugins. Plugins can be installed as Python packages using pip or, in most cases, conda or from within the napari GUI using the Plugin menu.

As a user, plugins are a way to extend the functionality of napari and access features that are not part of the core napari viewer experience. Often, they provide GUI access to complex Python functions or workflows. For a developer, plugins are a way to share code for image and data visualization, processing, and analysis in a robust way with the broader scientific community, using napari for the user interface and visualization capabilities.

For more information on plugins, please see the official Plugin documentation.

Important

Most plugins are developed by the broader napari scientific community. Plugins and their functionalities may be very specific and not be robust enough for every use case! Also, maintaining a plugin can be a burden and plugin developers may not keep up with napari or the broader scientific Python ecosystem.
If you encounter issues with a napari plugin, please check the plugin’s documentation and/or GitHub repository for more information or post on the image.sc forum.

Install Your First Plugins

• In napari, open
  Plugins>Install/Uninstall Plugins…

• Search for sample in the top search/filter bar, and then, in the bottom pane, you will see all of the plugins that match this search term.

• Click on the name of the plugin (underlined) to open a webpage with more information about the plugin, such as the documentation or the code repository (e.g. GitHub).

• Click the install button next to napari-bio-sample-data. If you want to know more about the version and installer used, you can click the Installation Info menu.

• Once the install completes, search for skimage and install the napari-skimage plugin as well.

  Tip

  • After clicking the install button, you can monitor the installing… process by clicking Show status at the bottom left. This can be helpful in trouble-shooting installation issues.

  • You can queue multiple plugins to install at once, by searching for a new plugin and clicking the install button.

• After installing plugins, it is recommended that you close napari and reopen it to continue.

Using Plugins

Plugins that provide reader functionality will automatically be used when you open a file type that the plugin supports. You may be prompted to select a reader plugin if there are multiple plugins that can read the same file type. Sample data provided by Plugins will be available in the File>Open Sample menu. Plugins that provide widgets to access functionality will be available in the Plugins menu, as well as in other declared menu locations, such as the Layers menu.

Tip

A good way to get a sense of the functionalities offered by a plugin is to use the napari Command Palette. You can open it by pressing Ctrl+Shift+P (or Cmd+Shift+P on macOS). In the floating text entry bar, you can type the name–or part of it–of a plugin. This will show you all the commands available from that plugin.

• Open File>Open Sample. napari-bio-sample-data should be a menu option.

  • Select: File>Open Sample>napari-bio-sample-data>3D nuclei

  This sample data includes the same nuclei data as before, but it also provides segmentations as a 3D Labels layer. Additionally, a Surface layer, which can be used to visualize precomputed surface meshes, is also present. You can explore these in the 3D view. Delete the surface layer once you’re ready to move on, as we will not be using it.

• Open Layers>Measure>Regionprops (labels) (napari-skimage)

  • Ensure that nuclei label is selected for the Label Layer and nuclei is selected for the Intensity Image Layer.

  • Select some properties to measure, you can use shift or control/command to multiselect. Refer to the scikit-image measure.regionprops documentation for more information

  • Click Analyze, which should yield a table of measurements—depending how many you checked, this could take a moment to run. You can save the results to a CSV file by clicking the Save Results button.

  Tip

  Grab the titlebar of the Table widget, it’s on the left edge, and drag it off the napari viewer to undock. Then you can resize the table for easier browsing. You can re-dock the widget by dragging it on the existing side dock or the bottom of the viewer, e.g. where the viewer buttons are, to dock it beneath the canvas.

  • Select the nuclei labels layer in the layer list and in the layer controls widget at the top, check the box show selected to only view the currently selected label. Now in the table, when you click a cell you will be shown the corresponding label. You could now use the brush and eraser tools to edit the segmentation and then re-run the analysis.

• Hide the Labels layer and close any open widgets.

• Explore a few of the other functions offered by the napari-skimage plugin. You can find them in the Layers menu (or the Plugins menu), or by using the Command Palette. For example, you can try Layers>Filter>Filtering>Gaussian filter. This open a widget that will allow you to apply a Gaussian filter to a specified Image layer and create a new Image layer with the output. The widget even provides a link to the documentation. You can also try one of the options in the Layers>Filter>Thresholding menu, which will allow you to apply a threshold to a specified Image layer.

A few other handy plugins

1. napari-animation: a plugin that lets you make (and export) animations in napari by simply setting key frames.

2. napari-ome-zarr: a reader plugin for for zarr backed OME-NGFF images (local or remote).

3. napari-tiff: a reader plugin that can open tiff files and variants like svs or ndpi and load them as multi-scale data.

4. napari-matplotlib: a plugin providing widgets with matplotlib based plots, such as histogram, scatter plot, line plot, etc.