Napari Brainbow Diagnose


Visualize and Diagnose brainbow dataset in color space.

License BSD-3 PyPI Python Version tests codecov napari hub

Explore image in channel coordinate space. Brainbow dataset have unique features that need to be addressed by specialized tools. This plugin aims at visualize and diagnose brainbow dataset. In particular we want to interact with the distribution of the dataset in the channel space.



You can also look at the demo notebook Find all menus under Plugins > napari-brainbow-diagnose > Diagnose Brainbow Image

Choose your dataset

If you want to use your dataset, you have to format it such as each channel is in one distinct napari.Layers You can open test dataset to try this plugin in File > Open Sample > napari-brainbow-diagnose.

  • The RGB Cube is an array with shape (3x256x256x256) cube : Great to check how the plugin work when all color are represented
  • Chrom Cortex Sample is an array with shape (3x256x256x256) #Hugo : Real life brainbow image (Cortex E18 Emx1Cre) !

Once you have your layers you can use the dropdown and select the corresponding layer. It is advised to match the red, green, blue order so the ratio you see on the napari viewer corresponds to the Hue-Saturation Wheel of the plugin.

Get Channel Ratio Density of the image

When you click on Compute brainbow image density you will populate the Hue-Saturation density Wheel. This should allow you to quickly see which ratio is more present in your image. You can see the corresponding ratio according to the "HS Color wheel" on the right. For example here on this screenshot we can see that:

  • there is a high number of non saturated red-only ratio. (2)
  • there is not a high number of non saturated magenta ratio. (3)


Create a selection of ratio on the channel coordinate system and apply it on the original image


Create a selection of pixel in the image and show where they are in the channel coordinate system



You can install napari-brainbow-diagnose via pip:

pip install napari-brainbow-diagnose

To install latest development version :

pip install git+


Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.


Distributed under the terms of the BSD-3 license, "napari-brainbow-diagnose" is free and open source software


If you encounter any problems, please file an issue along with a detailed description.

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.


  • 0.1.3

Last updated:

  • 06 March 2023

First released:

  • 25 January 2023


Supported data:

  • Information not submitted

Plugin type:

GitHub activity:

  • Stars: 1
  • Forks: 2
  • Issues + PRs: 5

Python versions supported:

Operating system:


  • numpy
  • magicgui
  • qtpy
  • pooch
  • matplotlib
  • scikit-image