napari

multi-dimensional image viewer for python

napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).

We're developing napari in the open! You can follow progress on our project roadmap, test out new versions as we release them, and contribute ideas and code.

If you want to refer to our documentation, please go to napari.org. If you want to contribute to it, please refer to the contributing section below.

We're working on tutorials, but you can also quickly get started by looking below.

try it out now!

Install uv to try napari. Then launch the program in a terminal window with the command:

uvx "napari[all]"

In the File menu, select Open Sample and select a sample image to get started.

installation

For a full installation, we recommend installing napari into a virtual environment, like this:

conda create -y -n napari-env -c conda-forge python=3.13
conda activate napari-env
python -m pip install "napari[all]"

If you prefer conda over pip, you can replace the last line with: conda install -c conda-forge napari pyqt6

See here for the full installation guide.

simple example

This example uses a data sample from the scikit-image library, but you can pass your own dataset as an array to imshow. From inside an IPython shell, you can open up an interactive viewer by calling

from skimage import data
import napari

viewer, layers = napari.imshow(data.cells3d(), channel_axis=1, ndisplay=3)

To use napari from inside a script, use napari.run():

from skimage import data
import napari

viewer, layers = napari.imshow(data.cells3d(), channel_axis=1, ndisplay=3)
napari.run()  # start the "event loop" and show the viewer

features

Check out the scripts in our examples folder to see some of the functionality we're developing!

napari supports six main different layer types, Image, Labels, Points, Vectors, Shapes, and Surface, each corresponding to a different data type, visualization, and interactivity. You can add multiple layers of different types into the viewer and then start working with them, adjusting their properties.

All our layer types support n-dimensional data and the viewer provides the ability to quickly browse and visualize either 2D or 3D slices of the data.

napari also supports bidirectional communication between the viewer and the Python kernel, which is especially useful when launching from jupyter notebooks or when using our built-in console. Using the console allows you to interactively load and save data from the viewer and control all the features of the viewer programmatically.

This is the beginning of the README. Read the rest at the original. Read the full README ↗