This release is a pre-release and may not be stable for production use.
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.
You can extend napari using custom shortcuts, key bindings, and mouse functions.
tutorials
For more details on how to use napari checkout our tutorials. These are still a work in progress, but we'll be updating them regularly.
mission, values, and roadmap
For more information about our plans for napari you can read our mission and values statement, which includes more details on our vision for supporting a plugin ecosystem around napari.
You can see details of the project roadmap here.
contributing
Contributions are encouraged! See CONTRIBUTING.md for resources to get started.
If you want to contribute to or edit our documentation, please go to napari/docs.
Visit our project weather report dashboard to see metrics and how development is progressing.
code of conduct
napari has a Code of Conduct that should be honored by everyone who participates in the napari community.
governance
You can learn more about how the napari project is organized and managed from our governance model, which includes information about, and ways to contact the @napari/steering-council and @napari/core-devs.
citing napari
If you find napari useful please cite this repository using its DOI as follows:
napari contributors (2019). napari: a multi-dimensional image viewer for python. doi:10.5281/zenodo.3555620
Note this DOI will resolve to all versions of napari. To cite a specific version please find the DOI of that version on our zenodo page. The DOI of the latest version is in the badge at the top of this page.
help
We're a community partner on the image.sc forum and all help and support requests should be posted on the forum with the tag napari. We look forward to interacting with you there.
Bug reports should be made on our GitHub issues using the bug report template. If you think something isn't working, don't hesitate to reach out - it is probably us and not you!
institutional and funding partners
6 maintainers
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