About

Vizarr is a minimal, purely client-side program for viewing zarr-based images.

• ⚡ GPU accelerated rendering with Viv
• 💻 Purely client-side zarr access with zarrita.js
• 🌎 A standalone web app for viewing entirely in the browser.
• 🐍 An anywidget Python API for programmatic control in notebooks.
• 📦 Supports any zarr-python store as a backend.

Getting started

Vizarr provides two primary interfaces for interacting with the core viewer:

1. Standalone Web App

You can use the standalone web app by copying and pasting a URL to a Zarr store as the ?source query parameter in the web app.

For example, to view this dataset from the IDR, navigate to the following URL:

https://hms-dbmi.github.io/vizarr/?source=https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr

2. Python API

The Python API is an anywidget, allowing programatic control of the viewer in computational notebooks like Jupyter, JupyterLab, Colab, and VS Code. The easiest way to get started is to open a Zarr store and load it into the viewer.

import vizarr
import zarr

store = zarr.open("./path/to/ome.zarr")
viewer = vizarr.Viewer()
viewer.add_image(store)
viewer

To learn more, see the getting started notebook.

Data types

Vizarr supports viewing 2D slices of n-Dimensional Zarr arrays, allowing users to choose a single channel or blended composites of multiple channels during analysis. It has special support for the developing OME-NGFF format for multiscale and multimodal images. Currently, Viv supports int8, int16, int32, uint8, uint16, uint32, float32, float64 arrays, but contributions are welcome to support more np.dtypes!

Limitations

vizarr was built to support the registration use case above where multiple, pyramidal OME-Zarr images are viewed within a Jupyter Notebook. Support for other Zarr arrays is supported but not as well tested. More information regarding the viewing of generic Zarr arrays can be found in the example notebooks.

Citation

If you are using Vizarr in your research, please cite our paper:

Trevor Manz, Ilan Gold, Nathan Heath Patterson, Chuck McCallum, Mark S Keller, Bruce W Herr II, Katy Börner, Jeffrey M Spraggins, Nils Gehlenborg, "Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web." Nature Methods (2022), doi:10.31219/10.1038/s41592-022-01482-7