view multiscale zarr images online and in notebooks
standalone app .
python api .
open in colab
Vizarr is a minimal, purely client-side program for viewing zarr-based images.
zarr-python
store
as a backend.
Vizarr provides two primary interfaces for interacting with the core viewer:
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
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.
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!
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.
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 BΓΆrner, 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