Appendices¶
OME-Zarr creation libraries¶
A number of libraries exist for creating OME-Zarr datasets from existing data. This table lists them, and drawbacks when working with 3D imaging data.
| Library | Drawbacks |
|---|---|
| ome-zarr-py | Not able to correctly downsample 3D images (see issue #262). |
| ngff-zarr |
OME-Zarr visualisation¶
There are many different viewers available for OME-Zarr images - see a full list in the NGFF documentation.
Napari¶
A good Python-based option is napari - see installation instructions on their website.
By default, napari supports opening Zarr arrays e.g.
import napari
# Data as a zarr array
heart_image = load_heart_data(array_type='zarr')
viewer = napari.Viewer()
viewer.add_image(heart_image)
napari.run()To open OME-Zarr images, you will need to install the napari-ome-zarr plugin.
Note: napari’s support for viewing large, multi-resolution images is still being developed / improved over time. You may find it difficult to browse very large Zarr images through this interface - if so, you may want to try other viewers such as webknossos, neuroglancer or BigDataViewer.