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@@ -1,47 +1,79 @@
+
- 
+ 
+
+
+
+ view multiscale zarr images online and in notebooks
+
+
+ app .
+ getting started
+
+
[](https://colab.research.google.com/github/hms-dbmi/vizarr/blob/main/python/notebooks/mandelbrot.ipynb)
-
+
+ 
+
-Vizarr is a minimal, purely client-side program for viewing Zarr-based images. It is built with
-[Viv](https://github.com/hms-dbmi/viv) and exposes a Python API using the
-[`imjoy-rpc`](https://github.com/imjoy-team/imjoy-rpc), allowing users to programatically view multiplex
-and multiscale images from within a Jupyter Notebook. The ImJoy plugin registers a codec for Python
-`zarr.Array` and `zarr.Group` objects, enabling Viv to securely request chunks lazily via
-[Zarr.js](https://github.com/gzuidhof/zarr.js/). This means that other valid zarr-python
-[stores](https://zarr.readthedocs.io/en/stable/api/storage.html) can be viewed remotely with Viv,
-enabling flexible workflows when working with large datasets.
+**Vizarr** is a minimal, purely client-side program for viewing zarr-based images.
-### Remote image registration workflow
-We created Vizarr to enhance interactive multimodal image alignment using the
-[wsireg](https://github.com/NHPatterson/wsireg) library. We describe a rapid workflow where
-comparison of registration methods as well as visual verification of alignnment can be assessed
-remotely, leveraging high-performance computational resources for rapid image processing and
-Viv for interactive web-based visualization in a laptop computer. The Jupyter Notebook containing
-the workflow described in the manuscript can be found in [`multimodal_registration_vizarr.ipynb`](multimodal_registration_vizarr.ipynb). For more information, please read our preprint [doi:10.31219/osf.io/wd2gu](https://doi.org/10.31219/osf.io/wd2gu).
-
-> Note: The data required to run this notebook is too large to include in this repository and can be made avaiable upon request.
+- ⚡ **GPU accelerated rendering** with [Viv](https://github.com/hms-dbmi/viv)
+- 💻 Purely **client-side** zarr access with [zarrita.js](https://github.com/manzt/zarrita.js)
+- 🌎 A **standalone [web app](https://hms-dbmi/vizarr)** for viewing entirely in the browser.
+- 🐍 An [anywidget](https://github.com/manzt/anywidget) **Python API** for
+ programmatic control in notebooks.
+- 📦 Supports any `zarr-python` [store](https://zarr.readthedocs.io/en/stable/api/storage.html)
+ as a backend.
### 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-Zarr format](https://github.com/ome/omero-ms-zarr/blob/master/spec.md)
-for multiscale and multimodal images. Currently [Viv](https://github.com/hms-dbmi/viv) supports
-`i1`, `i2`, `i4`, `u1`, `u2`, `u4`, and `f4` arrays, but contributions are welcome to support more `np.dtypes`!
-### Getting started
-The easiest way to get started with `vizarr` is to clone this repository and open one of
-the example [Jupyter Notebooks](example/).
+**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!
+
+### Getting started
+
+Copy and paste a URL to a Zarr store as the `?source` query parameter in the
+**[web app](https://hms-dbmi.github.io/vizarr/)**. For example, to view the
+[example data](https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr)
+from the IDR, you can use the following URL:
+
+```
+https://hms-dbmi.github.io/vizarr/?source=https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr
+```
+
+Otherwise you can try out the Python API in a Jupyter Notebook, following [the
+examples](./python/notebooks/getting_started.ipynb).
+
+```sh
+pip install vizarr
+```
+
+```python
+import vizarr
+import zarr
+
+store = zarr.open("./path/to/ome.zarr")
+viewer = vizarr.Viewer()
+viewer.add_image(store)
+viewer
+```
### 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,