{"id":25237187,"url":"https://github.com/hms-dbmi/vizarr","last_synced_at":"2026-06-05T00:31:36.892Z","repository":{"id":38442975,"uuid":"275174329","full_name":"hms-dbmi/vizarr","owner":"hms-dbmi","description":"A minimal Zarr image viewer based on Viv.","archived":false,"fork":false,"pushed_at":"2025-01-24T18:12:21.000Z","size":32630,"stargazers_count":134,"open_issues_count":30,"forks_count":18,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-01-28T19:44:20.828Z","etag":null,"topics":["gehlenborglab","imjoy","jupyter-notebooks","viv","zarr"],"latest_commit_sha":null,"homepage":"https://hms-dbmi.github.io/vizarr/?source=https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hms-dbmi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-26T14:24:04.000Z","updated_at":"2025-01-24T18:11:06.000Z","dependencies_parsed_at":"2023-02-06T12:30:32.929Z","dependency_job_id":"b7087265-dac5-4a49-8b84-9d28b5f1040c","html_url":"https://github.com/hms-dbmi/vizarr","commit_stats":{"total_commits":120,"total_committers":8,"mean_commits":15.0,"dds":0.2416666666666667,"last_synced_commit":"a88c4134dc3782003566c7ed51c9ff968b89f240"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hms-dbmi%2Fvizarr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hms-dbmi%2Fvizarr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hms-dbmi%2Fvizarr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hms-dbmi%2Fvizarr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hms-dbmi","download_url":"https://codeload.github.com/hms-dbmi/vizarr/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238320486,"owners_count":19452562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gehlenborglab","imjoy","jupyter-notebooks","viv","zarr"],"created_at":"2025-02-11T15:33:32.609Z","updated_at":"2025-10-26T12:30:38.191Z","avatar_url":"https://github.com/hms-dbmi.png","language":"TypeScript","funding_links":[],"categories":["Life sciences","🧩 OME-Zarr"],"sub_categories":["Visualization","Software tools"],"readme":"\u003ch1\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./assets/logo-wide.svg\" alt=\"vizarr\" width=\"200\"\u003e\n\u003c/h1\u003e\n\u003csamp\u003e\n  \u003cp align=\"center\"\u003e\n    \u003cspan\u003eview multiscale zarr images online and in notebooks\u003c/span\u003e\n      \u003cbr\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://hms-dbmi.github.io/vizarr/?source=https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr\"\u003estandalone app\u003c/a\u003e .\n      \u003ca href=\"./python/notebooks/getting_started.ipynb\"\u003epython api\u003c/a\u003e .\n      \u003ca href=\"https://colab.research.google.com/github/hms-dbmi/vizarr/blob/main/python/notebooks/mandelbrot.ipynb\"\u003eopen in colab\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/samp\u003e\n\u003c/p\u003e\n\n## About \n\n**Vizarr** is a minimal, purely client-side program for viewing zarr-based images.\n\n- ⚡ **GPU accelerated rendering** with [Viv](https://github.com/hms-dbmi/viv)\n- 💻 Purely **client-side** zarr access with [zarrita.js](https://github.com/manzt/zarrita.js)\n- 🌎 A **standalone [web app](https://hms-dbmi/vizarr)** for viewing entirely in the browser.\n- 🐍 An [anywidget](https://github.com/manzt/anywidget) **Python API** for\n  programmatic control in notebooks.\n- 📦 Supports any `zarr-python` [store](https://zarr.readthedocs.io/en/stable/api/storage.html)\n  as a backend.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./assets/screenshot.png\" alt=\"Multiscale OME-Zarr in Jupyter Notebook with Vizarr\" width=\"500\"\u003e\n\u003c/p\u003e\n\n## Getting started\n\n**Vizarr**  provides two primary interfaces for interacting with the core viewer:\n\n### 1. Standalone Web App\n\nYou 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](https://hms-dbmi.github.io/vizarr).\n\nFor example, to view [this dataset](https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr) from the IDR, navigate to the following URL:\n\n```\nhttps://hms-dbmi.github.io/vizarr/?source=https://minio-dev.openmicroscopy.org/idr/v0.3/idr0062-blin-nuclearsegmentation/6001240.zarr\n```\n\n### 2. Python API\n\nThe Python API is an [anywidget](https://github.com/manzt/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.\n\n```python\nimport vizarr\nimport zarr\n\nstore = zarr.open(\"./path/to/ome.zarr\")\nviewer = vizarr.Viewer()\nviewer.add_image(store)\nviewer\n```\n\nTo learn more, see the [getting started](./python/notebooks/getting_started.ipynb) notebook.\n\n## Data types\n\n**Vizarr** supports viewing 2D slices of n-Dimensional Zarr arrays, allowing\nusers to choose a single channel or blended composites of multiple channels\nduring analysis. It has special support for the developing OME-NGFF format for\nmultiscale and multimodal images. Currently, Viv supports `int8`, `int16`,\n`int32`, `uint8`, `uint16`, `uint32`, `float32`, `float64` arrays, but\ncontributions are welcome to support more np.dtypes!\n\n## Limitations\n\n`vizarr` was built to support the registration use case above where multiple, pyramidal OME-Zarr images\nare viewed within a Jupyter Notebook. Support for other Zarr arrays is supported but not as well tested. \nMore information regarding the viewing of generic Zarr arrays can be found in the example notebooks.\n\n## Citation\n\nIf you are using Vizarr in your research, please cite our paper:\n\n\u003e Trevor Manz, Ilan Gold, Nathan Heath Patterson, Chuck McCallum, Mark S Keller, Bruce W Herr II, Katy Börner, Jeffrey M Spraggins, Nils Gehlenborg,\n\u003e \"[Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web](https://www.nature.com/articles/s41592-022-01482-7).\"\n\u003e **Nature Methods** (2022), [doi:10.31219/10.1038/s41592-022-01482-7](https://doi.org/10.1038/s41592-022-01482-7)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhms-dbmi%2Fvizarr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhms-dbmi%2Fvizarr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhms-dbmi%2Fvizarr/lists"}