Point VisCy and waveorder links at their Biohub GitHub home

CITATION.cff

@@ -31,8 +31,8 @@ authors:
     family-names: Mehta
     affiliation: Chan Zuckerberg Biohub San Francisco
     orcid: "https://orcid.org/0000-0002-2542-3582"
-repository-code: "https://github.com/mehta-lab/VisCy"
-url: "https://github.com/mehta-lab/VisCy"
+repository-code: "https://github.com/Biohub/VisCy"
+url: "https://github.com/Biohub/VisCy"
 abstract: computer vision models for single-cell phenotyping
 keywords:
   - machine-learning

CONTRIBUTING.md

@@ -17,7 +17,7 @@ and how you can install and use the package.
 
 ## Issues
 
-We use [issues](https://github.com/mehta-lab/VisCy/issues) to track
+We use [issues](https://github.com/Biohub/VisCy/issues) to track
 bug reports, feature requests, and provide user support.
 
 Before opening a new issue, please first search existing issues (including closed ones)
@@ -26,7 +26,7 @@ to see if there is an existing discussion about it.
 ## Making changes
 
 Any change made to the `main` branch needs to be proposed in a
-[pull request](https://github.com/mehta-lab/VisCy/pulls) (PR).
+[pull request](https://github.com/Biohub/VisCy/pulls) (PR).
 
 If there is an issue that can be addressed by the PR, please reference it.
 If there is not a relevant issue, please either open an issue first,
@@ -50,11 +50,11 @@ See [uv installation docs](https://docs.astral.sh/uv/getting-started/installatio
 If you have push permission to the repository:
 
 ```sh
-git clone https://github.com/mehta-lab/VisCy.git
+git clone https://github.com/Biohub/VisCy.git
 ```
 
 Otherwise, you can follow [these instructions](https://docs.github.com/en/get-started/quickstart/fork-a-repo)
-to [fork](https://github.com/mehta-lab/VisCy/fork) the repository.
+to [fork](https://github.com/Biohub/VisCy/fork) the repository.
 
 #### Install dependencies
 

README.md

@@ -7,22 +7,22 @@
 |  **Meta**   | [![SPEC 0][badge-spec0]][link-spec0] [![Contributors][badge-contributors]][link-contributors] [![Stars][badge-stars]][link-repo]                              |
 |  **Cite**   | [![DOI][badge-doi]][link-doi]                                                                                                                                 |
 
-[badge-docs]: https://github.com/mehta-lab/VisCy/actions/workflows/docs.yml/badge.svg
-[badge-tests]: https://github.com/mehta-lab/VisCy/actions/workflows/test.yml/badge.svg
+[badge-docs]: https://github.com/Biohub/VisCy/actions/workflows/docs.yml/badge.svg
+[badge-tests]: https://github.com/Biohub/VisCy/actions/workflows/test.yml/badge.svg
 [badge-pypi]: https://img.shields.io/pypi/v/viscy.svg
 [badge-downloads]: https://img.shields.io/pypi/dm/viscy.svg
 [badge-spec0]: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038
-[badge-contributors]: https://img.shields.io/github/contributors-anon/mehta-lab/VisCy
-[badge-stars]: https://img.shields.io/github/stars/mehta-lab/VisCy
+[badge-contributors]: https://img.shields.io/github/contributors-anon/Biohub/VisCy
+[badge-stars]: https://img.shields.io/github/stars/Biohub/VisCy
 [badge-doi]: https://zenodo.org/badge/DOI/10.5281/zenodo.15022186.svg
 
-[link-docs]: https://mehta-lab.github.io/VisCy/stable/
-[link-tests]: https://github.com/mehta-lab/VisCy/actions/workflows/test.yml
+[link-docs]: https://biohub.github.io/VisCy/stable/
+[link-tests]: https://github.com/Biohub/VisCy/actions/workflows/test.yml
 [link-pypi]: https://pypi.org/project/viscy
 [link-downloads]: https://pypistats.org/packages/viscy
 [link-spec0]: https://scientific-python.org/specs/spec-0000/
-[link-contributors]: https://github.com/mehta-lab/VisCy/graphs/contributors
-[link-repo]: https://github.com/mehta-lab/VisCy
+[link-contributors]: https://github.com/Biohub/VisCy/graphs/contributors
+[link-repo]: https://github.com/Biohub/VisCy
 [link-doi]: https://doi.org/10.5281/zenodo.15022186
 
 VisCy (blend of `vision` and `cyto`) is a deep learning pipeline for training and deploying computer vision models for image-based phenotyping at single-cell resolution.
@@ -59,14 +59,14 @@ pip install viscy-models
 Or install from source with all development dependencies:
 
 ```sh
-git clone https://github.com/mehta-lab/VisCy.git
+git clone https://github.com/Biohub/VisCy.git
 cd VisCy
 uv sync
 ```
 
 ## Documentation
 
-Full documentation is hosted at <https://mehta-lab.github.io/VisCy/stable/>.
+Full documentation is hosted at <https://biohub.github.io/VisCy/stable/>.
 
 ## Development
 

applications/airtable/README.md

@@ -2,7 +2,7 @@
 
 Interface to the **Computational Imaging Database** on Airtable, with utilities for syncing experiment metadata between Airtable and OME-Zarr datasets.
 
-Part of the [VisCy](https://github.com/mehta-lab/VisCy) monorepo.
+Part of the [VisCy](https://github.com/Biohub/VisCy) monorepo.
 
 ---
 

applications/airtable/pyproject.toml

@@ -32,9 +32,9 @@ dependencies = [
 ]
 
 optional-dependencies.dev = [ "pytest" ]
-urls.Homepage = "https://github.com/mehta-lab/VisCy"
-urls.Issues = "https://github.com/mehta-lab/VisCy/issues"
-urls.Repository = "https://github.com/mehta-lab/VisCy"
+urls.Homepage = "https://github.com/Biohub/VisCy"
+urls.Issues = "https://github.com/Biohub/VisCy/issues"
+urls.Repository = "https://github.com/Biohub/VisCy"
 
 scripts.prepare = "airtable_utils.prepare_cli:main"
 

applications/cytoland/README.md

@@ -4,7 +4,7 @@ Robust virtual staining of landmark organelles from label-free microscopy.
 
 ![Cytoland virtual staining of nuclei and membrane from label-free phase contrast](./docs/asset/svideo_1.png)
 
-Part of the [VisCy](https://github.com/mehta-lab/VisCy) monorepo.
+Part of the [VisCy](https://github.com/Biohub/VisCy) monorepo.
 
 > **Paper:** [Robust virtual staining in Nature Machine Intelligence](https://www.nature.com/articles/s42256-025-01046-2)
 

applications/cytoland/examples/VS_model_inference/demo_vscyto2d.py

@@ -66,7 +66,7 @@
 For this example we will use the following parameters:
 For more information on the VSCyto2D model,
 see ``viscy.unet.networks.fcmae``
-([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/fcmae.py))
+([source code](https://github.com/Biohub/VisCy/blob/main/viscy/unet/networks/fcmae.py))
 for configuration details.
 """
 # %%

applications/cytoland/examples/VS_model_inference/demo_vscyto3d.py

@@ -74,7 +74,7 @@
 For this example we will use the following parameters:
 ### For more information on the VSCyto3D model:
 See ``viscy.unet.networks.unext2``
-([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/unext2.py))
+([source code](https://github.com/Biohub/VisCy/blob/main/viscy/unet/networks/unext2.py))
 for configuration details.
 """
 # %%

applications/cytoland/examples/VS_model_inference/demo_vsneuromast.py

@@ -72,7 +72,7 @@
 """
 For this example we will use the following parameters:
 ### For more information on the VSNeuromast model:
-See ``viscy.unet.networks.unext2`` ([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/unext2.py)) for configuration details.
+See ``viscy.unet.networks.unext2`` ([source code](https://github.com/Biohub/VisCy/blob/main/viscy/unet/networks/unext2.py)) for configuration details.
 """
 # %%
 # Setup the data module.

applications/cytoland/examples/dl-course-exercise/README.md

@@ -12,7 +12,7 @@ Written by Eduardo Hirata-Miyasaki, Ziwen Liu, and Shalin Mehta, CZ Biohub San F
 In this exercise, we will predict fluorescence images of nuclei and plasma membrane markers from quantitative phase images of cells, i.e., we will _virtually stain_ the nuclei and plasma membrane visible in the phase image.
 This is an example of an image translation task. We will apply spatial and intensity augmentations to train robust models and evaluate their performance. Finally, we will explore the opposite process of predicting a phase image from a fluorescence membrane label.
 
-[![HEK293T](https://raw.githubusercontent.com/mehta-lab/VisCy/main/docs/figures/svideo_1.png)](https://github.com/mehta-lab/VisCy/assets/67518483/d53a81eb-eb37-44f3-b522-8bd7bddc7755)
+[![HEK293T](https://raw.githubusercontent.com/Biohub/VisCy/main/docs/figures/svideo_1.png)](https://github.com/Biohub/VisCy/assets/67518483/d53a81eb-eb37-44f3-b522-8bd7bddc7755)
 (Click on image to play video)
 
 ## Goals
@@ -32,7 +32,7 @@ This is an example of an image translation task. We will apply spatial and inten
   - Evaluate the model using pixel-level and instance-level metrics.
 
 
-Checkout [VisCy](https://github.com/mehta-lab/VisCy/tree/main/examples/demos),
+Checkout [VisCy](https://github.com/Biohub/VisCy/tree/main/examples/demos),
 our deep learning pipeline for training and deploying computer vision models
 for image-based phenotyping including the robust virtual staining of landmark organelles.
 VisCy exploits recent advances in data and metadata formats
@@ -73,7 +73,7 @@ The script will:
 - Install `cytoland` + `viscy` (`>=0.5.0a0`) plus the tutorial extras:
   `cellpose`, `torchview`, `microssim`, `jupyter`, `ipykernel`,
   `ipywidgets`, `jupytext`. If you ran the script from inside a clone of
-  the [VisCy monorepo](https://github.com/mehta-lab/VisCy), it installs
+  the [VisCy monorepo](https://github.com/Biohub/VisCy), it installs
   `cytoland` editable from the local workspace; otherwise it installs from
   PyPI.
 - Register the venv as a Jupyter kernel named **`06_image_translation`**

applications/cytoland/examples/dl-course-exercise/solution.py

@@ -18,7 +18,7 @@
 # - Visualize the image transform learned by the model
 # - Understand the failure modes of the trained model
 #
-# [![HEK293T](https://raw.githubusercontent.com/mehta-lab/VisCy/main/docs/figures/svideo_1.png)](https://github.com/mehta-lab/VisCy/assets/67518483/d53a81eb-eb37-44f3-b522-8bd7bddc7755)
+# [![HEK293T](https://raw.githubusercontent.com/Biohub/VisCy/main/docs/figures/svideo_1.png)](https://github.com/Biohub/VisCy/assets/67518483/d53a81eb-eb37-44f3-b522-8bd7bddc7755)
 # (Click on image to play video)
 #
 # %% [markdown] tags=[]
@@ -43,7 +43,7 @@
 #   - Explore the model's regime of validity by applying blurring and scaling transforms to the input phase image.
 #
 # #### For more information:
-# Checkout [VisCy](https://github.com/mehta-lab/VisCy),
+# Checkout [VisCy](https://github.com/Biohub/VisCy),
 # our deep learning pipeline for training and deploying computer vision models
 # for image-based phenotyping including the robust virtual staining of landmark organelles.
 #
@@ -553,7 +553,7 @@ def log_batch_jupyter(batch):
 # [RandAffined](https://docs.monai.io/en/stable/transforms.html#randaffined),
 # [RandGaussianNoised](https://docs.monai.io/en/stable/transforms.html#randgaussiannoised).
 # You can also inspect any transform in a cell with `RandAffined?`.<br><br>
-# [Compare your choice of augmentations against the pretrained models and config files](https://github.com/mehta-lab/VisCy/releases/download/v0.1.0/VisCy-0.1.0-VS-models.zip).
+# [Compare your choice of augmentations against the pretrained models and config files](https://github.com/Biohub/VisCy/releases/download/v0.1.0/VisCy-0.1.0-VS-models.zip).
 # </div>
 # %% tags=["task"]
 # Here we turn on data augmentation and rerun setup
@@ -751,7 +751,7 @@ def log_batch_jupyter(batch):
 # - Start the training <br>
 #
 # <b> Note </b> <br>
-# See ``viscy.translation.engine.VSUNet`` ([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/translation/engine.py)) and ``viscy.unet.networks.fcmae`` ([source code](https://github.com/mehta-lab/VisCy/blob/main/viscy/unet/networks/fcmae.py)) to learn more about the configuration parameters and FCMAE architecture.
+# See ``viscy.translation.engine.VSUNet`` ([source code](https://github.com/Biohub/VisCy/blob/main/viscy/translation/engine.py)) and ``viscy.unet.networks.fcmae`` ([source code](https://github.com/Biohub/VisCy/blob/main/viscy/unet/networks/fcmae.py)) to learn more about the configuration parameters and FCMAE architecture.
 # </div>
 
 # %% tags=["task"]

applications/cytoland/examples/phase_contrast/solution.py

@@ -102,7 +102,7 @@
 For this example we will use the following parameters:
 ### For more information on the VSCyto3D model:
 See ``viscy.unet.networks.fcmae``
-([source code](https://github.com/mehta-lab/VisCy/blob/6a3457ec8f43ecdc51b1760092f1a678ed73244d/viscy/unet/networks/unext2.py#L252))
+([source code](https://github.com/Biohub/VisCy/blob/6a3457ec8f43ecdc51b1760092f1a678ed73244d/viscy/unet/networks/unext2.py#L252))
 for configuration details.
 """
 # %%

applications/cytoland/examples/vcp_tutorials/hek293t.ipynb

@@ -92,7 +92,7 @@
     "# `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub\n",
     "# monorepo: the prediction writer needs a viscy-data newer than its PyPI release.\n",
     "# Here stackview and ipycanvas are installed for visualization\n",
-    "!pip install \"viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland\" stackview ipycanvas==0.11"
+    "!pip install \"viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland\" stackview ipycanvas==0.11"
    ]
   },
   {

applications/cytoland/examples/vcp_tutorials/hek293t.py

@@ -59,7 +59,7 @@
 # `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub
 # monorepo: the prediction writer needs a viscy-data newer than its PyPI release.
 # Here stackview and ipycanvas are installed for visualization
-# !pip install "viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland" stackview ipycanvas==0.11
+# !pip install "viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland" stackview ipycanvas==0.11
 
 # %%
 # Restart kernel if running in Google Colab

applications/cytoland/examples/vcp_tutorials/neuromast.ipynb

@@ -100,7 +100,7 @@
     "# `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub\n",
     "# monorepo: the prediction writer needs a viscy-data newer than its PyPI release.\n",
     "# Here stackview and ipycanvas are installed for visualization, and iohub provides OME-Zarr access\n",
-    "!pip install \"viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland\" iohub stackview ipycanvas"
+    "!pip install \"viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland\" iohub stackview ipycanvas"
    ]
   },
   {

applications/cytoland/examples/vcp_tutorials/neuromast.py

@@ -67,7 +67,7 @@
 # `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub
 # monorepo: the prediction writer needs a viscy-data newer than its PyPI release.
 # Here stackview and ipycanvas are installed for visualization, and iohub provides OME-Zarr access
-# !pip install "viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland" iohub stackview ipycanvas
+# !pip install "viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland" iohub stackview ipycanvas
 
 # %%
 # Restart kernel if running in Google Colab

applications/cytoland/examples/vcp_tutorials/quick_start.ipynb

@@ -82,7 +82,7 @@
     "To run inference on your own data,\n",
     "convert them into the OME-Zarr data format using iohub or other\n",
     "[tools](https://ngff.openmicroscopy.org/tools/index.html#file-conversion),\n",
-    "and run [pre-processing](https://github.com/mehta-lab/VisCy/blob/main/docs/usage.md#preprocessing)\n",
+    "and run [pre-processing](https://github.com/Biohub/VisCy/blob/main/docs/usage.md#preprocessing)\n",
     "with the `viscy preprocess` CLI."
    ]
   },
@@ -122,7 +122,7 @@
     "# `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub\n",
     "# monorepo: the prediction writer needs a viscy-data newer than its PyPI\n",
     "# release. viscy-models and viscy-transforms resolve from PyPI.\n",
-    "!pip install \"viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland\" torchview cmap iohub"
+    "!pip install \"viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils\" \"viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data\" \"cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland\" torchview cmap iohub"
    ]
   },
   {

applications/cytoland/examples/vcp_tutorials/quick_start.py

@@ -61,7 +61,7 @@
 To run inference on your own data,
 convert them into the OME-Zarr data format using iohub or other
 [tools](https://ngff.openmicroscopy.org/tools/index.html#file-conversion),
-and run [pre-processing](https://github.com/mehta-lab/VisCy/blob/main/docs/usage.md#preprocessing)
+and run [pre-processing](https://github.com/Biohub/VisCy/blob/main/docs/usage.md#preprocessing)
 with the `viscy preprocess` CLI.
 """
 
@@ -90,7 +90,7 @@
 # `cytoland`, `viscy-utils` and `viscy-data` are installed from the GitHub
 # monorepo: the prediction writer needs a viscy-data newer than its PyPI
 # release. viscy-models and viscy-transforms resolve from PyPI.
-# !pip install "viscy-utils @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/mehta-lab/VisCy.git#subdirectory=applications/cytoland" torchview cmap iohub
+# !pip install "viscy-utils @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-utils" "viscy-data @ git+https://github.com/Biohub/VisCy.git#subdirectory=packages/viscy-data" "cytoland @ git+https://github.com/Biohub/VisCy.git#subdirectory=applications/cytoland" torchview cmap iohub
 
 # %%
 # restart kernel if running in Google Colab

applications/cytoland/pyproject.toml

@@ -42,9 +42,9 @@ dependencies = [
 optional-dependencies.metrics = [
   "cellpose",
 ]
-urls.Homepage = "https://github.com/mehta-lab/VisCy"
-urls.Issues = "https://github.com/mehta-lab/VisCy/issues"
-urls.Repository = "https://github.com/mehta-lab/VisCy"
+urls.Homepage = "https://github.com/Biohub/VisCy"
+urls.Issues = "https://github.com/Biohub/VisCy/issues"
+urls.Repository = "https://github.com/Biohub/VisCy"
 
 [dependency-groups]
 dev = [ { include-group = "test" } ]

applications/dynacell/CITATION.cff

@@ -50,7 +50,7 @@ authors:
   - given-names: Shalin B.
     family-names: Mehta
     affiliation: Biohub, San Francisco
-repository-code: "https://github.com/mehta-lab/VisCy"
+repository-code: "https://github.com/Biohub/VisCy"
 license: BSD-3-Clause
 abstract: >-
   The DynaCell evaluation suite assesses 3D time-lapse virtual staining of live

applications/dynacell/configs/benchmarks/virtual_staining/_internal/shared/model/model_overlays/pix2pix3d_unetvit_fit_modernized.yml

@@ -1,7 +1,7 @@
 # pix2pix3d_unetvit fit overlay — STYLEGAN2/R3GAN-STYLE MODERNIZED RECIPE.
 #
 # Opt-in modernization of the LSGAN baseline. See PR
-# https://github.com/mehta-lab/VisCy/pull/428 for the recipe and rationale.
+# https://github.com/Biohub/VisCy/pull/428 for the recipe and rationale.
 #
 # Differences from pix2pix3d_unetvit_fit.yml:
 #   - loss_type:   lsgan         -> nonsat (StyleGAN2 default; softplus-based)

applications/dynacell/configs/benchmarks/virtual_staining/er/pix2pix3d_unetvit/ipsc_confocal/train_4gpu_modernized.yml

@@ -1,7 +1,7 @@
 # pix2pix3d_unetvit MODERNIZED fit on ER (SEC61B) — 4-GPU DDP production leaf.
 #
 # Run A of the modernization recipe (see PR
-# https://github.com/mehta-lab/VisCy/pull/428).
+# https://github.com/Biohub/VisCy/pull/428).
 #
 # Sibling of train_4gpu.yml. Same experiment_id and W&B/save-dir structure,
 # but composes the MODERNIZED model overlay (nonsat + R1 + EMA + equal LR)