Update and run all pre commit hooks

.napari/DESCRIPTION.md

@@ -1,5 +1,5 @@
-
 <!---->
+
 <!--
 TODO : complete
 This file is designed to provide you with a starting template for documenting
@@ -10,6 +10,7 @@ The sections below are given as a guide for the flow of information only, and
 are in no way prescriptive. You should feel free to merge, remove, add and
 rename sections at will to make this document work best for your plugin.
 -->
+
 ## Description
 
 A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
@@ -46,6 +47,7 @@ Here is an example of an mp4 video embedded this way.
 
 https://user-images.githubusercontent.com/17995243/120088305-6c093380-c132-11eb-822d-620e81eb5f0e.mp4
 -->
+
 ## Intended Audience & Supported Data
 
 This plugin requires basic knowledge in machine learning;
@@ -68,16 +70,18 @@ make sure to mention this.
 If you know of researchers, groups or labs using your plugin, or if it has been cited
 anywhere, feel free to also include this information here.
 -->
+
 ## Quickstart
 
 Install from pip with `pip install napari-cellseg3d`
 
 OR
 
 - Install napari from pip with `pip install "napari[all]"`,
-then from the “Plugins” menu within the napari application, select “Install/Uninstall Package(s)...”
+  then from the “Plugins” menu within the napari application, select “Install/Uninstall Package(s)...”
 - Copy `napari-cellseg3d` and paste it where it says “Install by name/url…”
 - Click “Install”
+
 <!--
 
 
@@ -91,6 +95,7 @@ quick overview of the plugin's functionality, but you should definitely link out
 more complex and in-depth tutorials highlighting any intricacies of your plugin, and
 more detailed documentation if you have it.
 -->
+
 ## Additional Install Steps
 
 **Python >= 3.8 required**
@@ -114,10 +119,12 @@ over in `setup.cfg`. However, if your plugin has any more complex dependencies,
 requires any additional preparation before (or after) installation, you should add
 this information here.
 -->
+
 ## Getting Help
 
 If you would like to report an issue with the plugin,
 please open an [issue on Github](https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues)
+
 <!--
 This section should point users to your preferred support tools, whether this be raising
 an issue on GitHub, asking a question on image.sc, or using some other method of contact.
@@ -127,7 +134,6 @@ here.
 
 ## How to Cite
 
-
 <!--
 Many plugins may be used in the course of published (or publishable) research, as well as
 during conference talks and other public facing events. If you'd like to be cited in

.pre-commit-config.yaml

@@ -1,37 +1,40 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v6.0.0
     hooks:
-#      - id: check-docstring-first
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-yaml
       - id: check-toml
       # - id: check-added-large-files
-      # args: [--maxkb=5000]
-#  - repo: https://github.com/pycqa/isort
-#    rev: 5.12.0
-#    hooks:
-#      - id: isort
-#        args: ["--profile", "black", --line-length=79]
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    # Ruff version.
-    rev: 'v0.5.6'
+      #   args: [--maxkb=5000]
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.15.13
     hooks:
       - id: ruff
-        args: [ --fix, --exit-non-zero-on-fix ]
-  - repo: https://github.com/psf/black
-    rev: 24.8.0
+        args: [--fix, --unsafe-fixes, --exit-non-zero-on-fix]
+
+      - id: ruff-format
+
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 1.0.0
     hooks:
-      - id: black
-        args: [--line-length=79]
+      - id: mdformat
+        additional_dependencies:
+          - mdformat-myst
+
+  - repo: https://github.com/tox-dev/pyproject-fmt
+    rev: v2.21.2
+    hooks:
+      - id: pyproject-fmt
+
+  - repo: https://github.com/abravalheri/validate-pyproject
+    rev: v0.25
+    hooks:
+      - id: validate-pyproject
+
   - repo: https://github.com/tlambert03/napari-plugin-checks
     rev: v0.3.0
     hooks:
       - id: napari-plugin-checks
-  # https://mypy.readthedocs.io/en/stable/introduction.html
-  # you may wish to add this as well!
-  # - repo: https://github.com/pre-commit/mirrors-mypy
-  #   rev: v0.910-1
-  #   hooks:
-  #     - id: mypy

CODE_OF_CONDUCT.md

@@ -14,22 +14,22 @@ appearance, race, religion, or sexual identity and orientation.
 Examples of behavior that contributes to creating a positive environment
 include:
 
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
 
 Examples of unacceptable behavior by participants include:
 
-* The use of sexualized language or imagery and unwelcome sexual attention or
- advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
- address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
 
 ## Our Responsibilities
 
@@ -70,7 +70,7 @@ members of the project's leadership.
 This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
 available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
 
-[homepage]: https://www.contributor-covenant.org
-
 For answers to common questions about this code of conduct, see
 https://www.contributor-covenant.org/faq
+
+[homepage]: https://www.contributor-covenant.org

README.md

@@ -1,5 +1,7 @@
 # CellSeg3D: self-supervised (and supervised) 3D cell segmentation, primarily for mesoSPIM data!
+
 <!-- [![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari_cellseg3d) -->
+
 [![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg3d)
 [![Downloads](https://static.pepy.tech/badge/napari-cellseg3d)](https://pepy.tech/project/napari-cellseg3d)
 [![Downloads](https://static.pepy.tech/badge/napari-cellseg3d/month)](https://pepy.tech/project/napari-cellseg3d)
@@ -9,16 +11,13 @@
 
 <img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/838605d0-9723-4e43-83cd-6dbfe4adf36b/cellseg-logo.png?format=1500w" title="cellseg3d" alt="cellseg3d logo" width="150" align="right" vspace = "80"/>
 
-
 **A package for 3D cell segmentation with deep learning, including a napari plugin**: training, inference, and data review. In particular, this project was developed for analysis of confocal and mesoSPIM-acquired (cleared tissue + lightsheet) tissue datasets, but is not limited to this type of data. [Check out our publication for more information!](https://elifesciences.org/articles/99848)
 
-
 ![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)
 
-
 ## Installation
 
- 💻 See the [Installation page](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) in the documentation for detailed instructions.
+💻 See the [Installation page](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) in the documentation for detailed instructions.
 
 ## Documentation
 
@@ -33,21 +32,22 @@ pip install napari_cellseg3d
 ```
 
 To use the plugin, please run:
+
 ```
 napari
 ```
+
 Then go into `Plugins > napari_cellseg3d`, and choose which tool to use.
 
 - **Review (label)**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
 - **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
-- **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
+- **Train**: This module allows you to train segmentation algorithms from labeled volumes.
 - **Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; fragment images into smaller cubes for training; or converting labels from instance to segmentation and the opposite.
 
 ## Why use CellSeg3D?
 
 The strength of our approach is we can match supervised model performance with purely self-supervised learning, meaning users don't need to spend (hundreds) of hours on annotation. Here is a quick look of our key results. TL;DR see panel **f**, which shows that with minmal input data we can outperform supervised models:
 
-
 ![FIG1 (1)](https://github.com/user-attachments/assets/0d970b45-79ff-4c58-861f-e1e7dc9abc65)
 
 **Figure 1. Performance of 3D Semantic and Instance Segmentation Models.**
@@ -59,7 +59,6 @@ F1-score is computed from the Intersection over Union (IoU) with ground truth la
 **c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation.
 **d:** Illustration of our WNet3D architecture showcasing the dual 3D U-Net structure with our modifications.
 
-
 ## News
 
 ### **CellSeg3D now published at eLife**
@@ -69,15 +68,19 @@ Read the [article here !](https://elifesciences.org/articles/99848)
 ### **New version: v0.2.2**
 
 - v0.2.2:
+
   - Updated the Colab Notebooks for training and inference
   - New models available in the inference demo notebook
   - CRF optional post-processing adjustments (and pip install directly)
+
 - v0.2.1:
+
   - Updated plugin default behaviors across the board to be more readily applicable to demo data
   - Threshold value in inference is now automatically set by default according to performance on demo data on a per-model basis
   - Added a grid search utility to find best thresholds for supervised models
 
 - v0.2.0:
+
   - Changed project name to "napari_cellseg3d" to avoid setuptools deprecation
   - Small API changes for training/inference from a script
   - Some fixes to WandB integration and csv saving after training
@@ -97,7 +100,6 @@ Previous additions:
 - New utilities
 - Many small improvements and many bug fixes
 
-
 ## Requirements
 
 **Compatible with Python 3.8 to 3.10.**
@@ -118,39 +120,38 @@ Please reach out if you have any issues with the installation, we will be happy
 
 To avoid issues when installing on the ARM64 architecture, please follow these steps.
 
-1) Create a new conda env using the provided conda/napari_CellSeg3D_ARM64.yml file :
+1. Create a new conda env using the provided conda/napari_CellSeg3D_ARM64.yml file :
 
-        git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git
-        cd CellSeg3d
-        conda env create -f conda/napari_CellSeg3D_ARM64.yml
-        conda activate napari_CellSeg3D_ARM64
-
-
-2) Install a Qt backend (PySide or PyQt5)
-3) Launch napari, the plugin should be available in the plugins menu.
+   ```
+    git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git
+    cd CellSeg3d
+    conda env create -f conda/napari_CellSeg3D_ARM64.yml
+    conda activate napari_CellSeg3D_ARM64
+   ```
 
+1. Install a Qt backend (PySide or PyQt5)
 
+1. Launch napari, the plugin should be available in the plugins menu.
 
 ## Issues
 
 **Help us make the code better by reporting issues and adding your feature requests!**
 
-
 If you encounter any problems, please [file an issue] along with a detailed description.
 
 ## Testing
 
-You can generate docs locally by running ``make html`` in the docs/ folder.
+You can generate docs locally by running `make html` in the docs/ folder.
 
-Before testing, install all requirements using ``pip install napari-cellseg3d[test]``.
+Before testing, install all requirements using `pip install napari-cellseg3d[test]`.
 
-``pydensecrf`` is also required for testing.
+`pydensecrf` is also required for testing.
 
 To run tests locally:
 
-- Locally : run ``pytest napari_cellseg3d\_tests`` in the plugin folder.
-- Locally with coverage : In the plugin folder, run ``coverage run --source=napari_cellseg3d -m pytest`` then ``coverage xml`` to generate a .xml coverage file.
-- With tox : run ``tox`` in the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)
+- Locally : run `pytest napari_cellseg3d\_tests` in the plugin folder.
+- Locally with coverage : In the plugin folder, run `coverage run --source=napari_cellseg3d -m pytest` then `coverage xml` to generate a .xml coverage file.
+- With tox : run `tox` in the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)
 
 ## Contributing
 
@@ -170,23 +171,6 @@ Distributed under the terms of the [MIT] license.
 
 "napari-cellseg3d" is free and open source software.
 
-[napari-hub]: https://www.napari-hub.org/plugins/napari-cellseg3d
-
-[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
-[napari]: https://github.com/napari/napari
-[Cookiecutter]: https://github.com/audreyr/cookiecutter
-[@napari]: https://github.com/napari
-[MIT]: http://opensource.org/licenses/MIT
-[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
-[tox]: https://tox.readthedocs.io/en/latest/
-[pip]: https://pypi.org/project/pip/
-[PyPI]: https://pypi.org/
-[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3D/source/guides/installation_guide.html
-[the PyTorch website for installation instructions]: https://pytorch.org/get-started/locally/
-[PyTorch]: https://pytorch.org/get-started/locally/
-[MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
-[MONAI]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
-
 ## Citation
 
 ```
@@ -220,3 +204,14 @@ Please refer to the documentation for full acknowledgements.
 ## Plugin base
 
 This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
+
+[@napari]: https://github.com/napari
+[cookiecutter]: https://github.com/audreyr/cookiecutter
+[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
+[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
+[mit]: http://opensource.org/licenses/MIT
+[monai]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
+[monai's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
+[napari]: https://github.com/napari/napari
+[pytorch]: https://pytorch.org/get-started/locally/
+[the pytorch website for installation instructions]: https://pytorch.org/get-started/locally/

docs/TODO.md

@@ -1,8 +1,9 @@
-[//]: # (
+\[//\]: # (
 TODO:
+
 - [ ] Add a way to get the current version of the library
 - [x] Update all modules
 - [x] Better WNet3D tutorial
 - [x] Setup GH Actions
 - [ ] Add a bibliography
-)
+  )

docs/source/guides/training_wnet.rst

@@ -18,7 +18,7 @@ The WNet3D **does not require a large amount of data to train**, but **choosing
 
 You may find below some guidelines, based on our own data and testing.
 
-The WNet3D is a self-supervised learning approach for 3D cell segmentation, and relies on the assumption that structural and morphological features of cells can be inferred directly from unlabeled data. This involves leveraging inherent properties such as spatial coherence and local contrast in imaging volumes to distinguish cellular structures. This approach assumes that meaningful representations of cellular boundaries and nuclei can emerge solely from raw 3D volumes. Thus, we strongly recommend that you use WNet3D on stacks that have clear foreground/background segregation and limited noise. Even if your final samples have noise, it is best to train on data that is as clean as you can. 
+The WNet3D is a self-supervised learning approach for 3D cell segmentation, and relies on the assumption that structural and morphological features of cells can be inferred directly from unlabeled data. This involves leveraging inherent properties such as spatial coherence and local contrast in imaging volumes to distinguish cellular structures. This approach assumes that meaningful representations of cellular boundaries and nuclei can emerge solely from raw 3D volumes. Thus, we strongly recommend that you use WNet3D on stacks that have clear foreground/background segregation and limited noise. Even if your final samples have noise, it is best to train on data that is as clean as you can.
 
 
 .. important::

examples/README.md

@@ -5,4 +5,4 @@ All credits to the original authors of the data.
 You can install, launch `napari`, activate the CellSeg3D plugin app, and drag & drop this volume into the canvas.
 Then, for example, run the `Inference` module with one of our models.
 
-See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.
+See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.