A Jupyter Widget for Niivue based on anywidget.
Install ipyniivue using pip
:
pip install ipyniivue
In a Jupyter environment:
from ipyniivue import NiiVue
nv = NiiVue()
nv.load_volumes([{"path": "images/mni152.nii.gz"}])
nv
This will render an interactive Niivue widget within your notebook.
See the basic demo to learn more.
See the Documentation for usage.
ipyniivue uses the recommended hatchling
build system, which is convenient to use via the hatch
CLI. We recommend installing hatch
globally (e.g., via pipx
) and running the various commands defined within pyproject.toml
. hatch
will take care of creating and synchronizing a virtual environment with all dependencies defined in pyproject.toml
.
Run these commands from the root of the project:
Command | Description |
---|---|
hatch run format |
Format the project with ruff format . and apply linting with ruff --fix . |
hatch run lint |
Lint the project with ruff check . |
hatch run test |
Run unit tests with pytest |
hatch run docs |
Build docs with Sphinx |
Alternatively, you can manually create a virtual environment and manage installation and dependencies with pip
:
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
This is an anywidget project, meaning the codebase is a hybrid of Python and JavaScript. The JavaScript code resides under the js/
directory and uses esbuild for bundling. Whenever you make changes to the JavaScript code, you need to rebuild the files under src/ipyniivue/static
.
You have two options:
-
Build Once: Build the JavaScript code one time:
npm run build
-
Start Development Server: Start a development server that automatically rebuilds the code as you make changes:
npm run dev
We recommend this approach for a smoother development experience.
Working with Jupyter
Once the development server is running, you can start JupyterLab or Visual Studio Code to develop the widget. When you're finished, stop the development server with Ctrl+C
.
Note: To have
anywidget
automatically apply changes as you work, set the environment variableANYWIDGET_HMR=1
. You can set this directly in a notebook cell:%env ANYWIDGET_HMR=1or in the shell:
export ANYWIDGET_HMR=1
Releases are automated using GitHub Actions via the release.yml
workflow.
-
Commit Changes: Ensure all your changes are committed.
-
Create a Tag: Create a new tag matching the pattern
v*
:git tag -a vX.X.X -m "vX.X.X" git push --follow-tags
-
Workflow Actions: When triggered, the workflow will:
- Publish the package to PyPI with the tag version.
- Generate a changelog based on conventional commits.
- Create a GitHub Release with the changelog.
- We generate a changelog for GitHub releases with
antfu/changelogithub
. - Each changelog entry is grouped and rendered based on conventional commits.
- It's recommended to follow the Conventional Commits specification.