osl-dynamics 1.1.3

Models for infering dynamics in neuroimaging data

See the read the docs page for a description of this project: https://osl-dynamics.readthedocs.io.

Installation

osl-dynamics can be installed using the conda environment files in /envs. For a generic linux machine, osl-dynamics can be installed in editable mode with:

git clone https://github.com/OHBA-analysis/osl-dynamics.git
cd osl-dynamics
conda env create -f envs/linux.yml
conda activate osld
pip install -e .

If you’re installing on the Oxford BMRC server, use envs/bmrc.yml. If you’re installing on the OHBA workstation, use envs/hbaws.yml.

Developers might want to clone the repo using SSH:

git clone [e-mail]:OHBA-analysis/osl-dynamics.git

To use the HMM you also need to install armadillo:

conda install -c conda-forge armadillo

Documentation

The read the docs page should be automatically updated whenever there’s a new commit on the main branch.

The documentation is included as docstrings in the source code. Please write docstrings to any classes or functions you add following the numpy style. The API reference documentation will only be automatically generated if the docstrings are written correctly. The documentation directory /doc also contains .rst files that provide additional info regarding installation, development, the models, etc.

To compile the documentation locally you need to install the required packages (sphinx, etc.) in your conda environment:

cd osl-dynamics
pip install -r doc/requirements.txt

To compile the documentation locally use:

python setup.py build_sphinx

The local build of the documentation webpage can be found in build/sphinx/html/index.html.

Releases

The process of packaging a python project is described here: https://packaging.python.org/en/latest/tutorials/packaging-projects.

A couple packages are needed to build and upload a project to PyPI, these can be installed in your conda environment with:

pip install build twine

The following steps can be used to release a new version:

1. Update the version number on line 5 of setup.cfg.

2. Commit the updated setup.cfg to the main branch of the GitHub repo.

3. Delete any old distributions that have been built (if there are any): rm -r dist.

4. Build a distribution in the osl-dynamics root directory with python -m build. This will create a new directory called dist.

5. Test the build by installing in a test conda environment with cd dist; pip install <build>.whl.

6. Upload the distribution to PyPI with twine upload dist/*. You will need to enter the username and password that you used to register with https://pypi.org.

7. Tag the commit uploaded to PyPI with the version number using the ‘Create a new release’ link on the right of the GitHub repo webpage.

The uploaded distribution will then be available to be installed with:

pip install osl-dynamics

Editing Source Code

Formatting and Conventions

We use the python code formatter black to give a consistent code layout in our source files. To install:

conda activate <env>
pip install black

To format a source file:

black <filename>.py

Please run black on any edited files before commiting changes.

Git Workflow

We use git for version control. There is one main branch. To add changes:

Create a feature branch for changes:

git checkout main
git pull
git checkout -b <branch-name>

Make changes to file and commit it to the branch:

git add <file>
git commit -m "Short description of changes"

When writing commit messages please follow the conventions here.

Then either push the new branch to the remote repository:

git push --set-upstream origin <branch-name>

and create a pull request (recommended), or merge branch into main and push:

git checkout main
git merge <branch-name>
git push