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

If you find this toolbox useful, please cite the paper:

Gohil, C., Huang, R., Roberts, E., van Es, M. W., Quinn, A. J., Vidaurre, D., & Woolrich, M. W. (2024). osl-dynamics, a toolbox for modeling fast dynamic brain activity. Elife, 12, RP91949.

We recommend installing osl-dynamics using the conda environment files in /envs, which can be installed using Miniforge.

Miniforge (conda/mamba) can be installed with:

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" bash Miniforge3-$(uname)-$(uname -m).sh rm Miniforge3-$(uname)-$(uname -m).sh 

Different computers have their own environment files. For more information see the envs readme.

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/osld-tf.yml mamba env create -f osld-tf.yml rm osld-tf.yml 

If you have a GPU, then use the osld-tf-cuda.yml environment instead:

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/osld-tf-cuda.yml mamba env create -f osld-tf-cuda.yml rm osld-tf-cuda.yml 

If you have an M-series (M1, M2, M3) chip use:

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/osld-tf.yml mamba env create -f osld-tf.yml rm osld-tf.yml 

Otherwise, if you have an Intel chip use:

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/osld-tf-macos.yml mamba env create -f osld-tf-macos.yml rm osld-tf-macos.yml 

If you are using a Windows computer, we recommend first installing Linux (Ubuntu) as a Windows Subsystem by following the instructions here. Then follow the instructions for Linux above in the Ubuntu terminal.

On the OHBA workstation (hbaws), install Miniforge and Mamba using the instructions above and install osl-dynamics using:

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/hbaws.yml mamba env create -f hbaws.yml rm hbaws.yml 

On the Biomedical Research Computing (BMRC) cluster, conda and mamba are available as a software module:

module load Miniforge3 

and osl-dynamics can be installed with:

wget https://raw.githubusercontent.com/OHBA-analysis/osl-dynamics/refs/heads/main/envs/bmrc.yml mamba env create -f bmrc.yml rm bmrc.yml 

The above can be run on the login nodes (clusterX.bmrc.ox.ac.uk). On compg017 you will need to set the following to use conda:

unset https_proxy http_proxy no_proxy HTTPS_PROXY HTTP_PROXY NO_PROXY 

See here for useful information regarding how to use the BMRC cluster.

You should only need to do this if you need a feature or fix that has not been released on pip yet.

After you have created an osld environment you can install the latest code (development version) from the GitHub repository with:

conda activate osld pip install git+https://github.com/OHBA-analysis/osl-dynamics.git 

After you have created an osld environment you can install an editable local copy of the source code on your computer with:

git clone https://github.com/OHBA-analysis/osl-dynamics.git conda activate osld cd osl-dynamics pip install -e . 

You will run your local copy of the code when you import osl_dynamics.

If you are a developer, you may wish to clone the repository using SSH rather than HTTPS to make pushing branches/commits easier:

git clone git@github.com:OHBA-analysis/osl-dynamics.git 

You can use the following to check if TensorFlow is using any GPUs you have available:

python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" 

This should return a list of GPUs.

Simply delete the conda environment:

conda env remove -n osld conda clean --all 

And remove the GitHub repository if you have cloned it:

rm -rf osl-dynamics 

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. 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 conda activate osld pip install -r doc/requirements.txt 

To compile the documentation locally use:

sphinx-build -b html doc build 

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

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 on line 10 of pyproject.toml by removing dev from the version number.

2. Commit the updated pyproject.toml to the main branch of the GitHub repo.

3. Check the latest branch has compiled successfully on readthedocs.

4. Delete any old distributions that have been built (if there are any):

rm -r dist 

5. Build a distribution in the osl-dynamics root directory with:

python -m build 

This will create a new directory called dist.

6. Test the build by installing with

pip install dist/<build>.whl 

7. 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. You may need to setup 2FA and/or an API token, see API token instructions in your PyPI account settings.

8. 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. You will need to untick 'Set as a pre-release' and tick 'Set as the latest release'.

9. Change the version to X.Y.devZ in pyproject.toml and commit the new dev version to main.

10. Reinstall your editable development version in your osld conda environment:

pip install -e . 

11. Draft a new release (click 'Releases' on the right panel on the GitHub homepage, then 'Draft a new release') to help keep note of changes for the next release.