osl-dynamics 2.0.0

Models for infering dynamics in neuroimaging data

osl-dynamics

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

Citation

If you find this toolbox useful, please cite:

Chetan Gohil, Rukuang Huang, Evan Roberts, Mats WJ van Es, Andrew J Quinn, Diego Vidaurre, Mark W Woolrich (2024) osl-dynamics, a toolbox for modeling fast dynamic brain activity eLife 12:RP91949.

Installation

Conda

We recommend installing osl-dynamics within a virtual environment. You can do this with Anaconda (or miniconda.

Below we describe how to install osl-dynamics from source. We recommend using the conda environment files in /envs.

Linux

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 .

Mac

For a Mac, the installation of TensorFlow is slightly different to a Linux computer. We recommend using the lines above replacing the Linux environment file envs/linux.yml with the Mac environment file envs/mac.yml.

Note, you may also need to do

pip install tensorflow-metal==0.7.0

to get your GPUs working. See here for further details.

Windows

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

Within an osl environment

If you have already installed OSL you can install osl-dynamics in the osl environment with:

conda activate osl
cd osl-dynamics
pip install tensorflow==2.11.0
pip install tensorflow-probability==0.19.0
pip install -e .

Note, if you're using a Mac computer you need to install TensorFlow with the following instead:

pip install tensorflow-macos==2.11.0

You may also need to install tensorflow-metal with

pip install tensorflow-metal==0.7.0

to use any GPUs that maybe available. See here for further details.

TensorFlow versions

osl-dynamics has been tested with the following versions:

tensorflow	tensorflow-probability
2.11	0.19
2.12	0.19
2.13	0.20
2.14	0.22
2.15	0.22

Test GPUs are working

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

conda activate osld
python
>> import tensorflow as tf
>> print(tf.test.is_gpu_available())

This should print True if you have GPUs available (and False otherwise).

Removing osl-dynamics

Simply delete the conda environment and repository:

conda env remove -n osld
rm -rf osl-dynamics

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

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 5 of setup.cfg by removing dev from the version number.

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, e.g. with

conda create --name test python=3.10.14
conda activate test
pip install tensorflow==2.11.0 tensorflow-probability==0.19.0
pip install dist/<build>.whl
python examples/simulation/hmm_hmm-mvn.py
python examples/simulation/dynemo_hmm-mvn.py

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

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

8. Change the version to X.Y.devZ in setup.cfg and commit the new dev version to main.

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

pip install osl-dynamics

9. Optional: 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.

10. Activate the new version in the readthedocs project.

Editing Source Code

See here for useful info regarding how to use the Oxford BMRC cluster and how to edit the source code.