osl-dynamics 1.3.1

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 <https://osl-dynamics.readthedocs.io>_.

Citation

If you find this toolbox useful, please cite:

**Gohil C., Huang R., Roberts E., van Es M.W.J., Quinn A.J., Vidaurre D., Woolrich M.W. (2023) osl-dynamics: A toolbox for modelling fast dynamic brain activity. eLife 12:RP91949 https://doi.org/10.7554/eLife.91949.2**

Installation

Conda

We recommend installing osl-dynamics within a virtual environment. You can do this with Anaconda <https://docs.anaconda.com/free/anaconda/install/index.html>_ (or miniconda <https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html>_).

Linux

Here, we describe how to install osl-dynamics from source. We recommend using the conda environment files in /envs. For a generic linux machine, osl-dynamics can be installed in editable mode with:

.. code-block:: shell

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 .

Note, if you have a Mac you may want to use the envs/mac.yml environment file instead.

Windows

If you are using a Windows computer, we recommend first installing linux (Ubuntu) as a Windows Subsystem by following the instructions here <https://ubuntu.com/wsl>_. Then following the instructions above in the Ubuntu terminal.

Oxford specific computers

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. Note, the hbaws.yml environment will automatically install spyder and jupyter notebooks.

Within an osl environment

If you have already installed OSL <https://github.com/OHBA-analysis/osl>_ you can install osl-dynamics in the osl environment with:

.. code-block:: shell

conda activate osl
cd osl-dynamics
pip install tensorflow==2.9.1
pip install tensorflow-probability==0.17
pip install -e .

Note, if you're using a Mac computer you need to install TensorFlow with pip install tensorflow-macos==2.9.1 instead of tensorflow==2.9.1.

Developers

Developers might want to clone the repo using SSH instead of HTTPS:

.. code-block:: shell

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

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 <https://numpydoc.readthedocs.io/en/latest/format.html>_. 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:

.. code-block:: shell

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

To compile the documentation locally use:

.. code-block:: shell

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 <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:

.. code-block:: shell

pip install build twine

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

#. Update the version on line 5 of setup.cfg by removing dev from the version number. #. Commit the updated setup.cfg to the main branch of the GitHub repo. #. Delete any old distributions that have been built (if there are any): rm -r dist. #. Build a distribution in the osl-dynamics root directory with python -m build. This will create a new directory called dist. #. Test the build by installing in a test conda environment with cd dist; pip install <build>.whl. #. 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 <https://pypi.org>_. #. 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. #. 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:

.. code-block:: shell

pip install osl-dynamics

Editing Source Code

See here <https://github.com/OHBA-analysis/osl-dynamics/blob/main/doc/using_bmrc.rst>_ for useful info regarding how to use the BMRC cluster and how to edit the source code.