SCoT is a Python package for EEG/MEG source connectivity estimation. In particular, it includes measures of directed connectivity based on vector autoregressive modeling.
Use the following command to install the latest release:
pip install scot
Documentation is available at http://scot-dev.github.io/scot-doc/index.html.
SCoT requires numpy ≥ 1.8.2 and scipy ≥ 0.13.3. Optionally, matplotlib ≥ 1.4.0, scikit-learn ≥ 0.15.0, and mne ≥ 0.11.0 can be installed for additional functionality.
To run the examples on Linux, invoke the following commands inside the SCoT directory:
PYTHONPATH=. python examples/misc/connectivity.py
PYTHONPATH=. python examples/misc/timefrequency.py
etc.
Note that the example data from https://github.com/SCoT-dev/scot-data needs to be available. The scot-data package must be on Python's search path.
In February 2024 we managed to build the docs with the following package versions:
[tool.poetry.dependencies]
python = "^3.11"
sphinx = "^7.2.6"
matplotlib = "^3.8.3"
scipy = "^1.12.0"
scikit-learn = "^1.4.1.post1"
Note that these are the most recent versions at the moment, so it is likely that future versions will just work. When using a newer version of sphinx, it may be necessary to update the subrepository in doc/sphinxext/numpydoc.
