The Python 3 module spikeship which implements the fast, unsupervised dissimilarity measure described in Sotomayor-Gómez, B., L., Battaglia, F. and Vinck, M. (2023). "SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns". Article, PLoS CB.
The dependencies can be installed by running ./env_setup.sh <ENV_NAME> with the optional argument specifying the target environment (which must be source-able). To setup the module, run python setup.py install. A jupyter notebook is available in notebooks/, along with a demo dataset, showing an example workflow for the SpikeShip methods and its comparison with SPOTDis.
conda create -n spikeship python=3./env_setup.sh spikeshipsource activate spikeshippython setup.py install
conda create -n spikeship python=3.6.5conda activate spikeshipconda install numbaconda install ipykernelconda install matplotlibconda install scikit-learnconda install joblibpython setup.py install
The software requirements/dependencies are the same from the work , the implementation of the Spike Pattern Optimal Transport Dissimilarity described in Grossberger, L., Battaglia, F. and Vinck, M. (2018). Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS CB.