A semi-latent state-space model that combines movement, LFP, and ensemble single unit/multiunit information to identify periods of replay and decode its content.

NOTE: This code is still in production and prepublication.

replay_identification can be installed through pypi or conda. Conda is the best way to ensure that everything is installed properly.

pip install replay_identification
python setup.py install

Or

conda install -c edeno replay_identification
python setup.py install

See the notebooks (#1, #2) for more information on how to use the package.

You can also use the launch binder button at the top of the Readme to play with simulated data in your web browser.

• numpy
• scipy
• statsmodels
• numba
• matplotlib
• xarray
• scikit-learn
• regularized_glm

See the setup.pyor environment.yml file for the most up to date list of dependencies.

1. Install miniconda (or anaconda) if it isn't already installed. Type into bash (or install from the anaconda website):

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r

2. Go to the local repository (.../replay_identification) and install the anaconda environment for the repository. Type into bash:

conda update -q conda
conda info -a
conda env create -f environment.yml
source activate replay_identification
python setup.py develop