DeSMRRest-clustered-reactivation

Code for the publication "Clustered Reactivation During Retrieval"

This analysis was run with Python 3.9, but any version >3.8 should work.

1. Getting started

First install the requirements using pip pip install -r requirements.txt. It is recommended to run this in a dedicated environment not to mix up your current Python installation. You can do so e.g. using conda env.

conda create --name desmrrest python=3.9
conda activate desmrrest
# assuming you are in the folder of the repository
pip install -r requirements.txt

2. Download and setup

Then you need to specify your settings. Open settings.py and around line 117 insert where you want to store the data, or (if you have already downloaded it), where the data was saved. You can leave the other parameters the same.

data_dir = '/path/to/data/'           # directory containing the FIF files
cache_dir = f'{data_dir}/cache/'      # used for caching
plot_dir = f'{data_dir}/plots/'       # plots will be stored here
log_dir = f'{data_dir}/plots/logs/'   # log files will be created here

Download the experiment files from Zenodo into a common folder. Instead of downloading them individually, you they can be downloaded automatically by running python download_dataset.py . This will utilize the Python API pyzenodo3 and download the 100 GB dataset into your data_dir. This can take a while.

3. Run analysis

Now you can simply run run_analysis.py and after that run_supplement.py. I personally used Spyder to run the script, which also nicely annotates the cells. It's included in Anaconda, so you might already have it installed.