This repository contains the final stage of our data analyses. While it does not provide full access to the previous stages (mostly due to the fact that the whole workflow generated several dozens of TB of data), it contains all the records required to run the notebooks.
- The
code/
directory contains both a localbrainweb_tdcs
python package and the files required to run the Nextflow pipeline. - The
envs/
directory contains the Conda environment configuration used to run the notebooks. - The
notebooks/
directory contains the Jupyter notebooks used to produce the final results.
Here are the general steps to run the notebooks. You can runn them manually one-by-one or use an automation tool such as Papermill.
- Clone this repository.
cd
into the project directory.- Build the Conda environment using
conda env create -f envs/env.yaml
. - Activate the Conda environment using
conda activate brainweb-tdcs-analysis
. - Run Jupyter lab using
python -m jupyter lab
. - Choose a notebook in the
notebooks/
directory. - Set the value of
experiment_id
to a value between 0 and 5 (see below the corresponding experiments) anduse_gpr
toTrue
if you want to compute the results for the truncated normal distributions or toFalse
if you want to use the uniform distributions. - Run the notebook.
The experiment IDs from 0 to 5 correspond to :
- C3-C4 (MC)
- C3-Fp2 (MC)
- F3-F4 (dlPFC)
- F3-Fp2 (dlPFC)
- F7-F8 (vmPFC)
- P3-P4 (IPS)
Copyright (C) 2022 GIGA CRC In-Vivo Imaging, Liège, Belgium
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
For more information, refer to the full license.