MNE Python Tutorial 2021: Difference between revisions
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conda activate mne
=
The data is available from the resources below. A final jupyter notebook may be sent over email prior to the tutorial
=== NIMH Users: Download here ===
scp <USERNAME>@helix.nih.gov:/data/NIMH_scratch/mne_tutorial/mne_tutorial.tar.gz
tar -xvf mne_tutorial.tar.gz
|
Revision as of 08:59, 30 September 2021
Steps to prepare prior to tutorial
Prepare python environment - For 3D rendering and interactive plots, this should be installed on your local computer
!!IF YOU DO NOT HAVE MINICONDA/ANACONDA INSTALLED - have IT install miniconda under your user account!! !!If you already have an mne environment, you can use another name for the environment and adjust accordingly!! conda activate base conda install -n base mamba -c conda-forge -y mamba create -n mne conda-forge::mne main::pip main:jupyter -y
conda activate mne
Download the sample data and scripts.
The data is available from the resources below. A final jupyter notebook may be sent over email prior to the tutorial
NIMH Users: Download here
scp <USERNAME>@helix.nih.gov:/data/NIMH_scratch/mne_tutorial/mne_tutorial.tar.gz ./ tar -xvf mne_tutorial.tar.gz
Non-NIMH MEG Users: Download from MEG Data server
The data is located on the MEG data server and is accessible by all users with accounts An email will be sent with the path to the data
Start Jupyter Notebook
cd <NEW PATH>/nimh_tutorial_data jupyter notebook mne_tutorial_10_01_21.ipynb
Prep Non-tutorial Data
# Run Freesurfer on Dataset (takes 8+ hrs): # Does not run on BRIK/HEAD - convert to .nii first -- e.g (module load afni; 3dAFNItoNIFTI anat+orig. ) # Install and process freesurfer locally on computer https://surfer.nmr.mgh.harvard.edu/fswiki/rel7downloads recon-all -all -i <MRI Used w/ MEG> -s <SUBJECT> #INSTRUCTIONS FOR BIOWULF subjid= #Set Freesurfer ID mri= #Set MRI Name - must be a nifti file NOT BRIK/HEAD export SUBJECTS_DIR= #Set output folder, Make sure this directory exists module load freesurfer echo -e '#!/bin/bash\nrecon-all -all -i ' ${mri} -s ${subjid} | sbatch --mem=3g --time=24:00:00 #SUBMITS JOB TO SBATCH #In bash terminal - process the boundary element model - also requires freesurfer to be installed subjid=<SUBJECT> subjects_dir=<SUBJECTS_DIR> conda activate mne python -c "import mne; mne.bem.make_watershed_bem(subject='${subjid}', subjects_dir='${subjects_dir}')"