View source for MNE Python Tutorial 2021

=== Recording now available for download! ===

[https://megcore.nih.gov/MEG/NIMH-MEG-Core_MNE-Tutorial_10-01-21.mp4 MNE Python Tutorial Download]

=Steps to prepare prior to tutorial=
Prepare python environment - For 3D rendering and interactive plots, this should be installed on your local computer <br>
  !!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 (Approximately 1Gb compressed and 1.8Gb uncompressed).  A final jupyter notebook may be sent over email prior to the tutorial - this will replace the V1 version.
=== 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

=Running tutorial script - starting Jupyter Notebook=
  cd mne_tutorial
  conda activate mne
  jupyter notebook "MNE Python Tutorial-v1.ipynb"

=Prep Non-tutorial Data=
==MRI Prep==
===Freesurfer reconstruction===
  # 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. )
  
  # (OPTION1) - 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>
  
  # (OPTION2) - INSTRUCTIONS FOR BIOWULF - Run line by line on the bash terminal
  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}    #VERIFY THIS LOOKS CORRECT
  
  echo -e '#!/bin/bash\nrecon-all -all  -i ' ${mri} -s ${subjid} | sbatch  --mem=3g --time=24:00:00     #SUBMITS JOB TO SBATCH

===Watershed Processing for Boundary Element Model===
  #In bash terminal (5-10minutes)
  #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}')"

==MEG Prep==
  # The events of interest must be denoted in the MarkerFile.mrk
  https://megcore.nih.gov/index.php?title=Pyctf

  # The marker file can also be processed with the hv_proc code that is distributed with the data
  # A limited demonstration on the day of the tutorial will be given to process the data triggers into the MarkerFile.

==Copy Data To Tutorial Folder==
  #Move the subject's freesurfer reconstruction to the mne_tutorial folder.  If needed rename the output freesurfer subject folder to the 8 digit meg id found in the meg data
  cp -R ${SUBJECTS_DIR}/${subjid} mne_tutorial/SUBJECTS_DIR/    
  
  #Copy meg data to top level of mne_tutorial folder
  cp -R <MEGDATASET.ds> mne_tutorial/
  
  #Copy dataset used for the coregistration of the MRI
  cp {BRIK/HEAD w/ fiducials marked OR Brainsight .txt file } mne_tutorial/

==General Guidelines for processing==
  https://mne.tools/stable/overview/cookbook.html

==Additional Code (included w/tutorial data - automatically installed during jupyter notebook processing)==
  #Manual installation
  #conda activate ${CONDA ENV Name}
  pip install git+https://github.com/nih-megcore/pyctf-lite
  pip install git+https://github.com/nih-megcore/hv_proc 
  pip install git+https://github.com/nih-megcore/nih_to_mne

===pyctf-lite===
  More info: https://github.com/nih-megcore/pyctf-lite
  Utility functions for reading in ctf dataset

===hv_proc===
  More info:  https://github.com/nih-megcore/hv_proc
  hv_proc: Contains functions to evaluate triggers to generate events - write to dataframe - write to ctf MarkerFile.mrk.  
  MarkerFile.mrk reads into mne as raw.annotations - can be converted to events using events, event_ids = mne.events_from_annotations(raw)

===nih_to_mne===
  More info:  https://github.com/nih-megcore/nih_to_mne
  nih_to_mne: 
    calc_mnetrans.py -h  -- Creates MNE transformation matrix 
    bstags.py installed on commandline to use w/orthohull

==This section can be ignored==
===NOTES to reproduce===
  Backup of tutorial:  MegServer ...../mne_tutorial/mne_tutorial.tar.gz
  Inside backup tarball - environment/pip_freeze.txt  and environment/mne_tutorial_env.yml (conda)
  Also backup of full environment (linux:ubuntu) using conda-pack: envs_BAK/mne_tutorial1_env.tar.gz