Scientific Computing Working Group Workshop on performing analysis of neuroimaging data in Python

Developed by

• Jerry Jeyachandra (https://github.com/jerdra)
• Michael Joseph (https://github.com/josephmje)
• Olivia Stanley (https://github.com/ostanley)
• Jason Kai (https://github.com/kaitj)

Getting workshop material for SciNet workshops

If you're using SciNet's Jupyter System

Instructions with pictures

Open up a terminal and enter the following:

  ssh <user>@teach.scinet.utoronto.ca
  module load anaconda3
  source /scinet/course/ss2019/3/6_mripython/setup_workshop
  python -m ipykernel install --user --name mripython_conda

Open a new terminal and enter the following:

  ssh -L 8888:jupyterhub<X>:8000 <user>@teach.scinet.utoronto.ca -N

Where <X> is a number between 1-6.

If nothing happens that's great! Now open up your favourite browser and enter the following in your address bar:

  https://localhost:8888

You're ready to go!

If you're using Binder

Click the following button:

This will open up a jupyter terminal for you. Then just hit:

• New --> Terminal

This will open up a terminal. Once you're in here type the following:

./setup_workshop

Then leave it running in the background and switch tabs over back to the previous tab (says "Home" on Chrome)

Getting workshop material for CAMH Workshops

Method 1: Downloading directly from the repository

On the GitHub repo (this page), click the green button that says "Clone or download", then click Download ZIP. Once downloaded, extract the ZIP file.

Method 2: Using Git

Using this method requires a (very) useful piece of software called git. The process of installing git depends heavily on whether you're using MacOS, Windows or Linux. Follow the instructions in the link below to set up git on your PC:

Installing Git

Once you've installed git, open up your terminal and do the following:

git clone https://github.com/jerdra/SDC-BIDS-fMRI.git

This will download the repository directly into your current directory.

Setting up Python environment

We use python version 3.6.0, but any newer version should also work (Python 2 versions haven't been tested). There are many methods to setting up a python environment but we'd recommend using some sort of virtual environment as to not break your system python install. Two methods (of many) are listed below:

Method 1: Setting up conda environment (easiest) [Windows, Linux, MacOS]

For easy set-up we recommend Anaconda to manage python packages for scientific computing. Once installed, setting up the python environment can be done quite easily:

Windows

1. Install Anaconda Python version 3.7
2. Open Anaconda Navigator
3. Click on Environments on the left pane
4. Click Create then type in SDC-BIDS-fMRI
5. In the SDC-BIDS-fMRI entry click the play button then click Open Terminal
6. In terminal type:

   conda install -y numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda
   conda install -y -c conda-forge awscli
   pip install nilearn nibabel
   ./setup_workshop

7. Close the terminal, click on the play button again and open Jupyter Notebook
8. Navigate to SDC-BIDS-fMRI folder you downloaded earlier.
9. Done!

Linux and MacOS

After installing Anaconda, open terminal and type:

cd SDC-BIDS-fMRI
conda create -p ./sdc_bids_fmri
source activate $(pwd)/sdc_bids_fmri
conda install numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda
conda install -c conda-forge awscli
pip install nilearn nibabel
./setup_workshop

Method 2: Using pyenv (my favourite) [Linux, MacOS]

An alternative method uses pyenv with pyenv virtualenv. This is a favourite because it seamlessly integrates multiple python versions and environments into your system while maintaining use of pip (instead of conda).

cd SDC-BIDS-fMRI
pyenv virtualenv 3.6.0 sdc_bids_fmri
echo sdc_bids_fmri > .python-version
pip install --requirement requirements.txt
./setup_workshop

Finally open up the jupyter notebook to explore the tutorials:

cd SD-BIDS-fMRI

#Include below line if using anaconda environment
source activate $(pwd)/sdc_bids_fmri

jupyter notebook

Reference

[1] Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset [version 2; referees: 2 approved]. F1000Research 2017, 6:1262 (https://doi.org/10.12688/f1000research.11964.2)