This lesson is in the early stages of development (Alpha version)

Introduction to Working with MRI Data in Python: Setup

Overview

This lesson is designed to be run on either a personal computer or in a web-based interactive computational environment using Binder.

Binder

The Binder webpage for this lesson can be accessed by clicking the button below.

Binder

Binder enables you to run the collection of Python notebooks found in the code folder. All of the software and data used in the lesson are pre-installed in a custom computational environment and no additional software needs to be installed locally.

Binder Tips

If using Binder, please be aware that startup can take anywhere from 30 seconds to 10 minutes, depending on your internet connection and how recent the custom environment was used. Also, files can be uploaded to the Binder environment. However, the state of the environment is not permanent. If you are inactive for 10 minutes, the environment will shut down and all data will be lost. You should save any changed files to their computers in order to preserve them.

Local

Install Python

In this lesson, we will be using Python 3 with some of its most popular scientific and neuroimaging libraries. Although one can install a plain-vanilla Python and all required libraries by hand, we recommend installing Anaconda, a Python distribution that comes with everything we need for the lesson. Detailed installation instructions for various operating systems can be found on The Carpentries template website for workshops and in the Anaconda documentation.

Install Python Packages

Pip is the most common package installer for Python. This lesson requires a few additional neuroimaging-specific Python packages that can be installed with:

pip install nibabel nilearn pybids

Install dcm2niix

We will be using the dcm2niix software package for converting neuroimaging data from the DICOM format that is exported from the MRI scanner to the NIfTI format, which is much easier to work with. Detailed installation instructions for various operating systems can be found on the dcm2niix README. If using the Anaconda Python distribution described earlier, dcm2niix can be installed by running:

conda install -c conda-forge dcm2niix

Install DataLad

In order to obtain some of the data that will be used in this lesson, we will be using DataLad, a tool for managing and version controlling large datasets. Detailed installation instructions for various operating systems can be found in the Datalad Handbook. Again, if using Anaconda, DataLad can be easily installed by running:

conda install -c conda-forge datalad

Obtain lesson materials

Once DataLad has been installed, create a new folder on your Desktop and download the ds000030 dataset found on OpenNeuro.

mkdir ~/Desktop/dc-mri/data
cd ~/Desktop/dc-mri/data
datalad clone https://github.com/OpenNeuroDatasets/ds000030.git

Launch Python interface

To start working with Python, we need to launch a program that will interpret and execute our Python commands. Below we list several options. If you don’t have a preference, proceed with the top option in the list that is available on your machine. Otherwise, you may use any interface you like.

Option A: Jupyter Notebook

A Jupyter Notebook provides a browser-based interface for working with Python. If you installed Anaconda, you can launch a notebook in two ways:

Anaconda Navigator

  1. Launch Anaconda Navigator. It might ask you if you’d like to send anonymized usage information to Anaconda developers: Anaconda Navigator first launch Make your choice and click “Ok, and don’t show again” button.

  2. Find the “Notebook” tab and click on the “Launch” button: Anaconda Navigator Notebook launch Anaconda will open a new browser window or tab with a Notebook Dashboard showing you the contents of your Home (or User) folder.

  3. Navigate to the data directory by clicking on the directory names leading to it: Desktop, dc-mri, then data: Anaconda Navigator Notebook directory
  4. Launch the notebook by clicking on the “New” button and then selecting “Python 3”: Anaconda Navigator Notebook directory

Command line (Terminal)

1. Navigate to the data directory:

Unix shell

If you’re using a Unix shell application, such as Terminal app in macOS, Console or Terminal in Linux, or Git Bash on Windows, execute the following command:

cd ~/Desktop/dc-mri/data

Command Prompt (Windows)

On Windows, you can use its native Command Prompt program. The easiest way to start it up is pressing Windows Logo Key+R, entering cmd, and hitting Return. In the Command Prompt, use the following command to navigate to the data folder:

cd /D %userprofile%\Desktop\dc-mri\data

2. Start Jupyter server

Unix shell

jupyter notebook

Command Prompt (Windows)

python -m notebook

3. Launch the notebook by clicking on the “New” button on the right and selecting “Python 3” from the drop-down menu: Anaconda Navigator Notebook directory

 

Option B: IPython interpreter

IPython is an alternative solution situated somewhere in between the plain-vanilla Python interpreter and Jupyter Notebook. It provides an interactive command-line based interpreter with various convenience features and commands. You should have IPython on your system if you installed Anaconda.

To start using IPython, execute:

ipython

 

Option C: plain-vanilla Python interpreter

To launch a plain-vanilla Python interpreter, execute:

python

If you are using Git Bash on Windows, you have to call Python via winpty:

winpty python