Jupyter

Jupyter (https://jupyter.org) is a software platform that allows you to run code in a variety of languages (e.g., Python, R) through a web browser. Your code is run in "notebooks", which are documents that contain code cells, the output of the code you run, and annotations/commentary about the code. You can install and run Jupyter on your own computer, or run it off of our compute server, Architect.

In NCIL, we use Julyter more or elss exclusively for preprocessing and analysis of EEG/ERP data, as well as for behavioural data. It has a lot of advantages over other ways of running code:

1. The notebook files keep a record of what you did. This helps both in terms of auditing your code (e.g., checking what you did, giving you feedback to help you improve), and doing reproducible, open science. It's also awesome for you,when you come back to your code later to try to remember what you did.

2. The notebook files contain the output. In other ways of runing code, output either disappears when you quite the program your running, or ends up as a bunch of separate files (e.g., text files, image files, tables) that are hard to keep organized.

3. The ability to include annotations and other formatted text means that we can produce notebooks for teaching purposes, and also commentary on why we made certain choices during the analysis, interpretations of the results, etc..

Jupyter (pronounced like the planet, Jupiter) is a tool that was developed as an open-source project to allow users to run code in a type of file called a notebook. A notebook is a file that contains a set of cells. Each cell can contain code; clicking on a code cell than then hitting its "run" button will execute the code. Any results (including graphics, formatted tables, or raw text) will be printed out right below the cell that generated them. Cells in a notebook are kind of a cross between a REPL in the terminal, and a file containing a set of commands that is saved and then run. Like a REPL, a Jupyter notebook cell will run the commands as soon as you run the cell, and print the output below the cell. However, cells can each contain many lines of code (like small program files), and a notebook can have many cells, each with bits of code. Moreover, notebooks are files, meaning that the entire contents are saved together, and can be reopened and re-run later — making the process reproducible. Perhaps the greatest advantage of notebooks over files containing code, however, is that the output of each cell is saved when the notebook file is saved. This means that you can write code to perform a task, such as analyzing a dataset, and have the output right there with the commands that produced the output. Jupyter notebooks have revolutionized the practice of data science, and create an ideal environment for learning and communicating.

Another great feature of notebooks is that cells can contain either code, or text formatted in a simple language called Markdown. This means that in between your code cells, you can write blocks of text, for example to annotate or, essentially, "tell the story" of your data. Indeed, you could literally write a scientific manuscript in a Jupyter Notebook, with the advantage that it contained all of the reproducible code used to perform the analysis. We'll talk more about Markdown later.

Here's an example screenshot of a notebook file:

Jupyter notebooks have a number of other great design features. For one, notebooks were designed to run through a web browser, which means that you can run, and store, your analyses on a remote server (e.g., on in the lab you work in), meaning that you don't have to worry about the storage or computational capabilities of the computer you're working on (I'm guessing for most of you, this is a not-super-powerful laptop). For another, notebooks can run code in a wide range of different languages. Indeed, the name "jupyter" comes from the names of the three first programming languages it supported: Julia, Python, and R. There are now over 100 different languages supported in Jupyter notebooks! Typically though, a given notebook file only runs a single language, so you have to pick when you first create the file.

In this class, you will work extensively in Jupyter notebooks, and you will use them to complete and submit your assignments and projects, as well. There are lots of materials on the web to help you learn (or remember) how to use Jupyter notebooks, starting with their official web site: https://jupyter.org. This course also provides video tutorials on using Jupyter notebooks.