JupyterCards

JupyterCards is a tool for displaying interactive flash cards in Jupyter notebooks and Jupyter Book.

JupyterCards is part of my effort to make open source tools for developing modern, interactive textbooks.

• The other part of this effort is my interactive self-assessment quiz tool, JupyterQuiz.
• You can see both tools in action in my (in-progress) textbook Foundations of Data Science with Python.

Here is an animated GIF showing JupyterCards in action:

Flash card content can be loaded from:

• a Python list of dict,
• a JSON local file,
• via a URL to a JSON file.

As of version 1.7, JupyterCards supports switching cards by swiping left on touch devices.

Note: JupyterCards always requires MathJax to be loaded. If you are using JupyterBook, it may not always load MathJax if you do not have any LaTeX on your page. To resolve this, you can include the invisible math command $,!$ in any Markdown cell. I hope to remove this requirement in the future.

Installation

JupyterCards is available via pip:

pip install jupytercards

Notes on JSON File Format

The JSON file should contain a single JSON array of JSON objects. Each JSON object should have two keys that will be utilized:

• "front": a string containing the text to be shown on the front of the card
• "back": a string containing the text to be shown on the back of the card

Although using JSON objects for each flashcard is overkill, this model was chosen to support future extensions to this library.

Making flashcards in Markdown

As of version 1.9.0, I have added helper functions to convert flashcards created in Markdown to the JSON format that JupyterCards expects. In its simplest version, just put the front text in a Markdown heading (line starting with #) and put the back text below. See (Markdown-flashcards.ipynb) for more discussion and examples of how to use this functionality.

Example of generating flashcard files from JupyterBook notebooks

In my Jupyter notebooks that are used as input to JupyterBook, I use panels with the heading "DEFINITION" to call out definitions in the text. I have provided a helper program extractdefinitions.py that I use to scan files for the DEFINITION header and extract the appropriate lines that follow. The resulting terms and definitions are dumped to corresponding JSON files in a "flashcards" directory. An additional JSON file is generated for the whole chapter. extractdefinitions.py takes 2 arguments: the directory to parse and the chapter number to use to label the overall JSON definitions file. This program is very specific to my workflow and I am offering it only as reference in case it can help someone else with a similar situation.