Display Jupyter Notebooks with Academic

Learn how to blog in Academic using Jupyter notebooks

from IPython.core.display import Image
Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')

png

print("Welcome to Academic!")
Welcome to Academic!

Install Python and JupyterLab

Install Anaconda which includes Python 3 and JupyterLab.

Alternatively, install JupyterLab with pip3 install jupyterlab.

Create or upload a Jupyter notebook

Run the following commands in your Terminal, substituting <MY-WEBSITE-FOLDER> and <SHORT-POST-TITLE> with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:

mkdir -p <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
cd <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
jupyter lab index.ipynb

The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.

Edit your post metadata

The first cell of your Jupter notebook will contain your post metadata (front matter).

In Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:

---
title: My post's title
date: 2019-09-01

# Put any other Academic metadata here...
---

Edit the metadata of your post, using the documentation as a guide to the available options.

To set a featured image, place an image named featured into your post’s folder.

For other tips, such as using math, see the guide on writing content with Academic.

Convert notebook to Markdown

jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.

Example

This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter

Andrés Goens
Andrés Goens
Universitair Docent (Assistant Professor)

My primary research interest is in the intersection of programming languages and compilers. Much of my work has focused on using models of computation to reason about an efficient execution of concurrent programs in heterogeneous multicore systems. An overarching goal of my research is to find the right abstractions that help both programmers and compilers reason about the program and its execution. I am interested in using theorem proving software and type theory to help bridge this gap, be it for hardware development or pure mathematics. From the other side, I am also interested in machine learning on compilers and programming languages.