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

Alison Kleffner
Alison Kleffner
Assistant Professor

My research interests include environmental applications of statistics and spatial data visualization. Currently, I am working on a project which involves the development of a spatial clustering and interpolation method to estimate where ice cracks may form and to infer missing data. These methods were developed with motion data of Arctic sea ice from the RADARSTAT Geophysical Processing System, where chunks of data are missing due to the data collection method. This is important as when cracks form in the sea ice, the heat from the ocean is released into the atmosphere, thus playing an important role in the global climate. Additionally, I have been working on a project focusing on visualization variables that occupy the same spatial domain with the goal to derive relationships between the variables.