Getting started with VSCode for Jupyter users (Python)


Jupyter notebooks have become the staple integrated development environment (IDE) of many scientists around the world – and for good reason too. Just to list a few of the advantages of the Jupyter notebooks:

  • They very clearly demonstrate your workflow in the most intuitive way possible – as a single document that reads from top to bottom, with code output shown adjacent to the code that produced it.
  • Since cells in a Jupyter notebook must (or should) be executed consecutively from top to bottom, they inherently encourage you to develop a workflow that is reproducible.
  • Your code and output are easily shareable (e.g. as a .pdf), even to those who don’t have a programming environment set up on their own machines.
  • They’re perfect for rapid exploratory data analysis, as you can quickly and interactively make visualizations one after the other, preserving previous output within the same document.

However, there are certain situations in which these advantages of the Jupyter notebook become non-ideal. Again, to list just a few:

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Using machine learning and simple features to predict climate – part 1

Earth’s climate is one of the fundamental boundary conditions on many Earth surface processes. For this reason, global climate models (GCMs) are often a critical part of Earth science research. However, they remain highly computationally expensive to run, and often access to a super-computer is needed to run a GCM in a reasonable amount of time. This motivates the question: is it possible to reasonably predict climate without an expensive GCM?

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Topography art

Looking for a new background for your computer, tablet, or phone? Or just looking to make a cool visual of a place that you like? We can make an awesome “ridge plot” of a location using an elevation map, all in Python.

The Jupyter notebook in which this visualization is made can be found here, although commits after this post may change the most updated version of the code.

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