Evidence of global warming is everywhere.

Scatter plots are a great way to visualize the relationship between two variables. However, in a world where it’s easier than ever to analyze large datasets, they are becoming less and less appropriate for many common data analysis tasks. Surprisingly, I still see them published in papers all the time, even when they are not the appropriate choice (and I am probably guilty of this!).

Chat GPT-4 (https://chat.openai.com/) is taking off rapidly, and I’m admittedly a late-adopter. I recently used it for a quick project, and I can confidently say it drastically sped up my process.

This post shows a recipe for creating decent looking “hillshade” or “shaded relief” maps in python and matplotlib. I was inspired to do this after creating similar visualizations using the QGIS GUI. Using a GUI based program for making maps is, in my opinion, far superior to doing the same in python, since there are knobs and sliders for doing fine tuning of marker locations, colors, and the like. But sometimes you might want to display topography as part of another data visualization, in conjunction with other non-map data visuals. https://www.shadedrelief.com/ is a really cool website that shows off many examples of shaded relief maps.

The following link is for my 2021 AGU presentation. I describe a method to validate mountain precipitation estimates using streamflow, high resolution snow-lidar, simple hydrologic models, and bayesian inference https://youtu.be/Nune4HdeprI.

Use the ** operator in Python to clean up matplotlib plotting code

Emulating the Python unittesting module for non-unit test applications.

This blog is hosted on a digital ocean droplet running Ubuntu and nginix. The blog framework is “Jekyll”. I find that the easiest way to push content is to simply build the site on my local machine and then scp the contents to the server’s /var/www folder.