Quick Parallelization to get Quicker Code

I spend a lot of time letting models of stars evolve, hours and hours per week. Each model might take anywhere from 5 minutes to 30 minutes to finish (depending on the initial mass of the star, the resolution, and a few other factors that would distract from the main point of this post). TheContinue reading “Quick Parallelization to get Quicker Code”

Packaged code is usable code is happy code

After a pleasant break and a cancelled conference I’m back at astroBugs. This week I’m going to try and convince you that, for certain kinds of projects and code bases, you should wrap the source into a python package. “But Thomas” I hear you say “I already know this, if I want to distribute myContinue reading “Packaged code is usable code is happy code”

Jupyter For Data Exploration

I was chatting with a Linux using, terminal toting, office mate of mine earlier today about Jupyter and its use cases. She contented “Why would I use Jupyter when I can just quickly plot something in the terminal”. Now, I am not often one to push people away from using the command line. However, inContinue reading “Jupyter For Data Exploration”

Dynamically Debugging

Warning: This post may anger, perhaps for good reason, computer scientists and/or actual developers. Recently I was trying to evolve models of low-mass stars (near the fully convective transition mass or ~0.3Msolar) using the Dartmouth Stellar Evolution Program (DSEP). DSEP is an older piece of FORTRAN 77 code (with some of the 9x variants thrownContinue reading “Dynamically Debugging”

An awk.ward encounter pt. 2

Part One In the previous part we introduced some basic concepts surrounding the command line, shells, and utilities. Here we are going to dive more deeply into what I would call “The essential utilities”. There are two main properties which I think a tool needs to become essential: one, solve a problem, two, solve thatContinue reading “An awk.ward encounter pt. 2”