Git is my Captain’s Log. I can get back to a project that I left months, perhaps years ago, and retrace my steps to get ready to work on that project again. I can rewind and go back in time, then continue on a parallel history. Git works great for code. Except that in science not everything is code. How about data?
I was running
scikit-learnand realized that it was devouring all my cores. Moreover, I wrapped everything into a
joblibparallel loop, so my poor server was hanging there, starving for more power.
I love Python and its ecosystem of scientific packages because it makes it very easy to experiment with new techniques. My undergraduate research assistant Manon and I have been recently playing with Diffusion Weighted Imaging (DWI) using
Science should be reproducible. I like to think of an experiment as a recipe: you follow the steps described in the recipe, and you get results that are similar to the original ones (that amazing taste when your mom made it).