Thinking in arrays
Despite its reputation for being slow, Python is the leading language of scientific computing, which generally needs large-scale (fast) computations. This is because most scientific problems can be split into "metadata bookkeeping" and "number crunching," where the latter is performed by array-oriented (vectorized) calls into precompiled routines.
This tutorial is an introduction to array-oriented programming. We'll focus on techniques that are equally useful in any array library, with a particular focus on NumPy and JAX. You'll work in groups on four class projects: Conway's Game of Life using arrays, iterative computations on arrays, just-in-time (JIT) compilation for the Mandelbrot set, and exploring data in ragged arrays.
