gsrd

A small Gray-Scott reaction-diffusion simulator, packaged as a single CLI command:

gsrd input.yaml

It reads a YAML parameter file, runs a 2D reaction-diffusion integration, and writes the final fields to results.npz in the current directory.

Purpose

gsrd is built as the running example for a workflow-engine tutorial. Its interface is deliberately designed to emulate the conventions and quirks of real-world scientific codes — the kind of legacy tooling that workflow managers typically wrap. This makes it a realistic teaching target: many of the integration issues users encounter when wrapping established scientific software can be demonstrated here in a small, self-contained Python package.

Design choices

These are intentional design choices for pedagogical value, not bugs. The point is to give users a realistic surface to practise wrapping: parsing log output, handling fixed-name files, providing restart inputs, dealing with uninformative exit codes, and validating input schemas at the workflow layer rather than relying on the code itself.

Input format

A YAML file with the following keys:

F: 0.04          # feed rate
k: 0.065         # kill rate
du: 0.16         # diffusion coefficient of U
dv: 0.08         # diffusion coefficient of V
dt: 1.0          # time step
grid_size: 64    # grid is grid_size x grid_size
n_steps: 3000    # number of integration steps
seed: 42         # (optional) RNG seed

Output

On success, results.npz contains:

• U_final, V_final — final concentration fields, shape (grid_size, grid_size)
• params — the parameters as a JSON-serialised string

Scalar diagnostics (variance_V, mean_V) appear on stdout in the Diagnostics block; they are not written to the output file.