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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

Aspect Behaviour Mirrors
Invocation Single positional argument, no flags, no --help Minimal-CLI binaries from the Fortran era
Output location Hardcoded ./results.npz in the working directory Codes that write fixed-name files
Stdout Verbose startup banner, citation block, progress lines, formatted diagnostics Standard scientific-code log output
Scalar results Printed to stdout only (variance_V, mean_V); not stored in the output file Codes whose key numbers must be parsed from the log
Array results Written to results.npz (U_final, V_final) Binary outputs alongside textual logs
Parameter echo Required keys printed; extra YAML keys silently retained in the output's params record Tolerant input parsers with no schema enforcement
Restart mechanism If init/U_init.npy and init/V_init.npy exist in the working directory, they are used as initial fields Codes that pick up restart files from the run directory
Error reporting Short stderr message (ERR: ...); exit code is always 0; success is signalled by a JOB DONE marker on stdout Codes whose exit codes are not reliably informative
No-arguments behaviour Prints the banner and waits on stdin "Waiting for input" pattern of interactive scientific binaries

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.

Key dates

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License

MIT License

Requires

Python >=3.10

Provides Extra

docs gallery pre-commit tests

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