L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

simulation-and-recording

simulation-and-recording

Assemble and run Brian2 simulations with explicit or magic networks, clocks, snapshots, scheduling, progress, and runtime diagnostics.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: simulation-and-recording description: "Assemble and run Brian2 simulations with explicit or magic networks, clocks, snapshots, scheduling, progress, and runtime diagnostics." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Simulation and recording

Use this route to control a Brian2 simulation after its model objects have been specified. Prefer an explicit Network for reusable experiments, objects held in containers, staged construction, train/test loops, or any run that must be unambiguous. Use magic run only for a small, visible, single-scope model.

  • Build the network, choose clocks and scheduling, run, snapshot, restore, seed trials, and diagnose progress/profile/schedule here.
  • Send monitor construction, recording variables, and monitor result access to recording; send equations and units to modeling and units-and-equations; send devices, code-generation targets, and standalone builds to code-generation.
  • A network_operation is a control callback boundary: create/configure and schedule it here, but keep model/monitor implementation details in their owning routes.
  • Use collect() to inspect magic membership, but construct Network(collect()) and add hidden containers before the first run; it is not a transfer mechanism for objects that have already been simulated.

Read only the reference needed for the operation:

  • API reference — objects, arguments, and invariants.
  • Workflows — explicit/magic construction, clocks, scheduling, progress, and diagnostics.
  • Multiple runs — start_scope, trials, and store/restore/randomness.
  • Troubleshooting — install/import, optional dependencies, data/configuration, API misuse, and workflow errors.

For a tiny local smoke of explicit Network and snapshot/restore, use scripts/network_smoke.py. It is intentionally small and does not replace project-level verification.

Skills enregistrées