7803e147a7
Finishes the GPU path to a usable state: run_gpu_sweep vectorizes the expensive discharge across N single-stage configs via the validated batch integrator (numpy CPU / cupy GPU, same code). Setup (decode + build_stage_physics) is a fast python loop; the ODE batch is one call. - Extracted sim/stage.build_stage_physics so the CPU path (run_stage) and the GPU batch build IDENTICAL physics (coil model, eddy, saturation, circuit params) -- no divergence by construction. - Batch integrator hardened: stiff configs that overflow fixed-step RK4 are marked infeasible (blew_up), feasibility = actually-commutated (committed), warnings silenced via seterr. Single-pulse + eddy mirrored. - Analytic sensor trigger (constant-velocity approach) for the batch; inductive threshold check preserved. Honesty gate: test_batch_sweep validates GPU-path exit velocities against the CPU run_coilgun -- 0.0% divergence on the checked configs. Limitation stated plainly: single stage only (multi-stage stays on the CPU sweep); cupy on the server GPU (1070 passthrough) is the remaining infra step. 91 tests pass. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
gausse
Симулятор и оптимизатор многоступенчатого электромагнитного ускорителя (coilgun) на реальных, доступных в рознице компонентах.
Полный план и чек-лист этапов — в PLAN.md.
Установка (Docker — основной способ)
docker compose build
docker compose run --rm --entrypoint pytest gausse -q # тесты
docker compose run --rm gausse sweep --n 1000 # CLI (после реализации Этапа 6)
Результаты (SQLite, отчёты) должны сохраняться в ./results, примонтированный
в контейнер как /app/results (см. docker-compose.yml), чтобы переживать
пересборку образа.
Установка без Docker (локальная разработка)
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest