4f94811b49
The expensive part of the sim (the 4-state [Q,I,x,v] discharge ODE) now has a vectorized fixed-step RK4 integrator that runs N configs at once as arrays, with the same saturating-iron physics as the scipy path. One code path runs on numpy (CPU) or cupy (GPU) via a backend shim (gpu/backend.py) -- so it's developed and validated WITHOUT a GPU, then runs on GPU unchanged. Honesty gate (per the project's no-smoke-and-mirrors rule): validated against the scipy solve_ivp reference on 4 configs -- exit velocity, peak current, and dissipated energy all match within ~1-3%, plus a batched energy-conservation check. GPU speed is worthless if the numbers differ, so this test is the point. Benchmark: 5000 configs in 6.8s = 733/s single-threaded numpy, already faster than the 5-core scipy sweep (~430/s); cupy parallelizes the same arithmetic for the real win at large N. Not yet batched: flight-to-trigger, multi-stage chaining, genome decode (so this isn't a drop-in sweep replacement yet -- it's the validated core). Next: batch the full pipeline + run cupy on the server GPU. 89 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