scorepilot 0.1.3

Web-based tool for PCA/PLS model analysis in chemometrics.

ScorePilot

A web-based tool for PCA/PLS model analysis in chemometrics / multivariate data analysis. You build up model variants by changing preprocessing, including or excluding samples, and testing models on new data. The central collection of those variants is the Hangar; each model carries a Logbook entry recording its preprocessing, excluded samples, and validation history.

The numerical core is provided by the process-improve library (PCA, PLS, Hotelling's T², SPE/DModX, contributions, VIP, cross-validated Q²). ScorePilot wraps it behind a FastAPI backend and a SvelteKit + ECharts frontend, shipped as a single pip/uvx-installable package.

Architecture at a glance

• core/ - pure numerical functions over arrays/DataFrames; wraps process-improve. No web/DB imports.
• db/ - SQLAlchemy 2.0 ORM + repository interface (SQLite locally, Postgres in production). Model lineage is a self-referencing parent_id traversed with a recursive CTE.
• api/ - thin FastAPI routers translating HTTP to core/db.
• frontend/ - SvelteKit (Svelte 5 runes) + Vite + ECharts, built into src/scorepilot/web and served as static files.

The packaged app is one process: FastAPI serves /api and the built static frontend at /. No Node is required at runtime.

See docs/ARCHITECTURE.md for a fuller description, including the dataset / preprocessing-spec split and the reusable data grid.

What you can do today

• Import a CSV or Excel file; every column is kept and its data type (quantitative / qualitative / date-time) is inferred.
• Explore the data in a virtualized grid: assign identifier roles (primary / secondary / class), choose X / Y variables, and exclude observations (rows) or variables (columns).
• Check data quality: duplicate primary identifiers, invalid values in quantitative columns, and missing data.
• Inspect any variable: summary statistics, a frequency histogram, a sequence plot, and a non-destructive transform preview, plus a raw vs autoscaled view.

The dataset stays immutable; transforms, scaling, X/Y, and exclusions accumulate in a preprocessing recipe that will drive model variants in the next stage. A small PCA scores demo lives at /playground.

Run it (local)

Requires Python 3.12+ and uv.

Quickest start (newcomers): install git, make, curl, and Node 20+, then:

make setupenv   # one-time: installs uv, Python deps, and frontend deps
make debug      # builds the UI and runs the server at http://127.0.0.1:8000

make help lists every task. For a from-scratch, zero-experience walkthrough (including installing the tools) see docs/DEVELOPMENT.md.

Without make

uv sync                 # create the environment
uv run scorepilot       # boot uvicorn and open the browser

uv run scorepilot serves the bundled frontend (a placeholder until you build the real one - see below) and the API at http://127.0.0.1:8000. API docs are at http://127.0.0.1:8000/api/docs.

Useful flags:

uv run scorepilot --host 0.0.0.0 --port 8080 --no-browser

Develop

Run the backend and the Vite dev server side by side. The Vite server proxies /api to uvicorn, so the frontend gets hot-reload while talking to the real API.

# terminal A - backend (auto-reload)
uv run uvicorn scorepilot.app:create_app --factory --reload

# terminal B - frontend dev server
cd frontend
npm install
npm run dev

Open the URL printed by Vite (default http://localhost:5173).

Build the frontend into the package

The compiled bundle is not committed; it is built into src/scorepilot/web and force-included into the wheel at release time:

cd frontend
npm run build           # outputs to ../src/scorepilot/web

After building, uv run scorepilot serves the real UI.

Quality gates

uv run ruff check .     # lint
uv run ruff format .    # format
uv run pyright          # type check
uv run pytest           # tests

Install the git hooks once with uv run pre-commit install.

Database / migrations

SQLite is used locally (default sqlite:///./scorepilot.db); set SCOREPILOT_DATABASE_URL to point at Postgres in production.

uv run alembic upgrade head                         # apply migrations
uv run alembic revision --autogenerate -m "message" # create a new migration

Deployment

Pushes to main build a container image and auto-deploy it to a Hetzner server (Caddy for HTTPS, Postgres for storage), so you can test the latest build from any browser. See docs/DEPLOYMENT.md for server setup, the GitHub secrets to configure, and how to build/run the image locally (make image / make image-run).

Merges to main can also publish a wheel to PyPI (off until enabled). See docs/PUBLISHING.md.

License

MIT. See LICENSE.