quick_pp

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Python package for Petrophysical analysis.

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# quick_pp

Lightweight toolkit for quick-look petrophysical estimation and exploration.

This repository contains the Python backend and SvelteKit frontend used by the
quick_pp application, plus utilities for running ML training and simple
petrophysical workflows.

Goals of this README
- Give developers and users the minimal, practical steps to get the app
    running locally (backend, frontend and optional Docker services).

Project components
- Backend: FastAPI application, data services, model endpoints and plotting APIs (in `quick_pp/app/backend`).
- Frontend: SvelteKit UI (in `quick_pp/app/frontend`) providing data visualisations and tools.
- Docker: Compose assets to run backend + Postgres for development (`quick_pp/app/docker`).
- CLI: `quick_pp` CLI wrapper that starts services, runs training, prediction and deployment tasks (`quick_pp/cli.py`).
- Machine learning: training/prediction pipelines and MLflow integration (`quick_pp/machine_learning`).

Prerequisites
- Python 3.11+ (for backend and CLI)
- Node.js 18+ and npm or yarn (for frontend)
- Docker & Docker Compose (optional, for the packaged backend + DB)
- uv (optional, fast Python package installer from https://github.com/astral-sh/uv)

.env & Database (SQLite vs PostgreSQL)
- The application reads DB and other secrets from environment variables. For
    local development create a `.env` file in the repo root or use `quick_pp/app/docker/.env`
    when running the bundled Docker Compose stack.

- Minimal `.env` examples

SQLite (quick local testing)
    ```bash
    QPP_DATABASE_URL=sqlite:///./data/local.db
    QPP_SECRET_KEY=change-this-to-a-random-string
    ```

PostgreSQL (recommended for realistic usage / Docker)
    ```bash
    QPP_DATABASE_URL=postgresql://qpp_user:qpp_pass@postgres:5432/quick_pp
    QPP_SECRET_KEY=replace-with-secure-value
    # if you run DB externally, replace host with reachable hostname or IP
    ```

Which to choose
    - SQLite: easiest for quick, single-user experiments. No external DB
        server required but limited in concurrency and not recommended for
        multi-container deployments.
    - PostgreSQL: recommended for Docker and production-like setups; the
        `quick_pp/app/docker/docker-compose.yaml` in the repo is configured to create a
        Postgres service and a matching `.env` template.

Security note
    - Never commit secrets (`QPP_SECRET_KEY`, DB passwords) to version control.
        Use environment-specific `.env` files excluded via `.gitignore` or a
        secrets manager.

Quick checklist
- Ports: backend API `6312`, frontend dev `5173`, frontend prod `5469`, MLflow UI `5015`, model server `5555`.
- Backend CLI entrypoint: `python main.py` (or `quick_pp` if installed).

Clone & Python setup
1. Clone the repo and create a venv:

```bash
git clone https://github.com/imranfadhil/quick_pp.git
cd quick_pp
uv venv --python 3.11
# mac/linux
source .venv/bin/activate
# windows (cmd.exe)
.venv\Scripts\activate
```

2. Install Python dependencies:

```bash
uv pip install -r requirements.txt
# (optional) install package editable for CLI convenience
uv pip install -e .
```

Using Docker (recommended for a complete local stack)
- The repo provides Docker assets in `quick_pp/app/docker/` to start the backend and
    a Postgres data volume.

Quick docker compose (from repo root):

```bash
cd quick_pp/app/docker
docker-compose up -d
```

This will bring up services configured for development. Logs can be checked
with `docker-compose logs -f` in the same folder.

Frontend (SvelteKit)
The frontend is a SvelteKit application. For the best onboarding experience, use the CLI to start the frontend (see CLI commands below). The CLI handles dependency installation, building, and running the server automatically.

For manual setup or advanced development:
1. Install dependencies:

```bash
cd quick_pp/app/frontend
npm install
# Ensure Plotly is available for the UI components
npm install plotly.js-dist-min --save
```

2. Run the dev server:

```bash
npm run dev
```

3. Open the frontend at `http://localhost:5173` (SvelteKit default).

For production build:
- Run `npm run build` to build the app.
- Run `npm run preview` to preview the production build locally.
- The CLI can start the production server automatically (see CLI commands below).

Start the app using the project CLI
- From the repo root you can use the included CLI which orchestrates backend
    and frontend processes. Example (starts backend and, if available, frontend production server):

```bash
python main.py app
# or (if installed) the user-facing command
quick_pp app
```

Start backend only (dev):

```bash
python main.py backend --debug
# or (if installed) the user-facing command
quick_pp backend --debug
```

Start frontend only (dev):

```bash
python main.py frontend --dev
# or (if installed) the user-facing command
quick_pp frontend --dev
```

Start frontend only (prod):

```bash
python main.py frontend
# or (if installed) the user-facing command
quick_pp frontend
```

Common commands
- Run MLflow tracking UI (local): `python main.py mlflow_server`
- Deploy model server: `python main.py model_deployment`
- Train/predict via CLI: see `python main.py --help` or `quick_pp --help`
- Manage Docker services: `python main.py docker up -d` (see `python main.py docker --help` for options)

Testing
- Run unit tests with `pytest` in the repo root:

```bash
pytest -q
```

Troubleshooting & tips
- If the frontend does not render charts, ensure `plotly.js-dist-min` is
    installed in `quick_pp/app/frontend` (some components do dynamic imports).
- If the backend fails to start behind Docker, check `quick_pp/app/docker/.env` and
    the Postgres volumes under `quick_pp/app/docker/data/`.
- Use the CLI `python main.py` for convenience; it will open browser
    windows for the services it starts unless `--no-open` is provided.

Further reading
- API docs are available when the backend is running: `http://localhost:6312/docs`.
- Project documentation: https://quick-pp.readthedocs.io/en/latest/index.html

License
- See the `LICENSE` file in the repository root.

Contributions and feedback welcome — open an issue or a PR with improvements.

Jupyter Notebooks
-----------------
The repository includes several example notebooks under `notebooks/` that
demonstrate data handling, EDA and basic petrophysical workflows. Recommended
workflow for exploring the project locally:

1. Start the backend API (see CLI commands above) if a notebook calls the API.
2. Open a Python environment with the project dependencies installed.
3. Launch JupyterLab or Jupyter Notebook and open the notebooks in `notebooks/`.

Key notebooks:
- `01_data_handler.ipynb` — create and inspect a mock `qppp` project file.
- `02_EDA.ipynb` — quick exploratory data analysis patterns used in demos.
- `03_*` series — interpretation examples (porosity, saturation, rock typing).

Machine learning (Train / Predict / Deploy)
-----------------------------------------
The project includes ML training and prediction utilities integrated with
MLflow. High-level steps and helpful details:

1. Prepare input data
     - Training expects a Parquet file in `data/input/<data_hash>___.parquet`.
     - The feature set required by each modelling config is defined in
         `quick_pp/machine_learning/config.py` (or `MODELLING_CONFIG` used by
         training code). Ensure input columns match the configured features.

2. Train a model (local)

```bash
# from repo root, with virtualenv active
python main.py train <model_config> <data_hash>
# example
python main.py train mock mock
```

3. Run predictions

```bash
python main.py predict <model_config> <data_hash> [output_name] [--plot]
# example
python main.py predict mock mock results_test --plot
```

4. Deploy model server (serves registered MLflow models)

```bash
python main.py model_deployment
```

Notes:
- MLflow UI (tracking server) is available with `python main.py mlflow_server`.
- The `--plot` flag in `predict` saves visual outputs (if supported by the
    predict pipeline).
- For production or reproducible experiments, register models in MLflow and
    configure the model registry settings used by the deployment code.

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