sarpyx 1.0.1

A swissknife for SAR processing.

sarpyx is a specialized Python toolkit for Synthetic Aperture Radar (SAR) processing with tight integration to ESA SNAP. It focuses on reproducible SAR workflows, SNAP GPT orchestration, fast tiling, validation, and research features such as sub-aperture decomposition.

Highlights

• SNAP GPT integration with configurable graphs and operator chaining.
• WorldSAR preprocessing, tiling, validation, and H5-to-Zarr conversion.
• Generic pipeline CLI with built-in recipes for Sentinel-1, TSX, CSG, Biomass, NISAR, and Sentinel-1 InSAR.
• Sub-aperture decomposition for Sentinel-style BEAM-DIMAP products.
• Geocoded outputs ready for GIS and downstream ML.
• Utilities compatible with rasterio, geopandas, pyproj, h5py, zarr, and dask.

Commands

sarpyx --help           # Top-level command dispatcher
sarpyx worldsar --help  # WorldSAR preprocessing, tiling, validation, H5-to-Zarr
sarpyx pipeline --help  # Explicit built-in or external pipeline recipes
sarpyx-worldsar --help  # Compatibility WorldSAR entry point
sarpyx-pipeline --help  # Compatibility pipeline entry point

Documentation

• Documentation site
• Installation guide
• CLI usage examples
• User guide
• API reference

Install

The recommended installation uses conda first to provide ESA SNAP and gpt, then installs sarpyx with pip from this checkout. This keeps SNAP/native dependencies managed by conda while keeping the Python package editable.

conda create -n sarpyx -c sirbastiano/label/dev -c conda-forge \
  python=3.12 pip snap13=13.0.0

conda activate sarpyx
python -m pip install -e .

Verify the installation:

gpt -h
sarpyx --help
sarpyx worldsar --help
sarpyx pipeline --help

For development and tests:

python -m pip install -e ".[copernicus]"
python -m pip install pytest
pytest -q

Using uv for repository maintenance

uv sync
uv sync --group dev
uv sync --group dev --extra copernicus
uv run pytest -q
uv build

Published pip package

python -m pip install sarpyx

The pip package is suitable for Python-side usage, but SNAP GPT workflows require a working SNAP installation available in the environment.

Container usage

For container workflows, use the Docker Compose CLI plugin:

docker compose version
make recreate

At startup, the container expects a mounted grid file. Provide either GRID_PATH or place a *.geojson file under /workspace/grid.

mkdir -p ./grid
# put any grid GeoJSON here, e.g. ./grid/my_region.geojson
docker compose up

You can also pass --grid-path to sarpyx worldsar.

Project Links

• Contributing guide
• Security policy
• Citation metadata
• JOSS paper draft
• License

Maintainers: Roberto Del Prete, Gabriele Daga, Sebastian Fieldhouse, Juanfrancisco Amieva, Cedric Leonard, Valerio Marsocci, Eva Gmelich Mejling