pyshed 0.1.9

Python bindings for the shed watershed delineation engine

pyshed

Python bindings for the shed watershed delineation engine. pyshed loads HFX-format datasets and returns watershed polygons from a (lat, lon) outlet. The full native stack (GDAL, PROJ, GEOS, libtiff, SQLite, and more) is bundled inside the wheel — no system install required.

Install

pip install pyshed

Platform support (v0.1): Apple Silicon macOS only (macosx_11_0_arm64). Linux, Intel macOS, and Windows wheels are not yet built — community contributions are welcome. See CONTRIBUTING.md if you want to help port the build.

Quickstart

import pyshed

engine = pyshed.Engine("/path/to/hfx/dataset")
result = engine.delineate(lat=47.3769, lon=8.5417)
print(result.area_km2)

Snapping options belong on the constructor, not on delineate:

# Correct — snap_radius is an Engine constructor kwarg
engine = pyshed.Engine("/path/to/hfx/dataset", snap_radius=5000)
result = engine.delineate(lat=47.3769, lon=8.5417)

Engine also accepts dataset root URLs backed by the object-store integration:

local_engine = pyshed.Engine("/data/hfx/rhine")
file_url_engine = pyshed.Engine("file:///data/hfx/rhine")
s3_engine = pyshed.Engine("s3://bucket/path/to/hfx/rhine")
r2_engine = pyshed.Engine(
    "https://<account>.r2.cloudflarestorage.com/<bucket>/path/to/hfx/rhine"
)
public_r2_engine = pyshed.Engine(
    "https://basin-delineations-public.upstream.tech/global/hfx"
)

Remote dataset sessions cache manifest.json and graph.arrow under ~/.cache/hfx/<fabric_name>/<adapter_version>/ by default. Set HFX_CACHE_DIR=/path/to/cache before constructing pyshed.Engine(...) to use a different cache root. Parquet artifacts are read with object-store range reads; they are not copied into the cache wholesale.

GDAL raster URI and configuration plumbing is wired through the Python engine, but public Cloudflare R2 raster access still depends on the target bucket, credentials, and GDAL driver behavior. Verify the specific remote raster dataset you plan to use.

Verbose mode

Enable structured log output from both the Python and Rust layers:

import pyshed

pyshed.set_log_level("info")
engine = pyshed.Engine("https://basin-delineations-public.upstream.tech/global/hfx")
# INFO lines stream during manifest/graph/catchment loading
result = engine.delineate(lat=47.3769, lon=8.5417)

Valid levels: "trace", "debug", "info", "warn"/"warning", and "error"/"critical". Set PYSHED_LOG to one of those values to opt in at import time.

Speeding up repeated delineations

Enable the in-memory Parquet column-chunk cache to avoid redundant range reads across overlapping watersheds:

engine = pyshed.Engine(
    "https://basin-delineations-public.upstream.tech/global/hfx",
    parquet_cache=True,
    parquet_cache_max_mb=512,
)

The cache is off by default. parquet_cache_max_mb defaults to 2048 when parquet_cache=True. Cache state is per-Engine instance and is not persisted to disk.

Batch delineation with progress

import pyshed

# tqdm is a user dependency — not bundled with pyshed
from tqdm.auto import tqdm

url = "https://basin-delineations-public.upstream.tech/global/hfx"
engine = pyshed.Engine(url)

outlets = [
    {"lat": 47.3769, "lon": 8.5417},
    {"lat": 46.9480, "lon": 7.4474},
    {"lat": 48.1351, "lon": 11.5820},
]

bar = tqdm(total=len(outlets), unit="outlet")

def on_progress(event):
    bar.update(1)
    bar.set_postfix(status=event.get("status"), ms=event.get("duration_ms"))

results = engine.delineate_batch(outlets, progress=on_progress)
bar.close()

The progress callback receives a dict with keys index, total, lat, lon, duration_ms, status ("ok" or "error"), plus n_catchments on success and error on failure. Exceptions raised inside the callback are swallowed and logged; they do not interrupt the batch.

API Reference

For the full developer-oriented API surface, including argument types, return types, and the exception hierarchy, see API.md.

What it does

• Resolves the outlet coordinate to a terminal HFX atom (via snap.parquet or point-in-polygon on catchments.parquet).
• Walks the upstream graph in graph.arrow collecting all contributing atoms.
• Optionally refines the terminal atom geometry using flow_dir.tif / flow_acc.tif rasters when present.
• Returns a dissolved MultiPolygon + geodesic area in km².
• Bundles GDAL / PROJ / GEOS / libtiff / SQLite — no system GDAL install needed.

Links

• Source & issues: https://github.com/CooperBigFoot/shed
• HFX dataset spec: https://github.com/CooperBigFoot/hfx
• License: MIT for pyshed; bundled native libraries retain their own licenses — see LICENSES/.