geolibre-wasm 0.4.4

Run the GeoLibre / whitebox_next_gen geospatial tool suite (WASM/WASI) from Python.

geolibre-wasm (Python)

Run the geolibre-rust geospatial tool suite (the whitebox_next_gen tools plus GeoLibre's own) from Python. The tools are a single WebAssembly (WASI) module executed in-process via wasmtime, so there is no native install, no GDAL, and no server — just pip install.

This mirrors the JavaScript geolibre-wasm/tools API (list_tools, list_manifests, run_tool), so the two stay in sync.

The import package is geolibre_wasm (the distribution is geolibre-wasm), matching the npm package name and avoiding a clash with the separate geolibre application package.

Install

pip install geolibre-wasm

On first use the runtime (geolibre-cli.wasm, ~20 MB) is downloaded from the matching GitHub release and cached under ~/.cache/geolibre/. To use a local copy instead, set GEOLIBRE_WASM=/path/to/geolibre-cli.wasm or pass wasm_path= to any call.

Usage

Inputs are placed under /work; the tool reads/writes there and any new files come back as bytes. Each input value may be bytes, an http(s) URL to download, or a local file path -- the same for raster and vector inputs:

res = gl.run_tool(
    "write_geoparquet",
    args=["--input=/work/in.geojson", "--output=/work/out.parquet"],
    input={"in.geojson": "https://example.com/data/cities.geojson"},  # fetched for you
)

This downloads the whole file (no HTTP range reads). Works for cog.tif, data.parquet, data.fgb, data.geojson, etc.

Paths are sandboxed. Every path inside args refers to the tool's /work filesystem, not your host disk. input files are placed at /work/<name>, and res.files keys are paths relative to /work. Do your own open() / write() against host paths (e.g. /content/... on Colab) on the Python side, never inside args.

import geolibre_wasm as gl

# Discover tools (each manifest carries a "source": "geolibre" | "whitebox")
tools = gl.list_tools()
manifests = gl.list_manifests()

# Raster: compute slope from a DEM
dem = open("dem.tif", "rb").read()                     # read from host disk
res = gl.run_tool(
    "slope",
    args=["--input=/work/dem.tif", "--output=/work/slope.tif", "--units=degrees"],
    input={"dem.tif": dem},                            # -> /work/dem.tif
)
assert res.exit_code == 0, res.stdout                  # surfaces tool errors
open("slope.tif", "wb").write(res.files["slope.tif"])  # key is relative to /work

# Reproject (warp) to a target EPSG
res = gl.run_tool(
    "reproject_raster",
    args=["--input=/work/dem.tif", "--output=/work/wgs84.tif", "--epsg=4326"],
    input={"dem.tif": dem},
)

# Vector: GeoJSON -> GeoParquet (Hilbert-sorted, bbox covering, ZSTD by default)
gj = open("cities.geojson", "rb").read()
res = gl.run_tool(
    "write_geoparquet",
    args=["--input=/work/in.geojson", "--output=/work/out.parquet"],
    input={"in.geojson": gj},
)
open("cities.parquet", "wb").write(res.files["out.parquet"])

Tools that write a directory tree (e.g. raster_to_tiles) return nested keys:

res = gl.run_tool(
    "raster_to_tiles",
    args=["--input=/work/dem.tif", "--output_dir=/work/tiles", "--min_zoom=16", "--max_zoom=18"],
    input={"dem.tif": dem},
)
for path, data in res.files.items():
    # e.g. "tiles/16/9559/32767.png"
    ...

API

• list_tools(wasm_path=None) -> list[str]
• list_manifests(wasm_path=None) -> list[dict]
• run_tool(tool, args=None, input=None, wasm_path=None) -> ToolResult
• ToolResult(exit_code: int, stdout: list[str], files: dict[str, bytes])
• runtime_path(wasm_path=None) -> str — resolve the runtime (explicit > GEOLIBRE_WASM > cached download)
• download_runtime(dest=None) -> str — fetch the runtime ahead of time

License

MIT