Fast Google Polyline Encoding and Decoding
uv add pypolyline
pip install pypolyline
- Ensure you have a copy of
libpolylineffiandheader.hfrom https://github.com/urschrei/polyline-ffi/releases, and it's in thesrc/pypolylinesubdir - run
uv sync --dev - run
pytest .
Changes in pyx and pxd files, and the Rust library and header will bust the cache, triggering a rebuild when uv commands are run.
All currently supported Python versions.
- Linux (
manylinux*-compatible, x86_64 and aarch64) - macOS (x86_64 and arm64)
- Windows 64-bit
Coordinates must be in (Longitude, Latitude) order
from pypolyline.cutil import encode_coordinates, decode_polyline
coords = [
[52.64125, 23.70162],
[52.64938, 23.70154],
[52.64957, 23.68546],
[52.64122, 23.68549],
[52.64125, 23.70162]
]
# precision is 5 for Google Polyline, 6 for OSRM / Valhalla
polyline = encode_coordinates(coords, 5)
# polyline is 'ynh`IcftoCyq@Ne@ncBds@EEycB'
decoded_coords = decode_polyline(polyline, 5)Failure to encode coordinates, or to decode a supplied Polyline, will raise a RuntimeError containing information about the invalid input.
FFI and a Rust binary
…Yes.
You can verify this by installing the polyline package, then running benchmarks.py, a calibrated benchmark using cProfile.
On an M2 MBP, The pure-Python test runs in ~2500 ms, the Flexpolyline benchmark runs in ~1500 ms and The Rust + Cython benchmark runs in around 80 ms (30 x and 17.5 x faster, respectively).
The Blue Oak Model Licence 1.0.0
If Pypolyline has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing it as follows (example in APA style, 7th edition):
Hügel, S. (2021). Pypolyline (Version X.Y.Z) [Computer software]. https://doi.org/10.5281/zenodo.5774925
In Bibtex format:
@software{Hugel_Pypolyline_2021,
author = {Hügel, Stephan},
doi = {10.5281/zenodo.5774925},
license = {MIT},
month = {12},
title = {{Pypolyline}},
url = {https://github.com/urschrei/simplification},
version = {X.Y.Z},
year = {2021}
}