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Fast Google Polyline encoding and decoding using Rust FFI

Project description

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Fast Google Polyline Encoding and Decoding

Installation

pip install pypolyline
Please use a recent (>= 8.1.2) version of pip.

Supported Python Versions

  • Python 3.7
  • Python 3.8 (Linux and macOS Darwin only)
  • Python 3.9 (Linux and macOS Darwin only)
  • Python 3.10 (Linux and macOS Darwin only)

Supported Platforms

  • Linux (manylinux1-compatible)
  • macOS
  • Windows 32-bit / 64-bit

Usage

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)

Cython Module 🔥

If you're comfortable with a lack of built-in exceptions, you should use the compiled Cython version of the functions, giving a 3x speedup over the ctypes functions:

from pypolyline.cutil import encode_coordinates, decode_polyline
  • Longitude errors will return strings beginning with Longitude error:
  • Latitude errors will return strings beginning with Latitude error:
  • Polyline errors will return [[nan, nan]]

Otherwise, import from util instead, for a slower, ctypes-based interface. Attempts to decode an invalid Polyline will throw util.EncodingError
Attempts to encode invalid coordinates will throw util.DecodingError

How it Works

FFI and a Rust binary

Is It Fast

…Yes.
You can verify this by installing the polyline and cgpolyencode packages, then running benchmarks.py, a calibrated benchmark using cProfile.
On a 1.8 GHz Intel Core i7, The pure-Python test runs in ~21 s, the C++ (cgpolyencode.GPolyEncoder) test runs in around 600 ms, and The Rust + Cython benchmark runs in around 400 ms (33% faster).

License

MIT

Citing Pypolyline

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}
}

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