An attempt at speeding up the conversion between decimal longitude and latitude and British National Grid (epsg:27700) coordinates, using an external Rust binary and Python FFI, using the faster, less accurate Helmert transform, and the slower OSTN02-enabled for more accurate conversions.
Python is relatively slow; this type of conversion is usually carried out in bulk, so an order-of-magnitude improvement could save precious minutes
The Helmert transform used in convert_bng convert_lonlat and their threaded and vectorised versions is accurate to within 7 metres on average, and is not suitable for calculations or conversions used in e.g. surveying.
If higher accuracy is required, use the OSTN02-enabled functions, which adjust for local variation within the Terrestrial Reference Frame by incorporating OSTN02 data. See here for more information.
The OSTN02-enabled functions are:
Add the following to your Cargo.toml (the latest version is displayed on the second badge at the top of this screen)
lonlat_bng = "x.x.x"
Full library documentation is available here
Note that lon, lat coordinates outside the UK bounding box will be transformed to (9999, 9999), which cannot be mapped.
The functions exposed by the library can be found here
The FFI C-compatible functions exposed by the library are:
convert_to_bng_threaded(Array, Array) -> Array
convert_to_lonlat_threaded(Array, Array) -> Array
convert_to_osgb36_threaded(Array, Array) -> Array
convert_to_etrs89_threaded(Array, Array) -> Array)
convert_osgb36_to_ll_threaded(Array, Array) -> Array
convert_etrs89_to_ll_threaded(Array, Array) -> Array
convert_etrs89_to_osgb36_threaded(Array, Array) -> Array
convert_osgb36_to_etrs89_threaded(Array, Array) -> Array
And for freeing the memory allocated by the above
drop_float_array(Array) -> Null
The Arrays must contain c_double values. For examples, see the Array struct and tests in ffi.rs, and the _FFIArray class in convertbng.
If your library, module, or script uses the FFI functions, it must implement drop_float_array. Failing to do so may result in memory leaks.
Running cargo build --release will build an artefact called liblonlat_bng.dylib on OSX, and liblonlat_bng.a on *nix systems. Note that you'll have to generate liblonlat_bng.so for *nix hosts using the following steps:
ar -x target/release/liblonlat_bng.agcc -shared *.o -o target/release/liblonlat_bng.so -lrt convert_bng is available from PyPI for OSX and *nix:
pip install convertbng
More information is available in its repository
An IPython (sorry, Jupyter) notebook with some benchmarks is here
cargo build --release from the repo rootipython notebook, and open rust_BNG.Test machine:
- Late-2012 27" iMac
- 3.4 GHz Intel Core i7
- 4 cores (8 logical CPUs)
- 16GB 1600 MHz DDR3 RAM
| Coordinates | Method | Time (ms) | Speedup | |:---------------|:------:|:---------:|--------:| | 100k (50 runs) | Python | 547 | N/A | | |Rust| 73.9 |7.4x | | | Pyproj | 57.2 | 9.5x | | 1mm (50 runs) | Python | 5560 | N/A | | |Rust| 624 |8.9x | | | Pyproj | 510 | 10.9x | | 10mm (50 runs) | Python | 54500 | N/A | | |Rust| 6360 |8.6x | | | Pyproj | 4710 | 11.5 |
Using multithreading gives excellent performance (Pyproj – which is a compiled Cython binary – is now only ~20% faster than Rust, on average). Not bad, considering the relative youth of Rust as a language (let alone this library), and the maturity of the PROJ.4 project.
This software makes use of OSTN02 data, which is © Crown copyright, Ordnance Survey and the Ministry of Defence (MOD) 2002. All rights reserved. Provided under the BSD 2-clause license.
† Really, pyproj?
