simplification

Last week I looked into such simplification/decimation algorithms to simplify lines sent to a showlaser projector. Turns out there is a whole bunch of different algorithms for decimation, each with different trade-offs. It might be interesting to see how the edge cases mentioned in the article are impacted by switching to, for example, Visvalingam-Whyatt [0]. [0]: For a Python implementation: https://github.com/urschrei/simplification

Rust doesn’t need to copy the data. It’s trivial to pass e.g. Numpy arrays to Rust as slices via Cython (let alone originating in Cython!), modify them, and return them, or use them as input for a new returned struct. https://github.com/urschrei/simplification https://github.com/urschrei/lonlat_bng https://github.com/urschrei/pypolyline Each of those repos has links to the corresponding Rust “shim” libraries that provide FFIs for dealing with the incoming data, constructing Rust data structures from it, and then transforming it back on the way out. As a more general comment, using a GC language as the FFI target from a GC language is begging for difficult-if-not-impossible-to-debug crashes down the line.