simplification VS iminuit

Looks great! I have been using https://github.com/urschrei/simplification Visvalingam-Whyatt algo as per suggestion of Mike Bostock https://bost.ocks.org/mike/simplify/, don't know if it's comptetitive with the EffLTTB. It is surposesd to be fast and good in terms of shape preservation.

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.

- name: python-iminuit buildsystem: simple build-commands: - python3 setup.py build - python3 setup.py install --prefix=${FLATPAK_DEST} --root=/ --optimize=1 sources: - type: git url: https://github.com/scikit-hep/iminuit tag: v2.4.0

Have you tried numba+numpy? In my experience, it is much faster than Jax and can compile to cuda. It's not caveat free, but it also removes the hustle of labeling arrays as donated in Jax.

You may find this interesting https://github.com/scikit-hep/iminuit/blob/develop/tutorial/...