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Optimize Affine2D transforms #22191
Description
To address @timhoffm's other comment ("Does it still make sense to keep Affine2D._mtx as a numpy array if we only do element-wise operations?"): I'm quite convinced the whole transform stack would be faster if the transformation matrix was not a numpy array (because 3x3 is a size where the numpy's overhead is generally bigger than the gains from vectorization), but changing everything at once (even better would be to move things to C, but using plain C structs (or equivalently C++ objects) to store the coefficients) would be quite a big PR. So I'm doing the easy parts first :-)
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QuLogic commented on Jan 11, 2022
I also noted that there exist several packages that do affine transforms already. I have not attempted any conversions, and they may not be any better, but might be useful as inspiration.
There are some pure Python packages:
Some compiled options:
And some non-Python things that would need wrappers:
QuLogic commented on Dec 20, 2023
Since pybind11 has native support for Eigen, I've put together a branch https://github.com/QuLogic/matplotlib/tree/eigen-transform that attempts to use it. There are a few minor issues that need to be fixed, but it passes most tests now.
However, I've noticed that there is a cast to a NumPy array in the path extension here:
matplotlib/src/py_converters.cpp
Lines 320 to 323 in 8f296db
which means we miss out on avoiding NumPy right now. This will be made easier once #27087 is complete.
I've not run any benchmarks yet, but at least this points out the pain points with doing this change (and we got #27511 out of it.)
QuLogic commented on Dec 20, 2023
Also, taking a quick look at Agg's trans_affine, it looks to be fairly similar to Eigen. It may be just as easy to implement that way, and not require the addition of Eigen.