Re: ENH: improved, faster algorithm for array padding

Thanks guys! I'll be bringing a version of this over to scikit-image.utilin the very near future, to be used if NumPy < 1.8.x. On Monday, May 20, 2013 5:06:31 AM UTC-5, Chintak Sheth wrote: Cheers Josh!
On May 20, 2013 3:27 PM, "Stéfan van der Walt" <ste...@sun.ac.za<javascript:>> wrote:
Kudos to Josh Warner for getting this patch into NumPy so quickly!
---------- Forwarded message ---------- From: GitHub Date: Sun, May 19, 2013 at 9:50 PM Subject: [Numpy-svn] [numpy/numpy] 246c06: ENH: improved, faster algorithm for array padding
Branch: refs/heads/master Home: https://github.com/numpy/numpy Commit: 246c06d2475718ec36ba193494444464e497c69a
https://github.com/numpy/numpy/commit/246c06d2475718ec36ba193494444464e497c6... Author: Josh Warner (Mac) <warner...@mayo.edu <javascript:>> Date: 2013-05-19 (Sun, 19 May 2013)
Changed paths: M doc/release/1.8.0-notes.rst M numpy/lib/arraypad.py M numpy/lib/tests/test_arraypad.py
Log Message: ----------- ENH: improved, faster algorithm for array padding
New padding method which scales much better with dimensionality. This new implementation is fully vectorized, builds each abstracted n-dimensional padding block in a single step, and takes advantage of separability. The API is completely preserved, and the old algorithm is used if a vector function is input for `mode`.
The new algorithm is faster for all tested combinations of inputs, and scales much better with dimensionality. Execution time reductions from ~25% for small rank 1 arrays to >99% for rank 4+ arrays observed.
Commit: 7d188bf1c9ac5bf7ee41e0794d59771802b345bf
https://github.com/numpy/numpy/commit/7d188bf1c9ac5bf7ee41e0794d59771802b345... Author: Charles Harris <charles...@gmail.com <javascript:>> Date: 2013-05-19 (Sun, 19 May 2013)
Changed paths: M doc/release/1.8.0-notes.rst M numpy/lib/arraypad.py M numpy/lib/tests/test_arraypad.py
Log Message: ----------- Merge pull request #3329 from JDWarner/faster_arraypad
ENH: improved, faster algorithm for array padding
Compare: https://github.com/numpy/numpy/compare/5b28d185a494...7d188bf1c9ac
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Josh Warner