[Numpy-discussion] Numpy Generalized Ufuncs: Pointer Arithmetic and Segmentation Faults (Debugging?)
travis at continuum.io
Mon Oct 26 01:04:28 EDT 2015
Two things that might help you create generalized ufuncs:
1) Look at Numba --- it makes it very easy to write generalized ufuncs in
simple Python code. Numba will compile to machine code so it can be as
fast as writing in C. Here is the documentation for that specific
One wart of the interface is that scalars need to be treated as
1-element 1-d arrays (but still use '()' in the signature).
2) Look at the linear algebra module in NumPy which now wraps a bunch of
linear-algebra based generalized ufuncs (all written in C):
On Sun, Oct 25, 2015 at 7:06 AM, <eleanore.young at artorg.unibe.ch> wrote:
> Dear Numpy maintainers and developers,
> Thanks for providing such a great numerical library!
> I’m currently trying to implement the Dynamic Time Warping metric as a set
> of generalised numpy ufuncs, but unfortunately, I have lasting issues with
> pointer arithmetic and segmentation faults. Is there any way that I can
> use GDB or some such to debug a python/numpy extension? Furthermore: is it
> necessary to use pointer arithmetic to access the function arguments (as
> seen on http://docs.scipy.org/doc/numpy/user/c-info.ufunc-tutorial.html)
> or is element access (operator) also permissible?
> To break it down quickly, I need to have a fast DTW distance function
> dist_dtw() with two vector inputs (broadcasting should be possible), two
> scalar parameters and one scalar output (signature: (i), (j), (), () -> ())
> usable in python for a 1-Nearest Neighbor classification algorithm. The
> extension also implements two functions compute_envelope() and
> piecewise_mean_reduction() which are used for lower-bounding based on Keogh
> and Ratanamahatana, 2005. The source code is available at
> http://pastebin.com/MunNaP7V and the prominent segmentation fault happens
> somewhere in the chain dist_dtw() —> meta_dtw_dist() —> slow_dtw_dist(),
> but I fail to pin it down.
> Aside from my primary questions, I wonder how to approach
> errors/exceptions and unit testing when developing numpy ufuncs. Are there
> any examples apart from the numpy manual that I could use as reference
> implementations of generalised numpy ufuncs?
> I would greatly appreciate some insight into properly developing
> generalised ufuncs.
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