On a related note, does numpy's gufunc mechanism provide any thread safety, or is the responsibility on the extension writer to do that? For simple numpy array inputs, I would think that I don't have to worry about free-threaded python messing things up (unless I have a global state), I'm wondering if something like dask array inputs could mess up calls to a thread-unsafe function.
If it is on the extension writer, are there any examples on how to do that? Are there other guarantees (or lack thereof) that a gufunc writer should be aware of? How about reorderability? gufuncs operates on subarrays, so wouldn't dask inputs that are chunked potentially operate on the chunks in any order they like? Thanks, Ben Root On Tue, Jul 1, 2025 at 4:26 PM Benjamin Root <ben.v.r...@gmail.com> wrote: > Warren, > > The examples in ufunclab helped clear up a few things and I was able to > experiment and get a working gufunc! Thank you for your help! > > Ben Root > > On Fri, Jun 27, 2025 at 8:54 PM Benjamin Root <ben.v.r...@gmail.com> > wrote: > >> Warren, >> >> I'm fine with implementing it in C. I just didn't think gufuncs were for >> me. I couldn't tell from the description if it would be for my usecase >> since I wasn't looping over subarrays, and I didn't see any good examples. >> Maybe the documentation could be clearer. I'll have a look at your examples. >> >> I did try that signature with np.vectorize() with the signature keyword >> argument, but it didn't seem to work. Maybe it didn't work for the reasons >> in that open issue. >> >> Thank you, >> Ben Root >> >> On Fri, Jun 27, 2025 at 8:03 PM Warren Weckesser via NumPy-Discussion < >> numpy-discussion@python.org> wrote: >> >>> On Fri, Jun 27, 2025 at 5:29 PM Benjamin Root via NumPy-Discussion >>> <numpy-discussion@python.org> wrote: >>> > >>> > I'm looking at a situation where I like to wrap a C++ function that >>> takes two doubles as inputs, and returns an error code, a position vector, >>> and a velocity vector so that I essentially would have a function signature >>> of (N), (N) -> (N), (N, 3), (N, 3). When I try to use np.vectorize() or >>> np.frompyfunc() on the python version of this function, I keep running into >>> issues where it wants to make the outputs into object arrays of tuples. And >>> looking at utilizing PyUFunc_FromFuncAndData, it isn't clear to me how I >>> can tell it to expect those two output arrays to have a size 3 outer >>> dimension. >>> > >>> > Are ufuncs the wrong thing here? How should I go about this? Is it >>> even possible? >>> >>> Ben, >>> >>> It looks like the simplest signature for your core operation would be >>> (),()->(),(3),(3), with broadcasting taking care of higher dimensional >>> inputs. Because not all the core shapes are scalars, that would >>> require a *generalized* ufunc (gufunc). There is an open issue >>> (https://github.com/numpy/numpy/issues/14020) with a request for a >>> function to generate a gufunc from a Python function. >>> >>> numba has the @guvectorize decorator, but I haven't use it much, and >>> in my few quick attempts just now, it appeared to not accept fixed >>> integer sizes in the output shape. But wait to see if any numba gurus >>> respond with a definitive answer about whether or not it can handle >>> the shape signature (),()->(),(3),(3). >>> >>> You could implement the gufunc in a C or C++ extension module, if you >>> don't mind the additional development effort and packaging hassle. I >>> know that works--I've implemented quite a few gufuncs in ufunclab >>> (https://github.com/WarrenWeckesser/ufunclab). >>> >>> Warren >>> >>> >>> > >>> > Thanks in advance, >>> > Ben Root >>> > _______________________________________________ >>> > NumPy-Discussion mailing list -- numpy-discussion@python.org >>> > To unsubscribe send an email to numpy-discussion-le...@python.org >>> > https://mail.python.org/mailman3//lists/numpy-discussion.python.org >>> > Member address: warren.weckes...@gmail.com >>> _______________________________________________ >>> NumPy-Discussion mailing list -- numpy-discussion@python.org >>> To unsubscribe send an email to numpy-discussion-le...@python.org >>> https://mail.python.org/mailman3//lists/numpy-discussion.python.org >>> Member address: ben.v.r...@gmail.com >>> >>
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