On Tue, Sep 13, 2016 at 11:47 AM, Stephan Hoyer firstname.lastname@example.org wrote:
NumPy has the handy np.vectorize for turning Python code that operates on scalars into a function that vectorizes works like a ufunc, but no helper function for creating generalized ufuncs (http://docs.scipy.org/doc/ numpy/reference/c-api.generalized-ufuncs.html).
np.apply_along_axis accomplishes some of this, but it only allows a single core dimension on a single argument.
So I propose adding a new object, np.guvectorize(pyfunc, signature, otypes, ...), where pyfunc is defined over the core dimensions only of any inputs and signature is any valid gufunc signature (a string). Calling this object would apply the gufunc. This is inspired by the similar numba.guvectorize, which is currently the easiest way to write a gufunc in Python.
In addition to be handy like vectorize, such functionality would be especially useful for with working libraries that build upon NumPy to extend the capabilities of generalized ufuncs (e.g., xarray after https://github.com/pydata/xarray/pull/964).
First, this seems really cool. I hope it goes somewhere.
I'm curious whether you have a plan to deal with the python functional call overhead. Numba gets around this by JIT-compiling python functions - is there something analogous you can do in NumPy or will this always be limited by the overhead of repeatedly calling a Python implementation of the "core" operation?
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