[Numpy-discussion] Handle type convertion in C API

[Benoit Gschwind]

Hello Eric,

Thank you for pointing out ufunc, I implemented my binding using it,
it's working and it's simpler than my previous implementation, but I
still not have the flexibility to dynamically recast input array, i.e.
using a int64 array as input for a int32. For instance for testing my
code I use numpy.random that provide int64 array, I have to convert the
array manually before calling my ufunc, which is somehow annoying.

For function that have 1 or 2 parameters it's practical to have 4
variant of the function, but in case of 6-8 parameters it's becoming
more difficult.

Best regards

Le mardi 10 mars 2020 à 13:13 -0400, Eric Moore a écrit :
> Hi Benoit, 
> 
> Since you have a function that takes two scalars to one scalar, it
> sounds to me as though you would be best off creating a ufunc.  This
> will then handle the conversion to and looping over the arrays, etc
> for you.  The documentation is available here: 
> https://numpy.org/doc/1.18/user/c-info.ufunc-tutorial.html.
> 
> Regards,
> 
> Eric
> 
> On Tue, Mar 10, 2020 at 12:28 PM Benoit Gschwind <
> gschwind at gnu-log.net> wrote:
> > Hello,
> > 
> > I writing a python binding of one of our library. The binding
> > intend to
> > vectorize the function call. for exemple:
> > 
> > double foo(double, double) will be bound to a python:
> > 
> > <numpy.array of double> module.foo(<numpy.array>, <numpy.array>)
> > 
> > and the function foo will be called like :
> > 
> > for (int i = 0; i < size; ++i)
> >         outarr[i] = foo(inarr0[i], inarr[1]);
> > 
> > My question is about how can I handle type conversion of input
> > array,
> > preferably in efficient manner, given that each input array may
> > require
> > different input type.
> > 
> > Currently I basically enforce the type and no type conversion is
> > performed. But I would like relax it. I thought of several
> > possibility
> > starting from the obvious solution consisting on recasting in the
> > inner
> > loop that would give:
> > 
> > for (int i = 0; i < size; ++i)
> >         if (inarr[i] need recast)
> >                 in0 =
> > recast(inarr[i])
> >         else
> >                 in0 = inarr[i]
> >         [... same for all
> > inputs parameter ...]
> >         outarr[i] = foo(in0, in1, ...);
> > 
> > This solution is memory efficient, but not actually computationally
> > efficient.
> > 
> > The second solution is to copy&recast the entire inputs arrays, but
> > in
> > that case it's not memory efficient. And my final thought is to mix
> > the
> > first and the second by chunking the second method, i.e. converting
> > N
> > input in a raw, then applying the function to them en so on until
> > all
> > the array is processed.
> > 
> > Thus my questions are:
> >  - there is another way to do what I want?
> >  - there is an existing or recommended way to do it?
> > 
> > And a side question, I use the PyArray_FROM_OTF, but I do not
> > understand well it's semantic. If I pass a python list, it is
> > converted
> > to the desired type and requirement; when I pass a non-continuous
> > array
> > it is converted to the continuous one; but when I pass a numpy
> > array of
> > another type than the one specified I do not get the conversion.
> > There
> > is a function that do the conversion unconditionally? Did I missed
> > something ?
> > 
> > Thank you by advance for your help
> > 
> > Best regards
> > 
> > 
> > 
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