On 03.03.2012 17:07, Luis Pedro Coelho wrote:
> I sort of missed the big C++ discussion, but I'd like to give some examples of
> how writing code can become much simpler if you are based on C++. This is from
> my mahotas package, which has a thin C++ wrapper around numpy's C API
Here you are using NumPy arrays, not implementing them. That makes a big
difference. Your code would be even simpler if you had used Cython or
Fortran (f2py) instead of C++.
The only thing you have proved is that using NumPy arrays in C can be
messy, which I suspect everybody knows already.
C is a good systems programming language, but it sucks for any kind of
numerical computing. C++ sucks a little bit less. Using either for
numerical programming usually a mistake. But NumPy core is not a case of
numerical programming, it is a case of systems programming, for which C
is actually very nice.
And no, unmaintainable C is NOT an example of "very good C", rather the
opposite. This is a big mistake:
"If you compare this with the equivalent scipy.ndimage function,
which is very good C code (but mostly write-only—in fact, ndimage has
not been maintainable..."
Another thing to observe is that the ndimage C code you cited has a
major DRY violation: We have the best text (pre)processor there is to
our disposal: Python. So why are there macros and switch statements for
so many dtypes in there? As with C++ templates, generics in C is easy if
we just let Python generate the type-specialized code we need. (Actually
that is what NumPy's .src files do.) Generics is therefore not an
argument for using C++. We can augment standard C with syntax suger for
generics, or even NumPy arrays and Python types, using a Python script
as meta-compiler. We don't need C++ for that.
Sturla
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