Neal Becker wrote:
numpy frequently refers to 'casting'. I'm not sure if that term is ever
defined. I believe it has the same meaning as in C. In that case, it is
unfortunately used to mean 2 different things. There are casts that do not
change the underlying bits (such as a pointer cast), and there are casts
that actually convert to different bits (such as float - double).
I think numpy means the latter. When an array where the underlying data is
one type, a cast to another type means actually reallocating and converting
the data.
Yes, that is usually what people mean when they use _casting_ in the context of
numpy. It is the more frequently performed operation of the two. The former can
be accomplished with the .view(dtype) method of ndarrays.
It often occurs that I have an algorithm that can take any integral type,
because it is written with c++ templates. In that case, I don't want to
use PyArray_FROMANY, because I don't want to unecessarily convert the array
data. Instead, I'd like to inquire what is the preferred type of the data.
The solution I'm exploring is to use a function I
call 'preferred_array_type'. This uses the __array_struct__ interface to
find the native data type. I chose to use this interface, because then it
will work with both numpy arrays and other array-like types.
Any thoughts on all of this?
I'm not sure what you mean by preferred type of the data. Do you mean the
dtype of the array as it comes in? There are several functions and function
macros in the numpy C API which take differing amounts of information. For
example,
* PyArray_FROM_O(PyObject*onj) just takes an object.
* PyArray_FROM_OF(PyObject* obj, int req) takes an object and flags like
NPY_CONTIGUOUS.
* PyArray_FROM_OT(PyObject* obj, int typenum) takes an object and a type
number.
* PyArray_FROM_OTF(PyObject* obj, int typenum, int req) takes an object, type,
and flags.
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth.
-- Umberto Eco
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