Sebastian Haase wrote:
> Hi,
> This is a vaguely formulated question ...
> When I work with memmap'ed files/arrays I have a derived class
> that adds special attributes to the array class (referring to the MRC image
> file format used in medical / microscopy imaging)
>
> What are the pros and cons for asarray() vs. asanyarray()
>
> One obvious con for asanyarray is that its longer and asarray is what I have
> been using for the last few years ;-)
>
asarray() guarantees you have a base-class array. Thus, you are not
going to be thwarted by an re-definitions of infix operators, or other
changed methods or attributes which you might use in your routine.
asanyarray() allows a simple way of making sure your function returns
any sub-class so that, for example, matrices are passed seamlessly
through your function (matrix in and matrix out).
However, a big drawback of asanyarray is that you must be sure that the
way your function is written will not get confused by how a sub-class
may overwrite the array methods and attributes. This significantly
limits the application of asanyarray in my mind, as it is pretty
difficult to predict what a sub-class *might* do to it's methods
(including the special methods implementing the infix operators).
A better way to write a function that passes any sub-class is to use
asarray() so you are sure of the behavior of all methods and "infix"
operators and then use the __array_wrap__ method of the actual input
arguments (using __array_priority__ to choose between competing input
objects). I expect that a decorator that automates this process will be
added to NumPy eventually. Several examples have already been posted
on this list.
After getting the array result, you call the stored __array_wrap__
function which will take a base-class ndarray and return an object of
the right Python-type (without copying data if possible). This is how
the ufuncs work and why they can take sub-classes (actually anything
with an __array__ method) and the same kind of object.
-Travis
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