Dag Sverre Seljebotn, 08.05.2012 09:57:
On 05/07/2012 11:21 PM, mark florisson wrote:
On 7 May 2012 19:40, Dag Sverre Seljebotn wrote:
mark florisson wrote:
On 7 May 2012 17:00, Dag Sverre Seljebotn wrote:
On 05/07/2012 04:16 PM, Stefan Behnel wrote:
Stefan Behnel, 07.05.2012 15:04:
Dag Sverre Seljebotn, 07.05.2012 13:48:
BTW, with the coming of memoryviews, me and Mark talked about just
deprecating the "mytype[...]" meaning buffers, and rather treat it
as np.ndarray, array.array etc. being some sort of "template
types".
That is,
we disallow "object[int]" and require some special declarations in
the relevant pxd files.
Hmm, yes, it's unfortunate that we have two different types of
syntax now,
one that declares the item type before the brackets and one that
declares it afterwards.
Should we consider the
buffer interface syntax deprecated and focus on the memory view
syntax?
I think that's the very-long-term intention. Then again, it may be
too early
to really tell yet, we just need to see how the memory views play out
in
real life and whether they'll be able to replace np.ndarray[double]
among real users. We don't want to shove things down users throats.
But the use of the trailing-[] syntax needs some cleaning up. Me and
Mark agreed we'd put this proposal forward when we got around to it:
- Deprecate the "object[double]" form, where [dtype] can be stuck
on
any extension type
- But, do NOT (for the next year at least) deprecate
np.ndarray[double],
array.array[double], etc. Basically, there should be a magic flag
in
extension type declarations saying "I can be a buffer".
For one thing, that is sort of needed to open up things for templated
cdef classes/fused types cdef classes, if that is ever implemented.
Deprecating is definitely a good start. I think at least if you only
allow two types as buffers it will be at least reasonably clear when
one is dealing with fused types or buffers.
Basically, I think memoryviews should live up to demands of the users,
which would mean there would be no reason to keep the buffer syntax.
But they are different approaches -- use a different type/API, or just
try to speed up parts of NumPy..
One thing to do is make memoryviews coerce cheaply back to the
original objects if wanted (which is likely). Writting
np.asarray(mymemview) is kind of annoying.
It is going to be very confusing to have type(mymemview),
repr(mymemview), and so on come out as NumPy arrays, but not have the
full API of NumPy. Unless you auto-convert on getattr to...
Yeah, the idea is as very simple, as you mention, just keep the object
around cached, and when you slice construct one lazily.
If you want to eradicate the distinction between the backing array and
the memory view and make it transparent, I really suggest you kick back
alive np.ndarray (it can exist in some 'unrealized' state with delayed
construction after slicing, and so on). Implementation much the same
either way, it is all about how it is presented to the user.
You mean the buffer syntax?
Something like mymemview.asobject() could work though, and while not
much shorter, it would have some polymorphism that np.asarray does not
have (based probably on some custom PEP 3118 extension)
I was thinking you could allow the user to register a callback, and
use that to coerce from a memoryview back to an object (given a
memoryview object). For numpy this would be np.asarray, and the
implementation is allowed to cache the result (which it will).
It may be too magicky though... but it will be convenient. The
memoryview will act as a subclass, meaning that any of its methods
will override methods of the converted object.
My point was that this seems *way* to magicky.
Beyond "confusing users" and so on that are sort of subjective, here's a
fundamental problem for you: We're making it very difficult to type-infer
memoryviews. Consider:
cdef double[:] x = ...
y = x
print y.shape
Now, because y is not typed, you're semantically throwing in a conversion
on line 2, so that line 3 says that you want the attribute access to be
invoked on "whatever object x coerced back to". And we have no idea what
kind of object that is.
If you don't transparently convert to object, it'd be safe to
automatically
infer y as a double[:].
Why can't y be inferred as the type of x due to the assignment?
On a related note, I've said before that I dislike the notion of
cdef double[:] mview = obj
I'd rather like
cdef double[:] mview = double[:](obj)
Why? We currently allow
cdef char* s = some_py_bytes_string
Auto-coercion is a serious part of the language, and I don't see the
advantage of requiring the redundancy in the case above. It's clear enough
to me what the typed assignment is intended to mean: get me a buffer view
on the object, regardless of what it is.
I support Robert in that "np.ndarray[double]" is the syntax to use when
you
want this kind of transparent "be an object when I need to and a memory
view when I need to".
Proposal:
1) We NEVER deprecate "np.ndarray[double]", we commit to keeping that in
the language. It means exactly what you would like double[:] to mean,
i.e.
a variable that is memoryview when you need to and an object otherwise.
When you use this type, you bear the consequences of early-binding things
that could in theory be overridden.
2) double[:] is for when you want to access data of *any* Python object
in
a generic way. Raw PEP 3118. In those situations, access to the
underlying
object is much less useful.
2a) Therefore we require that you do "mview.asobject()" manually; doing
"mview.foo()" is a compile-time error
Sounds good. I think that would clean up the current syntax overlap very
nicely.
2b) To drive the point home among users, and aid type inference and
overall language clarity, we REMOVE the auto-acquisition and require that
you do
cdef double[:] mview = double[:](obj)
I don't see the point, as noted above. Either "obj" is statically typed
and
the bare assignment becomes a no-op, or it's not typed and the assignment
coerces by creating a view. As with all other typed assignments.
2c) Perhaps: Do not even coerce to a Python memoryview and disallow
"print mview"; instead require that you do "print mview.asmemoryview()"
or
"print memoryview(mview)" or somesuch.
This seems to depend on 2b.
