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.
> (A related proposal that's been up earlier has been that a variable can be
> annotated with many interfaces; e.g.
>
> cdef A|B|C obj
>
> ...and then when you do "obj.method", it is first looked up in C, then B,
> then A, then Python getattr. Not sure if we want to reopen that can of
> worms...)
Different topic - new thread?
Stefan
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