Travis, after reading all the post on this thread, my comments
Fist of all, I'm definitelly +1 on your suggestion. Below my rationale.
* I believe numpy scalars should provide all possible features needed
to smooth the difference between mutable, indexable 0-d arrays and
inmutable, non-indexable
A Thursday 21 February 2008, Konrad Hinsen escrigué:
> I agree. In fact, I'd rather see NumPy scalars move towards Python
> scalars rather than towards NumPy arrays in behaviour. In particular,
> their nasty habit of coercing everything they are combined with into
> arrays is still my #1 source of
A Friday 22 February 2008, Stefan van der Walt escrigué:
> Hi Travis,
>
> On Wed, Feb 20, 2008 at 10:14:07PM -0600, Travis E. Oliphant wrote:
> > In writing some generic code, I've encountered situations where it
> > would reduce code complexity to allow NumPy scalars to be "indexed"
> > in the sam
On 21.02.2008, at 18:40, Alan G Isaac wrote:
x = N.array([1,2],dtype='float')
x0 = x[0]
type(x0)
>
>
> So a "float64 value" is whatever a numpy.float64 is,
> and that is part of what is under discussion.
numpy.float64 is a very recent invention. During the first decade of
Travis E. Oliphant wrote:
>> Travis,
>>
>> You have been getting mostly objections so far;
> I wouldn't characterize it that way, but yes 2 people have pushed back a
> bit, although one not directly speaking to the proposed behavior.
>
> The issue is that [] notation does more than just "select
On 21/02/2008, Stefan van der Walt <[EMAIL PROTECTED]> wrote:
> Could I ask that we also consider implementing len() for 0-d arrays?
> numpy.asarray returns those as-is, and I would like to be able to
> handle them just as I do any other 1-dimensional array. I don't know
> if a length of 1 wo
Hi Travis,
On Wed, Feb 20, 2008 at 10:14:07PM -0600, Travis E. Oliphant wrote:
> In writing some generic code, I've encountered situations where it would
> reduce code complexity to allow NumPy scalars to be "indexed" in the
> same number of limited ways, that 0-d arrays support.
>
>
> For examp
On Thu, Feb 21, 2008 at 12:08:32PM -0500, Alan G Isaac wrote:
> On Thu, 21 Feb 2008, Konrad Hinsen apparently wrote:
>
> > What I see as more fundamental is the behaviour of Python container
> > objects (lists, sets, etc.). If you add an object to a container and
> > then access it as an element
On Thu, Feb 21, 2008 at 12:30 PM, Travis E. Oliphant <[EMAIL PROTECTED]>
wrote:
>
> > Travis,
> >
> > You have been getting mostly objections so far;
> I wouldn't characterize it that way, but yes 2 people have pushed back a
> bit, although one not directly speaking to the proposed behavior.
>
I
> Travis,
>
> You have been getting mostly objections so far;
I wouldn't characterize it that way, but yes 2 people have pushed back a
bit, although one not directly speaking to the proposed behavior.
The issue is that [] notation does more than just "select from a
container" for NumPy arrays
On Thu, 21 Feb 2008, Konrad Hinsen apparently wrote:
> A float64 array is thus a container of float64 values.
Well ... ok::
>>> x = N.array([1,2],dtype='float')
>>> x0 = x[0]
>>> type(x0)
>>>
So a "float64 value" is whatever a numpy.float64 is,
and that is part of what is u
Travis E. Oliphant wrote:
> Hi everybody,
>
> In writing some generic code, I've encountered situations where it would
> reduce code complexity to allow NumPy scalars to be "indexed" in the
> same number of limited ways, that 0-d arrays support.
>
> For example, 0-d arrays can be indexed with
>
On Feb 21, 2008, at 18:08, Alan G Isaac wrote:
> I do not think anyone has really defended this behavior,
> *but* the reply to me when I suggested that a matrix
> contains arrays and we should see that in its behavior
> was that, no, a matrix is a container of matrices so this is
> what you get.
On Thu, 21 Feb 2008, Konrad Hinsen apparently wrote:
> What I see as more fundamental is the behaviour of Python container
> objects (lists, sets, etc.). If you add an object to a container and
> then access it as an element of the container, you get the original
> object (or something that beh
On Feb 21, 2008, at 16:03, Travis E. Oliphant wrote:
> However, I think my proposal for limited indexing capabilities
> should be
> considered separately from coercion behavior of NumPy scalars. NumPy
> scalars are intentionally different from Python scalars, and I see
> this
> difference gro
Damian Eads wrote:
> While we are on the subject of indexing... I use xranges all over the
> place because I tend to loop over big data sets. Thus I try avoid to
> avoid allocating large chunks of memory unnecessarily with range. While
> I try to be careful not to let xranges propagate to the nd
While we are on the subject of indexing... I use xranges all over the
place because I tend to loop over big data sets. Thus I try avoid to
avoid allocating large chunks of memory unnecessarily with range. While
I try to be careful not to let xranges propagate to the ndarray's []
operator, there
Konrad Hinsen wrote:
> On 21.02.2008, at 08:41, Francesc Altet wrote:
>
>
>> Well, it seems like a non-intrusive modification, but I like the
>> scalars
>> to remain un-indexable, mainly because it would be useful to raise an
>> error when you are trying to index them. In fact, I thought that
In MATLAB, scalars are 1x1 arrays, and thus they can be indexed. There
have been situations in my use of Numpy when I would have liked to index
scalars to make my code more general.
It's not a very pressing issue for me but it is an interesting issue.
Whenever I index an array with a sequence o
Travis E. Oliphant wrote:
> Hi everybody,
>
> In writing some generic code, I've encountered situations where it would
> reduce code complexity to allow NumPy scalars to be "indexed" in the
> same number of limited ways, that 0-d arrays support.
>
> For example, 0-d arrays can be indexed with
>
>
On 21.02.2008, at 08:41, Francesc Altet wrote:
> Well, it seems like a non-intrusive modification, but I like the
> scalars
> to remain un-indexable, mainly because it would be useful to raise an
> error when you are trying to index them. In fact, I thought that when
> you want a kind of scalar
A Thursday 21 February 2008, Travis E. Oliphant escrigué:
> Hi everybody,
>
> In writing some generic code, I've encountered situations where it
> would reduce code complexity to allow NumPy scalars to be "indexed"
> in the same number of limited ways, that 0-d arrays support.
>
> For example, 0-d
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