[Numpy-discussion] Rank-0 arrays - reprise
Dag Sverre Seljebotn
d.s.seljebotn@astro.uio...
Sun Jan 6 01:58:58 CST 2013
On 01/05/2013 10:31 PM, Nathaniel Smith wrote:
> On 5 Jan 2013 12:16, "Matthew Brett" <matthew.brett@gmail.com> wrote:
>>
>> Hi,
>>
>> Following on from Nathaniel's explorations of the scalar - array
>> casting rules, some resources on rank-0 arrays.
>>
>> The discussion that Nathaniel tracked down on "rank-0 arrays"; it also
>> makes reference to casting. The rank-0 arrays seem to have been one
>> way of solving the problem of maintaining array dtypes other than bool
>> / float / int:
>>
>> http://mail.scipy.org/pipermail/numpy-discussion/2002-September/001612.html
>>
>> Quoting from an email from Travis in that thread, replying to an email
>> from Tim Hochberg:
>>
>> http://mail.scipy.org/pipermail/numpy-discussion/2002-September/001647.html
>>
>> <quote>
>>> Frankly, I have no idea what the implimentation details would be, but
>>> could we get rid of rank-0 arrays altogether? I have always simply found
>>> them strange and confusing... What are they really neccesary for
>>> (besides holding scalar values of different precision that standard
>>> Pyton scalars)?
>>
>> With new coercion rules this becomes a possibility. Arguments against it
>> are that special rank-0 arrays behave as more consistent numbers with the
>> rest of Numeric than Python scalars. In other words they have a length
>> and a shape and one can right N-dimensional code that works the same even
>> when the result is a scalar.
>>
>> Another advantage of having a Numeric scalar is that we can control the
>> behavior of floating point operations better.
>>
>> e.g.
>>
>> if only Python scalars were available and sum(a) returned 0, then
>>
>> 1 / sum(a) would behave as Python behaves (always raises error).
>>
>> while with our own scalars
>>
>> 1 / sum(a) could potentially behave however the user wanted.
>> </quote>
>>
>> There seemed then to be some impetus to remove rank-0 arrays and
>> replace them with Python scalar types with the various numpy
>> precisions :
>>
>> http://mail.scipy.org/pipermail/numpy-discussion/2002-September/013983.html
>>
>> Travis' recent email hints at something that seems similar, but I
>> don't understand what he means:
>>
>> http://mail.scipy.org/pipermail/numpy-discussion/2012-December/064795.html
>>
>> <quote>
>> Don't create array-scalars. Instead, make the data-type object a
>> meta-type object whose instances are the items returned from NumPy
>> arrays. There is no need for a separate array-scalar object and in
>> fact it's confusing to the type-system. I understand that now. I
>> did not understand that 5 years ago.
>> </quote>
>>
>> Travis - can you expand?
>
> Numpy has 3 partially overlapping concepts:
>
> A) scalars (what Travis calls "array scalars"): Things like "float64",
> "int32". These are ordinary Python classes; usually when you subscript
> an array, what you get back is an instance of one of these classes:
>
> In [1]: a = np.array([1, 2, 3])
>
> In [2]: a[0]
> Out[2]: 1
>
> In [3]: type(a[0])
> Out[3]: numpy.int64
>
> Note that even though they are called "array scalars", they have
> nothing to do with the actual ndarray type -- they are totally
> separate objects.
>
> B) dtypes: These are instances of class np.dtype. For every scalar
> type, there is a corresponding dtype object; plus you can create new
> dtype objects for things like record arrays (which correspond to
> scalars of type "np.void"; I don't really understand how void scalars
> work in detail):
>
> In [8]: int64_dtype = np.dtype(np.int64)
>
> In [9]: int64_dtype
> Out[9]: dtype('int64')
>
> In [10]: type(int64_dtype)
> Out[10]: numpy.dtype
>
> In [11]: int64_dtype.type
> Out[11]: numpy.int64
>
> C) rank-0 arrays: Plain old ndarray objects that happen to have ndim
> == 0, shape == (). These are arrays which are scalars, but they are
> not array scalars. Arrays HAVE-A dtype.
>
> In [15]: int64_arr = np.array(1)
>
> In [16]: int64_arr
> Out[16]: array(1)
>
> In [17]: int64_arr.dtype
> Out[17]: dtype('int64')
>
> ------------
>
> Okay given that background:
>
> What Travis was saying in that email was that he thought (A) and (B)
> should be combined. Instead of having np.float64-the-class and
> dtype(np.float64)-the-dtype-object, we should make dtype objects
> actually *be* the scalar classes. (They would still be dtype objects,
> which means they would be "metaclasses", which is just a fancy way to
> say, dtype would be a subclass of the Python class "type", and dtype
> objects would be class objects that had extra functionality.)
>
> Those old mailing list threads are debating about (A) versus (C). What
> we ended up with is what I described above -- we have "rank-0"
> (0-dimensional) arrays, and we have array scalar objects that are a
> different set of python types and objects entirely. The actual
> implementation is totally different -- to the point that we a 35,000
> line auto-generated C file implementing arithmetic for scalars, *and*
> a 10,000 line auto-generated C file implementing arithmetic for arrays
> (including 0-dim arrays), and these have different functionality and
> bugs:
> https://github.com/numpy/numpy/issues/593
>
> However, the actual goal of all this code is to make array scalars and
> 0-dim arrays entirely indistinguishable. Supposedly they have the same
> APIs and generally behave exactly the same, modulo bugs (but surely
> there can't be many of those...), and two things:
>
> 1) isinstance(scalar, np.int64) is a sorta-legitimate way to do a type
> check. But isinstance(zerodim_arr, np.int64) is always false. Instead
> you have to use issubdtype(zerodim_arr, np.int64). (I mean, obviously,
> right?)
>
> 2) Scalars are always read-only, like regular Python scalars. 0-dim
> arrays are in general writeable... unless you set them to read-only. I
> think the only behavioural difference between an array scalar and a
> read-only 0-dim array is that for read-only 0-dim arrays, in-place
> operations raise an exception:
>
> In [5]: scalar = np.int64(1)
>
> # same as 'scalar = scalar + 2', i.e., creates a new object
> In [6]: scalar += 2
>
> In [7]: scalar
> Out[7]: 3
>
> In [10]: zerodim = np.array(1)
>
> In [11]: zerodim.flags.writeable = False
>
> In [12]: zerodim += 2
> ValueError: return array is not writeable
>
> Also, scalar indexing of ndarrays returns scalar objects. Except when
> it returns a 0-dim array -- I'm pretty sure this can happen when the
> moon is right, though I forget the details. ndarray subclasses? custom
> dtypes? Maybe someone will remember.
>
> Q: We could make += work on read-only arrays with, like, a 2 line fix.
> So wouldn't it be simpler to throw away the tens of thousands of lines
> of code used to implement scalars, and just use 0-dim arrays
> everywhere instead? So like, np.array([1, 2, 3])[1] would return a
> read-only 0-dim array, which acted just like the current scalar
> objects in basically every way?
>
> A: Excellent question! So ndarrays would be similar to Python strings
> -- indexing an ndarray would return another ndarray, just like
> indexing a string returns another string?
>
> Q: Yeah. I mean, I remember that seemed weird when I first learned
> Python, but when have you ever felt the Python was really missing a
> "character" type like C has?
str is immutable which makes this a lot easier to deal with without
getting confused. So basically you have:
a[0:1] # read-write view
a[[0]] # read-write copy
a[0] # read-only view
AND, += are allowed on all read-only arrays, they just transparently
create a copy instead of doing the operation in-place.
Try to enumerate all the fundamentally different things (if you count
memory use/running time) that can happen for ndarrays a, b, and
arbitrary x here:
a += b[x]
That's already quite a lot, your proposal adds even more options. It's
certainly a lot more complicated than str.
To me it all sounds like a lot of rules introduced just to have the
result of a[0] be "kind of a scalar" without actually choosing that option.
BUT I should read up on that thread you posted on why that won't work,
didn't have time yet...
Dag Sverre
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