[Numpy-discussion] Behavior of array scalars

[Christopher Barker]

Hi all,

It just dawned on my that the numpy array scalars might give something I 
have wanted once in a while: mutable scalars. However, it seems that we 
almost, but no quite, have them. A few questions:

 >>> import numpy as N
 >>> N.__version__
'0.9.2'
 >>> N.array(5)
array(5)
 >>>
 >>> x = N.array(5)
 >>> x.shape
()

So it looks like a scalar.
 >>> y = x

Now I have two names bound to the same object.

 >>> x += 5

I expect this to change the object in place.

 >>> x
10

but what is this? is it no longer an array?

 >>> y
array(10)

y changed, so it looks like the object has changed in place.

 >>> type(x)
<type 'int32_arrtype'>
 >>> type(y)
<type 'numpy.ndarray'>

So why did x += 5 result in a different type of object?

Also:

I can see that we could use += and friends to mutate an array scalar, 
but what if I want to set it's value, as a mutation, like:

 >>> x = N.array((5,))
 >>> x
array([5])
 >>> x[0] = 10
 >>> x
array([10])

but I can't so that with an array scalar:

 >>> x = N.array(5)
 >>> x
array(5)
 >>> x[0] = 10
Traceback (most recent call last):
   File "<stdin>", line 1, in ?
IndexError: 0-d arrays can't be indexed.
 >>> x[] = 10
   File "<stdin>", line 1
     x[] = 10
       ^
SyntaxError: invalid syntax
 >>> x[:] = 10
Traceback (most recent call last):
   File "<stdin>", line 1, in ?
ValueError: cannot slice a scalar

Is there a way to set the value in place, without resorting to:

 >>> x *= 0
 >>> x += 34

I think it would be really handy to have a full featured, mutable scalar.

-Chris









-- 
Christopher Barker, Ph.D.
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Chris.Barker at noaa.gov