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@[...].gov


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