[Numpy-discussion] Response to PEP suggestions
konrad.hinsen at laposte.net
konrad.hinsen at laposte.net
Fri Feb 18 06:31:17 EST 2005
On Feb 17, 2005, at 23:22, Duncan Child wrote:
> I have attached some code that illustrates part of the pain we have
> experienced developing libraries of algorithms that can handle both
> arrays and scalars. The attached library is the reusable part. The
> other part of this problem is that we have lots of logic sprinkled
> throughout our algorithms to enable them to handle both arrays and
> scalars.
See comments below...
> Secondly, I have just been bitten by this declaration which suggests
> that the new Numeric might handle default values better:.
>
> _vp_mod = zeros(num_pts)
>
> It would be less surprising to someone developing numeric algorithms
> if functions like this defaulted to creating a double precision array
> rather than integers.
If you want a function that returns float arrays, it is trivial to
write and adds negligible overhead:
def float_array(array_spec):
return Numeric.array(array_spec, Numeric.Float)
No need to interfere with Numeric's principle of "smallest usable
type", which fits well into the Python type promotion hierarchy.
More generally, I don't think defaults should be chosen with a
particular application in mind. Arrays are a general and widely useful
datatype in many domains. I use integer arrays as much as float arrays,
even though my applications qualify as "numeric".
> """
> The following safe_ methods were written to handle both arrays amd
> scalars to
> save the developer of numerical methods having to clutter their code
> with tests
> to determine the type of the data.
> """
>
> def safe_take(a,indices):
> # Slice the input if it is an array but not if it is a scalar
This is a very bad example. Your function does not interpret scalars as
rank-0 arrays (for which take() would fail), but as something
completely different.
> def safe_copy(a):
> # Return a copy for both scalar and array input
That is a semantically reasonable application, but also one for which a
simple and standard solution already exists: copy.copy().
> def safe_min(a):
> # Return the minimum of the input array or the input if it is a
> scalar
I would argue that this is not a good example either, as "minimum over
an array" implies a reduction operation which is not defined for a
scalar. On the other hand, the operation you define certainly makes
sense.
> def safe_len(a):
> # Return the length of the input array or 1 if it is a scalar
That implies that a scalar is somehow equivalent to a rank-1 array of
length 1, which is not the case.
Actually, all of your examples look like an attempt to recreate Matlab
behaviour. But Python is not Matlab!
Konrad.
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