[SciPy-User] Return type of scipy.interpolate.splev for input array of length 1

Anne Archibald peridot.faceted@gmail....
Wed Jan 20 14:00:47 CST 2010

2010/1/19 Pauli Virtanen <pav+sp@iki.fi>:
> Mon, 18 Jan 2010 10:59:46 -0500, josef.pktd wrote:
>> On Sun, Jan 17, 2010 at 5:25 AM, Yves Frederix <yves.frederix@gmail.com>
>> wrote:
> [clip]
>>> It was rather unexpected that the type of input and output data are
>>> different. After checking interpolate/fitpack.py it seems that this
>>> behavior results from the fact that the length-1 case is explicitly
>>> treated differently (probably to be able to deal with the case of
>>> scalar input, for which scalar output is expected):
>>>  434 def splev(x,tck,der=0):
>>>  <snip>
>>>  487         if ier: raise TypeError,"An error occurred" 488
>>>  if len(y)>1: return y 489         return y[0]
>>>  490
>>> Wouldn't it be less confusing to have the return value always have the
>>> same type as the input data?
>> I don't know of any "official" policy.
> I think (unstructured) interpolation should respect
>        input.shape == output.shape
> also for 0-d. So yes, it's a wart, IMHO.
> Another question is: how many people actually have code that depends on
> this wart, and can it be fixed? I'd guess there's not much problem: (1,)
> arrays function nicely as scalars, but not vice versa because of
> mutability.

More generally, I think many functions should preserve the shape of
the input array. Unfortunately it's often a hassle to do this: a few
functions I have written start by checking whether the input is a
scalar, setting a boolean and converting it to an array of size one;
then at the end, I check the boolean and strip the array wrapping if
the input is a scalar. It's annoying boilerplate, and I suspect that
many functions don't handle this just because it's a nuisance. Some
handy utility code might help.

It would also be good to have a generic test one could apply to many
functions to check that they preserve array shapes (0-d, 1-d of size
1, many-dimensional, many-dimensional with a zero dimension),  and
scalarness. Together with a test for preservation of arbitrary array
subclasses (and correct functioning when handed matrices), one might
be able to shake out a lot of minor easy-to-fix nuisances.


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