The thing that I find I keep forgetting is that abs() is a built-in, but other simple functions are not. So it's abs(foo), but numpy.floor(foo) and numpy.ceil(foo). And then there's round() which is a built-in but can't be used with arrays, so
numpy.round_(foo). Seems like it would be more consistent to just add a numpy.abs() and numpy.round().
But I guess there's nothing numpy can do about it... you can't name a method the same as a built-in function, right? That's why we have
numpy.round_() instead of numpy.round(), no?
[...goes and checks]
Oh, you *can* name a module function the same as a built-in. Hmm... so then why isn't numpy.round_() just numpy.round()? Is it just so "from numpy import *" won't hide the built-in?
--bill
On 8/24/06, David M. Cooke <cookedm@physics.mcmaster.ca> wrote:
On Wed, 23 Aug 2006 13:51:02 -0700
Sebastian Haase <haase@msg.ucsf.edu> wrote:
> Hi!
> numpy renamed the *function* abs to absolute.
> Most functions like mean, min, max, average, ...
> have an equivalent array *method*.
>
> Why is absolute left out ?
> I think it should be added .
We've got __abs__ :-)
> Furthermore, looking at some line of code that have multiple calls to
> absolute [ like f(absolute(a), absolute(b), absolute(c)) ]
> I think "some people" might prefer less typing and less reading,
> like f( a.abs(), b.abs(), c.abs() ).
> One could even consider not requiring the "function call" parenthesis '()'
> at all - but I don't know about further implications that might have.
eh, no. things that return new arrays should be functions. (As opposed to
views of existing arrays, like a.T)
> PS: is there any performace hit in using the built-in abs function ?
Shouldn't be: abs(x) looks for the x.__abs__() method (which arrays have).