[Numpy-discussion] Args for ones, zeros, rand, eye, ones, empty (possible 1.0 change?)
Travis Oliphant
oliphant.travis at ieee.org
Mon Jul 3 02:51:06 CDT 2006
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Hmmm..... One thing that bothers me is that it seems that those
arguing *against* this behavior are relatively long-time users of Python
while those arguing *for* it are from what I can tell somewhat new to
Python/NumPy. I'm not sure what this means.
Is the current behavior a *wart* you get used to or a clear *feature*
providing some gain in programming efficiency.
If new users complain about something, my tendency is to listen openly
to the discussion and then discourage the implementation only if there
is a clear reason to.
With this one, I'm not so sure of the clear reason. I can see that
"program parsers" would have a harder time with a "flexible" calling
convention. But, in my calculus, user-interface benefit outweighs
programmer benefit (all things being equal).
It's easy as a programmer to get caught up in a very rigid system when
many users want flexibility.
I must confess that I don't like looking at ones((5,5)) either. I much
prefer ones(5,5) or even ones([5,5]).
But perhaps what this shows is something I've heard Robert discuss
before that ihas not received enough attention. NumPy really has at
least two "users" 1) application developers and 2) prototype developers
(the MATLAB crowd for lack of a better term).
These two groups usually want different functionality (and in reality
most of us probably fall in both groups on different occasions). The
first clamors for more rigidity and conformity even at the expense of
user interfaces. These people usually want
1) long_but_explanatory_function names
2) rigid calling syntax
3) strict name-space control
The second group which wants to get something prototyped and working
quickly wants
1) short easy-to-remember names
2) flexible calling syntax
3) all-in-one name-space control
My hypothesis is that when you first start with NumPy (especially with a
MATLAB or IDL history) you seem to start out in group 2 and stay there
for quick projects. Then, as code-size increases and applications get
larger, you become more like group 1.
I think both groups have valid points to make and both styles can be
useful and one point or another. Perhaps, the solution is the one I have
barely begun to recognize, that others of you have probably already seen.
A) Make numpy a good "library" for group 1.
B) Make a shallow "name-space" (like pylab) with the properties of group 2.
Perhaps a good solution is to formalize the discussion and actually
place in NumPy a name-space package much like Bill has done privately.
-Travis
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