[Numpy-discussion] Irregular arrays
Torgil Svensson
torgil.svensson at gmail.com
Tue Sep 5 14:49:34 CDT 2006
> Think about gridding physical problems expressed in cylindrical or
> spherical coordinates. The natural slices are not rectangles. You can
> use rectangular storage but only with O(n^3) waste.
I don't get this argument. Are you slicing your spherical coordinates
with a cartesian coordinate system? That's surely a waste. Many times
bases can be chosen so that you even in an array doesn't waste space.
I can't see the point attacking numpy with this "irregular"
generalisation. Numpy is a tool very good for it's purpose, we don't
really want to trash it with features for solving unspecified general
problems. Instead, specify a real world problem, and perhaps someone
might have a good tip, otherwise look for other tools. Pytables for
example can efficiently store varying length arrays.
Maybe start on developing an variable length array yourself that
integrates perfectly with Numpy where applicable?
//Torgil
On 9/5/06, rw679aq02 at sneakemail.com <rw679aq02 at sneakemail.com> wrote:
> Exalted presences and superior intellects aside, the point is not hard
> to get: Motivational examples are everywhere.
>
> Think about gridding physical problems expressed in cylindrical or
> spherical coordinates. The natural slices are not rectangles. You can
> use rectangular storage but only with O(n^3) waste.
>
> More abstract solution spaces of math and physics do not usually lend
> themselves to rectangular treatments. (I understand finite element
> techniques and am not referring to those.) Again, rectangular storage
> is possible only with O(n^d) waste, where commonly d>3.
>
> Granted one may overcome these issues with software development effort;
> that insight begs the question. I am looking for teaching software that
> already does so.
>
> I agree that rectangular storage is easiest for software programmers and
> hence common. It is not easiest for solving all problems. Students
> should explore solutiuon spaces in a proper setting. So I just asked
> what numpy could do in this regard. Now I have the plain answer, and am
> grateful for it.
>
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