Travis Oliphant wrote:

Sebastian Haase wrote:

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On Wednesday 09 August 2006 15:45, you wrote:

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Sebastian Haase wrote:

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On Wednesday 09 August 2006 15:18, Travis Oliphant wrote:

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If numarray supported it, then we should get NumPy to support it as well unless there is a compelling reason not to. I can't think of any except that it might be hard to make it work. What is '0i4' supposed to mean exactly? Do you get a zero-sized field or is the field not included? I think the former will be much easier than the latter. Would that be O.K.?

That's exactly what numarray did. The rest of my code is assuming that all fields exist (even if they are empty). Otherwise I get a name error which is worse than getting an empty array.

Do you have a simple code snippet that I could use as a test?

-Travis

I think this should do it:

a = N.arange(10, dtype=N.float32) a.shape = 5,2 type_descr = [("int", "<0i4"),("float", "<2f4")] a.dtype = type_descr

I'm not sure what a.shape = (5,2) is supposed to do. I left it in the unit-test out because assigning to the data-type you just defined already results in

a['float'].shape being (5,2)

If it is left in, then an extra dimension is pushed in and

a['float'].shape is (5,1,2)

This is due to the default behavior of assigning data-types when the new data-type has a larger but compatibile itemsize then the old itemsize.

I have to admit that I don't understand that statement. I thought - just "visually" - that a.shape = 5,2 would make a "table" with 2 columns. Then I could go on and give those columns names... Or is the problem that the type "2f4" refers to (some sort of) a "single column" with 2 floats grouped together !? Thanks for implementing it so quickly, Sebastian Haase