Robert Kern wrote: > On Wed, May 21, 2008 at 1:48 AM, Vincent Schut <[EMAIL PROTECTED]> wrote: >> Christopher Barker wrote: > >>> Also, if you image data is rgb, usually, that's a (width, height, 3) >>> array: rgbrgbrgbrgb... in memory. If you have a (3, width, height) >>> array, then that's rrrrrrr....gggggggg......bbbbbbbb. Some image libs >>> may give you that, I'm not sure. >> My data is. In fact, this is a simplification of my situation; I'm >> processing satellite data, which usually has more (and other) bands than >> just rgb. But the data is definitely in shape (bands, y, x). > > I don't think record arrays will help you much, then. Individual > records need to be contiguous (bar padding). You can't interleave > them. > Hmm, that was just what I was wondering about, when reading Stefan's reply. So in fact, recarrays aren't just another way to view some data, no matter in what shape it is.
So his solution: x.T.reshape((-1,x.shape[0])).view(dt).reshape(x.shape[1:]).T won't work, than. Or, at least, won't give me a view on my original dat, but would give me a recarray with a copy of my data. I guess I was misled by this text on the recarray wiki page: "We would like to represent a small colour image. The image is two pixels high and two pixels wide. Each pixel has a red, green and blue colour component, which is represented by a 32-bit floating point number between 0 and 1. Intuitively, we could represent the image as a 3x2x2 array, where the first dimension represents the color, and the last two the pixel positions, i.e. " Note the "3x2x2", which suggested imho that this would work with an image with (bands,y,x) shape, not with (x,y,bands) shape. But I understand that it's not shape, but internal representation in memory (contiguous or not, C/Fortran, etc) that matters? I know I can change the wiki text, but I'm afraid I still don't feel confident on this matter... _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion