Hello, Sorry to disturb again, but the topic still bugs me somehow... I'll try to rephrase the question: - What's the influence of the type of N-array representation with respect to TENSOR-calculus? - Are multiple representations possible? - I assume that the order of the dimensions plays a major role in for example TENSOR product. Is this assumption correct? As I said before, my math skills are lacking in this area... I hope you consider this a valid question. kind regards, Dieter On Fri, Jan 30, 2015 at 2:32 AM, Alexander Belopolsky <ndarray@mac.com> wrote:

On Mon, Jan 26, 2015 at 6:06 AM, Dieter Van Eessen < dieter.van.eessen@gmail.com> wrote:

I've read that numpy.array isn't arranged according to the 'right-hand-rule' (right-hand-rule => thumb = +x; index finger = +y, bend middle finder = +z). This is also confirmed by an old message I dug up from the mailing list archives. (see message below)

Dieter,

It looks like you are confusing dimensionality of the array with the dimensionality of a vector that it might store. If you are interested in using numpy for 3D modeling, you will likely only encounter 1-dimensional arrays (vectors) of size 3 and 2-dimensional arrays (matrices) of size 9 or shape (3, 3).

A 3-dimensional array is a stack of matrices and the 'right-hand-rule' does not really apply. The notion of C/F-contiguous deals with the order of axes (e.g. width first or depth first) while the right-hand-rule is about the direction of the axes (if you "flip" the middle finger right hand becomes left.) In the case of arrays this would probably correspond to little-endian vs. big-endian: is a[0] stored at a higher or lower address than a[1]. However, whatever the answer to this question is for a particular system, it is the same for all axes in the array, so right-hand - left-hand distinction does not apply.

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-- gtz, Dieter VE