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

Dieter VE