Ok, thanks for the reply!

Indeed, I know about the use of transformation matrices to manipulate points in space.
That's all matrix manipulation anyway....

But, (and perhaps this is not the right place to ask the following question):
But are there no known mathmatical algorithms which involve the use of 3n arrays (or higher dimensions)
to transform an object between one state and the other?

This is an open question, as my knowledge of math is lacking on this area.
I'm currently limited to 3D object manipulation and some statistics which all rely on matrix calculus...

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