[Numpy-discussion] 3D array and the right hand rule
klemm at phys.ethz.ch
Tue Mar 17 15:09:37 EDT 2015
> On 17 Mar 2015, at 09:11, Dieter Van Eessen <dieter.van.eessen at gmail.com> wrote:
> 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,
> On Fri, Jan 30, 2015 at 2:32 AM, Alexander Belopolsky <ndarray at mac.com> wrote:
> On Mon, Jan 26, 2015 at 6:06 AM, Dieter Van Eessen <dieter.van.eessen at 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)
> 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 stored at a higher or lower address than a. 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.
let us say you have an n-dimensional rank k tensor. Then you could represent that beast as a numpy array of dimension k*n. So, if n is 4 and k is 2 you have a 4D tensor of rank 2. This tensor would be an array of shape (4,4). If you want to have higher order tensors you have higher dimensional arrays, a tensor of rank 3 would be an array of shape (4,4,4) in this example. Of course you have to be careful over which axes you do tensor operations.
Something like V_ik = C_ijk v_j would then mean that you want to sum over the first axis of the 3-dimensional array C (of shape (4,4,4) and along the zero-axis of the 1-dimensional array v_j (of shape (4,)).
By the way, of you think about doing those things, np.einsum might help. Of course, knowing which axis in the numpy array corresponds to which axis (or index) in your tensor is quite important.
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