Hello,
I have 2*N arrays (let's say 2 arrays of length 4) that I combine using np.meshgrid
xxA, yyA = np.meshgrid(xA, yA)
xxB, yyB = np.meshgrid(xB, yB)
which gives me two meshes
xx.shape = yy.shape = (4,4)
wish represent N-dimensional mesh with 16 elements.
Now I want to evaluate a function f on every possible pair of N-dimensional points in the grid, resulting in a 16 x 16
matrix:
in a flattened notation, pA = (xxA, yyA)
f(pA[1]-pB[1]) f(pA[1]-pB[2]) f(pA[1]-pB[3]) ...
f(pA[2]-pB[1]) f(pA[2]-pB[2]) f(pA[2]-pB[3]) ...
f(pA[3]-pB[1]) f(pA[3]-pB[2]) f(pA[3]-pB[3]) ...
.
.
.
Let's say xA = yA = [1,2,3] and xB = yB = [10,20,30]
that gives me a mesh A:
(1,3) (2,3) (3,3)
(1,2) (2,2) (3,2)
(1,1) (2,1) (3,1)
and a mesh B alike.
My result matrix now should be of size 9 x 9:
f( (1,3), (10,30) ) f( (2,3), (20,30) ) f( (3,3), (30, 30) )
f( (1,2), (10,20) ) f( (2,2), (20,20) ) f( (3,2), (30, 20) )
...
f always takes two N-dimensional vectors and returns a scalar.
I hope I was able to explain what I want to achieve.
What is the best way to do that in numpy?
Thanks,
Florian
