Hi,
here is something I am thinking about for some time and I am wondering whether
there is a better solution
within numpy.
The task is:
I have an array (300000+ entries) with arrays each with length == 3, that is
initially empty like this:
n = 100 # for test otherwise ~300000
a1 = reshape(zeros(3*n).astype(float), (n,3))
(Speaking literally this is a field of displacements in a Finite-Element-Mesh)
Now I have a lot of triangles where the corners are the nodes, each with an
index between 0 and n-1
and I like to add a unique displacement for all three nodes to a1 like this
a2 = zeros(n).astype(int)
for indices, data in [...]:
#data = array((1.,2.,3.))
#indices = (1,5,60)
for index in indices:
a1[index] += data
a2[index] += 1
Now after filling a1 and a2 over and over (for a lot of triangles) I can
finally calculate the
averaged displacement on all points by this
meand = a1/reshape(a2,(n,1))
I am doing this in the old numeric package (and can do it in numpy).
I wonder whether there is a better way to do that in numpy. Numpy is in this
actual case 20times slower
than Numeric and I know that is due to the fact that I need to change only 3
values of the big array every
loop.
The basic problem is:
How can I add very few values to a big array with a good performance?
I thought about this:
m = zeros(n)
put(m, indices,1.) # only 3 ones in a long list of zeros!
a1 += outer(m, data)
a2 += m
which is in fact very slow due to the function outer.
Any help appriciated
Thomas
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