Tue, 08 Feb 2011 15:24:10 +0000, EMMEL Thomas wrote:
[clip]
> n = 100 # for test otherwise ~300000
> a1 = reshape(zeros(3*n).astype(float), (n,3))
>
> 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
You can (mis-)use `bincount` to vectorize summations.
Bincount does not support broadcasting and takes only 1-D
inputs, so some manual shape manipulation is necessary.
Something probably could/should be done to optimize Numpy's
performance for small arrays.
----
import numpy as np
n = 100 # 100 nodes
m = 1000 # 1000 triangles
# synthetic data
tri_nodes = np.random.randint(0, n, size=(m, 3))
tri_data = np.random.rand(m, 3)
# vectorize sums via bincount
a1 = np.zeros((n,3), float)
a2 = np.bincount(tri_nodes.ravel())
for j in range(3):
# repeat(..., 3) -> the same (3,) data vector is added to each
# node in the triangle
a1[:,j] = np.bincount(tri_nodes.ravel(), np.repeat(tri_data[:,j], 3))
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