Consider the following code using numpy arrays which is very slow :
# Intersection of an octree and a trajectory
def intersection(octree, trajectory):
# Initialize numpy arrays
ox = octree.get("x")
oy = octree.get("y")
oz = octree.get("z")
oe = octree.get("extent")/2
tx = trajectory.get("x")
ty = trajectory.get("y")
tz = trajectory.get("z")
result = np.zeros(np.size(ox))
# Loop over elements
for i in range(0, np.size(tx)):
for j in range(0, np.size(ox)):
if (tx[i] > ox[j]-oe[j] and
tx[i] < ox[j]+oe[j] and
ty[i] > oy[j]-oe[j] and
ty[i] < oy[j]+oe[j] and
tz[i] > oz[j]-oe[j] and
tz[i] < oz[j]+oe[j]):
result[j] += 1
# Finalize
return result
How to rewrite the function to speed up the calculation ? (np.size(tx) == 10000
and np.size(ox) == 100000
)
You are allocating 10000 lists of size 100000. The first thing to do would be to stop using range
for the nested j
loop and use the generator version xrange
instead. This will save you time and space allocating all those lists.
The next one would be to use vectorized operations:
for i in xrange(0, np.size(tx)):
index = (ox-oe < tx[i]) & (ox+oe > tx[i]) & (oy-oe < ty[i]) & (oy+oe > ty[i]) & (oz-oe < tz[i]) & (oz+oe > tz[i])
result[index] += 1
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