Applying Laplacian smoothing to vertices in a mesh
The code is not that bad at all.

Here are a few notes:




Use list comprehension – it's more readable than maps &amp; lambdas


indices = [x.ID for x in neighbors]






indices isn't really necessary. Instead of


indices = map(lambda x: x.ID, neighbors)
z = len(indices)

help = []
for j in indices:
    help.append([mesh.VPos[j]])
final += [sum(x)/z for x in zip(*help)]


you can simply do this


help = ([mesh.VPos[j.ID]] for j in neighbors)
z = len(neighbors)
final.extend(sum(x)/z for x in zip(*help))


help is a generator expression here, since you don't really need a list. That will probably save some overhead of creating the list. You can also avoid creating a new list of sum(x)/z and directly append all values to final using the extend method.





Instead of reassigning a numpy array to final, I'd just do


mesh.VPos = np.array(final)






I'm not completely sure, but I think the inner lists [mesh.VPos[j]] combined with zip(*help) will amount to the same as just


for j in indices:
    help.append(mesh.VPos[j])
final += [sum(help) / z]






You can create a numpy.ndarray right at the beginning instead of using a Python list and then converting it.




So after the edits it'll look something like this

n = mesh.VPos.shape[0]
final = np.empty(n)  # change dtype if you need to

for i in range(n):
    neighbors = mesh.vertices[i].getVertexNeighbors()
    help = (mesh.VPos[j.ID] for j in neighbors)
    final[i] = sum(help) / len(neighbors)

mesh.VPos = final


Alternatively you can use numpy.mean to compute sum(help) / len(neighbors):

n = mesh.VPos.shape[0]
final = np.empty(n)  # change dtype if you need to

for i in range(n):
    neighbors = mesh.vertices[i].getVertexNeighbors()
    help = [mesh.VPos[j.ID] for j in neighbors]
    final[i] = np.mean(help)

mesh.VPos = final


Note that you cannot give a generator argument to numpy.mean, it has to be a list (or anything with __len__ method).