What's the fastest way to calculate the midpoints of a sequence of edges?

I'm trying to create a list that calculates the midpoints of a sequence of edges.

Right now I'm doing it like this:

eList = []
for e in [e for e in bm.edges if e.select]:
pt_1, pt_2 = (ob.matrix_world * e.verts[0].co), (ob.matrix_world * e.verts[1].co)
eList.append((e, co))


I multiply each vert in the edge by the object's world matrix, then add the verts together. I divide each axis in the vert by 2 and covert them to a vector and then put the edge and the vector in a tuple and append that to a list.

It's probably not a very efficient process but I'm still pretty new to python and can't think of a faster way.

Is this something that could be done faster in numpy?

What would be the fastest way to perform this function over a large sequence of edges?

Some improvements.

• Use list comprehension
• Only need to multiply the local average edge coord by matrix world once.
• Multiply / divide a vector by a scalar, rather than on a per component basis.

Test script

import bpy
import bmesh

ob = bpy.context.edit_object
me = ob.data
bm = bmesh.from_edit_mesh(me)

mw = ob.matrix_world
elist = [(e, mw * (e.verts[0].co + e.verts[1].co) / 2)
for e in bm.edges if e.select]


could make a little helper routine.

def mid_edge(e):
return (e.verts[0].co + e.verts[1].co) / 2

elist = [(e, mw * mid_edge(e))
for e in bm.edges if e.select]

• Wow! I didn't realize you could do all that directly in the list comprehension. This is great, thanks! – andyV Jul 11 '18 at 6:32