I'm having trouble finding a solution to speed up a for loop in python. The following code does exactly what i want but is really slow :

# CoordEtAngle is an array containing 5 columns
# 1, 2 and 3 column are x y z coordinate of a points
# 4 and 5 values corresponding to those points


points=CoordEtAngle[:,0:3]
NumElem=np.zeros((len(allElem)), dtype=int)
CoordAxelist=[]

for idx,elemcur in enumerate(allElem):
    Coord = np.asarray(map(lambda i: allNodes[i].coordinates, elemcur.connectivity)) # gives me an array containing x,y and z coordinate of a box for each elements
    min_x=np.min(Coord[:,0])
    max_x=np.max(Coord[:,0])
    min_y=np.min(Coord[:,1])
    max_y=np.max(Coord[:,1])
    min_z=np.min(Coord[:,2])
    max_z=np.max(Coord[:,2])
    bound_x = np.logical_and(points[:, 0] > min_x, points[:, 0] < max_x)
    bound_y = np.logical_and(points[:, 1] > min_y, points[:, 1] < max_y)
    bound_z = np.logical_and(points[:, 2] > min_z, points[:, 2] < max_z)
    bb_filter = np.logical_and(np.logical_and(bound_x, bound_y), bound_z)
    NumElem[idx]=np.int(elemcur.label)
    CoordAxelist.append([np.mean(CoordEtAngle[bb_filter,3]),np.mean(CoordEtAngle[bb_filter,4])])
    
CoordAxelist=np.concatenate(CoordAxelist)

Thanks in advance for your input. I have a technical issue and i can only use numpy library ...

I did manage to gain some speed using lambda and map in some part of the loop but have no idea how to get even better results

CodePudding user response：

Thanks to both of you. allElem is not a numpy array but i could have it in numpy array, could it help ?

CodePudding user response：

I have several suggestions for you to try:

1. Try to pre allocate CoordAxelist before your loop starts
2. Try replacing enumerate with numpy.ndenumerate
3. You can profile your code with cProfile. You can read how to use this profiling tool in this question