Creating a numpy array of 3D coordinates from three 1D arrays

Question

Suppose I have three arbitrary 1D arrays, for example:

x_p = np.array((1.0, 2.0, 3.0, 4.0, 5.0))
y_p = np.array((2.0, 3.0, 4.0))
z_p = np.array((8.0, 9.0))
<         


        

Answer 1

You can use itertools.product:

def sol1():
    points = np.array( list(product(x_p, y_p, z_p)) )

Another solution using iterators and np.take showed to be about 3X faster for your case:

from itertools import izip, product

def sol2():
    points = np.empty((len(x_p)*len(y_p)*len(z_p),3))

    xi,yi,zi = izip(*product( xrange(len(x_p)),
                              xrange(len(y_p)),
                              xrange(len(z_p))  ))

    points[:,0] = np.take(x_p,xi)
    points[:,1] = np.take(y_p,yi)
    points[:,2] = np.take(z_p,zi)
    return points

Timing results:

In [3]: timeit sol1()
10000 loops, best of 3: 126 µs per loop

In [4]: timeit sol2()
10000 loops, best of 3: 42.9 µs per loop

In [6]: timeit ops()
10000 loops, best of 3: 59 µs per loop

In [11]: timeit joekingtons() # with your permission Joe...
10000 loops, best of 3: 56.2 µs per loop

So, only my second solution and Joe Kington's solution would give you some performance gain...