To use numpy mesh grid on the above example the following will work:
np.vstack(np.meshgrid(x_p,y_p,z_p)).reshape(3,-1).T
Numpy meshgrid for grids of more then two dimensions require numpy 1.7. To circumvent this and pulling the relevant data from the source code.
def ndmesh(*xi,**kwargs):
if len(xi) < 2:
msg = 'meshgrid() takes 2 or more arguments (%d given)' % int(len(xi) > 0)
raise ValueError(msg)
args = np.atleast_1d(*xi)
ndim = len(args)
copy_ = kwargs.get('copy', True)
s0 = (1,) * ndim
output = [x.reshape(s0[:i] + (-1,) + s0[i + 1::]) for i, x in enumerate(args)]
shape = [x.size for x in output]
# Return the full N-D matrix (not only the 1-D vector)
if copy_:
mult_fact = np.ones(shape, dtype=int)
return [x * mult_fact for x in output]
else:
return np.broadcast_arrays(*output)
Checking the result:
print np.vstack((ndmesh(x_p,y_p,z_p))).reshape(3,-1).T
[[ 1. 2. 8.]
[ 1. 2. 9.]
[ 1. 3. 8.]
....
[ 5. 3. 9.]
[ 5. 4. 8.]
[ 5. 4. 9.]]
For the above example:
%timeit sol2()
10000 loops, best of 3: 56.1 us per loop
%timeit np.vstack((ndmesh(x_p,y_p,z_p))).reshape(3,-1).T
10000 loops, best of 3: 55.1 us per loop
For when each dimension is 100:
%timeit sol2()
1 loops, best of 3: 655 ms per loop
In [10]:
%timeit points = np.vstack((ndmesh(x_p,y_p,z_p))).reshape(3,-1).T
10 loops, best of 3: 21.8 ms per loop
Depending on what you want to do with the data, you can return a view:
%timeit np.vstack((ndmesh(x_p,y_p,z_p,copy=False))).reshape(3,-1).T
100 loops, best of 3: 8.16 ms per loop