title: Python 3D interpolation speedup
body: <p>I have following code used to interpolate 3D volume data.</p>
  
  <pre><code>Y, X, Z = np.shape(volume)
  xs = np.arange(0, X)
  ys = np.arange(0, Y)
  zs = np.arange(0, Z)
  
  points = list(zip(np.ravel(result[:, :, :, 1]), np.ravel(result[:, :, :, 0]), np.ravel(result[:, :, :, 2])))
  interp = interpolate.RegularGridInterpolator((ys, xs, zs), volume,
                                               bounds_error=False, fill_value=0, method='linear')
  new_volume = interp(points)
  new_volume = np.reshape(new_volume, (Y, X, Z))
  </code></pre>
  
  <p>This code takes about 37 seconds to execute on 512x512x110 volume (about 29 millions of points), which results in more than one microsecond per voxel (which is unacceptable amount of time for me - what is more it uses 4 cores). Call <code>new_volume=interp(points)</code> takes about 80% of the prodecure time and the list creation almost whole remaining time. </p>
  
  <p>Is there any simple (or even more complex) way to make this computation faster? Or is there any good Python library, which provides faster interpolation? My volume and points change in every call to this prodecure.</p>