Hey,

quite often I want to evaluate a function on a grid in a n-D space.
What I end up doing (and what I really dislike) looks something like this:

  x = np.linspace(0, 5, 20)
  M1, M2 = np.meshgrid(x, x)
  X = np.column_stack([M1.flatten(), M2.flatten()])
  X.shape  # (400, 2)

  fancy_function(X)

I don't think I ever used `meshgrid` in any other way.
Is there a better way to create such a grid space?

I wrote myself a little helper function:

  def gridspace(linspaces):
      return np.column_stack([space.flatten()
                              for space in np.meshgrid(*linspaces)])

But maybe something like this should be part of numpy?


Best,
 Stefan