On Sun, May 10, 2015 at 4:40 AM, Stefan Otte <stefan.o...@gmail.com> wrote: > 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 feel like our "house style" has moved away from automatic flattening, and would maybe we should be nudging people towards something more like # using proposed np.stack from pull request #5605 X = np.stack(np.meshgrid(x, x), axis=-1) assert X.shape == (20, 20, 2) fancy_function(X) # vectorized to accept any array with shape (..., 2) -n -- Nathaniel J. Smith -- http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
