Array of hermite values in numpy

Question

I have a data structure that looks like a list values and I am trying to compute the (x,y) 2d hermite functions from them using numpy. I'm trying to use as many numpy arrays as possible due to the performance boost you get from getting to Fortran as quickly as possible (I'm expecting x to be in practice many thousands of 3-arrays). Specifically, my code looks like this:

x = np.array([[1., 2., 3.], [4., 5., 6.]])
coefs = np.array([[[1., 0.],[0., 1.]], [[0., 1.], [1., 0.]]])

z = np.array([0., 0.])

z[:] = hermval2d(x[:,0], x[:,1], coefs[:])

This returns an error about the shape of hermval2d, which according to just running the hermval2d function instead of assigning it:

In [XX]: hermval2d(x[:,0], x[:,1], coefs[:])
Out[XX]:
array([[  9.,  81.],
       [  6.,  18.]])

I would expect the hermval2d to be a scalar for every x, y, and coefficient matrix, which is what you would expect from the documentation. So what am I missing here? What's the score?

Answer 1

It's right there in the docs :)

hermval2d(x, y, c)

[...]

The shape of the result will be c.shape[2:] + x.shape

In your case this seems to return the Hermite values for x and y evaluated for each ith 2d array in c[:,:,i].