As I understand it, this is the basic usage of np.meshgrid:
import numpy as np
x = np.linspace(0, 1, 5)
y = np.linspace(0, 1, 5)
xx, yy = np.meshgrid(x, y)
z = xx * yy
plt.contourf(x,y,z)
plt.show()
Where xx is:
[[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]]
And yy is the transpose (in this case, at least).
What is not clear to me is why we need two separate 5x5 arrays. For example, why not use a 25x2 representation:
[[ 0. , 0. ],
[ 0. , 0.25],
[ 0. , 0.5 ],
... (etc)
Or even a 5x5x2 array.
Is there any clear advantage to using coordinate matrices? Linear algebra is a weak spot for me, so it's entire possible I'm missing something here.
Edit: I disagree with the duplicate tag. My question was more, "why is the output this strange format?", as opposed to "why would I want a meshgrid in the first place?"
With that said, I think Paul's comment mostly answers my question: In particular, the coordinate matrices make for cleaner vectorized operations when computing some z = f(x, y).