# Why does my code generate a figure whose kurtosis looks a little higher than the one I am trying to reproduce?

this figure (fig_1) is an univariate normal distribution in 3D, the corresponding equation is in the top left hand corner

it seems that x varies from -5 to 5; sigma varies from 0 to 4.

This Python code is trying to reproduce this figure

ax = plt.figure().gca(projection='3d')
xx, yy = np.meshgrid(np.arange(-5,5,.1), np.arange(0.1,4,.1))
zz = norm.pdf(xx,0,yy)
ax.plot_surface(xx, yy, zz, alpha=.5)

and I got this figure

and this guy

zz = norm.pdf(xx,0,np.sqrt(yy))

gives this figure

which is better although the kurtosis looks a little higher than fig_1.

any clue