Contour plotting orbitals in pyquante2 using matplotlib - numpy

Question

I'm currently writing line and contour plotting functions for my PyQuante quantum chemistry package using matplotlib. I have some great functions that evaluate basis sets along a (npts,3) array of points, e.g.
from somewhere import basisset, line
bfs = basisset(h2) # Generate a basis set
points = line((0,0,-5),(0,0,5)) # Create a line in 3d space
bfmesh = bfs.mesh(points)
for i in range(bfmesh.shape[1]):
plot(bfmesh[:,i])
This is fast because it evaluates all of the basis functions at once, and I got some great help from stackoverflow here and here to make them extra-nice.
I would now like to update this to do contour plotting as well. The slow way I've done this in the past is to create two one-d vectors using linspace(), mesh these into a 2D grid using meshgrid(), and then iterating over all xyz points and evaluating each one:
f = np.empty((50,50),dtype=float)
xvals = np.linspace(0,10)
yvals = np.linspace(0,20)
z = 0
for x in xvals:
for y in yvals:
f = bf(x,y,z)
X,Y = np.meshgrid(xvals,yvals)
contourplot(X,Y,f)
(this isn't real code -- may have done something dumb)
What I would like to do is to generate the mesh in more or less the same way I do in the contour plot example, "unravel" it to a (npts,3) list of points, evaluate the basis functions using my new fast routines, then "re-ravel" it back to X,Y matrices for plotting with contourplot.
The problem is that I don't have anything that I can simply call .ravel() on: I either have 1d meshes of xvals and yvals, the 2D versions X,Y, and the single z value.
Can anyone think of a nice, pythonic way to do this?

Answer 1

If you can express f as a function of X and Y, you could avoid the Python for-loops this way:
import matplotlib.pyplot as plt
import numpy as np
def bf(x, y):
return np.sin(np.sqrt(x**2+y**2))
xvals = np.linspace(0,10)
yvals = np.linspace(0,20)
X, Y = np.meshgrid(xvals,yvals)
f = bf(X,Y)
plt.contour(X,Y,f)
plt.show()
yields