Here's an example of some code I'm using:
import numpy as np
import numpy.linalg as linalg
A = np.random.random((10,10))
eigenValues,eigenVectors = linalg.eig(A)
idx = eigenValues.argsort()
eigenValues = eigenValues[idx]
eigenVectors = eigenVectors[:,idx]
What I'm trying to do is plot only the five smallest eigenvectors in a set of many more than five eigenvectors, and then plot them. So how could one choose the first five eigenvectors and then plot them in matplotlib?
The following will select the first five eigenvectors (assuming you've already done the sorting as in your example):
eigenVectors[:,:5]
As to how best to plot a ten-dimensional vector, I am not sure.
For future readers, here's a code sample that does exactly this:
from pylab import *
N = 10
k = 5
L = 1
x = linspace(-L, L, N)
H = random((N,N))
ls, vs = eig(H)
#find and plot k lowest eigenvalues and eigenvectors
min_values_indices = argsort(ls)[0:k]
for i in min_values_indices:
plot(x, vs[:,i])
show()
btw - I would've changed the subject of this question to something more informative (such as "numpy - find lowest k eigenvalues and eigenvectors")
