Calculating the singular values of one or more matrices using NumPy

# Example Python program that finds the singular values of
# multiple matrices in one go using the function linalg.svdvals()
import numpy

# Create an upper triangular matrix
elems     = numpy.arange(1, 18, 2)
upperTriangular  = elems.reshape(3, 3)

upperTriangular[1][0] = 0
upperTriangular[2][0] = 0
upperTriangular[2][1] = 0

print("Matrix 1:")
print(upperTriangular)

# Create a lower triangular matrix
elems     = numpy.arange(2, 19, 2)
lowerTriangular  = elems.reshape(3, 3)

lowerTriangular[0][1] = 0
lowerTriangular[0][2] = 0
lowerTriangular[1][2] = 0

print("Matrix 2:")
print(lowerTriangular)

svdValues = numpy.linalg.svdvals([upperTriangular, lowerTriangular])

print("Singular values of matrix 1:")
print(svdValues[0])

print("Singular values of matrix 2:")
print(svdValues[1])