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__all__ =['inf_norm','diag_sparse']
import numpy,scipy,scipy.sparse,scipy.weave
from numpy import ravel,arange
from scipy.sparse import isspmatrix,isspmatrix_csr,isspmatrix_csc, \
csr_matrix,csc_matrix,extract_diagonal
def inf_norm(A):
"""
Infinity norm of a sparse matrix (maximum absolute row sum). This serves
as an upper bound on spectral radius.
"""
if not isspmatrix_csr(A):
return ValueError,'expected csr_matrix'
abs_A = csr_matrix((abs(A.data),A.indices,A.indptr),dims=A.shape,check=False)
return (abs_A * numpy.ones(A.shape[1],dtype=A.dtype)).max()
def diag_sparse(A):
"""
If A is a sparse matrix (e.g. csr_matrix or csc_matrix)
- return the diagonal of A as an array
Otherwise
- return a csr_matrix with A on the diagonal
"""
if isspmatrix(A):
return extract_diagonal(A)
else:
return csr_matrix((A,arange(len(A)),arange(len(A)+1)),(len(A),len(A)))
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