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from warnings import warn
from numpy import asarray
from scipy.sparse import isspmatrix_csc, isspmatrix_csr, isspmatrix, \
SparseEfficiencyWarning, csc_matrix
import _superlu
noScikit = False
try:
import scikits.umfpack as umfpack
except ImportError:
import umfpack
noScikit = True
isUmfpack = hasattr( umfpack, 'UMFPACK_OK' )
useUmfpack = True
__all__ = [ 'use_solver', 'spsolve', 'splu', 'factorized' ]
#convert numpy char to superLU char
superLU_transtabl = {'f':'s', 'd':'d', 'F':'c', 'D':'z'}
def use_solver( **kwargs ):
"""
Valid keyword arguments with defaults (other ignored):
useUmfpack = True
assumeSortedIndices = False
The default sparse solver is umfpack when available. This can be changed by
passing useUmfpack = False, which then causes the always present SuperLU
based solver to be used.
Umfpack requires a CSR/CSC matrix to have sorted column/row indices. If
sure that the matrix fulfills this, pass assumeSortedIndices=True
to gain some speed.
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
if isUmfpack:
umfpack.configure( **kwargs )
def spsolve(A, b, permc_spec=2):
"""Solve the sparse linear system Ax=b
"""
if isspmatrix( b ):
b = b.toarray()
if b.ndim > 1:
if max( b.shape ) == b.size:
b = b.squeeze()
else:
raise ValueError, "rhs must be a vector (has shape %s)" % (b.shape,)
if not (isspmatrix_csc(A) or isspmatrix_csr(A)):
A = csc_matrix(A)
warn('spsolve requires CSC or CSR matrix format', SparseEfficiencyWarning)
A.sort_indices()
A = A.asfptype() #upcast to a floating point format
M, N = A.shape
if (M != N):
raise ValueError, "matrix must be square (has shape %s)" % (M,N)
if M != b.size:
raise ValueError, "matrix - rhs size mismatch (%s - %s)"\
% (A.shape, b.size)
if isUmfpack and useUmfpack:
if noScikit:
warn( 'scipy.sparse.linalg.dsolve.umfpack will be removed,'\
' install scikits.umfpack instead', DeprecationWarning )
if A.dtype.char not in 'dD':
raise ValueError, "convert matrix data to double, please, using"\
" .astype(), or set linsolve.useUmfpack = False"
b = asarray(b, dtype=A.dtype).reshape(-1)
family = {'d' : 'di', 'D' : 'zi'}
umf = umfpack.UmfpackContext( family[A.dtype.char] )
return umf.linsolve( umfpack.UMFPACK_A, A, b,
autoTranspose = True )
else:
if isspmatrix_csc(A):
flag = 1 # CSC format
else:
flag = 0 # CSR format
ftype = superLU_transtabl[A.dtype.char]
gssv = eval('_superlu.' + ftype + 'gssv')
b = asarray(b, dtype=A.dtype)
return gssv(N, A.nnz, A.data, A.indices, A.indptr, b, flag, permc_spec)[0]
def splu(A, permc_spec=2, diag_pivot_thresh=1.0,
drop_tol=0.0, relax=1, panel_size=10):
"""
A linear solver, for a sparse, square matrix A, using LU decomposition where
L is a lower triangular matrix and U is an upper triagular matrix.
Returns a factored_lu object. (scipy.sparse.linalg.dsolve._superlu.SciPyLUType)
See scipy.sparse.linalg.dsolve._superlu.dgstrf for more info.
"""
if not isspmatrix_csc(A):
A = csc_matrix(A)
warn('splu requires CSC matrix format', SparseEfficiencyWarning)
A.sort_indices()
A = A.asfptype() #upcast to a floating point format
M, N = A.shape
if (M != N):
raise ValueError, "can only factor square matrices" #is this true?
ftype = superLU_transtabl[A.dtype.char]
gstrf = eval('_superlu.' + ftype + 'gstrf')
return gstrf(N, A.nnz, A.data, A.indices, A.indptr, permc_spec,
diag_pivot_thresh, drop_tol, relax, panel_size)
def factorized( A ):
"""
Return a fuction for solving a sparse linear system, with A pre-factorized.
Example:
solve = factorized( A ) # Makes LU decomposition.
x1 = solve( rhs1 ) # Uses the LU factors.
x2 = solve( rhs2 ) # Uses again the LU factors.
"""
if isUmfpack and useUmfpack:
if noScikit:
warn( 'scipy.sparse.linalg.dsolve.umfpack will be removed,'\
' install scikits.umfpack instead', DeprecationWarning )
if not isspmatrix_csc(A):
A = csc_matrix(A)
warn('splu requires CSC matrix format', SparseEfficiencyWarning)
A.sort_indices()
A = A.asfptype() #upcast to a floating point format
if A.dtype.char not in 'dD':
raise ValueError, "convert matrix data to double, please, using"\
" .astype(), or set linsolve.useUmfpack = False"
family = {'d' : 'di', 'D' : 'zi'}
umf = umfpack.UmfpackContext( family[A.dtype.char] )
# Make LU decomposition.
umf.numeric( A )
def solve( b ):
return umf.solve( umfpack.UMFPACK_A, A, b, autoTranspose = True )
return solve
else:
return splu( A ).solve
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