def check_solve_sparse_rhs(self):
"""Solve with UMFPACK: double precision, sparse rhs"""
linsolve.use_solver( useUmfpack = True )
a = self.a.astype('d')
b = csc_matrix( self.b )
x = linsolve.spsolve(a, b)
#print x
#print "Error: ", a*x-b
assert_array_almost_equal(a*x, self.b)
def check_solve_without_umfpack(self):
"""Solve: single precision"""
linsolve.use_solver( useUmfpack = False )
a = self.a.astype('f')
b = self.b
x = linsolve.spsolve(a, b.astype('f'))
#print x
#print "Error: ", a*x-b
assert_array_almost_equal(a*x, b)
def check_solve_umfpack(self):
"""Solve with UMFPACK: double precision"""
linsolve.use_solver( useUmfpack = True )
a = self.a.astype('d')
b = self.b
x = linsolve.spsolve(a, b)
#print x
#print "Error: ", a*x-b
assert_array_almost_equal(a*x, b)
def check_factorized_without_umfpack(self):
"""Prefactorize matrix for solving with multiple rhs"""
linsolve.use_solver( useUmfpack = False )
a = self.a.astype('d')
solve = linsolve.factorized( a )
x1 = solve( self.b )
assert_array_almost_equal(a*x1, self.b)
x2 = solve( self.b2 )
assert_array_almost_equal(a*x2, self.b2)
def _testme():
from scipy.sparse import csc_matrix
from numpy import array
from scipy.linsolve import spdiags, spsolve, use_solver
print "Inverting a sparse linear system:"
print "The sparse matrix (constructed from diagonals):"
a = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5)
b = array([1, 2, 3, 4, 5])
print "Solve: single precision complex:"
use_solver( useUmfpack = False )
a = a.astype('F')
x = spsolve(a, b)
print x
print "Error: ", a*x-b
print "Solve: double precision complex:"
use_solver( useUmfpack = True )
a = a.astype('D')
x = spsolve(a, b)
print x
print "Error: ", a*x-b
print "Solve: double precision:"
a = a.astype('d')
x = spsolve(a, b)
print x
print "Error: ", a*x-b
print "Solve: single precision:"
use_solver( useUmfpack = False )
a = a.astype('f')
x = spsolve(a, b.astype('f'))
print x
print "Error: ", a*x-b
