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#!/usr/bin/env python
#
""" Test functions for UMFPACK wrappers
"""
from numpy import transpose, array, arange
import random
from numpy.testing import *
set_package_path()
from scipy import linsolve, rand, matrix, diag, eye
from scipy.sparse import csc_matrix, dok_matrix, spdiags
import numpy as nm
import scipy.linsolve.umfpack as um
restore_path()
class test_solvers(NumpyTestCase):
"""Tests inverting a sparse linear system"""
def check_solve_complex_without_umfpack(self):
"""Solve: single precision complex"""
linsolve.use_solver( useUmfpack = False )
a = self.a.astype('F')
b = self.b
x = linsolve.spsolve(a, b)
#print x
#print "Error: ", a*x-b
assert_array_almost_equal(a*x, 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_complex_umfpack(self):
"""Solve with UMFPACK: double precision complex"""
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_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_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_factorized_umfpack(self):
"""Prefactorize (with UMFPACK) matrix for solving with multiple rhs"""
linsolve.use_solver( useUmfpack = True )
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 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 setUp(self):
self.a = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5)
#print "The sparse matrix (constructed from diagonals):"
#print self.a
self.b = array([1, 2, 3, 4, 5])
self.b2 = array([5, 4, 3, 2, 1])
class test_factorization(NumpyTestCase):
"""Tests factorizing a sparse linear system"""
def check_complex_lu(self):
"""Getting factors of complex matrix"""
umfpack = um.UmfpackContext("zi")
for A in self.complex_matrices:
umfpack.numeric(A)
(L,U,P,Q,R,do_recip) = umfpack.lu(A)
L = L.todense()
U = U.todense()
A = A.todense()
if not do_recip: R = 1.0/R
R = matrix(diag(R))
P = eye(A.shape[0])[P,:]
Q = eye(A.shape[1])[:,Q]
assert_array_almost_equal(P*R*A*Q,L*U)
def check_real_lu(self):
"""Getting factors of real matrix"""
umfpack = um.UmfpackContext("di")
for A in self.real_matrices:
umfpack.numeric(A)
(L,U,P,Q,R,do_recip) = umfpack.lu(A)
L = L.todense()
U = U.todense()
A = A.todense()
if not do_recip: R = 1.0/R
R = matrix(diag(R))
P = eye(A.shape[0])[P,:]
Q = eye(A.shape[1])[:,Q]
assert_array_almost_equal(P*R*A*Q,L*U)
def setUp(self):
random.seed(0) #make tests repeatable
self.real_matrices = []
self.real_matrices.append(spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]],
[0, 1], 5, 5))
self.real_matrices.append(spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]],
[0, 1], 4, 5))
self.real_matrices.append(spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]],
[0, 2], 5, 5))
self.real_matrices.append(csc_matrix(rand(3,3)))
self.real_matrices.append(csc_matrix(rand(5,4)))
self.real_matrices.append(csc_matrix(rand(4,5)))
self.complex_matrices = [x.astype(nm.complex128)
for x in self.real_matrices]
if __name__ == "__main__":
NumpyTest().run()
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