def test_PowerMethod(self):
print ("test_BlockOperator")
N, M = 200, 300
niter = 10
ig = ImageGeometry(N, M)
Id = Identity(ig)
G = Gradient(ig)
uid = Id.domain_geometry().allocate(ImageGeometry.RANDOM_INT, seed=1)
a = LinearOperator.PowerMethod(Id, niter, uid)
#b = LinearOperator.PowerMethodNonsquare(Id, niter, uid)
b = LinearOperator.PowerMethod(Id, niter)
print ("Edo impl", a[0])
print ("None impl", b[0])
#self.assertAlmostEqual(a[0], b[0])
self.assertNumpyArrayAlmostEqual(a[0],b[0],decimal=6)
a = LinearOperator.PowerMethod(G, niter, uid)
b = LinearOperator.PowerMethod(G, niter)
#b = LinearOperator.PowerMethodNonsquare(G, niter, uid)
print ("Edo impl", a[0])
#print ("old impl", b[0])
self.assertNumpyArrayAlmostEqual(a[0],b[0],decimal=2)
def test_FiniteDifference(self):
print ("test FiniteDifference")
##
N, M = 2, 3
ig = ImageGeometry(N, M)
Id = Identity(ig)
FD = FiniteDiff(ig, direction = 0, bnd_cond = 'Neumann')
u = FD.domain_geometry().allocate('random_int')
res = FD.domain_geometry().allocate(ImageGeometry.RANDOM_INT)
FD.adjoint(u, out=res)
w = FD.adjoint(u)
self.assertNumpyArrayEqual(res.as_array(), w.as_array())
res = Id.domain_geometry().allocate(ImageGeometry.RANDOM_INT)
Id.adjoint(u, out=res)
w = Id.adjoint(u)
self.assertNumpyArrayEqual(res.as_array(), w.as_array())
self.assertNumpyArrayEqual(u.as_array(), w.as_array())
G = Gradient(ig)
u = G.range_geometry().allocate(ImageGeometry.RANDOM_INT)
res = G.domain_geometry().allocate()
G.adjoint(u, out=res)
w = G.adjoint(u)
self.assertNumpyArrayEqual(res.as_array(), w.as_array())
u = G.domain_geometry().allocate(ImageGeometry.RANDOM_INT)
res = G.range_geometry().allocate()
G.direct(u, out=res)
w = G.direct(u)
self.assertBlockDataContainerEqual(res, w)