def test_canonical_flip(self):
mpiprint(
0, '\n' + '=' * 50 + '\nCanonical Peps Flipping test\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 5
Ny = 5
d = 2
D = 3
chi = 10
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
normalize=False,
canonical=True)
norm0 = peps.calc_norm()
peps.flip()
norm1 = peps.calc_norm()
peps.flip()
norm2 = peps.calc_norm()
mpiprint(0, 'Norms = {},{},{}'.format(norm0, norm1, norm2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-3)
self.assertTrue(abs((norm0 - norm2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_flip_Z2_ctf(self):
mpiprint(
0, '\n' + '=' * 50 + '\nPeps Z2 Flipping test (ctf)\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 5
Ny = 5
d = 2
D = 6
Zn = 2
chi = 10
backend = 'ctf'
# Generate random PEPS
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False)
# Compute the norm (2 ways for comparison)
norm0 = peps.calc_norm(chi=chi)
peps_sparse = peps.make_sparse()
norm1 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Symmetric Dense Norm = {}'.format(norm0))
mpiprint(0, 'Symmetric Sparse Norm = {}'.format(norm1))
# Rotate the PEPS
peps.flip()
norm2 = peps.calc_norm(chi=chi)
peps_sparse = peps.make_sparse()
norm3 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Flipped Symmetric Dense Norm = {}'.format(norm2))
mpiprint(0, 'Flipped Symmetric Sparse Norm = {}'.format(norm3))
self.assertTrue(abs((norm0 - norm1) / norm1) < 1e-3)
self.assertTrue(abs((norm0 - norm2) / norm2) < 1e-3)
self.assertTrue(abs((norm0 - norm3) / norm3) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)