def test_canonical_normalization_z2(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nCanonical Peps Normalization test (Z2 Symmetry) \n' + '-' * 50)
Nx = 3
Ny = 5
d = 2
D = 3
chi = 10
Zn = 2
backend = 'numpy'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False,
canonical=True)
norm = peps.calc_norm(chi=chi)
norm = peps.normalize()
mpiprint(0, 'Norm = {}'.format(norm))
self.assertTrue(abs(1.0 - norm) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_normalization_Z2_ctf(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps (5x5) Normalization test with Z2 Symmetry (ctf)\n' +
'-' * 50)
Nx = 5
Ny = 5
d = 2
D = 6
chi = 10
Zn = 2 # Zn symmetry (here, Z2)
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))
# Normalize the PEPS
norm2 = peps.normalize()
peps_sparse = peps.make_sparse()
norm3 = peps_sparse.calc_norm(chi=chi)
mpiprint(0,
'Symmetric Dense Norm (After normalized) = {}'.format(norm2))
mpiprint(0,
'Symmetric Sparse Norm (After normalized) = {}'.format(norm3))
# Do some assertions to check if passed
self.assertTrue(abs((norm0 - norm1) / norm1) < 1e-3)
self.assertTrue(abs(1.0 - norm2) < 1e-3)
self.assertTrue(abs(1.0 - norm3) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
print_str = ''
for j in range(Ny-1):
print_str += '{} '.format(density_top[0][i][j])
print(print_str)
for i in range(Nx):
print_str = ''
for j in range(Ny-1):
print_str += '{} '.format(density_bot[0][i][j])
print(print_str)
print('Horizontal Density')
for i in range(Ny):
print_str = ''
for j in range(Nx-1):
print_str += '{} '.format(density_top[1][i][j])
print(print_str)
for i in range(Ny):
print_str = ''
for j in range(Nx-1):
print_str += '{} '.format(density_bot[1][i][j])
print(print_str)
# Increase PEPS D if wanted
if (len(D)-1 > ind):
if (D[ind+1] > D[ind]):
peps.increase_mbd(D[ind+1],chi=chi[ind+1])
peps.chi = chi[ind+1]
if (D[ind+1] > D[ind]):
pepsl.increase_mbd(D[ind+1],chi=chi[ind+1])
pepsl.chi = chi[ind+1]
peps.normalize()