def test_normalization_Z3(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps (5x5) Normalization test with Z3 Symmetry\n' + '-' * 50)
Nx = 5
Ny = 5
d = 2
D = 6
chi = 50
Zn = 3 # Zn symmetry (here, Z3)
dZn = 2
backend = 'ctf'
# Generate random PEPS
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
dZn=dZn,
backend=backend,
normalize=False)
# Compute the norm (2 ways for comparison)
peps_sparse = peps.make_sparse()
norm0 = peps.calc_norm(chi=chi)
norm1 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Symmetric Sparse Norm = {}'.format(norm1))
mpiprint(0, 'Symmetric Dense Norm = {}'.format(norm0))
self.assertTrue(abs((norm0 - norm1) / norm1) < 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)
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)
# Get inputs
Ny = int(argv[1])
Nx = int(argv[2])
d = 2
D = int(argv[3])
chi = int(argv[4])
Zn = int(argv[5])
backend = 'numpy'
# Create a random pep
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False)
# contract the norm
fname = argv[6] + '_norm_stats_Nx' + str(Nx) + '_Ny' + str(Ny) + '_d' + str(
d) + '_D' + str(D) + '_chi' + str(chi)
t0 = time.time()
cProfile.run('norm = peps.calc_norm(chi=chi)', fname + '_symtensor')
tf = time.time()
print('Symtensor Contraction Time = {}'.format(tf - t0))
peps_sparse = peps.make_sparse()
t0 = time.time()
cProfile.run('norm1 = peps_sparse.calc_norm(chi=chi)', fname + '_slow')
print('Slow Contraction Time = {}'.format(tf - t0))