def check_environments(self, vl, vm, vr, n):
# check if the environments of the n-th tensor have consistent dimensions
is_bug0 = False
bond = str()
if vl.shape[0] != vl.shape[1]:
is_bug0 = True
bond = 'LEFT'
if vm.shape[0] != vm.shape[1]:
is_bug0 = True
bond = 'MIDDLE'
if vr.shape[0] != vr.shape[1]:
is_bug0 = True
bond = 'RIGHT'
if is_bug0:
cprint(
'EnvError: for the %d-th tensor, the ' % n + bond +
' v is not square', 'magenta')
is_bug = False
if vl.shape[0] != self.virtual_dim[n]:
is_bug = True
bond = 'LEFT'
if vr.shape[0] != self.virtual_dim[n + 1]:
is_bug = True
bond = 'RIGHT'
if vm.shape[0] != self.mps[n].shape[1]:
bond = 'MIDDLE'
is_bug = True
if is_bug:
cprint(
'EnvError: for the %d-th tensor, the ' % n + bond +
' v has inconsistent dimension', 'magenta')
if is_bug0 or is_bug:
trace_stack()
def bound_vec_operator_right2left(tensor,
op=np.zeros(0),
v=np.zeros(0),
normalize=False,
symme=False):
s = tensor.shape
if op.size != 0: # deal with the operator
tensor1 = absorb_matrix2tensor(tensor, op.T, 1)
else: # no operator
tensor1 = tensor.copy()
if v.size == 0: # no input boundary vector v
tensor = tensor.reshape(s[0], s[1] * s[2]).conj()
tensor1 = tensor1.reshape(s[0], s[1] * s[2])
v1 = tensor.dot(tensor1.T)
else: # there is an input boundary vector v
if is_debug:
if v.shape[0] != s[2]:
cprint(
'BondDimError: the v_right has inconsistent dimension with the tensor',
'magenta')
cprint('v.shape = ' + str(v.shape) + '; T.shape = ' + str(s))
trace_stack()
tensor = tensor.reshape(s[0] * s[1], s[2]).conj().dot(v)
v1 = tensor.reshape(s[0], s[1] * s[2]).dot(
tensor1.reshape(s[0], s[1] * s[2]).T)
if normalize:
v1 = normalize_tensor(v1)[0]
if symme:
v1 = (v1 + v1.conj().T) / 2
return v1
def absorb_matrices2tensor_full_fast(tensor, mats):
# generally, recommend to use the function 'absorb_matrices2tensor'
# each bond will have a matrix to contract with
# the matrices must be in the right order
# contract the 1st bond of mat with tensor
nb = tensor.ndim
s = np.array(tensor.shape)
is_bug = False
if is_debug:
for n in range(0, nb):
if mats[n].shape[1] != s[n]:
cprint(
'Error: the %d-th matrix has inconsistent dimension with the tensor'
% n, 'magenta')
cprint(
'T.shape = ' + str(s) + ', mat.shape = ' +
str(mats[n].shape), 'magenta')
is_bug = True
for n in range(nb - 1, -1, -1):
tensor = tensor.reshape(np.prod(s[:nb - 1]), s[nb - 1]).dot(mats[n])
s[-1] = mats[n].shape[1]
ind = np.append(nb - 1, np.arange(0, nb - 1))
tensor = tensor.reshape(s).transpose(ind)
s = s[ind]
if is_debug and is_bug:
trace_stack()
return tensor
def check_virtual_bond_dimensions(self):
is_error = False
for n in range(1, self.length):
if self.virtual_dim[n] != self.mps[n].shape[0] or self.virtual_dim[
n] != self.mps[n - 1].shape[2]:
cprint(
'BondDimError: inconsistent dimension detected for the %d-th virtual bond'
% n, 'magenta')
is_error = True
if is_error:
trace_stack(2)
def norm_mps(self):
# calculate the norm of an MPS
if self._debug:
lc = self.check_orthogonal_center()
if lc != self.center:
cprint(
'CenterError: center should be at %d but at %d' %
self.center, lc, 'magenta')
trace_stack()
if self.center < -0.5:
v = self.contract_v_l0_to_l1(0, self.length)
norm = v[0, 0]
else:
norm = np.linalg.norm(self.mps[self.center].reshape(1, -1))
return norm
def normalize_tensor(tensor, if_flatten=False):
v = tensor.reshape(-1, )
norm = np.linalg.norm(v)
if norm < 1e-30:
cprint('InfWarning: norm is too small to normalize', 'magenta')
trace_stack()
if if_flatten:
return v, norm
else:
return tensor, norm
else:
if if_flatten:
return v/norm, norm
else:
return tensor/norm, norm
def check_orthogonal_center(self, expected_center=-2, if_print=True):
# Check if MPS has the correct center, or at the expected center
# if not, find the correct center, or recommend a new center while it is not central orthogonal
# NOTE: no central-orthogonalization in this function, only recommendation
if self.center > -0.5:
left = self.orthogonality[:self.center]
right = self.orthogonality[self.center + 1:]
if not (np.prod(left == -1) and np.prod(right == 1)):
if if_print:
cprint(
colored('self.center is incorrect. Change it to -1',
'magenta'))
trace_stack()
self.center = -1
if self.center < -0.5:
left = np.nonzero(self.orthogonality == -1)
left = left[0][-1]
right = np.nonzero(self.orthogonality == 1)
right = right[0][0]
if np.prod(self.orthogonality[:left + 1]) and np.prod(
self.orthogonality[right:]) and left + 2 == right:
self.center = left + 1
if if_print:
cprint(
colored('self.center is corrected to %g' % self.center,
'cyan'))
else:
if if_print:
cprint(
colored(
'MPS is not central orthogonal. self.center remains -1',
'cyan'))
else:
left = self.center - 1
if self.center > -0.5:
if expected_center > -0.5 and expected_center != self.center:
cprint('The center is at %d, not the expected position at %d' %
(self.center, expected_center))
recommend_center = self.center
else:
# if not central-orthogonal, recommend the tensor after the last left-orthogonal one as the new center
recommend_center = left + 1
return recommend_center
