def __mul__(self, l):
if type(l) == expr:
lhs = gpt.apply_expr_unary(self)
rhs = gpt.apply_expr_unary(l)
# Attempt to close before product to avoid exponential growth of terms.
# This does not work for sub-expressions without lattice fields, so
# lhs and rhs may still contain multiple terms.
if len(lhs.val) > 1:
lhs = expr(gpt.eval(lhs))
if len(rhs.val) > 1:
rhs = expr(gpt.eval(rhs))
return expr([(a[0] * b[0], a[1] + b[1]) for a in lhs.val for b in rhs.val])
elif type(l) == gpt.tensor and self.is_single(gpt.tensor):
ue, uf, to = self.get_single()
if ue == 0 and uf & factor_unary.BIT_TRANS != 0:
tag = l.otype.__name__
assert tag in to.otype.itab
mt = to.otype.itab[tag]
lhs = to.array
if uf & gpt.factor_unary.BIT_CONJ != 0:
lhs = lhs.conj()
res = gpt.tensor(np.tensordot(lhs, l.array, axes=mt[1]), mt[0]())
if res.otype == gpt.ot_singlet:
res = complex(res.array)
return res
assert 0
else:
return self.__mul__(expr(l))
def __mul__(self, l):
if type(l) == expr:
lhs = gpt.apply_expr_unary(self)
rhs = gpt.apply_expr_unary(l)
# close before product to avoid exponential growth of terms
if len(lhs.val) > 1:
lhs = expr(gpt.eval(lhs))
if len(rhs.val) > 1:
rhs = expr(gpt.eval(rhs))
assert (len(lhs.val) == 1 or len(rhs.val) == 1)
return expr([(a[0] * b[0], a[1] + b[1]) for a in lhs.val
for b in rhs.val])
elif type(l) == gpt.tensor and self.is_single(gpt.tensor):
ue, uf, to = self.get_single()
if ue == 0 and uf & factor_unary.BIT_TRANS != 0:
tag = (to.otype, l.otype)
assert (tag in gpt.otype.itab)
mt = gpt.otype.itab[tag]
lhs = to.array
if uf & gpt.factor_unary.BIT_CONJ != 0:
lhs = lhs.conj()
res = gpt.tensor(np.tensordot(lhs, l.array, axes=mt[1]), mt[0])
if res.otype == gpt.ot_complex:
res = complex(res.array)
return res
assert (0)
else:
return self.__mul__(expr(l))
def __add__(self, l):
if type(l) == expr:
if self.unary == l.unary:
return expr(self.val + l.val, self.unary)
else:
return expr(
gpt.apply_expr_unary(self).val + gpt.apply_expr_unary(l).val
)
else:
return self.__add__(expr(l))