def compute_fused_charge_degeneracies(
charges: List[BaseCharge],
flows: List[bool]) -> Tuple[BaseCharge, np.ndarray]:
"""
For a list of charges, computes all possible fused charges resulting
from fusing `charges` and their respective degeneracies
Args:
charges: List of `BaseCharge`, one for each leg of a
tensor.
flows: A list of bool, one for each leg of a tensor.
with values `False` or `True` denoting inflowing and
outflowing charge direction, respectively.
Returns:
BaseCharge: The unique fused charges.
np.ndarray: The degeneracies of each unqiue fused charge.
"""
if len(charges) == 1:
return (charges[0] * flows[0]).unique(return_counts=True, sort=False)
# get unique charges and their degeneracies on the first leg.
# We are fusing from "left" to "right".
accumulated_charges, accumulated_degeneracies = (charges[0] *
flows[0]).unique(
return_counts=True,
sort=False)
for n in range(1, len(charges)):
leg_charges, leg_degeneracies = charges[n].unique(return_counts=True,
sort=False)
fused_charges = accumulated_charges + leg_charges * flows[n]
fused_degeneracies = fuse_degeneracies(accumulated_degeneracies,
leg_degeneracies)
accumulated_charges = fused_charges.unique(sort=False)
accumulated_degeneracies = np.empty(len(accumulated_charges),
dtype=SIZE_T)
accumulated_degeneracies = np.array([
np.sum(fused_degeneracies[fused_charges.charge_labels ==
accumulated_charges.charge_labels[m]])
for m in range(len(accumulated_charges))
])
return accumulated_charges, accumulated_degeneracies
def test_fuse_degeneracies():
d1 = np.asarray([0, 1])
d2 = np.asarray([2, 3, 4])
fused_degeneracies = fuse_degeneracies(d1, d2)
np.testing.assert_allclose(fused_degeneracies, np.kron(d1, d2))