def __random__(self, index, which=0):
if which > 1:
sys.exit(
'SparseTensor.__eye__(self,index={0},which={1}): which has be to be 0 or 1.'
.format(index, which))
Ds = [copy.deepcopy(self._Ds[index]), copy.deepcopy(self._Ds[index])]
if which == 0:
qflow = tuple(
[utils.flipsigns(self._qflow[index]), self._qflow[index]])
elif which == 1:
qflow = tuple(
[self._qflow[index],
utils.flipsigns(self._qflow[index])])
mergelevel = tuple([self._mergelevel[index], self._mergelevel[index]])
keytoq = tuple([self._ktoq[index], self._ktoq[index]])
iden = SparseTensor(keys=[],
values=[],
Ds=Ds,
qflow=qflow,
keytoq=keytoq,
mergelevel=mergelevel,
dtype=self._dtype)
for k in self._Ds[index].keys():
key = tuple([k, k])
temp = self._Ds[index][k]
shape = tuple([temp, temp])
size = utils.prod(utils.flatten(temp))
iden.__insertmergedblock__(
key, shape,
np.random.rand(size, size).astype(self._dtype))
return iden
def __conj__(self, *args, **kwargs):
res = self.__unary_operations__(np.conj, *args, **kwargs)
res._qflow = utils.flipsigns(res._qflow)
return res
def __flippedflow__(self):
return utils.flipsigns(self._qflow)
def __flipflow__(self):
self._qflow = utils.flipsigns(self._qflow)
return self