def from_matrices(cls, Bs, Svs=None, norm=1.0):
sites = []
bss = []
Bs[0] = Bs[0] * norm
for b in Bs:
b = np.array(b)
bss.append(b.transpose([2, 0, 1])) #for some stupid reason
sites.append(Site(LegCharge.from_trivial(b.shape[2])))
return SimpleMPS(
tenpy.networks.mps.MPS.from_Bflat(sites, bss, Svs, form=None))
def random_MPS(L, d, chimax, func=randmat.standard_normal_complex, bc='finite', form='B'):
site = Site(charges.LegCharge.from_trivial(d))
chi = [chimax] * (L + 1)
if bc == 'finite':
for i in range(L // 2 + 1):
chi[i] = chi[L - i] = min(chi[i], d**i)
Bs = []
for i in range(L):
Bs.append(func((d, chi[i], chi[i + 1])))
dtype = np.common_type(*Bs)
psi = MPS.from_Bflat([site] * L, Bs, bc=bc, dtype=dtype, form=None)
if form is not None:
psi.canonical_form()
psi.convert_form(form)
return psi
def from_matrices(cls, Ws):
sites = []
wss = []
for w in Ws:
assert w.shape[2] == w.shape[
3] # For now only square MPO's are possible
sites.append(Site(LegCharge.from_trivial(w.shape[2])))
leg_i = LegCharge.from_trivial(w.shape[0])
leg_o = LegCharge.from_trivial(w.shape[1])
leg_p = LegCharge.from_trivial(w.shape[2])
Wn = npc.Array.from_ndarray(
w, [leg_i, leg_o.conj(), leg_p,
leg_p.conj()],
labels=["wL", "wR", "p", "p*"])
wss.append(Wn)
return SimpleMPO(tenpy.networks.mpo.MPO(sites, wss))