def B(matriz):
E = mp.eig(matriz, left=False, right=False)
E = mp.eig_sort(E)
bd = matriz
a = 0
for y in range(5):
a = a + 1
d = np.identity(100)
d[d == 1.0] = np.abs(mp.re(E[y]))
d1 = mp.matrix(d)
bd = bd + d1
o = mp.eig(bd, left=False, right=False)
o = mp.eig_sort(o)
if mp.re(o[0]) > 0.001:
break
B1 = mp.cholesky(bd)
return B1, a
print("L=" + str(L))
print(v)
print("................................")
#the following cicle takes care of the staggered hopping
for i in range(END):
j = (i + 1) % L
if i % 2 == 0:
H[i, j] = -v
H[j, i] = H[i, j]
elif j % 2 == 0:
H[i, j] = -1
H[j, i] = H[i, j]
#find its eigenvalues and vectors
eigval, eigvec = mp.eigsy(H)
eigval, eigvec = mp.eig_sort(eigval, eigvec)
Cij = Cij_0(eigvec, Np)
#From now on I will use the Free Fermions technique to calculate the entanglement entropies of the subsistems in units of log2
SB = FreeFermions(B, Cij) / mp.log(mp.mpf(2.0))
SAB = FreeFermions(A + B, Cij) / mp.log(mp.mpf(2.0))
SBC = FreeFermions(B + C, Cij) / mp.log(mp.mpf(2.0))
SABC = FreeFermions(D, Cij) / mp.log(mp.mpf(2.0))
#In the end I calculate Sqtopo ad proposed by Wen
Sq = (SAB + SBC - SB - SABC)
# estimar M0 yM1 M0_act = ((Y * Y.T) / 100) M0_in = (M0_act**-1) MP = (M0_act * M0_in) o = MP[0:5, 0:5] o0 = M0_in[0:5, 0:5] M1_act = ((Y * X.T) / 100) M0_ant = ((X * X.T) / 100) A = M1_act * (M0_ant**-1) BBT = M0_act - (A * M1_act.T) #### E = mp.eig(BBT, left=False, right=False) E = mp.eig_sort(E) # reajuste 0 d = np.identity(100) d[d == 1.0] = abs(-24.7174268506955680247383180094514) d1 = mp.matrix(d) BBT0 = BBT + d1 E0 = mp.eig(BBT0, left=False, right=False) E0 = mp.eig_sort(E0) # reajuste 0 d0 = np.identity(100) d0[d0 == 1.0] = abs(-0.00000062521499573893501604735293439538) d2 = mp.matrix(d0) BBT1 = BBT0 + d2 E1 = mp.eig(BBT1, left=False, right=False)