# Hp = Hamiltonian(pxp,pxp_syms)
# Hp.site_ops[1] = np.array([[0,0],[1,0]])
# Hp.site_ops[2] = np.array([[0,1],[0,0]])
# Hp.site_ops[4] = np.array([[0,0],[0,1]])
# Hp.model = np.array([[0,1,2,1,0,4,0],[0,1,2,1,0,4,0],[0,4,0,1,2,1,0],[0,4,0,1,2,1,0]])
# Hp.model_coef = np.array([1,1,1,1])
# Hp.uc_size = np.array([4,4,4,4])
# Hp.uc_pos = np.array([1,3,3,1])
z = zm_state(4, 1, pxp)
k = pxp_syms.find_k_ref(z.ref)
print(k)
# for n in range(0,np.size(k,axis=0)):
# Hp.gen(k[n])
Hp.gen()
Hm = Hp.herm_conj()
# Hz = 1/2 * com(Hp.sector.matrix(k[0]),Hm.sector.matrix(k[0]))
Hz = 1 / 2 * com(Hp.sector.matrix(), Hm.sector.matrix())
plt.matshow(np.abs(Hz))
plt.show()
e, u = np.linalg.eigh(Hz)
print(e)
from Diagnostics import print_wf
print_wf(u[:, 0], pxp, 1e-2)
print("\n")
print_wf(u[:, 1], pxp, 1e-2)
print("\n")
print_wf(u[:, 2], pxp, 1e-2)
print("\n")
print_wf(u[:, 3], pxp, 1e-2)
print("\n")
# Ip.gen()
# Im.gen()
# Kp.gen()
# Km.gen()
# Lp.gen()
# Lm.gen()
# Ip_pert.gen()
# Im_pert.gen()
# Kp_pert.gen()
# Km_pert.gen()
# Lp_pert.gen()
# Lm_pert.gen()
k = [0, 0]
Ip.gen(k)
Im = Ip.herm_conj()
Kp.gen(k)
Km = Kp.herm_conj()
Lp.gen(k)
Lm = Lp.herm_conj()
Ip_pert.gen(k)
Im_pert = Ip_pert.herm_conj()
Kp_pert.gen(k)
Km_pert = Kp_pert.herm_conj()
Lp_pert.gen(k)
Lm_pert = Lp_pert.herm_conj()
def gen_su3Basis(coef):
Ip_total = H_operations.add(Ip, Ip_pert, np.array([1, coef]))
Im_total = H_operations.add(Im, Im_pert, np.array([1, coef]))