def EF12(self):
if self.mat is None:
self.mat = matrices(self.xmol)
if self.eF12 is None:
x = self.xmol.no
v = self.xmol.nv
c = self.xmol.nc
#eps = np.array(self.xmol._vhf.mo_energy).reshape(x + v, 1)
#eij = eps[:x] + eps[:x].T
# here C_amp is with a negative sign
#V = self.mat.V.reshape(x * x, -1)
Camp = sp.linalg.solve(self.mat.B.reshape(x * x, -1), self.mat.V.reshape(x * x, -1))
ef12 = np.dot(np.conj(self.mat.V.transpose(0,1,3,2) - 2.0 * self.mat.V).reshape(-1), Camp.reshape(-1))
self.eF12 = ef12# + self.E1()
return self.eF12
def E1(self):
if self.mat is None:
self.mat = matrices(self.xmol)
Fcx = self.mat.TF.Fcx
Fcv = self.mat.TF.Fca
Fcc = self.mat.TF.Fcc
e1 = 0
for i in xrange(self.xmol.no):
ei = self.xmol._vhf.mo_energy[i]
ie = np.empty((self.xmol.nv, 1))
for a in xrange(self.xmol.nv):
ie[a] = 1.0 / (ei - self.xmol._vhf.mo_energy[a + self.xmol.no])
A = Fcc - ei * np.eye(self.xmol.nc) + np.einsum('a,pa,aq->pq', ie.reshape(-1), Fcv, HerConj(Fcv))
b = -Fcx[:, i]
ti = sp.linalg.solve(A, b, overwrite_a = True, overwrite_b = True)
e1 += np.dot(ti.reshape(-1), Fcx.conj().reshape(-1))
return e1 * 2.0