Esempio n. 1
0
class OneParticleGF(object):
    def __init__(self, cc, eta=0.01):
        self.cc = cc
        self.eomip = EOMIP(cc)
        self.eomea = EOMEA(cc)
        self.eta = eta

    def solve_ip(self, ps, qs, omegas):
        if not isinstance(ps, collections.Iterable): ps = [ps]
        if not isinstance(qs, collections.Iterable): qs = [qs]
        cc = self.cc
        print("solving ip portion")
        Sw = initial_ip_guess(cc)
        Sw += np.random.rand(Sw.shape[0])
        diag = self.eomip.get_diag()
        imds = self.eomip.make_imds()
        e_vector = list()
        for q in qs:
            e_vector.append(greens_e_vector_ip_rhf(cc, q))
        gfvals = np.zeros((len(ps), len(qs), len(omegas)), dtype=complex)
        for ip, p in enumerate(ps):
            print 'gf idx', ip
            b_vector = greens_b_vector_ip_rhf(cc, p)
            for iw, omega in enumerate(omegas):
                invprecond_multiply = lambda x: x / (omega + diag + 1j * self.
                                                     eta)

                def matr_multiply(vector, args=None):
                    return greens_func_multiply(self.eomip.matvec, vector,
                                                omega + 1j * self.eta, imds)

                size = len(b_vector)
                Ax = spla.LinearOperator((size, size), matr_multiply)
                mx = spla.LinearOperator((size, size), invprecond_multiply)
                Sw, info = spla.gmres(Ax, b_vector, x0=Sw, tol=1e-14, M=mx)
                if info != 0:
                    raise RuntimeError
                for iq, q in enumerate(qs):
                    gfvals[ip, iq, iw] = -np.dot(e_vector[iq], Sw)
        if len(ps) == 1 and len(qs) == 1:
            return gfvals[0, 0, :]
        else:
            return gfvals

    def solve_ea(self, ps, qs, omegas):
        if not isinstance(ps, collections.Iterable): ps = [ps]
        if not isinstance(qs, collections.Iterable): qs = [qs]
        cc = self.cc
        print("solving ea portion")
        Sw = initial_ea_guess(cc)
        diag = self.eomea.get_diag()
        e_vector = list()
        for p in ps:
            e_vector.append(greens_e_vector_ea_rhf(cc, p))
        gfvals = np.zeros((len(ps), len(qs), len(omegas)), dtype=complex)
        for iq, q in enumerate(qs):
            b_vector = greens_b_vector_ea_rhf(cc, q)
            for iw, omega in enumerate(omegas):
                invprecond_multiply = lambda x: x / (-omega + diag + 1j * self.
                                                     eta)

                def matr_multiply(vector, args=None):
                    return greens_func_multiply(self.eomea.matvec, vector,
                                                -omega + 1j * self.eta)

                size = len(b_vector)
                Ax = spla.LinearOperator((size, size), matr_multiply)
                mx = spla.LinearOperator((size, size), invprecond_multiply)
                Sw, info = spla.gmres(Ax, b_vector, x0=Sw, tol=1e-14, M=mx)
                for ip, p in enumerate(ps):
                    gfvals[ip, iq, iw] = np.dot(e_vector[ip], Sw)
        if len(ps) == 1 and len(qs) == 1:
            return gfvals[0, 0, :]
        else:
            return gfvals

    def kernel(self, p, q, omegas):
        return self.solve_ip(p, q, omegas)  #, self.solve_ea(p, q, omegas)
Esempio n. 2
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 def __init__(self, cc, eta=0.01):
     self.cc = cc
     self.eomip = EOMIP(cc)
     self.eomea = EOMEA(cc)
     self.eta = eta
Esempio n. 3
0
 def __init__(self, cc, nvir_act=0):
     EOMIP.__init__(self, cc)
     self.nvir_act = nvir_act