예제 #1
0
    def compute_ft(self, gvec):
        (gx, gy) = self._parse_ft_gvec(gvec)

        gabs = np.sqrt(np.abs(np.square(gx)) + np.abs(np.square(gy)))
        gabs += 1e-10 # To avoid numerical instability at zero

        ft = bd.exp(-1j*gx*self.x_cent - 1j*gy*self.y_cent)*self.r* \
                            2*np.pi/gabs*bd.bessel1(gabs*self.r)

        return ft
예제 #2
0
def S_T_matrices_TE(omega, g, eps_array, d_array):
    """
    Function to get a list of S and T matrices for D22 calculation
    """
    assert len(d_array)==len(eps_array)-2, \
        'd_array should have length = num_layers'
    chi_array = chi(omega, g, eps_array)

    S11 = (chi_array[:-1] + chi_array[1:])
    S12 = -chi_array[:-1] + chi_array[1:]
    S22 = S11
    S21 = S12
    S_matrices = 0.5 / chi_array[1:].reshape(-1,1,1) * \
        bd.array([[S11,S12],[S21,S22]]).transpose([2,0,1])
    T11 = bd.exp(1j*chi_array[1:-1]*d_array/2)
    T22 = bd.exp(-1j*chi_array[1:-1]*d_array/2)
    T_matrices = bd.array([[T11,bd.zeros(T11.shape)],
        [bd.zeros(T11.shape),T22]]).transpose([2,0,1])
    return S_matrices, T_matrices
예제 #3
0
    def compute_ft(self, gvec):
        """Compute Fourier transform of the polygon

        The polygon is assumed to take a value of 1 inside and a value of 0 
        outside.

        The Fourier transform calculation follows that of Lee, IEEE TAP (1984).

        Parameters
        ----------
        gvec : np.ndarray of shape (2, Ng)
            g-vectors at which FT is evaluated
        """
        (gx, gy) = self._parse_ft_gvec(gvec)

        (xj, yj) = self.x_edges, self.y_edges
        npts = xj.shape[0]
        ng = gx.shape[0]
        # Note: the paper uses +1j*g*x convention for FT while we use 
        # -1j*g*x everywhere in legume
        gx = -gx[:, bd.newaxis]
        gy = -gy[:, bd.newaxis]
        xj = xj[bd.newaxis, :]
        yj = yj[bd.newaxis, :]

        ft = bd.zeros((ng), dtype=bd.complex);

        aj = (bd.roll(xj, -1, axis=1) - xj + 1e-10) / \
                (bd.roll(yj, -1, axis=1) - yj + 1e-20)
        bj = xj - aj * yj

        # We first handle the Gx = 0 case
        ind_gx0 = np.abs(gx[:, 0]) < 1e-10
        ind_gx = ~ind_gx0
        if np.sum(ind_gx0) > 0:
            # And first the Gy = 0 case
            ind_gy0 = np.abs(gy[:, 0]) < 1e-10
            if np.sum(ind_gy0*ind_gx0) > 0:
                ft = ind_gx0*ind_gy0*bd.sum(xj * bd.roll(yj, -1, axis=1)-\
                                yj * bd.roll(xj, -1, axis=1))/2
                # Remove the Gx = 0, Gy = 0 component
                ind_gx0[ind_gy0] = False

            # Compute the remaining Gx = 0 components
            a2j = 1 / aj
            b2j = yj - a2j * xj
            bgtemp = gy * b2j
            agtemp1 = bd.dot(gx, xj) + bd.dot(gy, a2j * xj)
            agtemp2 = bd.dot(gx, bd.roll(xj, -1, axis=1)) + \
                    bd.dot(gy, a2j * bd.roll(xj, -1, axis=1))
            denom = gy * (gx + bd.dot(gy, a2j))
            ftemp = bd.sum(bd.exp(1j*bgtemp) * (bd.exp(1j*agtemp2) - \
                    bd.exp(1j*agtemp1)) * \
                    denom / (bd.square(denom) + 1e-50) , axis=1)
            ft = bd.where(ind_gx0, ftemp, ft)

        # Finally compute the general case for Gx != 0
        if np.sum(ind_gx) > 0:
            bgtemp = bd.dot(gx, bj)
            agtemp1 = bd.dot(gy, yj) + bd.dot(gx, aj * yj)
            agtemp2 = bd.dot(gy, bd.roll(yj, -1, axis=1)) + \
                        bd.dot(gx, aj * bd.roll(yj, -1, axis=1))
            denom = gx * (gy + bd.dot(gx, aj))
            ftemp = -bd.sum(bd.exp(1j*bgtemp) * (bd.exp(1j * agtemp2) - \
                    bd.exp(1j * agtemp1)) * \
                    denom / (bd.square(denom) + 1e-50) , axis=1)
            ft = bd.where(ind_gx, ftemp, ft)

        return ft