Пример #1
0
def createP_sigma(lx, ly, band):
    C = table.C_Sigma(ly / (2 / mp.sqrt(3)), lx / (mp.sqrt(3) / 2), band)
    #Values tabulated on a rectangular grid, flipped and compensated for.

    C = [
        C[0], C[1], C[2], C[3] / (3**(1 / 2)), C[4] / (3**(1 / 2)),
        C[5] / (3**(1 / 2))
    ]
    #For sqrt(3) normalisation, see paper for reference.

    f = lambda kx, ky, kz: C[0] * fi_As(lx, ly, kx, ky, kz) + C[1] * fi_Ax(
        lx, ly, kx, ky, kz) + C[2] * fi_Ay(lx, ly, kx, ky, kz) + C[3] * fi_Bs(
            lx, ly, kx, ky, kz) + C[4] * fi_Bx(lx, ly, kx, ky, kz) + C[
                5] * fi_By(lx, ly, kx, ky, kz)

    nrm = 1 / mp.sqrt(
        mp.conj(C[0]) * C[0] + mp.conj(C[1]) * C[1] + mp.conj(C[2]) * C[2] +
        3 *
        (mp.conj(C[3]) * C[3] + mp.conj(C[4]) * C[4] + mp.conj(C[5]) * C[5]) +
        2 * mp.re((mp.conj(C[0]) * C[3] * pss + mp.conj(C[0]) * C[4] * psx +
                   mp.conj(C[0]) * C[5] * psy + mp.conj(C[1]) * C[3] * psx +
                   mp.conj(C[1]) * C[4] * pxx + mp.conj(C[1]) * C[5] * pxy +
                   mp.conj(C[2]) * C[3] * psy + mp.conj(C[2]) * C[4] * pxy +
                   mp.conj(C[2]) * C[5] * pyy) *
                  (mp.exp(1j * mp.fdot([lx, ly, 0], R_1)) +
                   mp.exp(1j * mp.fdot([lx, ly, 0], R_2)) +
                   mp.exp(1j * mp.fdot([lx, ly, 0], R_3)))))

    return lambda kx, ky, kz: f(kx, ky, kz) * nrm
Пример #2
0
def fi_Bx(lx, ly, kx, ky, kz):
    return (mp.exp(1j * mp.fdot(
        [lx + kx / ct.hbar, ly + ky / ct.hbar, kz / ct.hbar], R_1)) +
            mp.exp(1j * mp.fdot(
                [lx + kx / ct.hbar, ly + ky / ct.hbar, kz / ct.hbar], R_2)) +
            mp.exp(1j * mp.fdot(
                [lx + kx / ct.hbar, ly + ky / ct.hbar, kz / ct.hbar], R_3))
            ) * w2px(kx, ky, kz)
Пример #3
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def createP_pi(lx, ly):
    fle = fl(lx, ly, 0)
    phi_l = -mp.atan(mp.im(fle) / mp.re(fle))

    f = lambda kx, ky, kz: (1 + 1 / (3**(1 / 2)) * mp.exp(1j * phi_l) * fl(
        lx + kx / ct.hbar, ly + ky / ct.hbar, kz / ct.hbar)) * w2pz(
            kx, ky, kz)

    #analytical
    #differential vectors, not used in nearest neighbor approximation
    R_12 = [R_1[0] - R_2[0], R_1[1] - R_2[1], R_1[2] - R_2[2]]
    R_23 = [R_2[0] - R_3[0], R_2[1] - R_3[1], R_2[2] - R_3[2]]
    R_31 = [R_3[0] - R_1[0], R_3[1] - R_1[1], R_3[2] - R_1[2]]

    nrm = mp.sqrt(1 / (
        2 + 2 * s / (3**(1 / 2)) *
        (mp.cos(phi_l + mp.fdot([lx, ly, 0], R_1)) +
         mp.cos(phi_l + mp.fdot([lx, ly, 0], R_2)) +
         mp.cos(phi_l + mp.fdot([lx, ly, 0], R_3))) + 2 * s2 *
        (mp.cos(mp.fdot([lx, ly, 0], R_12)) + mp.cos(mp.fdot(
            [lx, ly, 0], R_23)) + mp.cos(mp.fdot([lx, ly, 0], R_31)))))

    return lambda kx, ky, kz: f(kx, ky, kz) * nrm
Пример #4
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 def dot_exact(x, y):
     mp.dps = dps
     # convert to list first, see
     # <https://github.com/fredrik-johansson/mpmath/pull/385>
     return mp.fdot(x.tolist(), y.tolist())
Пример #5
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def fl(lx, ly, lz):
    l = [lx, ly, lz]

    out = mp.exp(1j * mp.fdot(l, R_1)) + mp.exp(1j * mp.fdot(l, R_2)) + mp.exp(
        1j * mp.fdot(l, R_3))
    return out