Exemple #1
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def psi_term(r, z, *p):
    n1, a1, b1 = p

    if rt1.check_coilcase(r, z):
        return 0.0
    else:
        return np.exp(-a1 * abs((rt1.psi(r, z, separatrix) - psix) / psi0)**2)
Exemple #2
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def B_term(r, z, *p):
    n1, a1, b1 = p

    br, bz = rt1.bvec(r, z, separatrix)
    bb = np.sqrt(br**2 + bz**2)

    if rt1.check_coilcase(r, z):
        return 0.0
    else:
        return (bb / rt1.b0(r, z, separatrix))**(-b1)
Exemple #3
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def ne_single_gaussian(r, z, *p):
	n1, a1, b1, rm = p

	br, bz = rt1.bvec(r, z, separatrix)
	bb = np.sqrt(br**2 + bz**2)

	if r == 0.0:
		return n1 * np.exp(-a1 * abs((rt1.psi(r, 0.0, separatrix)-psix)/psi0)**2)

	if rt1.check_coilcase(r, z):
		return 0.0
	else:
		return n1 * np.exp(-a1*abs((rt1.psi(r, z, separatrix) - psix)/psi0)**2) * (bb/rt1.b0(r, z, separatrix))**(-b1)
Exemple #4
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def view_profile(rm, p_opt):
    psix = rt1.psi(rm, 0.0, separatrix)  # psi上のBが最小となる直線上の密度最大値
    n1, a1, b1 = p_opt

    nl_y450 = 0.0
    nl_z620 = 0.0
    nl_z700 = 0.0

    nl_y450 = 0.0
    for i, x in enumerate(x450):
        nl_y450 = nl_y450 + np.exp(-a1 * abs(
            (psi_x450[i] - psix) / psi0)**2) * n1 * dx450
    nl_y450 = 2.0 * nl_y450
    error_y450 = (nl_y450 - nl_y450_mes)**2 / (nl_y450_mes)**2

    #   line integral along vertical y=60, 70 chord
    nl_z620 = 0.0
    for j, z in enumerate(z620):
        nl_z620 = nl_z620 + n1 * np.exp(-a1 * abs(
            (psi_z620[j] - psix) / psi0)**2) * (bb620[j] /
                                                b0620[j])**(-b1) * dz620
    error_z620 = (nl_z620 - nl_z620_mes)**2 / (nl_z620_mes)**2

    nl_z700 = 0.0
    for j, z in enumerate(z700):
        nl_z700 = nl_z700 + n1 * np.exp(-a1 * abs(
            (psi_z700[j] - psix) / psi0)**2) * (bb700[j] /
                                                b0700[j])**(-b1) * dz700
    error_z700 = (nl_z700 - nl_z700_mes)**2 / (nl_z700_mes)**2

    print('y450: ', nl_y450, '/', nl_y450_mes)
    print('z620: ', nl_z620, '/', nl_z620_mes)
    print('z700: ', nl_z700, '/', nl_z700_mes)

    print('error_y450: ', error_y450)
    print('error_z620: ', error_z620)
    print('error_z700: ', error_z700)

    #     Export figure
    rs = np.linspace(0.1, 1.001, 200)
    zs = np.linspace(-0.4, 0.401, 200)
    r_mesh, z_mesh = np.meshgrid(rs, zs)

    ne_profile = np.array([
        list(
            map(lambda r, z: ne_single_gaussian(r, z, *p_opt_best),
                r_mesh.ravel(), z_mesh.ravel()))
    ]).ravel().reshape(len(rs), len(zs))
    psi_term_profile = np.array([
        list(
            map(lambda r, z: psi_term(r, z, *p_opt_best), r_mesh.ravel(),
                z_mesh.ravel()))
    ]).ravel().reshape(len(rs), len(zs))
    B_term_profile = np.array([
        list(
            map(lambda r, z: B_term(r, z, *p_opt_best), r_mesh.ravel(),
                z_mesh.ravel()))
    ]).ravel().reshape(len(rs), len(zs))
    psi = np.array([
        list(map(lambda r, z: rt1.psi(r, z), r_mesh.ravel(), z_mesh.ravel()))
    ]).ravel().reshape(len(rs), len(zs))
    coilcase_truth_table = np.array([
        list(
            map(lambda r, z: rt1.check_coilcase(r, z), r_mesh.ravel(),
                z_mesh.ravel()))
    ]).ravel().reshape(len(rs), len(zs))
    psi[coilcase_truth_table == True] = 0
    np.save('ne2D_35_t20_r1', ne_profile)

    # density profileの表示
    levels = [
        0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014
    ]
    plt.figure(figsize=(8, 5))
    plt.subplot(111)
    img = plt.imshow(ne_profile,
                     origin='lower',
                     cmap='jet',
                     extent=(rs.min(), rs.max(), zs.min(), zs.max()))
    plt.contour(r_mesh, z_mesh, ne_profile, colors=['k'])
    plt.contour(r_mesh, z_mesh, psi, colors=['white'], levels=levels)
    plt.title(r'$n_\mathrm{e}$')
    plt.xlabel(r'$r\mathrm{\ [m]}$')
    plt.ylabel(r'$z\mathrm{\ [m]}$')
    # plt.gca():現在のAxesオブジェクトを返す
    divider = make_axes_locatable(plt.gca())
    # カラーバーの位置
    cax = divider.append_axes("right", "5%", pad="3%")
    cb = plt.colorbar(img, cax=cax)
    #cb.set_clim(0,6.4)
    cb.set_label(r'$\mathrm{[10^{16}\,m^{-3}]}$')
    plt.tight_layout(pad=1.0, w_pad=2.0, h_pad=1.0)
    plt.show()

    # psi term profileの表示
    plt.figure(figsize=(8, 5))
    plt.subplot(111)
    img = plt.imshow(psi_term_profile,
                     origin='lower',
                     cmap='plasma',
                     extent=(rs.min(), rs.max(), zs.min(), zs.max()))
    plt.contour(r_mesh, z_mesh, psi_term_profile, colors=['k'])
    plt.contour(r_mesh, z_mesh, psi, colors=['white'], levels=levels)
    plt.title(r'$\psi term$')
    plt.xlabel(r'$r\mathrm{\ [m]}$')
    plt.ylabel(r'$z\mathrm{\ [m]}$')
    # plt.gca():現在のAxesオブジェクトを返す
    divider = make_axes_locatable(plt.gca())
    # カラーバーの位置
    cax = divider.append_axes("right", "5%", pad="3%")
    cb = plt.colorbar(img, cax=cax)
    cb.set_clim(0, 6.4)
    cb.set_label(r'$\mathrm{[10^{16}\,m^{-3}]}$')
    plt.tight_layout(pad=1.0, w_pad=2.0, h_pad=1.0)
    plt.show()

    # B term profileの表示
    plt.figure(figsize=(8, 5))
    plt.subplot(111)
    img = plt.imshow(B_term_profile,
                     origin='lower',
                     cmap='plasma',
                     extent=(rs.min(), rs.max(), zs.min(), zs.max()))

    plt.contour(r_mesh, z_mesh, B_term_profile, colors=['k'])
    plt.contour(r_mesh, z_mesh, psi, colors=['white'], levels=levels)
    plt.title(r'$B term$')
    plt.xlabel(r'$r\mathrm{\ [m]}$')
    plt.ylabel(r'$z\mathrm{\ [m]}$')
    # plt.gca():現在のAxesオブジェクトを返す
    divider = make_axes_locatable(plt.gca())
    # カラーバーの位置
    cax = divider.append_axes("right", "5%", pad="3%")
    cb = plt.colorbar(img, cax=cax)
    cb.set_clim(0, 6.4)
    cb.set_label(r'$\mathrm{[10^{16}\,m^{-3}]}$')
    plt.tight_layout(pad=1.0, w_pad=2.0, h_pad=1.0)
    plt.show()

    # profile_zでのdensity profileの表示
    profile_z = 0.0  # プロファイルが見たい任意のz [m]
    profile_z_index = np.searchsorted(zs, profile_z)
    ne_profile_z0 = ne_profile[:][profile_z_index]

    fig, ax = plt.subplots(1)
    ax.plot(rs, ne_profile_z0)
    plt.draw()
    plt.show()

    #error at rms
    fig, ax = plt.subplots(1)
    ax.plot(rms, error_at_rms[:, 0])
    ax.plot(rms, error_at_rms[:, 1])
    plt.yscale('log')
    plt.xlabel('r [m]')
    plt.ylabel('Error at rm')
    plt.show()