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)
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)
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)
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()