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 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 err_single_gaussian(p, disp_flg):
n1, a1, b1, rm = p
psix = rt1.psi(rm, 0.0, separatrix)
# line integral along horizontal y=45 chord
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
error = [error_y450, error_z620, error_z700]
# print ( 'n1, a1, b1 =' , p)
# print ( 'y400: ', nl_y400, '/', nl_y400_mes)
# print ( 'y450: ', nl_y450, '/', nl_y450_mes)
# print ( 'y500: ', nl_y500, '/', nl_y500_mes)
# print ( 'y550: ', nl_y550, '/', nl_y550_mes)
# print ( 'y620: ', nl_y620, '/', nl_y620_mes)
# print ( 'z620: ', nl_z620, '/', nl_z620_mes)
# print ( 'z700: ', nl_z700, '/', nl_z700_mes)
# print ( 'z840: ', nl_z840, '/', nl_z840_mes)
# print (' err = ', sum(error[4:7]))
return sum(error[0:3])
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()
if view_mode is True:
print('Running with view mode')
rm_best = 0.5424
p_opt_best = [33.984, 8.652, 0.431]
t_start = time.time()
print('')
print(time.ctime())
print('')
dx450 = x450[1] - x450[0]
dz620 = z620[1] - z620[0]
dz700 = z700[1] - z700[0]
# calculate psi, bb, b0 along the line of sights beforehand
psi0 = rt1.psi(1.0, 0.0, separatrix) # psi at the vacuum chamber
psi_x450 = np.zeros(len(x450))
psi_z620 = np.zeros(len(z620))
psi_z700 = np.zeros(len(z700))
bb620 = np.zeros(len(z620))
bb700 = np.zeros(len(z700))
b0620 = np.zeros(len(z620))
b0700 = np.zeros(len(z700))
for i, x in enumerate(x450):
rx = np.sqrt(x**2 + r450_para**2)
psi_x450[i] = rt1.psi(rx, 0.0, separatrix)
for j, z in enumerate(z620):
psi_z620[j] = rt1.psi(r620_perp, z, separatrix)
br, bz = rt1.bvec(r620_perp, z, separatrix)
#p_opt_best = [35.389, 6.629, 1.800, 0.549]
#p_opt_best = [29.241, 4.295, 1.698, 0.550]
p_opt_best = [28.227, 4.897, 1.903, 0.542]
t_start = time.time()
print('')
print(time.ctime())
print('')
dx450 = x450[1] - x450[0]
dz620 = z620[1] - z620[0]
dz700 = z700[1] - z700[0]
# calculate psi, bb, b0 along the line of sights beforehand
psi0 = rt1.psi(1.0, 0.0, separatrix) # psi at the vacuum chamber
psi_x450 = np.zeros(len(x450))
psi_z620 = np.zeros(len(z620))
psi_z700 = np.zeros(len(z700))
bb620 = np.zeros(len(z620))
bb700 = np.zeros(len(z700))
b0620 = np.zeros(len(z620))
b0700 = np.zeros(len(z700))
for i, x in enumerate(x450):
rx = np.sqrt(x**2 + r450_para**2)
psi_x450[i] = rt1.psi(rx, 0.0, separatrix)
for j, z in enumerate(z620):
psi_z620[j] = rt1.psi(r620_perp, z, separatrix)
br, bz = rt1.bvec(r620_perp, z, separatrix)