def main():
ring = landau_cav.Ring()
# MAX IV
ring.E0 = 3e9
ring.nom_cur = 500e-3
ring.en_lost_rad = 856e3
ring.frf = 99.931e6
ring.peak_rf = 1.63e6
ring.harm_num = 176
ring.en_spread = 7.82e-4
ring.mom_cmpct = 3.07e-4
r = ring.en_lost_rad/ring.peak_rf
wrf = ring.wrf
krf = wrf/c
h = ring.harm_num
It = ring.nom_cur
sigz = ring.bunlen
hc = landau_cav.HarmCav(wrf, n=3, Q=21600, Rs=2.017e6, r=r) # MAX IV
F = np.exp(-(sigz*hc.num*wrf/c)**2)
hc.calc_flat_potential(ring, F=F)
hc.psi = -103.717*np.pi/180
zlim = 30*sigz
npoints = 1501
z = np.linspace(-zlim, zlim, npoints)
Ib = np.zeros(h, dtype=float)
s_fill = h
n_trains = 1
s_gap = (h - n_trains * s_fill) // n_trains
Ib[:] = It / s_fill / n_trains
for j in range(n_trains):
Ib[j * (s_fill + s_gap) + s_fill:(j + 1) * (s_fill+s_gap)] = 0
lamb = landau_cav.Lambda(z, Ib, ring)
_, _, dist_new = landau_cav.calc_equilibrium_potential(ring, lamb, hc, z,
epsilon=1e-10,
param_conv=15,
n_iters=1000)
lamb.dist = np.array(dist_new)
sigma_z_imp = lamb.get_bun_lens()
sigma_z_imp_fwhm = lamb.get_bun_lens_fwhm() / 2.35
z_ave_i = lamb.get_synch_phases()
bl_imp = np.mean(sigma_z_imp)
z_ave_ave_i = np.mean(z_ave_i)
print('IMPEDANCE')
hc.print_param(ring)
# fwhm = 2*np.sqrt(2*np.log(2))
mask = lamb.cur < 1e-8
sigma_z_imp[mask] = np.nan
z_ave_i[mask] = np.nan
ind_max = np.nanargmax(sigma_z_imp)
print('sync phase: {0:7.3f} mm'.format(z_ave_ave_i*1e3))
print('bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(bl_imp*1e3,
bl_imp*1e12/c))
print('max bun length factor: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp)/sigz, ind_max))
print('max bun length factor FWHM: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp_fwhm)/sigz, ind_max))
f = plt.figure(figsize=(10, 14))
gs = gridspec.GridSpec(4, 1)
gs.update(left=0.10, right=0.95, bottom=0.10,
top=0.97, wspace=0.35, hspace=0.25)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[1, 0])
ax3 = plt.subplot(gs[2, 0], sharex=ax2)
ax4 = plt.subplot(gs[3, 0], sharex=ax2)
ph = z*krf
ax1.plot(ph, dist_new[40, :], label='Distribution max bunch length')
ax2.plot(lamb.cur, label='Current - [mA]')
ax3.plot(sigma_z_imp/ring.bunlen, label='Bunch Lengthening factor')
ax4.plot(ring.synch_phase + z_ave_i*krf, label='Synch Phase')
ax1.legend(loc='best')
ax2.legend(loc='best')
ax3.legend(loc='best')
ax4.legend(loc='best')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
ax4.grid(True)
plt.show()
f.savefig('landau.png')
def main():
ring = landau_cav.Ring()
# MAX II
ring.E0 = 1.5e9
ring.en_lost_rad = 133.4e3
ring.mom_cmpct = 3.82e-3
ring.en_spread = 7.01e-4
ring.harm_num = 30
ring.nom_cur = 140e-3
ring.peak_rf = 358.19e3
ring.frf = 99.9606e6
r = ring.en_lost_rad/ring.peak_rf
wrf = ring.wrf
krf = wrf/c
h = ring.harm_num
It = ring.nom_cur
sigz = ring.bunlen
hc = landau_cav.HarmCav(wrf, n=5, Q=21720, Rs=1.57e6, r=r) # MAX II
F = np.exp(-(sigz*hc.num*wrf/c)**2)
hc.calc_flat_potential(ring, F=F)
hc.dw = 107.07e3*2*np.pi
zlim = 30*sigz
npoints = 1501
z = np.linspace(-zlim, zlim, npoints)
Ib = np.zeros(h, dtype=float)
s_fill = h
n_trains = 1
s_gap = (h - n_trains * s_fill) // n_trains
Ib[:] = It / s_fill / n_trains
for j in range(n_trains):
Ib[j * (s_fill + s_gap) + s_fill:(j + 1) * (s_fill+s_gap)] = 0
lamb = landau_cav.Lambda(z, Ib, ring)
_, _, dist_new = landau_cav.calc_equilibrium_potential(ring, lamb, hc, z,
epsilon=1e-5,
param_conv=15,
n_iters=1000)
lamb.dist = np.array(dist_new)
sigma_z_imp = lamb.get_bun_lens()
z_ave_i = lamb.get_synch_phases()
bl_imp = np.mean(sigma_z_imp)
z_ave_ave_i = np.mean(z_ave_i)
print('IMPEDANCE')
hc.print_param(ring)
# fwhm = 2*np.sqrt(2*np.log(2))
print('sync phase: {0:7.3f} mm'.format(z_ave_ave_i*1e3))
print('bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(bl_imp*1e3,
bl_imp*1e12/c))
plt.figure(figsize=(10, 14))
gs = gridspec.GridSpec(4, 1)
gs.update(left=0.10, right=0.95, bottom=0.10,
top=0.97, wspace=0.35, hspace=0.25)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[1, 0])
ax3 = plt.subplot(gs[2, 0], sharex=ax2)
ax4 = plt.subplot(gs[3, 0], sharex=ax2)
ph = z*krf
ax1.plot(ph, dist_new[0, :], label='Distribution 1st bunch')
ax2.plot(lamb.cur, label='Current - [mA]')
mask = lamb.cur < 1e-6
sigma_z_imp[mask] = np.nan
z_ave_i[mask] = np.nan
ax3.plot(sigma_z_imp/ring.bunlen, label='Bunch Lengthening factor')
ax4.plot(ring.synch_phase + z_ave_i*krf, label='Synch Phase')
ax1.legend(loc='best')
ax2.legend(loc='best')
ax3.legend(loc='best')
ax4.legend(loc='best')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
ax4.grid(True)
plt.show()
def main():
ring = landau_cav.Ring()
# SSRF
ring.E0 = 3.5e9
ring.en_spread = 9.8e-4
ring.frf = 500e6
ring.harm_num = 720
ring.mom_cmpct = 4.2e-4
ring.en_lost_rad = 1.44e6
ring.peak_rf = 4.8e6
ring.nom_cur = 300e-3
r = ring.en_lost_rad/ring.peak_rf
wrf = ring.wrf
krf = wrf/c
h = ring.harm_num
It = ring.nom_cur
sigz = ring.bunlen
Q = 1.5e5
Rs = 180*Q
hc = []
hc.append(landau_cav.HarmCav(wrf, n=3, Q=Q, Rs=Rs, r=r))
# hc.append(landau_cav.HarmCav(wrf, n=1, Q=1e5, Rs=3e6, r=r))
hc_landau = hc[0]
# hc_main = hc[1]
F = np.exp(-(sigz*hc_landau.num*wrf/c)**2)
hc_landau.calc_flat_potential(ring, F=F)
# print(hc_landau.dw*1e-3/2/np.pi)
# hc_landau.dw = 57e3 * 2 * np.pi
# hc_main.dw = hc_landau.dw
zlim = 15*sigz
npoints = 2501
z = np.linspace(-zlim, zlim, npoints)
Ib = np.zeros(h, dtype=float)
s_fill = h
n_trains = 1
s_gap = (h - n_trains * s_fill) // n_trains
Ib[:] = It / s_fill / n_trains
for j in range(n_trains):
Ib[j * (s_fill + s_gap) + s_fill:(j + 1) * (s_fill+s_gap)] = 0
lamb = landau_cav.Lambda(z, Ib, ring)
Vc, _, dist_new = landau_cav.calc_equilibrium_potential(ring, lamb, hc, z,
epsilon=1e-5,
param_conv=15,
n_iters=1000)
lamb.dist = np.array(dist_new)
sigma_z_imp = lamb.get_bun_lens()
sigma_z_imp_fwhm = lamb.get_bun_lens_fwhm() / 2.35
z_ave_i = lamb.get_synch_phases()
bl_imp = np.mean(sigma_z_imp)
z_ave_ave_i = np.mean(z_ave_i)
print('==============================')
print('HARMONIC CAVITY PARAMETERS')
print('==============================')
hc_landau.print_param(ring)
# fwhm = 2*np.sqrt(2*np.log(2))
mask = lamb.cur < 1e-6
sigma_z_imp_fwhm[mask] = np.nan
sigma_z_imp[mask] = np.nan
z_ave_i[mask] = np.nan
Vc = np.max(Vc, axis=1)
Vc[mask] = np.nan
ind_max = np.nanargmax(sigma_z_imp)
print('==============================')
print('BUNCH PARAMETERS')
print('==============================')
print('sync phase: {0:7.3f} mm'.format(z_ave_ave_i*1e3))
print('natural bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(sigz*1e3,
sigz*1e12/c))
print('average bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(bl_imp*1e3,
bl_imp*1e12/c))
print('average bun length factor: {0:1.3f}'.format(np.nanmean(sigma_z_imp)/sigz))
print('average bun length factor FWHM: {0:1.3f}'.format(np.nanmean(sigma_z_imp_fwhm)/sigz))
print('max bun length factor: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp)/sigz, ind_max))
print('max bun length factor FWHM: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp_fwhm)/sigz, ind_max))
print('mean harm. voltage: {0:1.3f} MV'.format(np.nanmean(Vc) * ring.E0 * 1e-6))
plt.figure(figsize=(10, 14))
gs = gridspec.GridSpec(5, 1)
gs.update(left=0.10, right=0.95, bottom=0.10,
top=0.97, wspace=0.35, hspace=0.25)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[1, 0])
ax3 = plt.subplot(gs[2, 0], sharex=ax2)
ax4 = plt.subplot(gs[3, 0], sharex=ax2)
ax5 = plt.subplot(gs[4, 0], sharex=ax2)
ph = z*krf
ax1.plot(ph, dist_new[ind_max, :], label='Distribution {0:1g}th bunch'.format(ind_max))
ax2.plot(lamb.cur, label='Current - [mA]')
ax3.plot(sigma_z_imp/ring.bunlen, label='Bunch Lengthening factor')
ax4.plot(ring.synch_phase + z_ave_i*krf - np.pi/2, label='Synch Phase')
ax5.plot(Vc * ring.E0 * 1e-6, label='Harmonic Voltage (MV)')
ax1.legend(loc='best')
ax2.legend(loc='best')
ax3.legend(loc='best')
ax4.legend(loc='best')
ax5.legend(loc='best')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
ax4.grid(True)
ax5.grid(True)
plt.show()
np.savetxt('data_no.txt', np.c_[ph, dist_new[ind_max, :]])
def main():
ring = landau_cav.Ring()
# SLS
ring.E0 = 1.9e9
ring.en_spread = 8.1e-4
ring.frf = 499.654e6
ring.harm_num = 328
ring.mom_cmpct = 1.62e-3
ring.en_lost_rad = 320e3
ring.peak_rf = 1.1e6
ring.nom_cur = 300e-3
r = ring.en_lost_rad/ring.peak_rf
wrf = ring.wrf
krf = wrf/c
h = ring.harm_num
It = ring.nom_cur
sigz = ring.bunlen
Q = 21e3
Rs = 3 * 80.4 * Q
hc = []
hc.append(landau_cav.HarmCav(wrf, n=3, Q=Q, Rs=Rs, r=r))
hc.append(landau_cav.HarmCav(wrf, n=1, Q=1e5, Rs=3e6, r=r))
hc_landau = hc[0]
hc_main = hc[1]
F = np.exp(-(sigz*hc_landau.num*wrf/c)**2)
hc_landau.calc_flat_potential(ring, F=F)
hc_landau.psi = np.pi/2 + 3*(-0.0275)
hc_main.psi = -hc_landau.psi
zlim = 15*sigz
npoints = 5501
z = np.linspace(-zlim, zlim, npoints)
Ib = np.zeros(h, dtype=float)
s_fill = 272
n_trains = 1
s_gap = (h - n_trains * s_fill) // n_trains
Ib[:] = It / s_fill / n_trains
for j in range(n_trains):
Ib[j * (s_fill + s_gap) + s_fill:(j + 1) * (s_fill+s_gap)] = 0
lamb = landau_cav.Lambda(z, Ib, ring)
Vc, _, dist_new = landau_cav.calc_equilibrium_potential(ring, lamb, hc, z,
epsilon=1e-5,
param_conv=15,
n_iters=1000)
lamb.dist = np.array(dist_new)
sigma_z_imp = lamb.get_bun_lens()
sigma_z_imp_fwhm = lamb.get_bun_lens_fwhm() / 2.35
z_ave_i = lamb.get_synch_phases()
bl_imp = np.mean(sigma_z_imp)
z_ave_ave_i = np.mean(z_ave_i)
print('==============================')
print('HARMONIC CAVITY PARAMETERS')
print('==============================')
hc_landau.print_param(ring)
# fwhm = 2*np.sqrt(2*np.log(2))
mask = lamb.cur < 1e-6
sigma_z_imp_fwhm[mask] = np.nan
sigma_z_imp[mask] = np.nan
z_ave_i[mask] = np.nan
# Vc = np.sqrt(np.mean(Vc * Vc, axis=1)) * ring.E0 * 1e-3
Vc = np.max(Vc, axis=1) * ring.E0 * 1e-3
Vc[mask] = np.nan
ind_max = np.nanargmax(sigma_z_imp)
print('==============================')
print('BUNCH PARAMETERS')
print('==============================')
print('sync phase: {0:7.3f} mm'.format(z_ave_ave_i*1e3))
print('natural bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(sigz*1e3,
sigz*1e12/c))
print('average bun length: {0:7.3f} mm ({1:7.3f} ps)'.format(bl_imp*1e3,
bl_imp*1e12/c))
print('average bun length factor: {0:1.3f}'.format(np.nanmean(sigma_z_imp)/sigz))
print('average bun length factor FWHM: {0:1.3f}'.format(np.nanmean(sigma_z_imp_fwhm)/sigz))
print('max bun length factor: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp)/sigz, ind_max))
print('max bun length factor FWHM: {0:1.3f} (Bunch number {1:1g})'.format(np.nanmax(sigma_z_imp_fwhm)/sigz, ind_max))
print('mean harm. voltage: {0:1.3f} kV'.format(np.nanmean(Vc)))
plt.figure(figsize=(10, 14))
gs = gridspec.GridSpec(5, 1)
gs.update(left=0.10, right=0.95, bottom=0.10,
top=0.97, wspace=0.35, hspace=0.25)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[1, 0])
ax3 = plt.subplot(gs[2, 0], sharex=ax2)
ax4 = plt.subplot(gs[3, 0], sharex=ax2)
ax5 = plt.subplot(gs[4, 0], sharex=ax2)
ph = z*krf
ax1.plot(ph, dist_new[ind_max, :], label='Distribution max bunch')
ax2.plot(lamb.cur, label='Current - [mA]')
ax3.plot(sigma_z_imp/ring.bunlen, label='Bunch Lengthening factor')
ax4.plot(ring.synch_phase + z_ave_i*krf - np.pi/2, label='Synch Phase')
ax5.plot(Vc, label='Harmonic Voltage (kV)')
ax1.legend(loc='best')
ax2.legend(loc='best')
ax3.legend(loc='best')
ax4.legend(loc='best')
ax5.legend(loc='best')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
ax4.grid(True)
ax5.grid(True)
plt.show()