machine.one_turn_map = new_one_turn_map
# generate a bunch
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=30000,
intensity=1.15e11,
epsn_x=epsn_x,
epsn_y=epsn_y,
sigma_z=0.2)
t_start_slice = time.mktime(time.localtime())
for ii in xrange(N_turns):
slices_list = bunch.extract_slices(slicer)
for slice_obj in slices_list[::-1]:
machine.track(slice_obj) #, verbose = True)
print 'Turn', ii
bunch = sum(slices_list)
machine.longitudinal_map.track(bunch)
for ec in ecloud_list:
ec.finalize_and_reinitialize()
t_end_slice = time.mktime(time.localtime())
print 'Sliced %.2e s per turn' % (
(t_end_slice - t_start_slice) / float(N_turns))
# Simulate bunch mode
machine_whole_bunch = SPS(n_segments=N_kicks,
machine_configuration='Q20-injection',
Dt_ref=25e-12,
pyecl_input_folder='./drift_sim',
x_aper=x_aper,
y_aper=y_aper,
Dh_sc=Dh_sc,
init_unif_edens_flag=init_unif_edens_flag,
init_unif_edens=init_unif_edens,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0)
# install ecloud in the machine
machine.install_after_each_transverse_segment(ecloud)
# setup transverse losses (to "protect" the ecloud)
import PyHEADTAIL.aperture.aperture as aperture
apt_xy = aperture.EllipticalApertureXY(x_aper=ecloud.cloudsim.chamb.x_aper,
y_aper=ecloud.cloudsim.chamb.y_aper)
machine.one_turn_map.append(apt_xy)
# generate a bunch
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=300000,
intensity=1.5e11,
epsn_x=epsn_x,
epsn_y=epsn_y,
sigma_z=.11)
# simulate
for i_turn in range(N_turns):
print('Turn', i_turn)
machine.track(bunch, verbose=True)
yp_before = bunch.yp[bunch.id<=n_part_per_turn] rms_err_x_list = [] rms_err_y_list = [] # pl.figure(1000) # pl.plot(bunch.z, bunch.id, '.', markersize=1) # pl.show() for ii in xrange(N_turns-1): slices_list = bunch.extract_slices(slicer) for slice_obj in slices_list[::-1]: machine.track(slice_obj)#, verbose = True) print 'Turn', ii bunch = sum(slices_list) machine.longitudinal_map.track(bunch) for ec in ecloud_list: ec.finalize_and_reinitialize() # id and momenta after track id_after = bunch.id[bunch.id<=n_part_per_turn] xp_after = bunch.xp[bunch.id<=n_part_per_turn] z_after = bunch.z[bunch.id<=n_part_per_turn]
bunch.xp[:n_part_per_turn] = xp[0,:] bunch.y[:n_part_per_turn] = y[0,:] bunch.yp[:n_part_per_turn] = yp[0,:] bunch.z[:n_part_per_turn] = z[0,:] bunch.dp[:n_part_per_turn] =zp[0,:] # save id and momenta before track id_before = bunch.id[bunch.id<=n_part_per_turn] xp_before = bunch.xp[bunch.id<=n_part_per_turn] yp_before = bunch.yp[bunch.id<=n_part_per_turn] rms_err_x_list = [] rms_err_y_list = [] for ii in xrange(N_turns-1): # track machine.track(bunch)#, verbose = True) print 'Turn', ii # id and momenta after track id_after = bunch.id[bunch.id<=n_part_per_turn] xp_after = bunch.xp[bunch.id<=n_part_per_turn] z_after = bunch.z[bunch.id<=n_part_per_turn] yp_after = bunch.yp[bunch.id<=n_part_per_turn] # sort id and momenta after track indsort = np.argsort(id_after) id_after = np.take(id_after, indsort) xp_after = np.take(xp_after, indsort) yp_after = np.take(yp_after, indsort) z_after = np.take(z_after, indsort)
id_before = bunch.id[bunch.id <= n_part_per_turn]
xp_before = bunch.xp[bunch.id <= n_part_per_turn]
yp_before = bunch.yp[bunch.id <= n_part_per_turn]
rms_err_x_list = []
rms_err_y_list = []
# pl.figure(1000)
# pl.plot(bunch.z, bunch.id, '.', markersize=1)
# pl.show()
for ii in range(N_turns - 1):
slices_list = bunch.extract_slices(slicer)
for slice_obj in slices_list[::-1]:
machine.track(slice_obj) # , verbose = True)
print('Turn', ii)
bunch = sum(slices_list)
machine.longitudinal_map.track(bunch)
for ec in ecloud_list:
ec.finalize_and_reinitialize()
# id and momenta after track
id_after = bunch.id[bunch.id <= n_part_per_turn]
xp_after = bunch.xp[bunch.id <= n_part_per_turn]
z_after = bunch.z[bunch.id <= n_part_per_turn]
yp_after = bunch.yp[bunch.id <= n_part_per_turn]