def init_all(self): self.n_slices = n_slices self.n_segments = n_segments # define the machine from LHC_custom import LHC self.machine = LHC(n_segments = n_segments, machine_configuration = machine_configuration) # define MP size nel_mp_ref_0 = init_unif_edens*4*x_aper*y_aper/N_MP_ele_init # prepare e-cloud import PyECLOUD.PyEC4PyHT as PyEC4PyHT ecloud = PyEC4PyHT.Ecloud(slice_by_slice_mode=True, L_ecloud=self.machine.circumference/n_segments, slicer=None , Dt_ref=Dt_ref, pyecl_input_folder=pyecl_input_folder, chamb_type = chamb_type, x_aper=x_aper, y_aper=y_aper, filename_chm=filename_chm, 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, B_multip=B_multip_per_eV*self.machine.p0/e*c) # setup transverse losses (to "protect" the ecloud) import PyHEADTAIL.aperture.aperture as aperture apt_xy = aperture.EllipticalApertureXY(x_aper=ecloud.impact_man.chamb.x_aper, y_aper=ecloud.impact_man.chamb.y_aper) self.machine.one_turn_map.append(apt_xy) n_non_parallelizable = 2 #rf and aperture # We suppose that all the object that cannot be slice parallelized are at the end of the ring i_end_parallel = len(self.machine.one_turn_map)-n_non_parallelizable # split the machine sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes) myid = self.ring_of_CPUs.myid i_start_part, i_end_part = sharing.my_part(myid) self.mypart = self.machine.one_turn_map[i_start_part:i_end_part] if self.ring_of_CPUs.I_am_a_worker: print 'I am id=%d/%d (worker) and my part is %d long'%(myid, self.ring_of_CPUs.N_nodes, len(self.mypart)) elif self.ring_of_CPUs.I_am_the_master: self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:] print 'I am id=%d/%d (master) and my part is %d long'%(myid, self.ring_of_CPUs.N_nodes, len(self.mypart)) #install eclouds in my part my_new_part = [] self.my_list_eclouds = [] for ele in self.mypart: my_new_part.append(ele) if ele in self.machine.transverse_map: #ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge() ecloud_new = DummyEcloud() my_new_part.append(ecloud_new) self.my_list_eclouds.append(ecloud_new) self.mypart = my_new_part
y_aper = 20 * sigma_y
Dh_sc = 2 * x_aper / 128
# define MP size
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
# define an electron cloud
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
from PyHEADTAIL.particles.slicing import UniformBinSlicer
slicer = UniformBinSlicer(n_slices=64, n_sigma_z=2.)
ecloud = PyEC4PyHT.Ecloud(L_ecloud=machine.circumference / n_segments,
slicer=slicer,
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)
list_slices += these_slices
# Build e-cloud
print('Build ecloud...')
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(
L_ecloud=1., slicer=None, slice_by_slice_mode=True,
Dt_ref=5e-12, pyecl_input_folder='./pyecloud_config',
chamb_type='polyg' ,
filename_chm='LHC_chm_ver.mat',
#init_unif_edens_flag=1,
#init_unif_edens=1e7,
#N_mp_max = 3000000,
#nel_mp_ref_0 = 1e7/(0.7*3000000),
#B_multip = [0.],
#~ PyPICmode = 'ShortleyWeller_WithTelescopicGrids',
#~ f_telescope = 0.3,
target_grid={'x_min_target': -5 * list_bunches[-1].sigma_x(), 'x_max_target': 5 * list_bunches[-1].sigma_x(),
'y_min_target': -5 * list_bunches[-1].sigma_y(), 'y_max_target': 5 * list_bunches[-1].sigma_y(),
'Dh_target': .2 * list_bunches[-1].sigma_x()},
#~ N_nodes_discard = 10.,
#~ N_min_Dh_main = 10,
#x_beam_offset = x_beam_offset,
#y_beam_offset = y_beam_offset,
#probes_position = probes_position,
save_pyecl_outp_as='test_saving',
sparse_solver='PyKLU')
print('Done.')
# REMEBMBER TO START POPPING FROM THE RIGHT SIDE
print('Start cloud sim')
# initialize ion cloud with single kick per bunch
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud_sk = PyEC4PyHT.Ecloud(L_ecloud=machine.circumference / n_segments,
slicer=bunch_slicer,
Dt_ref=Dt_ref,
pyecl_input_folder='./pyecloud_config',
beam_monitor=beam_monitor,
chamb_type=chamb_type,
PyPICmode='FFT_OpenBoundary',
x_aper=x_aper,
y_aper=y_aper,
filename_chm=filename_chm,
Dh_sc=Dh_sc,
init_unif_edens_flag=init_unif_edens_flag,
init_unif_edens=init_unif_edens,
cloud_mass=ion_mass,
cloud_charge=ion_charge,
gas_ion_flag=gas_ion_flag,
unif_frac=unif_frac,
P_nTorr=P_nTorr,
sigma_ion_MBarn=sigma_ion_MBarn,
Temp_K=Temp_K,
E_init_ion=E_init_ion,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_per_eV * machine.p0 / e * c,
switch_model='perfect_absorber',
kick_mode_for_beam_field=True,
verbose=True)
# print grid size
pp.N_mp_max_dip = 500000
nel_mp_ref_0 = pp.init_unif_edens_dip * 4 * pp.x_aper * pp.y_aper / pp.N_MP_ele_init_dip
ecloud = PyEC4PyHT.Ecloud(slice_by_slice_mode=True,
L_ecloud=1.,
slicer=None,
force_interp_at_substeps_interacting_slices=True,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type=pp.chamb_type,
x_aper=pp.x_aper,
y_aper=pp.y_aper,
filename_chm=pp.filename_chm,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid=target_grid_arcs,
init_unif_edens_flag=pp.init_unif_edens_flag_dip,
init_unif_edens=pp.init_unif_edens_dip,
N_mp_max=pp.N_mp_max_dip,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=[0.],
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y,
kick_mode_for_beam_field=False)
sc = ecloud.beam_PyPIC_state.pic_internal
i_y0 = np.argmin(np.abs(sc.yg))
def init_all(self):
print('Exec init...')
from LHC_custom import LHC
self.machine = LHC(n_segments = n_segments, machine_configuration = machine_configuration,
Qp_x=Qp_x, Qp_y=Qp_y,
octupole_knob=octupole_knob)
self.n_non_parallelizable = 1 #RF
inj_optics = self.machine.transverse_map.get_injection_optics()
sigma_x_smooth = np.sqrt(inj_optics['beta_x']*epsn_x/self.machine.betagamma)
sigma_y_smooth = np.sqrt(inj_optics['beta_y']*epsn_y/self.machine.betagamma)
if flag_aperture:
# setup transverse losses (to "protect" the ecloud)
import PyHEADTAIL.aperture.aperture as aperture
apt_xy = aperture.EllipticalApertureXY(x_aper=target_size_internal_grid_sigma*sigma_x_smooth,
y_aper=target_size_internal_grid_sigma*sigma_x_smooth)
self.machine.one_turn_map.append(apt_xy)
self.n_non_parallelizable +=1
if enable_transverse_damper:
# setup transverse damper
from PyHEADTAIL.feedback.transverse_damper import TransverseDamper
damper = TransverseDamper(dampingrate_x=dampingrate_x, dampingrate_y=dampingrate_y)
self.machine.one_turn_map.append(damper)
self.n_non_parallelizable +=1
if enable_ecloud:
print('Build ecloud...')
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(
L_ecloud=L_ecloud_tot/n_segments, slicer=None, slice_by_slice_mode=True,
Dt_ref=5e-12, pyecl_input_folder='./pyecloud_config',
chamb_type = 'polyg' ,
filename_chm= 'LHC_chm_ver.mat',
#init_unif_edens_flag=1,
#init_unif_edens=1e7,
#N_mp_max = 3000000,
#nel_mp_ref_0 = 1e7/(0.7*3000000),
#B_multip = [0.],
#~ PyPICmode = 'ShortleyWeller_WithTelescopicGrids',
#~ f_telescope = 0.3,
target_grid = {'x_min_target':-target_size_internal_grid_sigma*sigma_x_smooth, 'x_max_target':target_size_internal_grid_sigma*sigma_x_smooth,
'y_min_target':-target_size_internal_grid_sigma*sigma_y_smooth,'y_max_target':target_size_internal_grid_sigma*sigma_y_smooth,
'Dh_target':.2*sigma_x_smooth},
#~ N_nodes_discard = 10.,
#~ N_min_Dh_main = 10,
#x_beam_offset = x_beam_offset,
#y_beam_offset = y_beam_offset,
#probes_position = probes_position,
save_pyecl_outp_as = 'cloud_evol_ring%d'%self.ring_of_CPUs.myring,
save_only = ['lam_t_array', 'nel_hist', 'Nel_timep', 't', 't_hist', 'xg_hist'],
sparse_solver = 'PyKLU', enable_kick_x=enable_kick_x, enable_kick_y=enable_kick_y)
print('Done.')
# split the machine
i_end_parallel = len(self.machine.one_turn_map)-self.n_non_parallelizable
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes_per_ring)
i_start_part, i_end_part = sharing.my_part(self.ring_of_CPUs.myid_in_ring)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_at_end_ring:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
#install eclouds in my part
if enable_ecloud:
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
if ele in self.machine.transverse_map:
ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge()
# we save buildup info only for the first cloud in each ring
if self.ring_of_CPUs.myid_in_ring>0 or len(self.my_list_eclouds)>0:
ecloud_new.remove_savers()
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
my_new_part.append(ele)
self.mypart = my_new_part
print('Hello, I am %d.%d, my part looks like: %s. Saver status: %s'%(
self.ring_of_CPUs.myring, self.ring_of_CPUs.myid_in_ring, self.mypart,
[(ec.cloudsim.cloud_list[0].pyeclsaver is not None) for ec in self.my_list_eclouds]))
init_unif_edens_flag = 1
init_unif_edens = 1e11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init * 4.
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
Dh_ext = 2e-3
ecloud = PyEC4PyHT.Ecloud(L_ecloud=machine.circumference / machine.transverse_map.n_segments, slicer=slicer,
Dt_ref=25e-12, pyecl_input_folder='./drift_sim',
x_aper=x_aper, y_aper=y_aper,
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,
B_multip=B_multip,
PyPICmode='ShortleyWeller_WithTelescopicGrids',
f_telescope=0.3,
Dh_sc=Dh_ext,
target_grid={'x_min_target': -4 * bunch.sigma_x(), 'x_max_target': 4 * bunch.sigma_x(),
'y_min_target': -4 * bunch.sigma_y(), 'y_max_target': 4 * bunch.sigma_y(),
'Dh_target': Dh_sc},
N_nodes_discard=10.,
N_min_Dh_main=10,)
# generate a bunch
bunch_for_map = machine.generate_6D_Gaussian_bunch(n_macroparticles=500000,
intensity=1.15e11, epsn_x=epsn_x, epsn_y=epsn_y, sigma_z=0.2)
ecloud.track_once_and_replace_with_recorded_field_map(bunch_for_map)
machine.install_after_each_transverse_segment(ecloud)
init_unif_edens = 1e11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init * 4.
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
new_one_turn_map = []
ecloud_list = []
for ele in machine.one_turn_map:
new_one_turn_map.append(ele)
if ele in machine.transverse_map:
new_ecloud = PyEC4PyHT.Ecloud(slice_by_slice_mode=True,
L_ecloud=machine.circumference / machine.transverse_map.n_segments,
slicer=None,
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,
B_multip=B_multip)
new_one_turn_map.append(new_ecloud)
ecloud_list.append(new_ecloud)
machine.one_turn_map = new_one_turn_map
# generate a bunch
bunch_for_map = machine.generate_6D_Gaussian_bunch(n_macroparticles=500000,
intensity=1.15e11, epsn_x=epsn_x, epsn_y=epsn_y, sigma_z=0.2)
slices_list_for_map = bunch.extract_slices(slicer)
for ec in ecloud_list:
def init_all(self):
n_slices = 100
z_cut = 2.5e-9 * c
self.n_slices = n_slices
self.z_cut = z_cut
n_segments = 70
from LHC import LHC
self.machine = LHC(machine_configuration='Injection',
n_segments=n_segments,
D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - 1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long' % (
myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long' % (
myid, len(self.mypart))
# config e-cloud
chamb_type = 'polyg'
x_aper = 2.300000e-02
y_aper = 1.800000e-02
filename_chm = '../pyecloud_config/LHC_chm_ver.mat'
B_multip_per_eV = [1.190000e-12]
B_multip_per_eV = np.array(B_multip_per_eV)
fraction_device = 0.65
intensity = 1.150000e+11
epsn_x = 2.5e-6
epsn_y = 2.5e-6
init_unif_edens_flag = 1
init_unif_edens = 9.000000e+11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init * 4.
Dh_sc = .2e-3
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(
L_ecloud=self.machine.circumference / n_segments,
slicer=None,
Dt_ref=10e-12,
pyecl_input_folder='../pyecloud_config',
chamb_type=chamb_type,
x_aper=x_aper,
y_aper=y_aper,
filename_chm=filename_chm,
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,
B_multip=B_multip_per_eV * self.machine.p0 / e * c,
slice_by_slice_mode=True)
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge(
)
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
self.mypart = my_new_part
{'x' : x_beam_offset, 'y': y_beam_offset + (2 * Dy_probe)},
{'x' : x_beam_offset, 'y': y_beam_offset - (2 * Dy_probe)},
{'x' : x_beam_offset + (2 * Dx_probe), 'y': y_beam_offset},
{'x' : x_beam_offset - (2 * Dx_probe), 'y': y_beam_offset}]
n_probes = len(probes_position)
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud_singlegrid = PyEC4PyHT.Ecloud(
L_ecloud=L_ecloud, slicer=slicer,
Dt_ref=20e-12, pyecl_input_folder='./pyecloud_config_LHC',
chamb_type='polyg' ,
filename_chm='LHC_chm_ver.mat', Dh_sc=.2 * bunch.sigma_x(),
init_unif_edens_flag=1,
init_unif_edens=1e7,
N_mp_max=3000000,
nel_mp_ref_0=1e7 / (0.7 * 3000000),
B_multip=[0.],
x_beam_offset=x_beam_offset,
y_beam_offset=y_beam_offset,
probes_position=probes_position,
sparse_solver=sparse_solver)
ecloud_multigrid = PyEC4PyHT.Ecloud(
L_ecloud=L_ecloud, slicer=slicer,
Dt_ref=20e-12, pyecl_input_folder='./pyecloud_config_LHC',
chamb_type='polyg' ,
filename_chm='LHC_chm_ver.mat', Dh_sc=1e-3,
init_unif_edens_flag=1,
init_unif_edens=1e7,
N_mp_max=3000000,
machine_configuration = 'HLLHC-injection'
machine = LHC(n_segments=1, machine_configuration=machine_configuration)
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=300000,
intensity=1.15e11,
epsn_x=2.5e-6,
epsn_y=2.5e-6,
sigma_z=0.11)
bunch.x[bunch.z < 5e-2] += 1e-3
ecloud_ele = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=1.,
slicer=None,
Dt_ref=25e-12,
pyecl_input_folder='pyecloud_config',
)
n_slices = 150
z_cut = 2.5e-9 / 2 * clight
slicer = UniformBinSlicer(n_slices=n_slices, z_cuts=(-z_cut, z_cut))
slices_list_for_map = bunch.extract_slices(slicer)
ecloud_ele.save_ele_distributions_last_track = True
ecloud_ele.save_ele_field = True
ecloud_ele._reinitialize()
z_centers = []
def init_all(self): self.n_slices = n_slices self.n_segments = n_segments from machines_for_testing import SPS self.machine = SPS(n_segments = n_segments, machine_configuration = 'Q20-injection', accQ_x=20., accQ_y=20., RF_at='end_of_transverse') # We suppose that all the object that cannot be slice parallelized are at the end of the ring i_end_parallel = len(self.machine.one_turn_map)-1 #only RF is not parallelizable # split the machine sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes) myid = self.ring_of_CPUs.myid i_start_part, i_end_part = sharing.my_part(myid) self.mypart = self.machine.one_turn_map[i_start_part:i_end_part] if self.ring_of_CPUs.I_am_a_worker: print 'I am id=%d (worker) and my part is %d long'%(myid, len(self.mypart)) elif self.ring_of_CPUs.I_am_the_master: self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:] print 'I am id=%d (master) and my part is %d long'%(myid, len(self.mypart)) # config e-cloud init_unif_edens_flag=1 init_unif_edens=2e11 N_MP_ele_init = 100000 N_mp_max = N_MP_ele_init*4. # define apertures and Dh_sc to simulate headtail inj_optics = self.machine.transverse_map.get_injection_optics() sigma_x = np.sqrt(inj_optics['beta_x']*epsn_x/self.machine.betagamma) sigma_y = np.sqrt(inj_optics['beta_y']*epsn_y/self.machine.betagamma) x_aper = 20*sigma_x y_aper = 20*sigma_y Dh_sc = 2*x_aper/128/2 # initial MP size nel_mp_ref_0 = init_unif_edens*4*x_aper*y_aper/N_MP_ele_init import PyECLOUD.PyEC4PyHT as PyEC4PyHT ecloud = PyEC4PyHT.Ecloud(slice_by_slice_mode=True, L_ecloud=self.machine.circumference/n_segments, slicer=None, Dt_ref=25e-12, pyecl_input_folder='../../PyECLOUD/testing/tests_PyEC4PyHT/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_ele_init=N_MP_ele_init, N_mp_max=N_mp_max, nel_mp_ref_0=nel_mp_ref_0, B_multip=B_multip) my_new_part = [] self.my_list_eclouds = [] for ele in self.mypart: my_new_part.append(ele) if ele in self.machine.transverse_map: ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge() my_new_part.append(ecloud_new) self.my_list_eclouds.append(ecloud_new) self.mypart = my_new_part
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud_multigrid = PyEC4PyHT.Ecloud(
L_ecloud=L_ecloud,
slicer=slicer,
Dt_ref=20e-12,
pyecl_input_folder='./pyecloud_config_LHC',
chamb_type='polyg',
filename_chm='LHC_chm_ver.mat',
#chamb_type='ellip', x_aper=2e-2, y_aper=2e-2,
Dh_sc=1e-3,
init_unif_edens_flag=1,
init_unif_edens=1e7,
N_mp_max=3000000,
nel_mp_ref_0=1e7 * np.pi * 2e-2**2 / (0.7 * 3000000),
B_multip=[0.],
PyPICmode='ShortleyWeller_WithTelescopicGrids',
f_telescope=0.3,
target_grid={
'x_min_target': -5 * bunch.sigma_x(),
'x_max_target': 5 * bunch.sigma_x(),
'y_min_target': -5 * bunch.sigma_y(),
'y_max_target': 5 * bunch.sigma_y(),
'Dh_target': .1 * bunch.sigma_x()
},
N_nodes_discard=10.,
N_min_Dh_main=10,
sparse_solver=sparse_solver,
verbose=True)
ecloud_multigrid.save_ele_distributions_last_track = True
def init_all(self):
self.n_slices = pp.n_slices
# read the optics if needed
if pp.optics_pickle_file is not None:
with open(pp.optics_pickle_file) as fid:
optics = pickle.load(fid)
self.n_kick_smooth = np.sum(
['_kick_smooth_' in nn for nn in optics['name']])
else:
optics = None
self.n_kick_smooth = pp.n_segments
# define the machine
from LHC_custom import LHC
self.machine = LHC(n_segments=pp.n_segments,
machine_configuration=pp.machine_configuration,
beta_x=pp.beta_x,
beta_y=pp.beta_y,
accQ_x=pp.Q_x,
accQ_y=pp.Q_y,
Qp_x=pp.Qp_x,
Qp_y=pp.Qp_y,
octupole_knob=pp.octupole_knob,
optics_dict=optics,
V_RF=pp.V_RF)
self.n_segments = self.machine.transverse_map.n_segments
# compute sigma
inj_opt = self.machine.transverse_map.get_injection_optics()
sigma_x_inj = np.sqrt(inj_opt['beta_x'] * pp.epsn_x /
self.machine.betagamma)
sigma_y_inj = np.sqrt(inj_opt['beta_y'] * pp.epsn_y /
self.machine.betagamma)
if pp.optics_pickle_file is None:
sigma_x_smooth = sigma_x_inj
sigma_y_smooth = sigma_y_inj
else:
beta_x_smooth = None
beta_y_smooth = None
for ele in self.machine.one_turn_map:
if ele in self.machine.transverse_map:
if '_kick_smooth_' in ele.name1:
if beta_x_smooth is None:
beta_x_smooth = ele.beta_x1
beta_y_smooth = ele.beta_y1
else:
if beta_x_smooth != ele.beta_x1 or beta_y_smooth != ele.beta_y1:
raise ValueError(
'Smooth kicks must have all the same beta')
if beta_x_smooth is None:
sigma_x_smooth = None
sigma_y_smooth = None
else:
sigma_x_smooth = np.sqrt(beta_x_smooth * pp.epsn_x /
self.machine.betagamma)
sigma_y_smooth = np.sqrt(beta_y_smooth * pp.epsn_y /
self.machine.betagamma)
# define MP size
nel_mp_ref_0 = pp.init_unif_edens_dip * 4 * pp.x_aper * pp.y_aper / pp.N_MP_ele_init_dip
# prepare e-cloud
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
if pp.custom_target_grid_arcs is not None:
target_grid_arcs = pp.custom_target_grid_arcs
else:
target_grid_arcs = {
'x_min_target':
-pp.target_size_internal_grid_sigma * sigma_x_smooth,
'x_max_target':
pp.target_size_internal_grid_sigma * sigma_x_smooth,
'y_min_target':
-pp.target_size_internal_grid_sigma * sigma_y_smooth,
'y_max_target':
pp.target_size_internal_grid_sigma * sigma_y_smooth,
'Dh_target': pp.target_Dh_internal_grid_sigma * sigma_x_smooth
}
self.target_grid_arcs = target_grid_arcs
if pp.enable_arc_dip:
ecloud_dip = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=self.machine.circumference / self.n_kick_smooth *
pp.fraction_device_dip,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type=pp.chamb_type,
x_aper=pp.x_aper,
y_aper=pp.y_aper,
filename_chm=pp.filename_chm,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid=target_grid_arcs,
init_unif_edens_flag=pp.init_unif_edens_flag_dip,
init_unif_edens=pp.init_unif_edens_dip,
N_mp_max=pp.N_mp_max_dip,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=pp.B_multip_dip,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y)
if pp.enable_arc_quad:
ecloud_quad = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=self.machine.circumference / self.n_kick_smooth *
pp.fraction_device_quad,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type=pp.chamb_type,
x_aper=pp.x_aper,
y_aper=pp.y_aper,
filename_chm=pp.filename_chm,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid=target_grid_arcs,
N_mp_max=pp.N_mp_max_quad,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=pp.B_multip_quad,
filename_init_MP_state=pp.filename_init_MP_state_quad,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y)
if self.ring_of_CPUs.I_am_the_master and pp.enable_arc_dip:
with open('multigrid_config_dip.txt', 'w') as fid:
if hasattr(ecloud_dip.spacech_ele.PyPICobj, 'grids'):
fid.write(repr(ecloud_dip.spacech_ele.PyPICobj.grids))
else:
fid.write("Single grid.")
with open('multigrid_config_dip.pkl', 'w') as fid:
if hasattr(ecloud_dip.spacech_ele.PyPICobj, 'grids'):
pickle.dump(ecloud_dip.spacech_ele.PyPICobj.grids, fid)
else:
pickle.dump('Single grid.', fid)
if self.ring_of_CPUs.I_am_the_master and pp.enable_arc_quad:
with open('multigrid_config_quad.txt', 'w') as fid:
if hasattr(ecloud_quad.spacech_ele.PyPICobj, 'grids'):
fid.write(repr(ecloud_quad.spacech_ele.PyPICobj.grids))
else:
fid.write("Single grid.")
with open('multigrid_config_quad.pkl', 'w') as fid:
if hasattr(ecloud_quad.spacech_ele.PyPICobj, 'grids'):
pickle.dump(ecloud_quad.spacech_ele.PyPICobj.grids, fid)
else:
pickle.dump('Single grid.', fid)
# setup transverse losses (to "protect" the ecloud)
import PyHEADTAIL.aperture.aperture as aperture
apt_xy = aperture.EllipticalApertureXY(
x_aper=pp.target_size_internal_grid_sigma * sigma_x_inj,
y_aper=pp.target_size_internal_grid_sigma * sigma_y_inj)
self.machine.one_turn_map.append(apt_xy)
if pp.enable_transverse_damper:
# setup transverse damper
from PyHEADTAIL.feedback.transverse_damper import TransverseDamper
damper = TransverseDamper(dampingrate_x=pp.dampingrate_x,
dampingrate_y=pp.dampingrate_y)
self.machine.one_turn_map.append(damper)
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - pp.n_non_parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d/%d (worker) and my part is %d long' % (
myid, self.ring_of_CPUs.N_nodes, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d/%d (master) and my part is %d long' % (
myid, self.ring_of_CPUs.N_nodes, len(self.mypart))
#install eclouds in my part
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
if pp.optics_pickle_file is None or '_kick_smooth_' in ele.name1:
if pp.enable_arc_dip:
ecloud_dip_new = ecloud_dip.generate_twin_ecloud_with_shared_space_charge(
)
my_new_part.append(ecloud_dip_new)
self.my_list_eclouds.append(ecloud_dip_new)
if pp.enable_arc_quad:
ecloud_quad_new = ecloud_quad.generate_twin_ecloud_with_shared_space_charge(
)
my_new_part.append(ecloud_quad_new)
self.my_list_eclouds.append(ecloud_quad_new)
elif '_kick_element_' in ele.name1 and pp.enable_eclouds_at_kick_elements:
i_in_optics = list(optics['name']).index(ele.name1)
kick_name = optics['name'][i_in_optics]
element_name = kick_name.split('_kick_element_')[-1]
L_curr = optics['L_interaction'][i_in_optics]
buildup_folder = pp.path_buildup_simulations_kick_elements.replace(
'!!!NAME!!!', element_name)
chamber_fname = '%s_chamber.mat' % (element_name)
B_multip_curr = [0., optics['gradB'][i_in_optics]]
x_beam_offset = optics['x'][i_in_optics] * pp.orbit_factor
y_beam_offset = optics['y'][i_in_optics] * pp.orbit_factor
sigma_x_local = np.sqrt(optics['beta_x'][i_in_optics] *
pp.epsn_x / self.machine.betagamma)
sigma_y_local = np.sqrt(optics['beta_y'][i_in_optics] *
pp.epsn_y / self.machine.betagamma)
ecloud_ele = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=L_curr,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type='polyg',
x_aper=None,
y_aper=None,
filename_chm=buildup_folder + '/' + chamber_fname,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid={
'x_min_target':
-pp.target_size_internal_grid_sigma * sigma_x_local
+ x_beam_offset,
'x_max_target':
pp.target_size_internal_grid_sigma * sigma_x_local
+ x_beam_offset,
'y_min_target':
-pp.target_size_internal_grid_sigma * sigma_y_local
+ y_beam_offset,
'y_max_target':
pp.target_size_internal_grid_sigma * sigma_y_local
+ y_beam_offset,
'Dh_target':
pp.target_Dh_internal_grid_sigma * sigma_y_local
},
N_mp_max=pp.N_mp_max_quad,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_curr,
filename_init_MP_state=buildup_folder + '/' +
pp.name_MP_state_file_kick_elements,
x_beam_offset=x_beam_offset,
y_beam_offset=y_beam_offset,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y)
my_new_part.append(ecloud_ele)
self.my_list_eclouds.append(ecloud_ele)
self.mypart = my_new_part
if pp.footprint_mode:
print 'Proc. %d computing maps' % myid
# generate a bunch
bunch_for_map = self.machine.generate_6D_Gaussian_bunch_matched(
n_macroparticles=pp.n_macroparticles_for_footprint_map,
intensity=pp.intensity,
epsn_x=pp.epsn_x,
epsn_y=pp.epsn_y,
sigma_z=pp.sigma_z)
# Slice the bunch
slicer_for_map = UniformBinSlicer(n_slices=pp.n_slices,
z_cuts=(-pp.z_cut, pp.z_cut))
slices_list_for_map = bunch_for_map.extract_slices(slicer_for_map)
#Track the previous part of the machine
for ele in self.machine.one_turn_map[:i_start_part]:
for ss in slices_list_for_map:
ele.track(ss)
# Measure optics, track and replace clouds with maps
list_ele_type = []
list_meas_beta_x = []
list_meas_alpha_x = []
list_meas_beta_y = []
list_meas_alpha_y = []
for ele in self.mypart:
list_ele_type.append(str(type(ele)))
# Measure optics
bbb = sum(slices_list_for_map)
list_meas_beta_x.append(bbb.beta_Twiss_x())
list_meas_alpha_x.append(bbb.alpha_Twiss_x())
list_meas_beta_y.append(bbb.beta_Twiss_y())
list_meas_alpha_y.append(bbb.alpha_Twiss_y())
if ele in self.my_list_eclouds:
ele.track_once_and_replace_with_recorded_field_map(
slices_list_for_map)
else:
for ss in slices_list_for_map:
ele.track(ss)
print 'Proc. %d done with maps' % myid
with open('measured_optics_%d.pkl' % myid, 'wb') as fid:
pickle.dump(
{
'ele_type': list_ele_type,
'beta_x': list_meas_beta_x,
'alpha_x': list_meas_alpha_x,
'beta_y': list_meas_beta_y,
'alpha_y': list_meas_alpha_y,
}, fid)
#remove RF
if self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part.remove(self.machine.longitudinal_map)
def _generate_parent_eclouds(self):
pp = self.pp
sigma_x_inj = self.sigma_x_inj
sigma_y_inj = self.sigma_y_inj
sigma_x_smooth = self.sigma_x_smooth
sigma_y_smooth = self.sigma_y_smooth
# prepare e-cloud
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
if pp.custom_target_grid_arcs is not None:
target_grid_arcs = pp.custom_target_grid_arcs
else:
target_grid_arcs = {
"x_min_target":
-pp.target_size_internal_grid_sigma * sigma_x_smooth,
"x_max_target":
pp.target_size_internal_grid_sigma * sigma_x_smooth,
"y_min_target":
-pp.target_size_internal_grid_sigma * sigma_y_smooth,
"y_max_target":
pp.target_size_internal_grid_sigma * sigma_y_smooth,
"Dh_target": pp.target_Dh_internal_grid_sigma * sigma_x_smooth,
}
self.target_grid_arcs = target_grid_arcs
self.parent_eclouds = []
nel_mp_ref_0 = (pp.init_unif_edens_dip * 4 * pp.x_aper * pp.y_aper /
pp.N_MP_ele_init_dip)
if pp.enable_arc_dip:
# define MP size
ecloud_dip = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=self.machine.circumference / self.n_kick_smooth *
pp.fraction_device_dip,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type=pp.chamb_type,
x_aper=pp.x_aper,
y_aper=pp.y_aper,
filename_chm=pp.filename_chm,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid=target_grid_arcs,
init_unif_edens_flag=pp.init_unif_edens_flag_dip,
init_unif_edens=pp.init_unif_edens_dip,
N_mp_max=pp.N_mp_max_dip,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=pp.B_multip_dip,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y,
force_interp_at_substeps_interacting_slices=pp.
force_interp_at_substeps_interacting_slices,
)
self.parent_eclouds.append(ecloud_dip)
if pp.enable_arc_quad:
ecloud_quad = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=self.machine.circumference / self.n_kick_smooth *
pp.fraction_device_quad,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type=pp.chamb_type,
x_aper=pp.x_aper,
y_aper=pp.y_aper,
filename_chm=pp.filename_chm,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid=target_grid_arcs,
N_mp_max=pp.N_mp_max_quad,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=pp.B_multip_quad,
filename_init_MP_state=pp.filename_init_MP_state_quad,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y,
force_interp_at_substeps_interacting_slices=pp.
force_interp_at_substeps_interacting_slices,
)
self.parent_eclouds.append(ecloud_quad)
if self.ring_of_CPUs.I_am_the_master and pp.enable_arc_dip:
with open("multigrid_config_dip.txt", "w") as fid:
if hasattr(ecloud_dip.spacech_ele.PyPICobj, "grids"):
fid.write(repr(ecloud_dip.spacech_ele.PyPICobj.grids))
else:
fid.write("Single grid.")
with open("multigrid_config_dip.pkl", "wb") as fid:
if hasattr(ecloud_dip.spacech_ele.PyPICobj, "grids"):
pickle.dump(ecloud_dip.spacech_ele.PyPICobj.grids, fid)
else:
pickle.dump("Single grid.", fid)
if self.ring_of_CPUs.I_am_the_master and pp.enable_arc_quad:
with open("multigrid_config_quad.txt", "w") as fid:
if hasattr(ecloud_quad.spacech_ele.PyPICobj, "grids"):
fid.write(repr(ecloud_quad.spacech_ele.PyPICobj.grids))
else:
fid.write("Single grid.")
with open("multigrid_config_quad.pkl", "wb") as fid:
if hasattr(ecloud_quad.spacech_ele.PyPICobj, "grids"):
pickle.dump(ecloud_quad.spacech_ele.PyPICobj.grids, fid)
else:
pickle.dump("Single grid.", fid)
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
new_one_turn_map = []
ecloud_list = []
for ele in machine.one_turn_map:
new_one_turn_map.append(ele)
if ele in machine.transverse_map:
new_ecloud = PyEC4PyHT.Ecloud(
L_ecloud=machine.circumference / N_kicks,
slicer=slicer,
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_ele_init=N_MP_ele_init,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip,
slice_by_slice_mode=True)
new_one_turn_map.append(new_ecloud)
ecloud_list.append(new_ecloud)
machine.one_turn_map = new_one_turn_map
# generate a bunch
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=30000,
intensity=1.15e11,
def _install_eclouds_in_machine_part(self):
# install eclouds in my part
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
if not self.optics_from_pickle or "_kick_smooth_" in ele.name1:
for ee in self.parent_eclouds:
ecloud_new = (
ee.generate_twin_ecloud_with_shared_space_charge())
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
elif ("_kick_element_" in ele.name1
and pp.enable_eclouds_at_kick_elements):
i_in_optics = list(optics["name"]).index(ele.name1)
kick_name = optics["name"][i_in_optics]
element_name = kick_name.split("_kick_element_")[-1]
L_curr = optics["L_interaction"][i_in_optics]
buildup_folder = pp.path_buildup_simulations_kick_elements.replace(
"!!!NAME!!!", element_name)
chamber_fname = "%s_chamber.mat" % (element_name)
B_multip_curr = [0.0, optics["gradB"][i_in_optics]]
x_beam_offset = optics["x"][i_in_optics] * pp.orbit_factor
y_beam_offset = optics["y"][i_in_optics] * pp.orbit_factor
sigma_x_local = np.sqrt(optics["beta_x"][i_in_optics] *
pp.epsn_x / self.machine.betagamma)
sigma_y_local = np.sqrt(optics["beta_y"][i_in_optics] *
pp.epsn_y / self.machine.betagamma)
ecloud_ele = PyEC4PyHT.Ecloud(
slice_by_slice_mode=True,
L_ecloud=L_curr,
slicer=None,
Dt_ref=pp.Dt_ref,
pyecl_input_folder=pp.pyecl_input_folder,
chamb_type="polyg",
x_aper=None,
y_aper=None,
filename_chm=buildup_folder + "/" + chamber_fname,
PyPICmode=pp.PyPICmode,
Dh_sc=pp.Dh_sc_ext,
N_min_Dh_main=pp.N_min_Dh_main,
f_telescope=pp.f_telescope,
N_nodes_discard=pp.N_nodes_discard,
target_grid={
"x_min_target":
-pp.target_size_internal_grid_sigma * sigma_x_local
+ x_beam_offset,
"x_max_target":
pp.target_size_internal_grid_sigma * sigma_x_local
+ x_beam_offset,
"y_min_target":
-pp.target_size_internal_grid_sigma * sigma_y_local
+ y_beam_offset,
"y_max_target":
pp.target_size_internal_grid_sigma * sigma_y_local
+ y_beam_offset,
"Dh_target":
pp.target_Dh_internal_grid_sigma * sigma_y_local,
},
N_mp_max=pp.N_mp_max_quad,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_curr,
filename_init_MP_state=buildup_folder + "/" +
pp.name_MP_state_file_kick_elements,
x_beam_offset=x_beam_offset,
y_beam_offset=y_beam_offset,
enable_kick_x=pp.enable_kick_x,
enable_kick_y=pp.enable_kick_y,
force_interp_at_substeps_interacting_slices=pp.
force_interp_at_substeps_interacting_slices,
)
my_new_part.append(ecloud_ele)
self.my_list_eclouds.append(ecloud_ele)
self.mypart = my_new_part
Dh_sc = .2e-3
# define MP size
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
# define an electron cloud
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
from PyHEADTAIL.particles.slicing import UniformBinSlicer
slicer = UniformBinSlicer(n_slices=64, n_sigma_z=2.)
ecloud = PyEC4PyHT.Ecloud(L_ecloud=machine.circumference / n_segments,
slicer=slicer,
Dt_ref=10e-12,
pyecl_input_folder='./pyecloud_config',
chamb_type=chamb_type,
x_aper=x_aper,
y_aper=y_aper,
filename_chm=filename_chm,
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,
B_multip=B_multip_per_eV * machine.p0 / e * c)
# 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.impact_man.chamb.x_aper,
y_aper=ecloud.impact_man.chamb.y_aper)
machine.one_turn_map.append(apt_xy)
from PyHEADTAIL.particles.slicing import UniformBinSlicer slicer = UniformBinSlicer(n_slices = 64, n_sigma_z = 3.) init_unif_edens_flag=1 init_unif_edens=2e11 N_MP_ele_init = 100000 N_mp_max = N_MP_ele_init*4. nel_mp_ref_0 = init_unif_edens*4*x_aper*y_aper/N_MP_ele_init ecloud = PyEC4PyHT.Ecloud(L_ecloud=machine.circumference/N_kicks, slicer=slicer, Dt_ref=25e-12, pyecl_input_folder='../../PyECLOUD/testing/tests_PyEC4PyHT/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_ele_init=N_MP_ele_init, N_mp_max=N_mp_max, nel_mp_ref_0=nel_mp_ref_0, B_multip=B_multip) machine.install_after_each_transverse_segment(ecloud) if show_movie: ecloud.save_ele_distributions_last_track = True ecloud.save_ele_potential_and_field = True # generate a bunch bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=300000, intensity=1.15e11, epsn_x=epsn_x, epsn_y=epsn_y, sigma_z=0.2) # replace first particles with HEADTAIL ones