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_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 bunch.x[:n_part_per_turn] = x[0,:] 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]
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,
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)
class Simulation(object):
def __init__(self):
self.N_turns = N_turns_to_simulate
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
def init_master(self):
# beam parameters
sigma_z = 0.2
intensity = 1.15e11
macroparticlenumber_track = 300000
# initialization bunch
bunch = self.machine.generate_6D_Gaussian_bunch(
macroparticlenumber_track, intensity, epsn_x, epsn_y, sigma_z=sigma_z)
print 'Bunch initialized.'
#replace particles with HDTL ones
self.n_part_per_turn = 5000
appo = np.loadtxt(filename)
parid = np.reshape(appo[:,0], (-1, self.n_part_per_turn))[::self.n_segments,:]
x = np.reshape(appo[:,1], (-1, self.n_part_per_turn))[::self.n_segments,:]
xp = np.reshape(appo[:,2], (-1, self.n_part_per_turn))[::self.n_segments,:]
y = np.reshape(appo[:,3], (-1, self.n_part_per_turn))[::self.n_segments,:]
yp =np.reshape(appo[:,4], (-1, self.n_part_per_turn))[::self.n_segments,:]
z = np.reshape(appo[:,5], (-1, self.n_part_per_turn))[::self.n_segments,:]
zp = np.reshape(appo[:,6], (-1, self.n_part_per_turn))[::self.n_segments,:]
# replace first particles with HEADTAIL ones
bunch.x[:self.n_part_per_turn] = x[0,:]
bunch.xp[:self.n_part_per_turn] = xp[0,:]
bunch.y[:self.n_part_per_turn] = y[0,:]
bunch.yp[:self.n_part_per_turn] = yp[0,:]
bunch.z[:self.n_part_per_turn] = z[0,:]
bunch.dp[:self.n_part_per_turn] =zp[0,:]
# save id and momenta before track
self.id_before = bunch.id[bunch.id<=self.n_part_per_turn]
self.xp_before = bunch.xp[bunch.id<=self.n_part_per_turn]
self.yp_before = bunch.yp[bunch.id<=self.n_part_per_turn]
# initial slicing
from PyHEADTAIL.particles.slicing import UniformBinSlicer
self.slicer = UniformBinSlicer(n_slices = self.n_slices, n_sigma_z = 3.)
self.rms_err_x_list = []
self.rms_err_y_list = []
#slice for the first turn
slice_obj_list = bunch.extract_slices(self.slicer)
pieces_to_be_treated = slice_obj_list
print 'N_turns', self.N_turns
return pieces_to_be_treated
def init_worker(self):
pass
def treat_piece(self, piece):
for ele in self.mypart:
ele.track(piece)
def finalize_turn_on_master(self, pieces_treated):
# re-merge bunch
bunch = sum(pieces_treated)
#finalize present turn (with non parallel part, e.g. synchrotron motion)
for ele in self.non_parallel_part:
ele.track(bunch)
# id and momenta after track
id_after = bunch.id[bunch.id<=self.n_part_per_turn]
xp_after = bunch.xp[bunch.id<=self.n_part_per_turn]
z_after = bunch.z[bunch.id<=self.n_part_per_turn]
yp_after = bunch.yp[bunch.id<=self.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)
# save results
import myfilemanager as mfm
mfm.save_dict_to_h5('particles_at_turn_%d.h5'%self.ring_of_CPUs.i_turn,{\
'id_after': id_after,
'xp_after': xp_after,
'yp_after': yp_after,
'z_after': z_after,
'id_before':self.id_before,
'xp_before':self.xp_before,
'yp_before':self.yp_before})
# prepare next turn (re-slice)
new_pieces_to_be_treated = bunch.extract_slices(self.slicer)
orders_to_pass = ['reset_clouds']
return orders_to_pass, new_pieces_to_be_treated
def execute_orders_from_master(self, orders_from_master):
if 'reset_clouds' in orders_from_master:
for ec in self.my_list_eclouds: ec.finalize_and_reinitialize()
def finalize_simulation(self):
pass
def piece_to_buffer(self, piece):
buf = ch.beam_2_buffer(piece)
return buf
def buffer_to_piece(self, buf):
piece = ch.buffer_2_beam(buf)
return piece
