def test_slicemonitor(self):
'''
Test whether the slicemonitor works as excpected, use the mock slicer
'''
nslices = 3
mock_slicer = self.generate_mock_slicer(nslices)
mock_bunch = self.generate_mock_bunch()
slice_monitor = SliceMonitor(filename=self.s_fn, n_steps=self.n_turns,
slicer=mock_slicer, buffer_size=11, write_buffer_every=9,
slice_stats_to_store=['propertyA'],
bunch_stats_to_store=['mean_x', 'macrop'])
for i in xrange(self.n_turns):
slice_monitor.dump(mock_bunch)
s = hp.File(self.s_fn + '.h5')
sd = s['Slices']
sb = s['Bunch']
self.assertTrue(np.allclose(sb['mean_x'],
np.arange(start=1, stop=self.n_turns+0.5)))
self.assertTrue(np.allclose(sb['macrop'], 99*np.ones(self.n_turns)))
for k in xrange(nslices):
for j in xrange(self.n_turns):
self.assertTrue(np.allclose(sd['propertyA'][k,j],
k + (j+1)*1000), 'Slices part of SliceMonitor wrong')
def init_master(self):
# Manage multi-job operation
if pp.footprint_mode:
if pp.N_turns != pp.N_turns_target:
raise ValueError(
'In footprint mode you need to set N_turns_target=N_turns_per_run!'
)
import Save_Load_Status as SLS
SimSt = SLS.SimulationStatus(N_turns_per_run=pp.N_turns,
check_for_resubmit=True,
N_turns_target=pp.N_turns_target)
SimSt.before_simulation()
self.SimSt = SimSt
# generate a bunch
if pp.footprint_mode:
self.bunch = self.machine.generate_6D_Gaussian_bunch_matched(
n_macroparticles=pp.n_macroparticles_for_footprint_track,
intensity=pp.intensity,
epsn_x=pp.epsn_x,
epsn_y=pp.epsn_y,
sigma_z=pp.sigma_z)
elif SimSt.first_run:
self.bunch = self.machine.generate_6D_Gaussian_bunch_matched(
n_macroparticles=pp.n_macroparticles,
intensity=pp.intensity,
epsn_x=pp.epsn_x,
epsn_y=pp.epsn_y,
sigma_z=pp.sigma_z)
# compute initial displacements
inj_opt = self.machine.transverse_map.get_injection_optics()
sigma_x = np.sqrt(inj_opt['beta_x'] * pp.epsn_x /
self.machine.betagamma)
sigma_y = np.sqrt(inj_opt['beta_y'] * pp.epsn_y /
self.machine.betagamma)
x_kick = pp.x_kick_in_sigmas * sigma_x
y_kick = pp.y_kick_in_sigmas * sigma_y
# apply initial displacement
if not pp.footprint_mode:
self.bunch.x += x_kick
self.bunch.y += y_kick
print 'Bunch initialized.'
else:
print 'Loading bunch from file...'
with h5py.File(
'bunch_status_part%02d.h5' %
(SimSt.present_simulation_part - 1), 'r') as fid:
self.bunch = self.buffer_to_piece(
np.array(fid['bunch']).copy())
print 'Bunch loaded from file.'
# initial slicing
self.slicer = UniformBinSlicer(n_slices=pp.n_slices,
z_cuts=(-pp.z_cut, pp.z_cut))
# define a bunch monitor
from PyHEADTAIL.monitors.monitors import BunchMonitor
self.bunch_monitor = BunchMonitor(
'bunch_evolution_%02d' % self.SimSt.present_simulation_part,
pp.N_turns, {'Comment': 'PyHDTL simulation'},
write_buffer_every=3)
# define a slice monitor
from PyHEADTAIL.monitors.monitors import SliceMonitor
self.slice_monitor = SliceMonitor(
'slice_evolution_%02d' % self.SimSt.present_simulation_part,
pp.N_turns,
self.slicer, {'Comment': 'PyHDTL simulation'},
write_buffer_every=3)
#slice for the first turn
slice_obj_list = self.bunch.extract_slices(self.slicer)
pieces_to_be_treated = slice_obj_list
print 'N_turns', self.N_turns
if pp.footprint_mode:
self.recorded_particles = ParticleTrajectories(
pp.n_macroparticles_for_footprint_track, self.N_turns)
return pieces_to_be_treated
class Simulation(object):
def __init__(self):
self.N_turns = pp.N_turns
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)
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 init_master(self):
# Manage multi-job operation
if pp.footprint_mode:
if pp.N_turns != pp.N_turns_target:
raise ValueError(
'In footprint mode you need to set N_turns_target=N_turns_per_run!'
)
import Save_Load_Status as SLS
SimSt = SLS.SimulationStatus(N_turns_per_run=pp.N_turns,
check_for_resubmit=True,
N_turns_target=pp.N_turns_target)
SimSt.before_simulation()
self.SimSt = SimSt
# generate a bunch
if pp.footprint_mode:
self.bunch = self.machine.generate_6D_Gaussian_bunch_matched(
n_macroparticles=pp.n_macroparticles_for_footprint_track,
intensity=pp.intensity,
epsn_x=pp.epsn_x,
epsn_y=pp.epsn_y,
sigma_z=pp.sigma_z)
elif SimSt.first_run:
self.bunch = self.machine.generate_6D_Gaussian_bunch_matched(
n_macroparticles=pp.n_macroparticles,
intensity=pp.intensity,
epsn_x=pp.epsn_x,
epsn_y=pp.epsn_y,
sigma_z=pp.sigma_z)
# compute initial displacements
inj_opt = self.machine.transverse_map.get_injection_optics()
sigma_x = np.sqrt(inj_opt['beta_x'] * pp.epsn_x /
self.machine.betagamma)
sigma_y = np.sqrt(inj_opt['beta_y'] * pp.epsn_y /
self.machine.betagamma)
x_kick = pp.x_kick_in_sigmas * sigma_x
y_kick = pp.y_kick_in_sigmas * sigma_y
# apply initial displacement
if not pp.footprint_mode:
self.bunch.x += x_kick
self.bunch.y += y_kick
print 'Bunch initialized.'
else:
print 'Loading bunch from file...'
with h5py.File(
'bunch_status_part%02d.h5' %
(SimSt.present_simulation_part - 1), 'r') as fid:
self.bunch = self.buffer_to_piece(
np.array(fid['bunch']).copy())
print 'Bunch loaded from file.'
# initial slicing
self.slicer = UniformBinSlicer(n_slices=pp.n_slices,
z_cuts=(-pp.z_cut, pp.z_cut))
# define a bunch monitor
from PyHEADTAIL.monitors.monitors import BunchMonitor
self.bunch_monitor = BunchMonitor(
'bunch_evolution_%02d' % self.SimSt.present_simulation_part,
pp.N_turns, {'Comment': 'PyHDTL simulation'},
write_buffer_every=3)
# define a slice monitor
from PyHEADTAIL.monitors.monitors import SliceMonitor
self.slice_monitor = SliceMonitor(
'slice_evolution_%02d' % self.SimSt.present_simulation_part,
pp.N_turns,
self.slicer, {'Comment': 'PyHDTL simulation'},
write_buffer_every=3)
#slice for the first turn
slice_obj_list = self.bunch.extract_slices(self.slicer)
pieces_to_be_treated = slice_obj_list
print 'N_turns', self.N_turns
if pp.footprint_mode:
self.recorded_particles = ParticleTrajectories(
pp.n_macroparticles_for_footprint_track, 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
self.bunch = sum(pieces_treated)
#finalize present turn (with non parallel part, e.g. synchrotron motion)
for ele in self.non_parallel_part:
ele.track(self.bunch)
# save results
#print '%s Turn %d'%(time.strftime("%d/%m/%Y %H:%M:%S", time.localtime()), i_turn)
self.bunch_monitor.dump(self.bunch)
self.slice_monitor.dump(self.bunch)
# prepare next turn (re-slice)
new_pieces_to_be_treated = self.bunch.extract_slices(self.slicer)
# order reset of all clouds
orders_to_pass = ['reset_clouds']
if pp.footprint_mode:
self.recorded_particles.dump(self.bunch)
# check if simulation has to be stopped
# 1. for beam losses
if not pp.footprint_mode and self.bunch.macroparticlenumber < pp.sim_stop_frac * pp.n_macroparticles:
orders_to_pass.append('stop')
self.SimSt.check_for_resubmit = False
print 'Stop simulation due to beam losses.'
# 2. for the emittance growth
if pp.flag_check_emittance_growth:
epsn_x_max = (pp.epsn_x) * (1 + pp.epsn_x_max_growth_fraction)
epsn_y_max = (pp.epsn_y) * (1 + pp.epsn_y_max_growth_fraction)
if not pp.footprint_mode and (self.bunch.epsn_x() > epsn_x_max
or self.bunch.epsn_y() > epsn_y_max):
orders_to_pass.append('stop')
self.SimSt.check_for_resubmit = False
print 'Stop simulation due to emittance growth.'
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):
if pp.footprint_mode:
# Tunes
import NAFFlib
print 'NAFFlib spectral analysis...'
qx_i = np.empty_like(self.recorded_particles.x_i[:, 0])
qy_i = np.empty_like(self.recorded_particles.x_i[:, 0])
for ii in range(len(qx_i)):
qx_i[ii] = NAFFlib.get_tune(self.recorded_particles.x_i[ii] +
1j *
self.recorded_particles.xp_i[ii])
qy_i[ii] = NAFFlib.get_tune(self.recorded_particles.y_i[ii] +
1j *
self.recorded_particles.yp_i[ii])
print 'NAFFlib spectral analysis done.'
# Save
import h5py
dict_beam_status = {\
'x_init': np.squeeze(self.recorded_particles.x_i[:,0]),
'xp_init': np.squeeze(self.recorded_particles.xp_i[:,0]),
'y_init': np.squeeze(self.recorded_particles.y_i[:,0]),
'yp_init': np.squeeze(self.recorded_particles.yp_i[:,0]),
'z_init': np.squeeze(self.recorded_particles.z_i[:,0]),
'qx_i': qx_i,
'qy_i': qy_i,
'x_centroid': np.mean(self.recorded_particles.x_i, axis=1),
'y_centroid': np.mean(self.recorded_particles.y_i, axis=1)}
with h5py.File('footprint.h5', 'w') as fid:
for kk in dict_beam_status.keys():
fid[kk] = dict_beam_status[kk]
else:
#save data for multijob operation and launch new job
import h5py
with h5py.File(
'bunch_status_part%02d.h5' %
(self.SimSt.present_simulation_part), 'w') as fid:
fid['bunch'] = self.piece_to_buffer(self.bunch)
if not self.SimSt.first_run:
os.system('rm bunch_status_part%02d.h5' %
(self.SimSt.present_simulation_part - 1))
self.SimSt.after_simulation()
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
def run():
# HELPERS
def read_all_data(bfile, sfile, pfile):
bunchdata = hp.File(bfile + '.h5')
slicedata = hp.File(sfile + '.h5')
particledata = hp.File(pfile + '.h5part')
# Bunchdata
bdata = bunchdata['Bunch']
n_turns = len(bdata['mean_x'])
_ = np.empty(n_turns)
for key in bdata.keys():
_[:] = bdata[key][:]
# Slicedata
sdata = slicedata['Slices']
sbdata = slicedata['Bunch']
n_turns = len(sbdata['mean_x'])
_ = np.empty(n_turns)
for key in sbdata.keys():
_[:] = sbdata[key][:]
n_slices, n_turns = sdata['mean_x'].shape
_ = np.empty((n_slices, n_turns))
for key in sdata.keys():
_[:,:] = sdata[key][:,:]
# Particledata
pdata = particledata['Step#0']
n_particles = len(pdata['x'])
n_steps = len(particledata.keys())
_ = np.empty(n_particles)
for i in xrange(n_steps):
step = 'Step#%d' % i
for key in particledata[step].keys():
_[:] = particledata[step][key][:]
bunchdata.close()
slicedata.close()
particledata.close()
def read_n_plot_data(bfile, sfile, pfile):
bunchdata = hp.File(bfile + '.h5')
slicedata = hp.File(sfile + '.h5')
particledata = hp.File(pfile + '.h5part')
fig = plt.figure(figsize=(16, 16))
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
ax1.plot(bunchdata['Bunch']['mean_x'][:])
ax2.plot(slicedata['Slices']['mean_x'][:,:])
ax3.plot(particledata['Step#0']['x'][:])
#ax2.plot(slicedata[])
plt.show()
bunchdata.close()
slicedata.close()
particledata.close()
def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, alpha_0, Q_s, R):
intensity = 1.05e11
sigma_z = 0.059958
gamma = 3730.26
eta = alpha_0 - 1. / gamma**2
gamma_t = 1. / np.sqrt(alpha_0)
p0 = np.sqrt(gamma**2 - 1) * m_p * c
beta_z = eta * R / Q_s
epsn_x = 3.75e-6 # [m rad]
epsn_y = 3.75e-6 # [m rad]
epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) # WITH OR WITHOUT 4 PIjQuery202047649151738733053_1414145430832?
bunch = generators.generate_Gaussian6DTwiss(
macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
gamma=gamma, mass=m_p, circumference=C,
alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
beta_z=beta_z, epsn_z=epsn_z)
return bunch
# In[4]:
# Basic parameters.
n_turns = 2
n_segments = 5
n_macroparticles = 500
Q_x = 64.28
Q_y = 59.31
Q_s = 0.0020443
C = 26658.883
R = C / (2.*np.pi)
alpha_x_inj = 0.
alpha_y_inj = 0.
beta_x_inj = 66.0064
beta_y_inj = 71.5376
alpha_0 = 0.0003225
# ##### Things tested: - Instantiation of the three monitors BunchMonitor, SliceMonitor, ParticleMonitor. - dump(beam) method for all the three. - read data from file. Plot example data from Bunch-, Slice- and Particle-Monitors. - SliceMonitor: does it handle/request slice_sets correctly? - Buffers are on for Bunch- and SliceMonitors. Look at one of the files in hdfview to check the units, attributes, ...
# In[5]:
# Parameters for transverse map.
s = np.arange(0, n_segments + 1) * C / n_segments
alpha_x = alpha_x_inj * np.ones(n_segments)
beta_x = beta_x_inj * np.ones(n_segments)
D_x = np.zeros(n_segments)
alpha_y = alpha_y_inj * np.ones(n_segments)
beta_y = beta_y_inj * np.ones(n_segments)
D_y = np.zeros(n_segments)
# In[6]:
# Instantiate BunchMonitor, SliceMonitor and ParticleMonitor and dump data to file.
bunch = generate_bunch(
n_macroparticles, alpha_x_inj, alpha_y_inj, beta_x_inj, beta_y_inj,
alpha_0, Q_s, R)
trans_map = TransverseMap(
s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y, Q_x, Q_y)
# Slicer config for SliceMonitor.
unibin_slicer = UniformBinSlicer(n_slices=10, n_sigma_z=None, z_cuts=None)
# Monitors
bunch_filename = 'bunch_mon'
slice_filename = 'slice_mon'
particle_filename = 'particle_mon'
bunch_monitor = BunchMonitor(filename=bunch_filename, n_steps=n_turns, parameters_dict={'Q_x': Q_x},
write_buffer_every=20)
slice_monitor = SliceMonitor(
filename=slice_filename, n_steps=n_turns, slicer=unibin_slicer, parameters_dict={'Q_x': Q_x},
write_buffer_every=20)
particle_monitor = ParticleMonitor(filename=particle_filename, stride=10, parameters_dict={'Q_x': Q_x})
arrays_dict = {}
map_ = trans_map
for i in xrange(n_turns):
for m_ in map_:
m_.track(bunch)
bunch_monitor.dump(bunch)
slice_monitor.dump(bunch)
slice_set_pmon = bunch.get_slices(unibin_slicer)
arrays_dict.update({'slidx': slice_set_pmon.slice_index_of_particle, 'zz': bunch.z})
particle_monitor.dump(bunch, arrays_dict)
read_all_data(bunch_filename, slice_filename, particle_filename)
os.remove(bunch_filename + '.h5')
os.remove(slice_filename + '.h5')
os.remove(particle_filename + '.h5part')
def run():
# HELPERS
def read_all_data(bfile, sfile, pfile):
bunchdata = hp.File(bfile + '.h5')
slicedata = hp.File(sfile + '.h5')
particledata = hp.File(pfile + '.h5part')
# Bunchdata
bdata = bunchdata['Bunch']
n_turns = len(bdata['mean_x'])
_ = np.empty(n_turns)
for key in list(bdata.keys()):
_[:] = bdata[key][:]
# Slicedata
sdata = slicedata['Slices']
sbdata = slicedata['Bunch']
n_turns = len(sbdata['mean_x'])
_ = np.empty(n_turns)
for key in list(sbdata.keys()):
_[:] = sbdata[key][:]
n_slices, n_turns = sdata['mean_x'].shape
_ = np.empty((n_slices, n_turns))
for key in list(sdata.keys()):
_[:, :] = sdata[key][:, :]
# Particledata
pdata = particledata['Step#0']
n_particles = len(pdata['x'])
n_steps = len(list(particledata.keys()))
_ = np.empty(n_particles)
for i in range(n_steps):
step = 'Step#%d' % i
for key in list(particledata[step].keys()):
_[:] = particledata[step][key][:]
bunchdata.close()
slicedata.close()
particledata.close()
def read_n_plot_data(bfile, sfile, pfile):
bunchdata = hp.File(bfile + '.h5')
slicedata = hp.File(sfile + '.h5')
particledata = hp.File(pfile + '.h5part')
fig = plt.figure(figsize=(16, 16))
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
ax1.plot(bunchdata['Bunch']['mean_x'][:])
ax2.plot(slicedata['Slices']['mean_x'][:, :])
ax3.plot(particledata['Step#0']['x'][:])
#ax2.plot(slicedata[])
plt.show()
bunchdata.close()
slicedata.close()
particledata.close()
def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y,
alpha_0, Q_s, R):
intensity = 1.05e11
sigma_z = 0.059958
gamma = 3730.26
eta = alpha_0 - 1. / gamma**2
gamma_t = 1. / np.sqrt(alpha_0)
p0 = np.sqrt(gamma**2 - 1) * m_p * c
beta_z = eta * R / Q_s
epsn_x = 3.75e-6 # [m rad]
epsn_y = 3.75e-6 # [m rad]
epsn_z = 4 * np.pi * sigma_z**2 * p0 / (
beta_z * e
) # WITH OR WITHOUT 4 PIjQuery202047649151738733053_1414145430832?
bunch = generators.generate_Gaussian6DTwiss(
macroparticlenumber=n_macroparticles,
intensity=intensity,
charge=e,
gamma=gamma,
mass=m_p,
circumference=C,
alpha_x=alpha_x,
beta_x=beta_x,
epsn_x=epsn_x,
alpha_y=alpha_y,
beta_y=beta_y,
epsn_y=epsn_y,
beta_z=beta_z,
epsn_z=epsn_z)
return bunch
# In[4]:
# Basic parameters.
n_turns = 2
n_segments = 5
n_macroparticles = 500
Q_x = 64.28
Q_y = 59.31
Q_s = 0.0020443
C = 26658.883
R = C / (2. * np.pi)
alpha_x_inj = 0.
alpha_y_inj = 0.
beta_x_inj = 66.0064
beta_y_inj = 71.5376
alpha_0 = 0.0003225
# ##### Things tested: - Instantiation of the three monitors BunchMonitor, SliceMonitor, ParticleMonitor. - dump(beam) method for all the three. - read data from file. Plot example data from Bunch-, Slice- and Particle-Monitors. - SliceMonitor: does it handle/request slice_sets correctly? - Buffers are on for Bunch- and SliceMonitors. Look at one of the files in hdfview to check the units, attributes, ...
# In[5]:
# Parameters for transverse map.
s = np.arange(0, n_segments + 1) * C / n_segments
alpha_x = alpha_x_inj * np.ones(n_segments)
beta_x = beta_x_inj * np.ones(n_segments)
D_x = np.zeros(n_segments)
alpha_y = alpha_y_inj * np.ones(n_segments)
beta_y = beta_y_inj * np.ones(n_segments)
D_y = np.zeros(n_segments)
# In[6]:
# Instantiate BunchMonitor, SliceMonitor and ParticleMonitor and dump data to file.
bunch = generate_bunch(n_macroparticles, alpha_x_inj, alpha_y_inj,
beta_x_inj, beta_y_inj, alpha_0, Q_s, R)
trans_map = TransverseMap(s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y,
Q_x, Q_y)
# Slicer config for SliceMonitor.
unibin_slicer = UniformBinSlicer(n_slices=10, n_sigma_z=None, z_cuts=None)
# Monitors
bunch_filename = 'bunch_mon'
slice_filename = 'slice_mon'
particle_filename = 'particle_mon'
bunch_monitor = BunchMonitor(filename=bunch_filename,
n_steps=n_turns,
parameters_dict={'Q_x': Q_x},
write_buffer_every=20)
slice_monitor = SliceMonitor(filename=slice_filename,
n_steps=n_turns,
slicer=unibin_slicer,
parameters_dict={'Q_x': Q_x},
write_buffer_every=20)
particle_monitor = ParticleMonitor(filename=particle_filename,
stride=10,
parameters_dict={'Q_x': Q_x})
arrays_dict = {}
map_ = trans_map
for i in range(n_turns):
for m_ in map_:
m_.track(bunch)
bunch_monitor.dump(bunch)
slice_monitor.dump(bunch)
slice_set_pmon = bunch.get_slices(unibin_slicer)
arrays_dict.update({
'slidx': slice_set_pmon.slice_index_of_particle,
'zz': bunch.z
})
particle_monitor.dump(bunch, arrays_dict)
read_all_data(bunch_filename, slice_filename, particle_filename)
os.remove(bunch_filename + '.h5')
os.remove(slice_filename + '.h5')
os.remove(particle_filename + '.h5part')
def run(job_id,accQ_y):
it = job_id
# SIMULATION PARAMETERS
# =====================
# Simulation parameters
n_turns = 10000
n_macroparticles = 100000 # per bunch
# MACHINE PARAMETERS
# ==================
intensity = 2e13 # protons
Ek = 71e6 # Kinetic energy [eV]
p0 = np.sqrt((m_p_MeV+Ek)**2 - m_p_MeV**2) * e /c
print('Beam kinetic energy: ' + str(Ek*1e-6) + ' MeV')
print('Beam momentum: ' + str(p0*1e-6*c/e) + ' MeV/c')
accQ_x = 4.31 # Horizontal tune
# accQ_y = 3.80 # Vertical tune is an input argument
Q_s=0.02 # Longitudinal tune
chroma=-1.4 # Chromaticity
alpha = 5.034**-2 # momentum compaction
circumference = 160. # [meters]
# Approximated average beta functions (lumped wake normalizations)
beta_x = circumference / (2.*np.pi*accQ_x)
beta_y = circumference / (2.*np.pi*accQ_y)
# Harmonic number for RF
h_RF = 2
h_bunch = h_RF
V_RF = 2e5
p_increment = 0.
dphi_RF = 0.
longitudinal_mode = 'linear'
optics_mode = 'smooth'
n_segments = 1
s = None
alpha_x = None
alpha_y = None
beta_x = circumference / (2.*np.pi*accQ_x)
beta_y = circumference / (2.*np.pi*accQ_y)
D_x = 0
D_y = 0
charge = e
mass = m_p
name = None
app_x = 0
app_y = 0
app_xy = 0
# Creates PyHEADTAIL object for the synchotron
machine = Synchrotron(optics_mode=optics_mode, circumference=circumference,
n_segments=n_segments, s=s, name=name,
alpha_x=alpha_x, beta_x=beta_x, D_x=D_x,
alpha_y=alpha_y, beta_y=beta_y, D_y=D_y,
accQ_x=accQ_x, accQ_y=accQ_y, Qp_x=chroma, Qp_y=chroma,
app_x=app_x, app_y=app_y, app_xy=app_xy,
alpha_mom_compaction=alpha, longitudinal_mode=longitudinal_mode,
h_RF=np.atleast_1d(h_RF), V_RF=np.atleast_1d(V_RF),
dphi_RF=np.atleast_1d(dphi_RF), p0=p0, p_increment=p_increment,
charge=charge, mass=mass)
print()
print('machine.beta: ')
print(machine.beta)
print()
epsn_x = 300e-6
epsn_y = 300e-6
sigma_z = 450e-9*c*machine.beta/4.
allbunches = machine.generate_6D_Gaussian_bunch(n_macroparticles, intensity, epsn_x, epsn_y, sigma_z)
# Slicer object, which used for wakefields and slice monitors
slicer = UniformBinSlicer(50, z_cuts=(-4.*sigma_z, 4.*sigma_z))
# WAKE FIELDS
# ===========
# Length of the wake function in turns, wake
n_turns_wake = 150
# Parameters for a resonator
# frequency is in the units of (mode-Q_frac), where
# mode: integer number of coupled bunch mode (1 matches to the observations)
# Q_frac: resonance fractional tune
f_r = (1-0.83)*1./(circumference/(c*machine.beta))
Q = 15
R = 1.0e6
# Renator wake object, which is added to the one turn map
wakes = CircularResonator(R, f_r, Q, n_turns_wake=n_turns_wake)
wake_field = WakeField(slicer, wakes)
machine.one_turn_map.append(wake_field)
# CREATE MONITORS
# ===============
simulation_parameters_dict = {'gamma' : machine.gamma,\
'intensity' : intensity,\
'Qx' : accQ_x,\
'Qy' : accQ_y,\
'Qs' : Q_s,\
'beta_x' : beta_x,\
'beta_y' : beta_y,\
# 'beta_z' : bucket.beta_z,\
'epsn_x' : epsn_x,\
'epsn_y' : epsn_y,\
'sigma_z' : sigma_z,\
}
# Bunch monitor strores bunch average positions for all the bunches
bunchmonitor = BunchMonitor(outputpath + '/bunchmonitor_{:04d}'.format(it), n_turns,
simulation_parameters_dict,
write_buffer_every=32, buffer_size=32)
# Slice monitors saves slice-by-slice data for each bunch
slicemonitor = SliceMonitor(
outputpath + '/slicemonitor_{:01d}_{:04d}'.format(0,it),
16, slicer,
simulation_parameters_dict, write_buffer_every=16, buffer_size=16)
# Counter for a number of turns stored to slice monitors
s_cnt = 0
# TRACKING LOOP
# =============
monitor_active = False
print('\n--> Begin tracking...\n')
for i in range(n_turns):
t0 = time.clock()
# Tracks beam through the one turn map simulation map
machine.track(allbunches)
# Stores bunch mean coordinate values
bunchmonitor.dump(allbunches)
# If the total oscillation amplitude of bunches exceeds the threshold
# or the simulation is running on the last turns, triggers the slice
# monitors for headtail motion data
if (allbunches.mean_x() > 1e0 or allbunches.mean_y() > 1e0 or i > (n_turns-64)):
monitor_active = True
# saves slice monitor data if monitors are activated and less than
# 64 turns have been stored
if monitor_active and s_cnt<64:
slicemonitor.dump(allbunches)
s_cnt += 1
elif s_cnt == 64:
break
# If this script is runnin on the first processor, prints the current
# bunch coordinates and emittances
if (i%100 == 0):
print('{:4d} \t {:+3e} \t {:+3e} \t {:+3e} \t {:3e} \t {:3e} \t {:3f} \t {:3f} \t {:3f} \t {:3s}'.format(i, allbunches.mean_x(), allbunches.mean_y(), allbunches.mean_z(), allbunches.epsn_x(), allbunches.epsn_y(), allbunches.epsn_z(), allbunches.sigma_z(), allbunches.sigma_dp(), str(time.clock() - t0)))
def run(intensity, chroma=0, i_oct=0):
'''Arguments:
- intensity: integer number of charges in beam
- chroma: first-order chromaticity Q'_{x,y}, identical
for both transverse planes
- i_oct: octupole current in A (positive i_oct means
LOF = i_oct > 0 and LOD = -i_oct < 0)
'''
# BEAM AND MACHINE PARAMETERS
# ============================
from LHC import LHC
# energy set above will enter get_nonlinear_params p0
assert machine_configuration == 'LHC_6.5TeV_collision_2016'
machine = LHC(n_segments=1,
machine_configuration=machine_configuration,
**get_nonlinear_params(chroma=chroma, i_oct=i_oct))
# BEAM
# ====
epsn_x = 3.e-6 # normalised horizontal emittance
epsn_y = 3.e-6 # normalised vertical emittance
sigma_z = 1.2e-9 * machine.beta * c / 4. # RMS bunch length in meters
bunch = machine.generate_6D_Gaussian_bunch_matched(n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
print("\n--> Bunch length and emittance: {:g} m, {:g} eVs.".format(
bunch.sigma_z(), bunch.epsn_z()))
# CREATE BEAM SLICERS
# ===================
slicer_for_slicemonitor = UniformBinSlicer(50,
z_cuts=(-3 * sigma_z,
3 * sigma_z))
slicer_for_wakefields = UniformBinSlicer(500,
z_cuts=(-3 * sigma_z,
3 * sigma_z))
# CREATE WAKES
# ============
wake_table1 = WakeTable(
wakefile,
[
'time',
'dipole_x',
'dipole_y',
'quadrupole_x',
'quadrupole_y',
# 'noquadrupole_x', 'noquadrupole_y',
'dipole_xy',
'dipole_yx',
# 'nodipole_xy', 'nodipole_yx',
])
wake_field = WakeField(slicer_for_wakefields, wake_table1)
# CREATE DAMPER
# =============
dampingrate = 50
damper = TransverseDamper(dampingrate, dampingrate)
# CREATE MONITORS
# ===============
try:
bucket = machine.longitudinal_map.get_bucket(bunch)
except AttributeError:
bucket = machine.rfbucket
simulation_parameters_dict = {
'gamma': machine.gamma,
'intensity': intensity,
'Qx': machine.Q_x,
'Qy': machine.Q_y,
'Qs': bucket.Q_s,
'beta_x': bunch.beta_Twiss_x(),
'beta_y': bunch.beta_Twiss_y(),
'beta_z': bucket.beta_z,
'epsn_x': bunch.epsn_x(),
'epsn_y': bunch.epsn_y(),
'sigma_z': bunch.sigma_z(),
}
bunchmonitor = BunchMonitor(
outputpath + '/bunchmonitor_{:04d}_chroma={:g}'.format(it, chroma),
n_turns,
simulation_parameters_dict,
write_buffer_every=100)
slicemonitor = SliceMonitor(
outputpath + '/slicemonitor_{:04d}_chroma={:g}'.format(it, chroma),
n_turns_slicemon,
slicer_for_slicemonitor,
simulation_parameters_dict,
write_buffer_every=1,
buffer_size=n_turns_slicemon)
# TRACKING LOOP
# =============
machine.one_turn_map.append(damper)
machine.one_turn_map.append(wake_field)
# for slice statistics monitoring:
s_cnt = 0
monitorswitch = False
print('\n--> Begin tracking...\n')
# GO!!!
for i in range(n_turns):
t0 = time.clock()
# track the beam around the machine for one turn:
machine.track(bunch)
ex, ey, ez = bunch.epsn_x(), bunch.epsn_y(), bunch.epsn_z()
mx, my, mz = bunch.mean_x(), bunch.mean_y(), bunch.mean_z()
# monitor the bunch statistics (once per turn):
bunchmonitor.dump(bunch)
# if the centroid becomes unstable (>1cm motion)
# then monitor the slice statistics:
if not monitorswitch:
if mx > 1e-2 or my > 1e-2 or i > n_turns - n_turns_slicemon:
print("--> Activate slice monitor")
monitorswitch = True
else:
if s_cnt < n_turns_slicemon:
slicemonitor.dump(bunch)
s_cnt += 1
# stop the tracking as soon as we have not-a-number values:
if not all(np.isfinite(c) for c in [ex, ey, ez, mx, my, mz]):
print('*** STOPPING SIMULATION: non-finite bunch stats!')
break
# print status all 1000 turns:
if i % 1000 == 0:
t1 = time.clock()
print('Emittances: ({:.3g}, {:.3g}, {:.3g}) '
'& Centroids: ({:.3g}, {:.3g}, {:.3g})'
'@ turn {:d}, {:g} ms, {:s}'.format(
ex, ey, ez, mx, my, mz, i, (t1 - t0) * 1e3,
time.strftime("%d/%m/%Y %H:%M:%S", time.localtime())))
print('\n*** Successfully completed!')