def test_transverse_damper(self):
'''
Track through a transverse damper
'''
bunch_cpu = self.create_all1_bunch()
bunch_gpu = self.create_all1_bunch()
dampingrate_x = 0.01
dampingrate_y = 0.05
damp = TransverseDamper(dampingrate_x, dampingrate_y)
self.assertTrue(self._track_cpu_gpu([damp], bunch_cpu, bunch_gpu),
'Tracking TransverseDamper CPU/GPU differs')
def _install_damper(self):
pp = self.pp
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)
self.n_non_parallelizable += 1
self.dampers = [damper]
else:
self.dampers = []
# PARAMETERS FOR LONGITUDINAL MAP # ======================= alpha = 1.9e-3 Q_s = 0.0035 h1, h2 = 4620, 9240 V1, V2 = 4.5e6, 0e6 dphi1, dphi2 = 0, np.pi p_increment = 0 * e/c * circumference/(beta*c) #longitudinal_map = LinearMap([alpha], circumference, Q_s) # CREATE DAMPER # ============= dampingrate_y = 10 #40 damper = TransverseDamper(dampingrate_x, dampingrate_y) # CREATE BEAM # =========== macroparticlenumber = 100000 charge = e mass = m_p intensity = 1.5e11 R = circumference/(2*np.pi) eta = alpha-1/gamma**2 beta_z = np.abs(eta)*R/Q_s epsn_x = 2e-6 epsn_y = 2e-6 epsn_z = 2.5
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 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]))
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!')
