def test_wakefield_platesresonator(self):
'''
Track through a ParallelPlatesResonator wakefield
'''
Dx = np.append(np.linspace(0., 20., self.nsegments), [0])
# add some dispersion/alpha
lhc = m.LHC(n_segments=self.nsegments,
machine_configuration='450GeV',
app_x=1e-9,
app_y=2e-9,
app_xy=-1.5e-11,
chromaticity_on=False,
amplitude_detuning_on=True,
alpha_x=1.2 * np.ones(self.nsegments),
D_x=Dx,
printer=SilentPrinter())
self.n_macroparticles = 200000
bunch_cpu = self.create_lhc_bunch(lhc) #self.create_gaussian_bunch()
bunch_gpu = self.create_lhc_bunch(lhc) #self.create_gaussian_bunch()
n_slices = 50 #5
frequency = 8e8 #1e9
R_shunt = 23e3 # [Ohm]
Q = 1.
unif_bin_slicer = UniformBinSlicer(n_slices=n_slices, n_sigma_z=1)
#res = CircularResonator(R_shunt=R_shunt, frequency=frequency, Q=Q)
res = ParallelPlatesResonator(R_shunt=R_shunt,
frequency=frequency,
Q=Q,
printer=SilentPrinter())
wake_field = WakeField(unif_bin_slicer, res)
self.assertTrue(
self._track_cpu_gpu([wake_field], bunch_cpu, bunch_gpu),
'Tracking Wakefield CircularResonator CPU/GPU differs')
def test_wakefield_wakefile(self):
'''
Track an LHC bunch and a LHC wakefield
'''
wakefile = 'autoruntests/wake_table.dat' #'./wakeforhdtl_PyZbase_Allthemachine_450GeV_B1_LHC_inj_450GeV_B1.dat'
Qp_x, Qp_y = 1., 1.
Q_s = 0.0049
n_macroparticles = 10
intensity = 1e11
longitudinal_focusing = 'linear'
machine = m.LHC(n_segments=1,
machine_configuration='450GeV',
longitudinal_focusing=longitudinal_focusing,
Qp_x=[Qp_x],
Qp_y=[Qp_y],
Q_s=Q_s,
beta_x=[65.9756],
beta_y=[71.5255],
printer=SilentPrinter())
epsn_x = 3.5e-6
epsn_y = 3.5e-6
sigma_z = 1.56e-9 * c / 4.
np.random.seed(0)
bunch_cpu = machine.generate_6D_Gaussian_bunch(n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
np.random.seed(0)
bunch_gpu = machine.generate_6D_Gaussian_bunch(n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
n_slices_wakefields = 55
n_sigma_z_wakefields = 3
slicer_for_wakefields_cpu = UniformBinSlicer(
n_slices_wakefields, n_sigma_z=n_sigma_z_wakefields)
wake_components = [
'time', 'dipole_x', 'dipole_y', 'no_quadrupole_x',
'no_quadrupole_y', 'no_dipole_xy', 'no_dipole_yx'
]
wake_table_cpu = WakeTable(wakefile,
wake_components,
printer=SilentPrinter())
wake_field_cpu = WakeField(slicer_for_wakefields_cpu, wake_table_cpu)
# also checked for 100 turns!
self.assertTrue(
self._track_cpu_gpu([wake_field_cpu],
bunch_cpu,
bunch_gpu,
nturns=2),
'Tracking through WakeField(waketable) differs')
)
ww1 = WakeTable(
wakefile1,
['time', 'dipole_x', 'dipole_y', 'quadrupole_x', 'quadrupole_y'],
n_turns_wake=n_turns_wake)
# only dipolar kick
#my_length = len(ww1.wake_table['quadrupole_x'])
#ww1.wake_table['quadrupole_x'] = np.zeros(my_length)
#ww1.wake_table['quadrupole_y'] = np.zeros(my_length)
# only quadrupolar kick
#my_length = len(ww1.wake_table['dipole_x'])
#ww1.wake_table['dipole_x'] = np.zeros(my_length)
#ww1.wake_table['dipole_y'] = np.zeros(my_length)
wake_field_complete = WakeField(slicer_for_wakefields,
ww1) #, beta_x=beta_x, beta_y=beta_y)
# CREATE TRANSVERSE AND LONGITUDINAL MAPS
# =======================================
scale_factor = 2 * bunch.p0 # scale the detuning coefficients in pyheadtail units
transverse_map = TransverseMap(
s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y, Q_x, Q_y, [
Chromaticity(Qp_x, Qp_y),
AmplitudeDetuning(app_x * scale_factor, app_y * scale_factor,
app_xy * scale_factor)
])
longitudinal_map = LinearMap([alpha], circumference, Q_s)
# ======================================================================
# SET UP ACCELERATOR MAP AND START TRACKING
def run(job_id, accQ_y):
it = job_id
# SIMULATION PARAMETERS
# =====================
# Simulation parameters
n_turns = 10000
n_macroparticles = 100000 # per bunch
# MACHINE PARAMETERS
# ==================
intensity = 1e13 # protons
# intensity = 2*4e12 # protons
E0 = 71e6 # Kinetic energy [eV]
p0 = np.sqrt((m_p_MeV + E0)**2 - m_p_MeV**2) * e / c
print('Beam kinetic energy: ' + str(E0 * 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
chroma = -1.4 # Chromaticity
alpha = 5.034**-2 # momentum compaction factor
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] # a list of harmonic number for RF
V_RF = [5e3 * 2] # a list of RF voltages
p_increment = 0.
dphi_RF = [np.pi] # a list of RF phases
# dphi_RF = 0.
# non-linear longitudinal mode includes RF, otherwise linear needs synhotron tune Q_s
longitudinal_mode = 'non-linear'
# Q_s=0.02 # Longitudinal tune
optics_mode = 'smooth'
n_segments = 1
s = None
alpha_x = None
alpha_y = None
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('')
print('')
print('machine.gamma: ')
print(machine.gamma)
print('')
epsn_x = 300e-6
epsn_y = 300e-6
sigma_z = 15 # bunch length in meters to be matched to the bucket
# Creates transverse macroparticle distribution
allbunches = machine.generate_6D_Gaussian_bunch(n_macroparticles,
intensity, epsn_x, epsn_y,
sigma_z)
# Creates longitudinal macroparticle distribution
rfb = RFBucket(circumference, machine.gamma, m_p, e, [alpha], 0., h_RF,
V_RF, dphi_RF)
rfb_matcher = RFBucketMatcher(rfb, WaterbagDistribution, sigma_z=sigma_z)
rfb_matcher.integrationmethod = 'cumtrapz'
z, dp, _, _ = rfb_matcher.generate(n_macroparticles)
np.copyto(allbunches.z, z)
np.copyto(allbunches.dp, dp)
# Slicer object, which used for wakefields and slice monitors
slicer = UniformBinSlicer(75, z_cuts=(-2. * sigma_z, 2. * 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),
60,
slicer,
simulation_parameters_dict,
write_buffer_every=60,
buffer_size=60)
# 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() > 1e-1 or allbunches.mean_y() > 1e-1 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)))
# ==========
n_turns_wake = 1 # for the moment we consider that the wakefield decays after 1 turn
#wakefile1 = ('/afs/cern.ch/work/n/natriant/private/pyheadtail_example_crabcavity/wakefields/newkickers_Q26_2018_modified.txt')
wakefile1 = ('/afs/cern.ch/work/n/natriant/private/pyheadtail_example_crabcavity/wakefields/SPS_complete_wake_model_2018_Q26.txt')
ww1 = WakeTable(wakefile1, ['time', 'dipole_x', 'dipole_y', 'quadrupole_x', 'quadrupole_y'], n_turns_wake=n_turns_wake)
# only dipolar kick
#my_length = len(ww1.wake_table['quadrupole_x'])
#ww1.wake_table['quadrupole_x'] = np.zeros(my_length)
#ww1.wake_table['quadrupole_y'] = np.zeros(my_length)
# only quadrupolar kick
#my_length = len(ww1.wake_table['dipole_x'])
#ww1.wake_table['dipole_x'] = np.zeros(my_length)
#ww1.wake_table['dipole_y'] = np.zeros(my_length)
wake_field_kicker = WakeField(slicer_for_wakefields, ww1)#, beta_x=beta_x, beta_y=beta_y)
# CREATE TRANSVERSE AND LONGITUDINAL MAPS
# =======================================
scale_factor = 2*bunch.p0 # scale the detuning coefficients in pyheadtail units
transverse_map = TransverseMap(s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y, Q_x, Q_y,
[Chromaticity(Qp_x, Qp_y),
AmplitudeDetuning(app_x*scale_factor, app_y*scale_factor, app_xy*scale_factor)])
longitudinal_map = LinearMap([alpha], circumference, Q_s)
# ======================================================================
# SET UP ACCELERATOR MAP AND START TRACKING
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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-injection'
machine = LHC(n_segments=1,
machine_configuration=machine_configuration,
**get_nonlinear_params(chroma=chroma,
i_oct=i_oct,
p0=0.45e12 * e / c))
# 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(n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z,
matched=True)
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(
50,
z_cuts=(-3 * sigma_z, 3 * sigma_z),
circumference=machine.circumference,
h_bunch=machine.h_bunch)
print("Slice")
# 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, mpi=True)
# CREATE DAMPER
# =============
dampingtime = 50.
gain = 2. / dampingtime
damper = IdealBunchFeedback(gain)
# 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_to_file_every=512,
buffer_size=4096)
slicemonitor = SliceMonitor(
outputpath + '/slicemonitor_{:04d}_chroma={:g}'.format(it, chroma),
n_turns_slicemon,
slicer_for_slicemonitor,
simulation_parameters_dict,
write_buffer_to_file_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 % 100 == 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!')
def run():
def track_n_save(bunch, map_):
mean_x = np.empty(n_turns)
mean_y = np.empty(n_turns)
sigma_z = np.empty(n_turns)
for i in xrange(n_turns):
mean_x[i] = bunch.mean_x()
mean_y[i] = bunch.mean_y()
sigma_z[i] = bunch.sigma_z()
for m_ in map_:
m_.track(bunch)
return mean_x, mean_y, sigma_z
def my_fft(data):
t = np.arange(len(data))
fft = np.fft.rfft(data)
fft_freq = np.fft.rfftfreq(t.shape[-1])
return fft_freq, np.abs(fft.real)
def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y,
linear_map):
intensity = 1.05e11
sigma_z = 0.059958
gamma = 3730.26
p0 = np.sqrt(gamma**2 - 1) * m_p * c
beta_z = (linear_map.eta(dp=0, gamma=gamma) *
linear_map.circumference / (2 * np.pi * linear_map.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)
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)
# print ('bunch sigma_z=' + bunch.sigma_z())
return bunch
def track_n_show(bunch, slicer, map_woWakes, wake_field):
fig, ((ax1, ax2)) = plt.subplots(2, 1, figsize=(16, 16))
xp_diff = np.zeros(n_macroparticles)
for i in xrange(n_turns):
for m_ in map_woWakes:
m_.track(bunch)
# Dipole X kick.
if i == (n_turns - 1):
xp_old = bunch.xp.copy()
wake_field.track(bunch)
if i == (n_turns - 1):
xp_diff[:] = bunch.xp[:] - xp_old[:]
# Plot bunch.z vs. slice index of particle. Mark particles within
# z cuts in green.
nsigmaz_lbl = ' (nsigmaz =' + str(n_sigma_z) + ')'
slice_set = bunch.get_slices(slicer)
pidx = slice_set.particles_within_cuts
slidx = slice_set.slice_index_of_particle
z_cut_tail, z_cut_head = slice_set.z_cut_tail, slice_set.z_cut_head
# In[4]:
# Basic parameters.
n_turns = 10
n_segments = 1
n_macroparticles = 50
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]
#waketable filename
fn = os.path.join(os.path.dirname(__file__), 'wake_table.dat')
# 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]:
# In[7]:
# In[8]:
# CASE TEST SETUP
trans_map = TransverseMap(s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y,
Q_x, Q_y)
long_map = LinearMap(alpha_0, C, Q_s)
bunch = generate_bunch(n_macroparticles, alpha_x_inj, alpha_y_inj,
beta_x_inj, beta_y_inj, long_map)
# In[9]:
# CASE I
# Transverse and long. tracking (linear), and wakes from WakeTable source.
# DIPOLE X, UniformBinSlicer
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
wake_file_columns = [
'time', 'dipole_x', 'no_dipole_y', 'no_quadrupole_x',
'no_quadrupole_y', 'no_dipole_xy', 'no_dipole_yx'
]
table = WakeTable(fn,
wake_file_columns,
printer=SilentPrinter(),
warningprinter=SilentPrinter())
wake_field = WakeField(uniform_bin_slicer, table)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[10]:
# CASE II
# Transverse and long. tracking (linear), and wakes from WakeTable source.
# DIPOLE X, UniformChargeSlicer
n_sigma_z = 2
n_slices = 15
uniform_charge_slicer = UniformChargeSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
wake_file_columns = [
'time', 'dipole_x', 'no_dipole_y', 'no_quadrupole_x',
'no_quadrupole_y', 'no_dipole_xy', 'no_dipole_yx'
]
table = WakeTable(fn,
wake_file_columns,
printer=SilentPrinter(),
warningprinter=SilentPrinter())
wake_field = WakeField(uniform_charge_slicer, table)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[11]:
# CASE III
# Transverse and long. tracking (linear), and wakes from WakeTable source.
# Quadrupole X, UniformChargeSlicer
n_sigma_z = 2
n_slices = 15
uniform_charge_slicer = UniformChargeSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
wake_file_columns = [
'time', 'no_dipole_x', 'no_dipole_y', 'quadrupole_x',
'no_quadrupole_y', 'no_dipole_xy', 'no_dipole_yx'
]
table = WakeTable(fn,
wake_file_columns,
printer=SilentPrinter(),
warningprinter=SilentPrinter())
wake_field = WakeField(uniform_charge_slicer, table)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[12]:
# CASE IV
# Transverse and long. tracking (linear), and wakes from WakeTable source.
# Quadrupole X, UniformBinSlicer
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
wake_file_columns = [
'time', 'no_dipole_x', 'no_dipole_y', 'quadrupole_x',
'no_quadrupole_y', 'no_dipole_xy', 'no_dipole_yx'
]
table = WakeTable(fn,
wake_file_columns,
printer=SilentPrinter(),
warningprinter=SilentPrinter())
wake_field = WakeField(uniform_bin_slicer, table)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[15]:
# CASE V
# Transverse and long. tracking (linear),
# Resonator circular
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
reson_circ = CircularResonator(R_shunt=1e6, frequency=1e8, Q=1)
wake_field = WakeField(uniform_bin_slicer, reson_circ)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[16]:
# CASE V b.
# Transverse and long. tracking (linear),
# Several Resonators circular
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
reson_circ = CircularResonator(R_shunt=1e6, frequency=1e8, Q=1)
reson_circ2 = CircularResonator(R_shunt=1e6, frequency=1e9, Q=0.8)
reson_circ3 = CircularResonator(R_shunt=5e6, frequency=1e6, Q=0.2)
wake_field = WakeField(uniform_bin_slicer, reson_circ, reson_circ2,
reson_circ3)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[17]:
# CASE V c.
# Transverse and long. tracking (linear),
# Resonator parallel_plates
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
reson_para = ParallelPlatesResonator(R_shunt=1e6, frequency=1e8, Q=1)
wake_field = WakeField(uniform_bin_slicer, reson_para)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[18]:
# CASE V d.
# Transverse and long. tracking (linear),
# Resonator w. longitudinal wake
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
reson = Resonator(R_shunt=1e6,
frequency=1e8,
Q=1,
Yokoya_X1=1,
Yokoya_X2=1,
Yokoya_Y1=1,
Yokoya_Y2=1,
switch_Z=True)
wake_field = WakeField(uniform_bin_slicer, reson)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[19]:
# CASE VI
# Transverse and long. tracking (linear),
# ResistiveWall circular
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
resis_circ = CircularResistiveWall(pipe_radius=5e-2,
resistive_wall_length=C,
conductivity=1e6,
dt_min=1e-3)
wake_field = WakeField(uniform_bin_slicer, resis_circ)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[20]:
# CASE VI b.
# Transverse and long. tracking (linear),
# ResistiveWall parallel_plates
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
resis_para = ParallelPlatesResistiveWall(pipe_radius=5e-2,
resistive_wall_length=C,
conductivity=1e6,
dt_min=1e-3)
wake_field = WakeField(uniform_bin_slicer, resis_para)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
# In[21]:
# CASE VII.
# Transverse and long. tracking (linear),
# Pass mixture of WakeSources to define WakeField.
n_sigma_z = 2
n_slices = 15
uniform_bin_slicer = UniformBinSlicer(n_slices=n_slices,
n_sigma_z=n_sigma_z)
# Definition of WakeField as a composition of different sources.
resis_circ = CircularResistiveWall(pipe_radius=5e-2,
resistive_wall_length=C,
conductivity=1e6,
dt_min=1e-3)
reson_para = ParallelPlatesResonator(R_shunt=1e6, frequency=1e8, Q=1)
wake_file_columns = [
'time', 'dipole_x', 'dipole_y', 'quadrupole_x', 'quadrupole_y',
'dipole_xy', 'dipole_yx'
]
table = WakeTable(fn,
wake_file_columns,
printer=SilentPrinter(),
warningprinter=SilentPrinter())
wake_field = WakeField(uniform_bin_slicer, resis_circ, reson_para, table)
trans_map = [m for m in trans_map]
map_woWakes = trans_map + [long_map]
track_n_save(bunch, map_woWakes)
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-injection'
machine = LHC(n_segments=1,
machine_configuration=machine_configuration,
**get_nonlinear_params(chroma=chroma,
i_oct=i_oct,
p0=0.45e12 * e / c))
# BEAM
# ====
#print(filling_scheme)
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
beam = machine.generate_6D_Gaussian_bunch(n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z,
matched=True,
filling_scheme=filling_scheme)
bunch_list = beam.split_to_views()
for b in bunch_list:
if b.bucket_id[0] < batch_length:
b.x += 1e-3
b.y += 1e-3
bunch = bunch_list[0]
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(
50,
z_cuts=(-3 * sigma_z, 3 * sigma_z),
circumference=machine.circumference,
h_bunch=machine.h_bunch)
# 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,
mpi='linear_mpi_full_ring_fft')
# CREATE DAMPER
# =============
from PyHEADTAIL_feedback.feedback import OneboxFeedback
from PyHEADTAIL_feedback.processors.multiplication import ChargeWeighter
from PyHEADTAIL_feedback.processors.register import TurnFIRFilter
from PyHEADTAIL_feedback.processors.convolution import Lowpass, FIRFilter
from PyHEADTAIL_feedback.processors.resampling import DAC, HarmonicADC, BackToOriginalBins, Upsampler
from MD4063_filter_functions import calculate_coefficients_3_tap, calculate_hilbert_notch_coefficients
# from PyHEADTAIL_feedback.processors.addition import NoiseGenerator
dampingtime = 20.
gain = 2. / dampingtime
lowpass100kHz = [
1703, 1169, 1550, 1998, 2517, 3108, 3773, 4513, 5328, 6217, 7174, 8198,
9282, 10417, 11598, 12813, 14052, 15304, 16555, 17793, 19005, 20176,
21294, 22345, 23315, 24193, 24969, 25631, 26171, 26583, 26860, 27000,
27000, 26860, 26583, 26171, 25631, 24969, 24193, 23315, 22345, 21294,
20176, 19005, 17793, 16555, 15304, 14052, 12813, 11598, 10417, 9282,
8198, 7174, 6217, 5328, 4513, 3773, 3108, 2517, 1998, 1550, 1169, 1703
]
lowpassEnhanced = [
490, 177, -478, -820, -370, 573, 1065, 428, -909, -1632, -799, 1015,
2015, 901, -1592, -3053, -1675, 1642, 3670, 1841, -2828, -6010, -3929,
2459, 7233, 4322, -6384, -17305, -18296, -5077, 16097, 32000, 32000,
16097, -5077, -18296, -17305, -6384, 4322, 7233, 2459, -3929, -6010,
-2828, 1841, 3670, 1642, -1675, -3053, -1592, 901, 2015, 1015, -799,
-1632, -909, 428, 1065, 573, -370, -820, -478, 177, 490
]
lowpass20MHz = [
38, 118, 182, 112, -133, -389, -385, -45, 318, 257, -259, -665, -361,
473, 877, 180, -996, -1187, 162, 1670, 1329, -954, -2648, -1219, 2427,
4007, 419, -5623, -6590, 2893, 19575, 32700, 32700, 19575, 2893, -6590,
-5623, 419, 4007, 2427, -1219, -2648, -954, 1329, 1670, 162, -1187,
-996, 180, 877, 473, -361, -665, -259, 257, 318, -45, -385, -389, -133,
112, 182, 118, 38
]
phaseEqualizer = [
2, 4, 7, 10, 12, 16, 19, 22, 27, 31, 36, 42, 49, 57, 67, 77, 90, 104,
121, 141, 164, 191, 223, 261, 305, 358, 422, 498, 589, 700, 836, 1004,
1215, 1483, 1832, 2301, 2956, 3944, 5600, 9184, 25000, -16746, -4256,
-2056, -1195, -769, -523, -372, -271, -202, -153, -118, -91, -71, -56,
-44, -34, -27, -20, -15, -11, -7, -4, -1
]
FIR_phase_filter = np.loadtxt(
'./injection_error_input_data/FIR_Phase_40MSPS.csv')
FIR_phase_filter = np.array(phaseEqualizer)
FIR_phase_filter = FIR_phase_filter / float(np.sum(FIR_phase_filter))
FIR_gain_filter = np.array(lowpass20MHz)
FIR_gain_filter = FIR_gain_filter / float(np.sum(lowpass20MHz))
# Cut-off frequency of the kicker system
fc = 1.0e6
ADC_bits = 16
ADC_range = (-1e-3, 1e-3)
# signal processing delay in turns before the first measurements is applied
delay = 1
extra_adc_bins = 10
# betatron phase advance between the pickup and the kicker. The value 0.25
# corresponds to the 90 deg phase change from from the pickup measurements
# in x-plane to correction kicks in xp-plane.
additional_phase = 0.25 # Kicker-to-pickup phase advance 0 deg
# additional_phase = 0. # Kicker-to-pickup phase advance 90 deg
f_RF = 1. / (machine.circumference / c / (float(machine.h_RF)))
# turn_phase_filter_x = calculate_hilbert_notch_coefficients(machine.Q_x, delay, additional_phase)
# turn_phase_filter_y = calculate_hilbert_notch_coefficients(machine.Q_y, delay, additional_phase)
turn_phase_filter_x = calculate_coefficients_3_tap(machine.Q_x, delay,
additional_phase)
turn_phase_filter_y = calculate_coefficients_3_tap(machine.Q_y, delay,
additional_phase)
print('f_RF: ' + str(f_RF))
processors_detailed_x = [
Bypass(),
ChargeWeighter(normalization='segment_average'),
# NoiseGenerator(RMS_noise_level, debug=False),
HarmonicADC(1 * f_RF / 10.,
ADC_bits,
ADC_range,
n_extras=extra_adc_bins),
TurnFIRFilter(turn_phase_filter_x, machine.Q_x, delay=delay),
FIRFilter(FIR_phase_filter, zero_tap=40),
Upsampler(3, [1.5, 1.5, 0]),
FIRFilter(FIR_gain_filter, zero_tap=34),
DAC(ADC_bits, ADC_range),
Lowpass(fc, f_cutoff_2nd=10 * fc),
BackToOriginalBins(),
]
processors_detailed_y = [
Bypass(),
ChargeWeighter(normalization='segment_average'),
# NoiseGenerator(RMS_noise_level, debug=False),
HarmonicADC(1 * f_RF / 10.,
ADC_bits,
ADC_range,
n_extras=extra_adc_bins),
TurnFIRFilter(turn_phase_filter_y, machine.Q_y, delay=delay),
FIRFilter(FIR_phase_filter, zero_tap=40),
Upsampler(3, [1.5, 1.5, 0]),
FIRFilter(FIR_gain_filter, zero_tap=34),
DAC(ADC_bits, ADC_range),
Lowpass(fc, f_cutoff_2nd=10 * fc),
BackToOriginalBins(),
]
# Kicker-to-pickup phase advance 0 deg
damper = OneboxFeedback(gain,
slicer_for_wakefields,
processors_detailed_x,
processors_detailed_y,
pickup_axis='displacement',
kicker_axis='divergence',
mpi=True,
beta_x=machine.beta_x,
beta_y=machine.beta_y)
# # Kicker-to-pickup phase advance 90 deg
# damper = OneboxFeedback(gain,slicer_for_wakefields,
# processors_detailed_x,processors_detailed_y, mpi=True,
# pickup_axis='displacement', kicker_axis='displacement')
# 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_to_file_every=512,
buffer_size=4096,
mpi=True,
filling_scheme=filling_scheme)
# slicemonitor = SliceMonitor(
# outputpath+'/slicemonitor_{:04d}_chroma={:g}_bunch_{:04d}'.format(it, chroma, bunch.bucket_id[0]),
# n_turns_slicemon,
# slicer_for_slicemonitor, simulation_parameters_dict,
# write_buffer_to_file_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(beam)
bunch_list = beam.split_to_views()
bunch = bunch_list[0]
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(beam)
# 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 % 100 == 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!')
# multiply with a factor 2
#ww1.wake_table['dipole_y'] = 2*ww1.wake_table['dipole_y'] # for the analytical step wake
ww2.wake_table['dipole_y'] = 1.034*ww2.wake_table['dipole_y'] # for the analytical step wake
ww2.wake_table['dipole_x'] = 1.034*ww2.wake_table['dipole_x'] # for the analytical step wake
ww2.wake_table['quadrupole_y'] = 1.034*ww2.wake_table['quadrupole_y'] # for the analytical step wake
ww2.wake_table['quadrupole_x'] = 1.034*ww2.wake_table['quadrupole_x'] # for the analytical step wake
#wake_field_kicker = WakeField(slicer_for_wakefields, ww1)#, beta_x=beta_x, beta_y=beta_y)
#wake_field_1 = WakeField(slicer_for_wakefields, ww1)#, beta_x=beta_x, beta_y=beta_y)
wake_field_2 = WakeField(slicer_for_wakefields, ww2)#, beta_x=beta_x, beta_y=beta_y)
wake_field_3 = WakeField(slicer_for_wakefields, ww3)#, beta_x=beta_x, beta_y=beta_y)
# CREATE TRANSVERSE AND LONGITUDINAL MAPS
# =======================================
scale_factor = 2*bunch.p0 # scale the detuning coefficients in pyheadtail units
transverse_map = TransverseMap(s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y, Q_x, Q_y,
[Chromaticity(Qp_x, Qp_y),
AmplitudeDetuning(app_x*scale_factor, app_y*scale_factor, app_xy*scale_factor)])
longitudinal_map = LinearMap([alpha], circumference, Q_s)
# ======================================================================
# 1) Load the SPS imepdance model
n_turns_wake = 1 # for the moment we consider that the wakefield decays after 1 turn
wakefile1 = ('/afs/cern.ch/work/n/natriant/private/pyheadtail_example_crabcavity/wakefields/SPS_complete_wake_model_2018_Q26.txt')
ww1 = WakeTable(wakefile1, ['time', 'dipole_x', 'dipole_y', 'quadrupole_x', 'quadrupole_y'], n_turns_wake=n_turns_wake)
# only dipolar kick- uncomment the following 3 lines
#my_length = len(ww1.wake_table['quadrupole_x'])
#ww1.wake_table['quadrupole_x'] = np.zeros(my_length)
#ww1.wake_table['quadrupole_y'] = np.zeros(my_length)
# only quadrupolar kick, uncomment the following 3 lines
#my_length = len(ww1.wake_table['dipole_x'])
#ww1.wake_table['dipole_x'] = np.zeros(my_length)
#ww1.wake_table['dipole_y'] = np.zeros(my_length)
wake_field_complete_sps = WakeField(slicer_for_wakefields, ww1)#, beta_x=beta_x, beta_y=beta_y)
# 2) Definition of wakefield of a circular resonator
reson_circ = CircularResonator(R_shunt=2.2e6, frequency=1.92e9, Q=59000) # assuming here that Q is the quality factor in 1e4
wake_field_reson_circ = WakeField(slicer_for_wakefields, reson_circ)
# CREATE TRANSVERSE AND LONGITUDINAL MAPS
# =======================================
scale_factor = 2*bunch.p0 # scale the detuning coefficients in pyheadtail units
transverse_map = TransverseMap(s, alpha_x, beta_x, D_x, alpha_y, beta_y, D_y, Q_x, Q_y,
[Chromaticity(Qp_x, Qp_y),
AmplitudeDetuning(app_x*scale_factor, app_y*scale_factor, app_xy*scale_factor)])
longitudinal_map = LinearMap([alpha], circumference, Q_s)