def init_all(self):
n_slices = 100
z_cut = 2.5e-9 * c
self.n_slices = n_slices
self.z_cut = z_cut
from LHC import LHC
self.machine = LHC(machine_configuration='Injection',
n_segments=43,
D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - 1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long' % (
myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long' % (
myid, len(self.mypart))
def init_all(self): n_slices = 100 z_cut = 2.5e-9*c self.n_slices = n_slices self.z_cut = z_cut from LHC import LHC self.machine = LHC(machine_configuration='Injection', n_segments=43, D_x=0., RF_at='end_of_transverse') # We suppose that all the object that cannot be slice parallelized are at the end of the ring i_end_parallel = len(self.machine.one_turn_map)-1 #only RF is not parallelizable # split the machine sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes) myid = self.ring_of_CPUs.myid i_start_part, i_end_part = sharing.my_part(myid) self.mypart = self.machine.one_turn_map[i_start_part:i_end_part] if self.ring_of_CPUs.I_am_a_worker: print 'I am id=%d (worker) and my part is %d long'%(myid, len(self.mypart)) elif self.ring_of_CPUs.I_am_the_master: self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:] print 'I am id=%d (master) and my part is %d long'%(myid, len(self.mypart))
class Simulation(object):
def __init__(self):
self.N_turns = 128
self.N_buffer_float_size = 500000
self.N_buffer_int_size = 450
def init_all(self):
n_slices = 100
z_cut = 2.5e-9*c
self.n_slices = n_slices
self.z_cut = z_cut
from LHC import LHC
self.machine = LHC(machine_configuration='Injection', n_segments=43, D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(self.machine.one_turn_map)-1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long'%(myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long'%(myid, len(self.mypart))
def init_master(self):
# beam parameters
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
macroparticlenumber_track = 50000
# initialization bunch
bunch = self.machine.generate_6D_Gaussian_bunch_matched(
macroparticlenumber_track, intensity, epsn_x, epsn_y, sigma_z=sigma_z)
print 'Bunch initialized.'
# initial slicing
from PyHEADTAIL.particles.slicing import UniformBinSlicer
self.slicer = UniformBinSlicer(n_slices = self.n_slices, z_cuts=(-self.z_cut, self.z_cut))
#slice for the first turn
slice_obj_list = bunch.extract_slices(self.slicer)
#prepare to save results
self.beam_x, self.beam_y, self.beam_z = [], [], []
self.sx, self.sy, self.sz = [], [], []
self.epsx, self.epsy, self.epsz = [], [], []
pieces_to_be_treated = slice_obj_list
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)
#save results
self.beam_x.append(bunch.mean_x())
self.beam_y.append(bunch.mean_y())
self.beam_z.append(bunch.mean_z())
self.sx.append(bunch.sigma_x())
self.sy.append(bunch.sigma_y())
self.sz.append(bunch.sigma_z())
self.epsx.append(bunch.epsn_x()*1e6)
self.epsy.append(bunch.epsn_y()*1e6)
self.epsz.append(bunch.epsn_z())
# prepare next turn (re-slice)
new_pieces_to_be_treated = bunch.extract_slices(self.slicer)
orders_to_pass = []
return orders_to_pass, new_pieces_to_be_treated
def execute_orders_from_master(self, orders_from_master):
pass
def finalize_simulation(self):
# save results
import PyPARIS.myfilemanager as mfm
mfm.dict_to_h5({\
'beam_x':self.beam_x,
'beam_y':self.beam_y,
'beam_z':self.beam_z,
'sx':self.sx,
'sy':self.sy,
'sz':self.sz,
'epsx':self.epsx,
'epsy':self.epsy,
'epsz':self.epsz},
'beam_coord.h5')
# output plots
if False:
beam_x = self.beam_x
beam_y = self.beam_y
beam_z = self.beam_z
sx = self.sx
sy = self.sy
sz = self.sz
epsx = self.epsx
epsy = self.epsy
epsz = self.epsz
import pylab as plt
plt.figure(2, figsize=(16, 8), tight_layout=True)
plt.subplot(2,3,1)
plt.plot(beam_x)
plt.ylabel('x [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,2)
plt.plot(beam_y)
plt.ylabel('y [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,3)
plt.plot(beam_z)
plt.ylabel('z [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,4)
plt.plot(np.fft.rfftfreq(len(beam_x), d=1.), np.abs(np.fft.rfft(beam_x)))
plt.ylabel('Amplitude');plt.xlabel('Qx')
plt.subplot(2,3,5)
plt.plot(np.fft.rfftfreq(len(beam_y), d=1.), np.abs(np.fft.rfft(beam_y)))
plt.ylabel('Amplitude');plt.xlabel('Qy')
plt.subplot(2,3,6)
plt.plot(np.fft.rfftfreq(len(beam_z), d=1.), np.abs(np.fft.rfft(beam_z)))
plt.xlim(0, 0.1)
plt.ylabel('Amplitude');plt.xlabel('Qz')
fig, axes = plt.subplots(3, figsize=(16, 8), tight_layout=True)
twax = [plt.twinx(ax) for ax in axes]
axes[0].plot(sx)
twax[0].plot(epsx, '-g')
axes[0].set_xlabel('Turns')
axes[0].set_ylabel(r'$\sigma_x$')
twax[0].set_ylabel(r'$\varepsilon_y$')
axes[1].plot(sy)
twax[1].plot(epsy, '-g')
axes[1].set_xlabel('Turns')
axes[1].set_ylabel(r'$\sigma_x$')
twax[1].set_ylabel(r'$\varepsilon_y$')
axes[2].plot(sz)
twax[2].plot(epsz, '-g')
axes[2].set_xlabel('Turns')
axes[2].set_ylabel(r'$\sigma_x$')
twax[2].set_ylabel(r'$\varepsilon_y$')
axes[0].grid()
axes[1].grid()
axes[2].grid()
for ax in list(axes)+list(twax):
ax.ticklabel_format(useOffset=False, style='sci', scilimits=(0,0),axis='y')
plt.show()
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(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!')
n_turns = 512 * 4
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
mode = 'smooth'
#mode = 'non-smooth'
import pickle
from LHC import LHC
if mode == 'smooth':
machine = LHC(machine_configuration='Injection', n_segments=29, D_x=10)
elif mode == 'non-smooth':
with open('lhc_2015_80cm_optics.pkl') as fid:
optics = pickle.load(fid)
optics.pop('circumference')
machine = LHC(machine_configuration='Injection',
optics_mode='non-smooth',
V_RF=10e6,
**optics)
print('Create bunch for optics...')
bunch = machine.generate_6D_Gaussian_bunch_matched(macroparticlenumber_optics,
intensity,
epsn_x,
epsn_y,
p0_GeV = 2000.
# LHC
machine_configuration = '6.5_TeV_collision_tunes'
intensity = 1.2e11
epsn_x = 2.5e-6
epsn_y = 2.5e-6
sigma_z = 7e-2
n_macroparticles = 1000000
sparse_solver = 'PyKLU'
machine = LHC(machine_configuration=machine_configuration,
optics_mode='smooth',
n_segments=1,
p0=p0_GeV * 1e9 * e / c)
# generate beam
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=n_macroparticles,
intensity=intensity,
epsn_x=epsn_x,
epsn_y=epsn_y,
sigma_z=sigma_z)
# Single grid parameters
Dh_single = 0.5 * bunch.sigma_x() #.3
# Bassetti-Erskine parameters
Dh_BE = 0.3 * bunch.sigma_x()
class Simulation(object):
def __init__(self):
self.N_turns = 2
def init_all(self):
n_slices = 100
z_cut = 2.5e-9 * c
self.n_slices = n_slices
self.z_cut = z_cut
n_segments = 70
from LHC import LHC
self.machine = LHC(machine_configuration='Injection',
n_segments=n_segments,
D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - 1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long' % (
myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long' % (
myid, len(self.mypart))
# config e-cloud
chamb_type = 'polyg'
x_aper = 2.300000e-02
y_aper = 1.800000e-02
filename_chm = '../pyecloud_config/LHC_chm_ver.mat'
B_multip_per_eV = [1.190000e-12]
B_multip_per_eV = np.array(B_multip_per_eV)
fraction_device = 0.65
intensity = 1.150000e+11
epsn_x = 2.5e-6
epsn_y = 2.5e-6
init_unif_edens_flag = 1
init_unif_edens = 9.000000e+11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init * 4.
Dh_sc = .2e-3
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(
L_ecloud=self.machine.circumference / n_segments,
slicer=None,
Dt_ref=10e-12,
pyecl_input_folder='../pyecloud_config',
chamb_type=chamb_type,
x_aper=x_aper,
y_aper=y_aper,
filename_chm=filename_chm,
Dh_sc=Dh_sc,
init_unif_edens_flag=init_unif_edens_flag,
init_unif_edens=init_unif_edens,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_per_eV * self.machine.p0 / e * c,
slice_by_slice_mode=True)
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge(
)
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
self.mypart = my_new_part
def init_master(self):
# beam parameters
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
macroparticlenumber_track = 50000
# initialization bunch
bunch = self.machine.generate_6D_Gaussian_bunch_matched(
macroparticlenumber_track,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
print 'Bunch initialized.'
# initial slicing
from PyHEADTAIL.particles.slicing import UniformBinSlicer
self.slicer = UniformBinSlicer(n_slices=self.n_slices,
z_cuts=(-self.z_cut, self.z_cut))
#slice for the first turn
slice_obj_list = bunch.extract_slices(self.slicer)
#prepare to save results
self.beam_x, self.beam_y, self.beam_z = [], [], []
self.sx, self.sy, self.sz = [], [], []
self.epsx, self.epsy, self.epsz = [], [], []
pieces_to_be_treated = slice_obj_list
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)
#csave results
self.beam_x.append(bunch.mean_x())
self.beam_y.append(bunch.mean_y())
self.beam_z.append(bunch.mean_z())
self.sx.append(bunch.sigma_x())
self.sy.append(bunch.sigma_y())
self.sz.append(bunch.sigma_z())
self.epsx.append(bunch.epsn_x() * 1e6)
self.epsy.append(bunch.epsn_y() * 1e6)
self.epsz.append(bunch.epsn_z())
# 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):
# save results
import myfilemanager as mfm
mfm.save_dict_to_h5('beam_coord.h5',{\
'beam_x':self.beam_x,
'beam_y':self.beam_y,
'beam_z':self.beam_z,
'sx':self.sx,
'sy':self.sy,
'sz':self.sz,
'epsx':self.epsx,
'epsy':self.epsy,
'epsz':self.epsz})
# output plots
if False:
beam_x = self.beam_x
beam_y = self.beam_y
beam_z = self.beam_z
sx = self.sx
sy = self.sy
sz = self.sz
epsx = self.epsx
epsy = self.epsy
epsz = self.epsz
import pylab as plt
plt.figure(2, figsize=(16, 8), tight_layout=True)
plt.subplot(2, 3, 1)
plt.plot(beam_x)
plt.ylabel('x [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 2)
plt.plot(beam_y)
plt.ylabel('y [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 3)
plt.plot(beam_z)
plt.ylabel('z [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 4)
plt.plot(np.fft.rfftfreq(len(beam_x), d=1.),
np.abs(np.fft.rfft(beam_x)))
plt.ylabel('Amplitude')
plt.xlabel('Qx')
plt.subplot(2, 3, 5)
plt.plot(np.fft.rfftfreq(len(beam_y), d=1.),
np.abs(np.fft.rfft(beam_y)))
plt.ylabel('Amplitude')
plt.xlabel('Qy')
plt.subplot(2, 3, 6)
plt.plot(np.fft.rfftfreq(len(beam_z), d=1.),
np.abs(np.fft.rfft(beam_z)))
plt.xlim(0, 0.1)
plt.ylabel('Amplitude')
plt.xlabel('Qz')
fig, axes = plt.subplots(3, figsize=(16, 8), tight_layout=True)
twax = [plt.twinx(ax) for ax in axes]
axes[0].plot(sx)
twax[0].plot(epsx, '-g')
axes[0].set_xlabel('Turns')
axes[0].set_ylabel(r'$\sigma_x$')
twax[0].set_ylabel(r'$\varepsilon_y$')
axes[1].plot(sy)
twax[1].plot(epsy, '-g')
axes[1].set_xlabel('Turns')
axes[1].set_ylabel(r'$\sigma_x$')
twax[1].set_ylabel(r'$\varepsilon_y$')
axes[2].plot(sz)
twax[2].plot(epsz, '-g')
axes[2].set_xlabel('Turns')
axes[2].set_ylabel(r'$\sigma_x$')
twax[2].set_ylabel(r'$\varepsilon_y$')
axes[0].grid()
axes[1].grid()
axes[2].grid()
for ax in list(axes) + list(twax):
ax.ticklabel_format(useOffset=False,
style='sci',
scilimits=(0, 0),
axis='y')
plt.show()
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 init_all(self):
n_slices = 100
z_cut = 2.5e-9 * c
self.n_slices = n_slices
self.z_cut = z_cut
n_segments = 70
from LHC import LHC
self.machine = LHC(machine_configuration='Injection',
n_segments=n_segments,
D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - 1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long' % (
myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long' % (
myid, len(self.mypart))
# config e-cloud
chamb_type = 'polyg'
x_aper = 2.300000e-02
y_aper = 1.800000e-02
filename_chm = '../pyecloud_config/LHC_chm_ver.mat'
B_multip_per_eV = [1.190000e-12]
B_multip_per_eV = np.array(B_multip_per_eV)
fraction_device = 0.65
intensity = 1.150000e+11
epsn_x = 2.5e-6
epsn_y = 2.5e-6
init_unif_edens_flag = 1
init_unif_edens = 9.000000e+11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init * 4.
Dh_sc = .2e-3
nel_mp_ref_0 = init_unif_edens * 4 * x_aper * y_aper / N_MP_ele_init
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(
L_ecloud=self.machine.circumference / n_segments,
slicer=None,
Dt_ref=10e-12,
pyecl_input_folder='../pyecloud_config',
chamb_type=chamb_type,
x_aper=x_aper,
y_aper=y_aper,
filename_chm=filename_chm,
Dh_sc=Dh_sc,
init_unif_edens_flag=init_unif_edens_flag,
init_unif_edens=init_unif_edens,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_per_eV * self.machine.p0 / e * c,
slice_by_slice_mode=True)
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge(
)
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
self.mypart = my_new_part
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!')
from LHC import LHC
import matplotlib.pyplot as plt
import numpy as np
import Damper
from scipy.constants import e, m_p, c
print("Generating machine")
machine = LHC(machine_configuration='6.5_TeV_collision_tunes',
n_segments=29,
D_x=10)
print("Machine generated")
damper = Damper.Damper(machine)
machine.one_turn_map.append(damper.damper)
macroparticlenumber_track = 50000
intensity = 1e11
chroma = 0.
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
bunch = machine.generate_6D_Gaussian_bunch_matched(macroparticlenumber_track,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
n_turns = 512
beam_x = []
n_turns = 512*4
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
#mode = 'smooth'
mode = 'non-smooth'
import pickle
from LHC import LHC
if mode == 'smooth':
machine = LHC(machine_configuration='Injection', n_segments=29, D_x=10)
elif mode == 'non-smooth':
with open('lhc_2015_80cm_optics.pkl') as fid:
optics = pickle.load(fid)
optics.pop('circumference')
machine = LHC(machine_configuration='Injection', optics_mode = 'non-smooth', V_RF=10e6, **optics)
print 'Create bunch for optics...'
bunch = machine.generate_6D_Gaussian_bunch_matched(
macroparticlenumber_optics, intensity, epsn_x, epsn_y, sigma_z=sigma_z)
print 'Done.'
bunch.x += 10.
bunch.y += 20.
bunch.z += .020
if nDvar <= 3:
nCons = 1 # Tell DOT how many contraint equations are present
else:
nCons = 2 # Tell DOT how many contraint equations are present
NEWITR = 0 # Set the current iteration to zero
JWRITE = 21 # File number to write iteration history to.
IWRITE = 20 # File number for printed output
FDCH = 0.001
FDCHM = 0.0001
# // Obtain 10 different design points (starting points) from LHC function
from LHC import LHC
s = 10 #number of different starting points
sp, cl, cu = LHC(nDvar, s)
# //
# ----------------------------------------------------------------------------------------------------------------
# In case more than one algorithm needs to be tested, but the same starting points. Unindent the following lines and
# manually add the starting points from the "starting_points.txt" file. This is to be done before the second algorithm
# is started. During the first, leave the file as is and for the 2nd, unindent the lines below and follow the steps
# set out below.
# NB !!!! - Remember to indent lines 173-175
# - Change the algorithm type in line 273
# - In the "graphs.py" file unindent the lines explained there, only just before the second algorithm's
# optimisation is started
# - Change the lines as explained in "File_paths.py"
# sp = nm.empty([10,3])
y_aper = 25e-3
Dh_single = 0.5e-3
#~ # machine parameters
optics_mode = 'smooth'
n_segments = 1
# beam parameters
n_macroparticles = 1000000
#LHC
machine_configuration = '6.5_TeV_collision_tunes'
intensity = 1.2e11
epsn_x = .5e-6
epsn_y = 3e-6
sigma_z = 7e-2
machine = LHC(machine_configuration=machine_configuration,
optics_mode=optics_mode,
n_segments=n_segments)
# build chamber
chamber = ell.ellip_cham_geom_object(x_aper=x_aper, y_aper=y_aper)
Vx, Vy = chamber.points_on_boundary(N_points=200)
# generate beam for singlegrid
bunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=n_macroparticles,
intensity=intensity,
epsn_x=epsn_x,
epsn_y=epsn_y,
sigma_z=sigma_z)
# generate beam for state1
bunch1 = machine.generate_6D_Gaussian_bunch(n_macroparticles=n_macroparticles,
class Simulation(object):
def __init__(self):
self.N_turns = 2
def init_all(self):
n_slices = 100
z_cut = 2.5e-9*c
self.n_slices = n_slices
self.z_cut = z_cut
n_segments=70
from LHC import LHC
self.machine = LHC(machine_configuration='Injection', n_segments=n_segments, D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(self.machine.one_turn_map)-1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long'%(myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long'%(myid, len(self.mypart))
# config e-cloud
chamb_type = 'polyg'
x_aper = 2.300000e-02
y_aper = 1.800000e-02
filename_chm = '../pyecloud_config/LHC_chm_ver.mat'
B_multip_per_eV = [1.190000e-12]
B_multip_per_eV = np.array(B_multip_per_eV)
fraction_device = 0.65
intensity = 1.150000e+11
epsn_x = 2.5e-6
epsn_y = 2.5e-6
init_unif_edens_flag = 1
init_unif_edens = 9.000000e+11
N_MP_ele_init = 100000
N_mp_max = N_MP_ele_init*4.
Dh_sc = .2e-3
nel_mp_ref_0 = init_unif_edens*4*x_aper*y_aper/N_MP_ele_init
import PyECLOUD.PyEC4PyHT as PyEC4PyHT
ecloud = PyEC4PyHT.Ecloud(L_ecloud=self.machine.circumference/n_segments, slicer=None,
Dt_ref=10e-12, pyecl_input_folder='../pyecloud_config',
chamb_type = chamb_type,
x_aper=x_aper, y_aper=y_aper,
filename_chm=filename_chm, Dh_sc=Dh_sc,
init_unif_edens_flag=init_unif_edens_flag,
init_unif_edens=init_unif_edens,
N_mp_max=N_mp_max,
nel_mp_ref_0=nel_mp_ref_0,
B_multip=B_multip_per_eV*self.machine.p0/e*c,
slice_by_slice_mode=True)
my_new_part = []
self.my_list_eclouds = []
for ele in self.mypart:
my_new_part.append(ele)
if ele in self.machine.transverse_map:
ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge()
my_new_part.append(ecloud_new)
self.my_list_eclouds.append(ecloud_new)
self.mypart = my_new_part
def init_master(self):
# beam parameters
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
macroparticlenumber_track = 50000
# initialization bunch
bunch = self.machine.generate_6D_Gaussian_bunch_matched(
macroparticlenumber_track, intensity, epsn_x, epsn_y, sigma_z=sigma_z)
print 'Bunch initialized.'
# initial slicing
from PyHEADTAIL.particles.slicing import UniformBinSlicer
self.slicer = UniformBinSlicer(n_slices = self.n_slices, z_cuts=(-self.z_cut, self.z_cut))
#slice for the first turn
slice_obj_list = bunch.extract_slices(self.slicer)
#prepare to save results
self.beam_x, self.beam_y, self.beam_z = [], [], []
self.sx, self.sy, self.sz = [], [], []
self.epsx, self.epsy, self.epsz = [], [], []
pieces_to_be_treated = slice_obj_list
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)
#csave results
self.beam_x.append(bunch.mean_x())
self.beam_y.append(bunch.mean_y())
self.beam_z.append(bunch.mean_z())
self.sx.append(bunch.sigma_x())
self.sy.append(bunch.sigma_y())
self.sz.append(bunch.sigma_z())
self.epsx.append(bunch.epsn_x()*1e6)
self.epsy.append(bunch.epsn_y()*1e6)
self.epsz.append(bunch.epsn_z())
# 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):
# save results
import myfilemanager as mfm
mfm.save_dict_to_h5('beam_coord.h5',{\
'beam_x':self.beam_x,
'beam_y':self.beam_y,
'beam_z':self.beam_z,
'sx':self.sx,
'sy':self.sy,
'sz':self.sz,
'epsx':self.epsx,
'epsy':self.epsy,
'epsz':self.epsz})
# output plots
if False:
beam_x = self.beam_x
beam_y = self.beam_y
beam_z = self.beam_z
sx = self.sx
sy = self.sy
sz = self.sz
epsx = self.epsx
epsy = self.epsy
epsz = self.epsz
import pylab as plt
plt.figure(2, figsize=(16, 8), tight_layout=True)
plt.subplot(2,3,1)
plt.plot(beam_x)
plt.ylabel('x [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,2)
plt.plot(beam_y)
plt.ylabel('y [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,3)
plt.plot(beam_z)
plt.ylabel('z [m]');plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0,0),axis='y')
plt.subplot(2,3,4)
plt.plot(np.fft.rfftfreq(len(beam_x), d=1.), np.abs(np.fft.rfft(beam_x)))
plt.ylabel('Amplitude');plt.xlabel('Qx')
plt.subplot(2,3,5)
plt.plot(np.fft.rfftfreq(len(beam_y), d=1.), np.abs(np.fft.rfft(beam_y)))
plt.ylabel('Amplitude');plt.xlabel('Qy')
plt.subplot(2,3,6)
plt.plot(np.fft.rfftfreq(len(beam_z), d=1.), np.abs(np.fft.rfft(beam_z)))
plt.xlim(0, 0.1)
plt.ylabel('Amplitude');plt.xlabel('Qz')
fig, axes = plt.subplots(3, figsize=(16, 8), tight_layout=True)
twax = [plt.twinx(ax) for ax in axes]
axes[0].plot(sx)
twax[0].plot(epsx, '-g')
axes[0].set_xlabel('Turns')
axes[0].set_ylabel(r'$\sigma_x$')
twax[0].set_ylabel(r'$\varepsilon_y$')
axes[1].plot(sy)
twax[1].plot(epsy, '-g')
axes[1].set_xlabel('Turns')
axes[1].set_ylabel(r'$\sigma_x$')
twax[1].set_ylabel(r'$\varepsilon_y$')
axes[2].plot(sz)
twax[2].plot(epsz, '-g')
axes[2].set_xlabel('Turns')
axes[2].set_ylabel(r'$\sigma_x$')
twax[2].set_ylabel(r'$\varepsilon_y$')
axes[0].grid()
axes[1].grid()
axes[2].grid()
for ax in list(axes)+list(twax):
ax.ticklabel_format(useOffset=False, style='sci', scilimits=(0,0),axis='y')
plt.show()
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 init_all(self): n_slices = 100 z_cut = 2.5e-9*c self.n_slices = n_slices self.z_cut = z_cut n_segments=70 from LHC import LHC self.machine = LHC(machine_configuration='Injection', n_segments=n_segments, D_x=0., RF_at='end_of_transverse') # We suppose that all the object that cannot be slice parallelized are at the end of the ring i_end_parallel = len(self.machine.one_turn_map)-1 #only RF is not parallelizable # split the machine sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes) myid = self.ring_of_CPUs.myid i_start_part, i_end_part = sharing.my_part(myid) self.mypart = self.machine.one_turn_map[i_start_part:i_end_part] if self.ring_of_CPUs.I_am_a_worker: print 'I am id=%d (worker) and my part is %d long'%(myid, len(self.mypart)) elif self.ring_of_CPUs.I_am_the_master: self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:] print 'I am id=%d (master) and my part is %d long'%(myid, len(self.mypart)) # config e-cloud chamb_type = 'polyg' x_aper = 2.300000e-02 y_aper = 1.800000e-02 filename_chm = '../pyecloud_config/LHC_chm_ver.mat' B_multip_per_eV = [1.190000e-12] B_multip_per_eV = np.array(B_multip_per_eV) fraction_device = 0.65 intensity = 1.150000e+11 epsn_x = 2.5e-6 epsn_y = 2.5e-6 init_unif_edens_flag = 1 init_unif_edens = 9.000000e+11 N_MP_ele_init = 100000 N_mp_max = N_MP_ele_init*4. Dh_sc = .2e-3 nel_mp_ref_0 = init_unif_edens*4*x_aper*y_aper/N_MP_ele_init import PyECLOUD.PyEC4PyHT as PyEC4PyHT ecloud = PyEC4PyHT.Ecloud(L_ecloud=self.machine.circumference/n_segments, slicer=None, Dt_ref=10e-12, pyecl_input_folder='../pyecloud_config', chamb_type = chamb_type, x_aper=x_aper, y_aper=y_aper, filename_chm=filename_chm, Dh_sc=Dh_sc, init_unif_edens_flag=init_unif_edens_flag, init_unif_edens=init_unif_edens, N_mp_max=N_mp_max, nel_mp_ref_0=nel_mp_ref_0, B_multip=B_multip_per_eV*self.machine.p0/e*c, slice_by_slice_mode=True) my_new_part = [] self.my_list_eclouds = [] for ele in self.mypart: my_new_part.append(ele) if ele in self.machine.transverse_map: ecloud_new = ecloud.generate_twin_ecloud_with_shared_space_charge() my_new_part.append(ecloud_new) self.my_list_eclouds.append(ecloud_new) self.mypart = my_new_part
class Simulation(object):
def __init__(self):
self.N_turns = 128
def init_all(self):
n_slices = 100
z_cut = 2.5e-9 * c
self.n_slices = n_slices
self.z_cut = z_cut
from LHC import LHC
self.machine = LHC(machine_configuration='Injection',
n_segments=43,
D_x=0.,
RF_at='end_of_transverse')
# We suppose that all the object that cannot be slice parallelized are at the end of the ring
i_end_parallel = len(
self.machine.one_turn_map) - 1 #only RF is not parallelizable
# split the machine
sharing = shs.ShareSegments(i_end_parallel, self.ring_of_CPUs.N_nodes)
myid = self.ring_of_CPUs.myid
i_start_part, i_end_part = sharing.my_part(myid)
self.mypart = self.machine.one_turn_map[i_start_part:i_end_part]
if self.ring_of_CPUs.I_am_a_worker:
print 'I am id=%d (worker) and my part is %d long' % (
myid, len(self.mypart))
elif self.ring_of_CPUs.I_am_the_master:
self.non_parallel_part = self.machine.one_turn_map[i_end_parallel:]
print 'I am id=%d (master) and my part is %d long' % (
myid, len(self.mypart))
def init_master(self):
# beam parameters
epsn_x = 2.5e-6
epsn_y = 3.5e-6
sigma_z = 0.05
intensity = 1e11
macroparticlenumber_track = 50000
# initialization bunch
bunch = self.machine.generate_6D_Gaussian_bunch_matched(
macroparticlenumber_track,
intensity,
epsn_x,
epsn_y,
sigma_z=sigma_z)
print 'Bunch initialized.'
# initial slicing
from PyHEADTAIL.particles.slicing import UniformBinSlicer
self.slicer = UniformBinSlicer(n_slices=self.n_slices,
z_cuts=(-self.z_cut, self.z_cut))
#slice for the first turn
slice_obj_list = bunch.extract_slices(self.slicer)
#prepare to save results
self.beam_x, self.beam_y, self.beam_z = [], [], []
self.sx, self.sy, self.sz = [], [], []
self.epsx, self.epsy, self.epsz = [], [], []
pieces_to_be_treated = slice_obj_list
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)
#save results
self.beam_x.append(bunch.mean_x())
self.beam_y.append(bunch.mean_y())
self.beam_z.append(bunch.mean_z())
self.sx.append(bunch.sigma_x())
self.sy.append(bunch.sigma_y())
self.sz.append(bunch.sigma_z())
self.epsx.append(bunch.epsn_x() * 1e6)
self.epsy.append(bunch.epsn_y() * 1e6)
self.epsz.append(bunch.epsn_z())
# prepare next turn (re-slice)
new_pieces_to_be_treated = bunch.extract_slices(self.slicer)
orders_to_pass = []
return orders_to_pass, new_pieces_to_be_treated
def execute_orders_from_master(self, orders_from_master):
pass
def finalize_simulation(self):
# save results
import myfilemanager as mfm
mfm.save_dict_to_h5('beam_coord.h5',{\
'beam_x':self.beam_x,
'beam_y':self.beam_y,
'beam_z':self.beam_z,
'sx':self.sx,
'sy':self.sy,
'sz':self.sz,
'epsx':self.epsx,
'epsy':self.epsy,
'epsz':self.epsz})
# output plots
if False:
beam_x = self.beam_x
beam_y = self.beam_y
beam_z = self.beam_z
sx = self.sx
sy = self.sy
sz = self.sz
epsx = self.epsx
epsy = self.epsy
epsz = self.epsz
import pylab as plt
plt.figure(2, figsize=(16, 8), tight_layout=True)
plt.subplot(2, 3, 1)
plt.plot(beam_x)
plt.ylabel('x [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 2)
plt.plot(beam_y)
plt.ylabel('y [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 3)
plt.plot(beam_z)
plt.ylabel('z [m]')
plt.xlabel('Turn')
plt.gca().ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
plt.subplot(2, 3, 4)
plt.plot(np.fft.rfftfreq(len(beam_x), d=1.),
np.abs(np.fft.rfft(beam_x)))
plt.ylabel('Amplitude')
plt.xlabel('Qx')
plt.subplot(2, 3, 5)
plt.plot(np.fft.rfftfreq(len(beam_y), d=1.),
np.abs(np.fft.rfft(beam_y)))
plt.ylabel('Amplitude')
plt.xlabel('Qy')
plt.subplot(2, 3, 6)
plt.plot(np.fft.rfftfreq(len(beam_z), d=1.),
np.abs(np.fft.rfft(beam_z)))
plt.xlim(0, 0.1)
plt.ylabel('Amplitude')
plt.xlabel('Qz')
fig, axes = plt.subplots(3, figsize=(16, 8), tight_layout=True)
twax = [plt.twinx(ax) for ax in axes]
axes[0].plot(sx)
twax[0].plot(epsx, '-g')
axes[0].set_xlabel('Turns')
axes[0].set_ylabel(r'$\sigma_x$')
twax[0].set_ylabel(r'$\varepsilon_y$')
axes[1].plot(sy)
twax[1].plot(epsy, '-g')
axes[1].set_xlabel('Turns')
axes[1].set_ylabel(r'$\sigma_x$')
twax[1].set_ylabel(r'$\varepsilon_y$')
axes[2].plot(sz)
twax[2].plot(epsz, '-g')
axes[2].set_xlabel('Turns')
axes[2].set_ylabel(r'$\sigma_x$')
twax[2].set_ylabel(r'$\varepsilon_y$')
axes[0].grid()
axes[1].grid()
axes[2].grid()
for ax in list(axes) + list(twax):
ax.ticklabel_format(useOffset=False,
style='sci',
scilimits=(0, 0),
axis='y')
plt.show()
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
