def generate_6D_Gaussian_bunch_matched(
self, n_macroparticles, intensity, epsn_x, epsn_y,
sigma_z=None, epsn_z=None, margin=0):
'''Generate a 6D Gaussian distribution of particles which is
transversely as well as longitudinally matched.
The distribution is found iteratively to exactly yield the
given bunch length while at the same time being stationary in
the non-linear bucket. Thus, the bunch length should amount
to the one specificed and should not change significantly
during the synchrotron motion.
Requires self.longitudinal_focusing == 'non-linear'
for the bucket.
'''
assert self.longitudinal_focusing == 'non-linear'
epsx_geo = epsn_x/self.betagamma
epsy_geo = epsn_y/self.betagamma
bunch = gen.ParticleGenerator(macroparticlenumber=n_macroparticles,
intensity=intensity, charge=self.charge, mass=self.mass,
circumference=self.circumference, gamma=self.gamma,
distribution_x=gen.gaussian2D(epsx_geo),
alpha_x=self.alpha_x[0], beta_x=self.beta_x[0], D_x=self.D_x[0],
distribution_y=gen.gaussian2D(epsy_geo),
alpha_y=self.alpha_y[0], beta_y=self.beta_y[0], D_y=self.D_y[0],
distribution_z=gen.RF_bucket_distribution(
rfbucket=self.longitudinal_map.get_bucket(gamma=self.gamma),
sigma_z=sigma_z, epsn_z=epsn_z, margin=margin,
printer=self._printer)
).generate()
return bunch
def test_rf_bucket_distribution(self):
'''Tests the functionality of the rf-bucket matchor'''
#SPS Q20 flattop
nparticles = 100
h1 = 4620
h2 = 4 * 4620
V1 = 10e6
V2 = 1e6
dphi1 = 0
dphi2 = 0
alpha = 0.00308
p_increment = 0
long_map = RFSystems(self.circumference, [h1, h2], [V1, V2],
[dphi1, dphi2], [alpha],
self.gamma,
p_increment,
charge=self.charge,
mass=self.mass)
bucket = long_map.get_bucket(gamma=self.gamma)
bunch = gf.ParticleGenerator(nparticles,
1e11,
constants.e,
constants.m_p,
self.circumference,
self.gamma,
distribution_z=gf.RF_bucket_distribution(
bucket,
epsn_z=0.002,
printer=SilentPrinter())).generate()
def generate_6D_Gaussian_bunch_matched(
self, n_macroparticles, intensity, epsn_x, epsn_y, sigma_z=None, epsn_z=None
):
"""Generate a 6D Gaussian distribution of particles which is
transversely as well as longitudinally matched.
The distribution is found iteratively to exactly yield the
given bunch length while at the same time being stationary in
the non-linear bucket. Thus, the bunch length should amount
to the one specificed and should not change significantly
during the synchrotron motion.
Requires self.longitudinal_mode == 'non-linear'
for the bucket.
"""
if self.longitudinal_mode == 'linear':
assert(sigma_z is not None)
bunch = self.generate_6D_Gaussian_bunch(n_macroparticles, intensity,
epsn_x, epsn_y, sigma_z)
elif self.longitudinal_mode == "non-linear":
epsx_geo = epsn_x / self.betagamma
epsy_geo = epsn_y / self.betagamma
injection_optics = self.transverse_map.get_injection_optics()
bunch = generators.ParticleGenerator(
macroparticlenumber=n_macroparticles,
intensity=intensity,
charge=self.charge,
mass=self.mass,
circumference=self.circumference,
gamma=self.gamma,
distribution_x=generators.gaussian2D(epsx_geo),
alpha_x=injection_optics["alpha_x"],
beta_x=injection_optics["beta_x"],
D_x=injection_optics["D_x"],
distribution_y=generators.gaussian2D(epsy_geo),
alpha_y=injection_optics["alpha_y"],
beta_y=injection_optics["beta_y"],
D_y=injection_optics["D_y"],
distribution_z=generators.RF_bucket_distribution(
self.longitudinal_map.get_bucket(gamma=self.gamma),
sigma_z=sigma_z,
epsn_z=epsn_z,
),
).generate()
else:
raise ValueError('Unknown longitudinal mode!')
return bunch
def generate_6D_Gaussian_bunch_matched_parabolic(self,
n_macroparticles,
intensity,
epsn_x,
epsn_y,
sigma_z=None,
epsn_z=None,
margin=0):
'''
Modified version to use parabolic longitudinal profile
instead of the gaussian one
Added 06.04.2018
'''
assert self.longitudinal_focusing == 'non-linear'
epsx_geo = epsn_x / self.betagamma
epsy_geo = epsn_y / self.betagamma
bunch = gen.ParticleGenerator(
macroparticlenumber=n_macroparticles,
intensity=intensity,
charge=self.charge,
mass=self.mass,
circumference=self.circumference,
gamma=self.gamma,
distribution_x=gen.gaussian2D(epsx_geo),
alpha_x=self.alpha_x[0],
beta_x=self.beta_x[0],
D_x=self.D_x[0],
distribution_y=gen.gaussian2D(epsy_geo),
alpha_y=self.alpha_y[0],
beta_y=self.beta_y[0],
D_y=self.D_y[0],
distribution_z=gen.RF_bucket_distribution(
rfbucket=self.longitudinal_map.get_bucket(gamma=self.gamma),
distribution_type=ParabolicDistribution,
sigma_z=sigma_z,
epsn_z=epsn_z,
margin=margin,
printer=self._printer)).generate()
return bunch