def _create_new_fbpic_species(self, s, layout, initialize_self_field):
# - For the case of a plasma defined in a gridded layout
if (type(s.initial_distribution)==PICMI_AnalyticDistribution) and \
(type(layout) == PICMI_GriddedLayout):
assert initialize_self_field == False
import numexpr
density_expression = s.initial_distribution.density_expression
if s.density_scale is not None:
density_expression = "%f*(%s)" \
%(s.density_scale, density_expression)
def dens_func(z, r):
n = numexpr.evaluate(density_expression)
return n
p_nr = layout.n_macroparticle_per_cell[0]
p_nt = layout.n_macroparticle_per_cell[1]
p_nz = layout.n_macroparticle_per_cell[2]
fbpic_species = self.fbpic_sim.add_new_species(
q=s.charge,
m=s.mass,
n=1.,
dens_func=dens_func,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt,
p_zmin=s.initial_distribution.lower_bound[-1],
p_zmax=s.initial_distribution.upper_bound[-1],
continuous_injection=s.initial_distribution.fill_in)
# - For the case of a Gaussian beam
elif (type(s.initial_distribution)==PICMI_GaussianBunchDistribution) \
and (type(layout) == PICMI_PseudoRandomLayout):
dist = s.initial_distribution
gamma0_beta0 = dist.centroid_velocity[-1] / c
gamma0 = (1 + gamma0_beta0**2)**.5
sig_r = dist.rms_bunch_size[0]
sig_z = dist.rms_bunch_size[-1]
sig_gamma = dist.rms_velocity[-1] / c
sig_vr = dist.rms_velocity[0] / gamma0
if sig_vr != 0:
tf = -sig_r**2 / sig_vr**2 * dist.velocity_divergence[0]
else:
tf = 0.
zf = dist.centroid_position[-1] + \
dist.centroid_velocity[-1]/gamma0 * tf
# Calculate size at focus and emittance
sig_r0 = (sig_r**2 - (sig_vr * tf)**2)**0.5
n_emit = gamma0 * sig_r0 * sig_vr / c
# Get the number of physical particles
n_physical_particles = dist.n_physical_particles
if s.density_scale is not None:
n_physical_particles *= s.density_scale
fbpic_species = add_particle_bunch_gaussian(
self.fbpic_sim,
q=s.charge,
m=s.mass,
gamma0=gamma0,
sig_gamma=sig_gamma,
sig_r=sig_r0,
sig_z=sig_z,
n_emit=n_emit,
n_physical_particles=n_physical_particles,
n_macroparticles=layout.n_macroparticles,
zf=zf,
tf=tf,
initialize_self_field=initialize_self_field)
# - For the case of an empty species
elif (s.initial_distribution is None) and (layout is None):
fbpic_species = self.fbpic_sim.add_new_species(q=s.charge,
m=s.mass)
else:
raise ValueError('Unknown combination of layout and distribution')
return fbpic_species
def _create_new_fbpic_species(self, s, layout, injection_plane_position,
injection_plane_normal_vector,
initialize_self_field):
# - For the case of a plasma/beam defined in a gridded layout
if type(layout) == PICMI_GriddedLayout:
# - Uniform distribution
if type(s.initial_distribution) == PICMI_UniformDistribution:
n0 = s.initial_distribution.density
dens_func = None
# - Analytic distribution
elif type(s.initial_distribution) == PICMI_AnalyticDistribution:
import numexpr
density_expression = s.initial_distribution.density_expression
if s.density_scale is not None:
n0 = s.density_scale
else:
n0 = 1.
def dens_func(x, y, z):
d = locals()
d.update(s.initial_distribution.user_defined_kw)
n = numexpr.evaluate(density_expression, local_dict=d)
return n
else:
raise ValueError(
'Unknown combination of layout and distribution')
p_nr = layout.n_macroparticle_per_cell[0]
p_nt = layout.n_macroparticle_per_cell[1]
p_nz = layout.n_macroparticle_per_cell[2]
if initialize_self_field or (injection_plane_position is not None):
assert s.initial_distribution.fill_in != True
if injection_plane_position is None:
z_injection_plane = None
else:
z_injection_plane = injection_plane_position[-1]
gamma0_beta0 = s.initial_distribution.directed_velocity[-1] / c
gamma0 = (1 + gamma0_beta0**2)**.5
dist = s.initial_distribution
fbpic_species = add_particle_bunch(
self.fbpic_sim,
q=s.charge,
m=s.mass,
gamma0=gamma0,
n=n0,
dens_func=dens_func,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt,
p_zmin=dist.lower_bound[-1]
if dist.lower_bound[-1] is not None else -np.inf,
p_zmax=dist.upper_bound[-1]
if dist.upper_bound[-1] is not None else +np.inf,
p_rmin=0,
p_rmax=dist.upper_bound[0]
if dist.upper_bound[0] is not None else +np.inf,
boost=self.fbpic_sim.boost,
z_injection_plane=z_injection_plane,
initialize_self_field=initialize_self_field,
boost_positions_in_dens_func=True)
else:
dist = s.initial_distribution
fbpic_species = self.fbpic_sim.add_new_species(
q=s.charge,
m=s.mass,
n=n0,
dens_func=dens_func,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt,
p_zmin=dist.lower_bound[-1]
if dist.lower_bound[-1] is not None else -np.inf,
p_zmax=dist.upper_bound[-1]
if dist.upper_bound[-1] is not None else +np.inf,
p_rmax=dist.upper_bound[0]
if dist.upper_bound[0] is not None else +np.inf,
continuous_injection=s.initial_distribution.fill_in,
boost_positions_in_dens_func=True)
# - For the case of a Gaussian beam
elif (type(s.initial_distribution)==PICMI_GaussianBunchDistribution) \
and (type(layout) == PICMI_PseudoRandomLayout):
dist = s.initial_distribution
gamma0_beta0 = dist.centroid_velocity[-1] / c
gamma0 = (1 + gamma0_beta0**2)**.5
sig_r = dist.rms_bunch_size[0]
sig_z = dist.rms_bunch_size[-1]
sig_gamma = dist.rms_velocity[-1] / c
sig_vr = dist.rms_velocity[0] / gamma0
if sig_vr != 0:
tf = -sig_r**2 / sig_vr**2 * dist.velocity_divergence[0]
else:
tf = 0.
zf = dist.centroid_position[-1] + \
dist.centroid_velocity[-1]/gamma0 * tf
# Calculate size at focus and emittance
sig_r0 = (sig_r**2 - (sig_vr * tf)**2)**0.5
n_emit = gamma0 * sig_r0 * sig_vr / c
# Get the number of physical particles
n_physical_particles = dist.n_physical_particles
if s.density_scale is not None:
n_physical_particles *= s.density_scale
fbpic_species = add_particle_bunch_gaussian(
self.fbpic_sim,
q=s.charge,
m=s.mass,
gamma0=gamma0,
sig_gamma=sig_gamma,
sig_r=sig_r0,
sig_z=sig_z,
n_emit=n_emit,
n_physical_particles=n_physical_particles,
n_macroparticles=layout.n_macroparticles,
zf=zf,
tf=tf,
boost=self.fbpic_sim.boost,
initialize_self_field=initialize_self_field)
# - For the case of an empty species
elif (s.initial_distribution is None) and (layout is None):
fbpic_species = self.fbpic_sim.add_new_species(q=s.charge,
m=s.mass)
else:
raise ValueError('Unknown combination of layout and distribution')
return fbpic_species
n=n_e,
dens_func=dens_func,
p_zmin=p_zmin,
p_zmax=p_zmax,
p_rmax=p_rmax,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt)
beam = add_particle_bunch_gaussian(
sim,
q=-e,
m=m_e,
sig_r=sig_r,
sig_z=sig_z,
n_emit=0, #initial emittance is 0
gamma0=gamma0,
sig_gamma=sig_gamma,
n_physical_particles=Qtot / e,
n_macroparticles=5000,
zf=L0,
tf=(L0 - z0Beam) / c,
z_injection_plane=L0)
# Load initial fields
# Add a laser to the fields of the simulation
#add_laser( sim, a0, w0, ctau, z0 )
#add_laser_pulse(sim,PulseTrain_profile,method="antenna",z0_antenna=z0Antenna,v_antenna=0) #fw_propagating=True
add_laser_pulse(sim,
UniformPulseTrainN(NumberOfPulses, UniformPulseSpacing),
method="antenna",
z0_antenna=z0Antenna,