def check_fields(interp1_complex,
z,
r,
info_in_real_part,
z0,
gamma_b,
forward_propagating,
show_difference=False):
"""
Check the real and imaginary part of the interpolation grid agree
with the theory by:
- Checking that the part (real or imaginary) that does not
carry information is zero
- Extracting the a0 from the other part and comparing it
to the predicted value
- Using the extracted value of a0 to compare the simulated
profile with a gaussian profile
"""
# Extract the part that has information
if info_in_real_part:
interp1 = interp1_complex.real
zero_part = interp1_complex.imag
else:
interp1 = interp1_complex.imag
zero_part = interp1_complex.real
# Control that the part that has no information is 0
assert np.allclose(0., zero_part, atol=1.e-6 * interp1.max())
# Get the predicted properties of the laser in the boosted frame
if gamma_b is None:
boost = BoostConverter(1.)
else:
boost = BoostConverter(gamma_b)
ctau_b, lambda0_b, Lprop_b, z0_b = \
boost.copropag_length([ctau, 0.8e-6, Lprop, z0])
# Take into account whether the pulse is propagating forward or backward
if not forward_propagating:
Lprop_b = -Lprop_b
# Fit the on-axis profile to extract a0
def fit_function(z, a0, z0_phase):
return (gaussian_laser(z, r[0], a0, z0_phase, z0_b + Lprop_b, ctau_b,
lambda0_b))
fit_result = curve_fit(fit_function,
z,
interp1[:, 0],
p0=np.array([a0, z0_b + Lprop_b]))
a0_fit, z0_fit = fit_result[0]
# Check that the a0 agrees within 5% of the predicted value
assert abs(abs(a0_fit) - a0) / a0 < 0.05
# Calculate predicted fields
r2d, z2d = np.meshgrid(r, z)
# Factor 0.5 due to the definition of the interpolation grid
interp1_predicted = gaussian_laser(z2d, r2d, a0_fit, z0_fit,
z0_b + Lprop_b, ctau_b, lambda0_b)
# Plot the difference
if show_difference:
import matplotlib.pyplot as plt
plt.subplot(311)
plt.imshow(interp1.T)
plt.colorbar()
plt.subplot(312)
plt.imshow(interp1_predicted.T)
plt.colorbar()
plt.subplot(313)
plt.imshow((interp1_predicted - interp1).T)
plt.colorbar()
plt.show()
# Control the values (with a precision of 3%)
assert np.allclose(interp1_predicted, interp1, atol=3.e-2 * interp1.max())
def run_simulation(gamma_boost, use_separate_electron_species):
"""
Run a simulation with a laser pulse going through a gas jet of ionizable
N5+ atoms, and check the fraction of atoms that are in the N5+ state.
Parameters
----------
gamma_boost: float
The Lorentz factor of the frame in which the simulation is carried out.
use_separate_electron_species: bool
Whether to use separate electron species for each level, or
a single electron species for all levels.
"""
# The simulation box
zmax_lab = 20.e-6 # Length of the box along z (meters)
zmin_lab = 0.e-6
Nr = 3 # Number of gridpoints along r
rmax = 10.e-6 # Length of the box along r (meters)
Nm = 2 # Number of modes used
# The particles of the plasma
p_zmin = 5.e-6 # Position of the beginning of the plasma (meters)
p_zmax = 15.e-6
p_rmin = 0. # Minimal radial position of the plasma (meters)
p_rmax = 100.e-6 # Maximal radial position of the plasma (meters)
n_atoms = 0.2 # The atomic density is chosen very low,
# to avoid collective effects
p_nz = 2 # Number of particles per cell along z
p_nr = 1 # Number of particles per cell along r
p_nt = 4 # Number of particles per cell along theta
# Boosted frame
boost = BoostConverter(gamma_boost)
# Boost the different quantities
beta_boost = np.sqrt(1. - 1. / gamma_boost**2)
zmin, zmax = boost.static_length([zmin_lab, zmax_lab])
p_zmin, p_zmax = boost.static_length([p_zmin, p_zmax])
n_atoms, = boost.static_density([n_atoms])
# Increase the number of particles per cell in order to keep sufficient
# statistics for the evaluation of the ionization fraction
if gamma_boost > 1:
p_nz = int(2 * gamma_boost * (1 + beta_boost) * p_nz)
# The laser
a0 = 1.8 # Laser amplitude
lambda0_lab = 0.8e-6 # Laser wavelength
# Boost the laser wavelength before calculating the laser amplitude
lambda0, = boost.copropag_length([lambda0_lab], beta_object=1.)
# Duration and initial position of the laser
ctau = 10. * lambda0
z0 = -2 * ctau
# Calculate laser amplitude
omega = 2 * np.pi * c / lambda0
E0 = a0 * m_e * c * omega / e
B0 = E0 / c
def laser_func(F, x, y, z, t, amplitude, length_scale):
"""
Function that describes a Gaussian laser with infinite waist
"""
return( F + amplitude * math.cos( 2*np.pi*(z-c*t)/lambda0 ) * \
math.exp( - (z - c*t - z0)**2/ctau**2 ) )
# Resolution and number of timesteps
dz = lambda0 / 16.
dt = dz / c
Nz = int((zmax - zmin) / dz) + 1
N_step = int(
(2. * 40. * lambda0 + zmax - zmin) / (dz * (1 + beta_boost))) + 1
# Get the speed of the plasma
uz_m, = boost.longitudinal_momentum([0.])
v_plasma, = boost.velocity([0.])
# The diagnostics
diag_period = N_step - 1 # Period of the diagnostics in number of timesteps
# Initial ionization level of the Nitrogen atoms
level_start = 2
# Initialize the simulation object, with the neutralizing electrons
# No particles are created because we do not pass the density
sim = Simulation(Nz,
zmax,
Nr,
rmax,
Nm,
dt,
zmin=zmin,
v_comoving=v_plasma,
use_galilean=False,
boundaries='open',
use_cuda=use_cuda)
# Add the charge-neutralizing electrons
elec = sim.add_new_species(q=-e,
m=m_e,
n=level_start * n_atoms,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt,
p_zmin=p_zmin,
p_zmax=p_zmax,
p_rmin=p_rmin,
p_rmax=p_rmax,
continuous_injection=False,
uz_m=uz_m)
# Add the N atoms
ions = sim.add_new_species(q=0,
m=14. * m_p,
n=n_atoms,
p_nz=p_nz,
p_nr=p_nr,
p_nt=p_nt,
p_zmin=p_zmin,
p_zmax=p_zmax,
p_rmin=p_rmin,
p_rmax=p_rmax,
continuous_injection=False,
uz_m=uz_m)
# Add the target electrons
if use_separate_electron_species:
# Use a dictionary of electron species: one per ionizable level
target_species = {}
level_max = 6 # N can go up to N7+, but here we stop at N6+
for i_level in range(level_start, level_max):
target_species[i_level] = sim.add_new_species(q=-e, m=m_e)
else:
# Use the pre-existing, charge-neutralizing electrons
target_species = elec
level_max = None # Default is going up to N7+
# Define ionization
ions.make_ionizable(element='N',
level_start=level_start,
level_max=level_max,
target_species=target_species)
# Set the moving window
sim.set_moving_window(v=v_plasma)
# Add a laser to the fields of the simulation (external fields)
sim.external_fields = [
ExternalField(laser_func, 'Ex', E0, 0.),
ExternalField(laser_func, 'By', B0, 0.)
]
# Add a particle diagnostic
sim.diags = [
ParticleDiagnostic(
diag_period,
{"ions": ions},
particle_data=["position", "gamma", "weighting", "E", "B"],
# Test output of fields and gamma for standard
# (non-boosted) particle diagnostics
write_dir='tests/diags',
comm=sim.comm)
]
if gamma_boost > 1:
T_sim_lab = (2. * 40. * lambda0_lab + zmax_lab - zmin_lab) / c
sim.diags.append(
BackTransformedParticleDiagnostic(zmin_lab,
zmax_lab,
v_lab=0.,
dt_snapshots_lab=T_sim_lab / 2.,
Ntot_snapshots_lab=3,
gamma_boost=gamma_boost,
period=diag_period,
fldobject=sim.fld,
species={"ions": ions},
comm=sim.comm,
write_dir='tests/lab_diags'))
# Run the simulation
sim.step(N_step, use_true_rho=True)
# Check the fraction of N5+ ions at the end of the simulation
w = ions.w
ioniz_level = ions.ionizer.ionization_level
# Get the total number of N atoms/ions (all ionization levels together)
ntot = w.sum()
# Get the total number of N5+ ions
n_N5 = w[ioniz_level == 5].sum()
# Get the fraction of N5+ ions, and check that it is close to 0.32
N5_fraction = n_N5 / ntot
print('N5+ fraction: %.4f' % N5_fraction)
assert ((N5_fraction > 0.30) and (N5_fraction < 0.34))
# When different electron species are created, check the fraction of
# each electron species
if use_separate_electron_species:
for i_level in range(level_start, level_max):
n_N = w[ioniz_level == i_level].sum()
assert np.allclose(target_species[i_level].w.sum(), n_N)
# Check consistency in the regular openPMD diagnostics
ts = OpenPMDTimeSeries('./tests/diags/hdf5/')
last_iteration = ts.iterations[-1]
w, q = ts.get_particle(['w', 'charge'],
species="ions",
iteration=last_iteration)
# Check that the openPMD file contains the same number of N5+ ions
n_N5_openpmd = np.sum(w[(4.5 * e < q) & (q < 5.5 * e)])
assert np.isclose(n_N5_openpmd, n_N5)
# Remove openPMD files
shutil.rmtree('./tests/diags/')
# Check consistency of the back-transformed openPMD diagnostics
if gamma_boost > 1.:
ts = OpenPMDTimeSeries('./tests/lab_diags/hdf5/')
last_iteration = ts.iterations[-1]
w, q = ts.get_particle(['w', 'charge'],
species="ions",
iteration=last_iteration)
# Check that the openPMD file contains the same number of N5+ ions
n_N5_openpmd = np.sum(w[(4.5 * e < q) & (q < 5.5 * e)])
assert np.isclose(n_N5_openpmd, n_N5)
# Remove openPMD files
shutil.rmtree('./tests/lab_diags/')
n = np.where(z >= ramp_up + plateau + ramp_down, 0, n)
# Add transverse guiding parabolic profile
n = n * (1. + rel_delta_n_over_w2 * r**2)
return (n)
# The bunch
bunch_zmin = z0 - 10.e-6
bunch_zmax = bunch_zmin + 4.e-6
bunch_rmax = 10.e-6
bunch_gamma = 400.
bunch_n = 5.e23
# Convert parameters to boosted frame
bunch_beta = np.sqrt(1. - 1. / bunch_gamma**2)
bunch_zmin, bunch_zmax = \
boost.copropag_length( [ bunch_zmin, bunch_zmax ], beta_object=bunch_beta )
bunch_n, = boost.copropag_density([bunch_n], beta_object=bunch_beta)
bunch_gamma, = boost.gamma([bunch_gamma])
# The moving window (moves with the group velocity in a plasma)
v_window = c * (1 - 0.5 * n_e / 1.75e27)
# Convert parameter to boosted frame
v_window, = boost.velocity([v_window])
# The diagnostics
diag_period = 50 # Period of the diagnostics in number of timesteps
# Whether to write the fields in the lab frame
Ntot_snapshot_lab = 20
dt_snapshot_lab = (zmax - zmin) / c
track_bunch = False # Whether to tag and track the particles of the bunch
def run_simulation(gamma_boost, show):
"""
Run a simulation with a relativistic electron bunch crosses a laser
Parameters
----------
gamma_boost: float
The Lorentz factor of the frame in which the simulation is carried out.
show: bool
Whether to show a plot of the angular distribution
"""
# Boosted frame
boost = BoostConverter(gamma_boost)
# The simulation timestep
diag_period = 100
N_step = 101 # Number of iterations to perform
# Calculate timestep to resolve the interaction with enough points
laser_duration_boosted, = boost.copropag_length([laser_duration],
beta_object=-1)
bunch_sigma_z_boosted, = boost.copropag_length([bunch_sigma_z],
beta_object=1)
dt = (4 * laser_duration_boosted + bunch_sigma_z_boosted / c) / N_step
# Initialize the simulation object
zmax, zmin = boost.copropag_length([zmax_lab, zmin_lab], beta_object=1.)
sim = Simulation(Nz,
zmax,
Nr,
rmax,
Nm,
dt,
p_zmin=0,
p_zmax=0,
p_rmin=0,
p_rmax=0,
p_nz=1,
p_nr=1,
p_nt=1,
n_e=1,
dens_func=None,
zmin=zmin,
boundaries='periodic',
use_cuda=use_cuda)
# Remove particles that were previously created
sim.ptcl = []
print('Initialized simulation')
# Add electron bunch (automatically converted to boosted-frame)
add_elec_bunch_gaussian(sim,
sig_r=1.e-6,
sig_z=bunch_sigma_z,
n_emit=0.,
gamma0=gamma_bunch_mean,
sig_gamma=gamma_bunch_rms,
Q=Q_bunch,
N=N_bunch,
tf=0.0,
zf=0.5 * (zmax + zmin),
boost=boost)
elec = sim.ptcl[0]
print('Initialized electron bunch')
# Add a photon species
photons = Particles(q=0,
m=0,
n=0,
Npz=1,
zmin=0,
zmax=0,
Npr=1,
rmin=0,
rmax=0,
Nptheta=1,
dt=sim.dt,
ux_m=0.,
uy_m=0.,
uz_m=0.,
ux_th=0.,
uy_th=0.,
uz_th=0.,
dens_func=None,
continuous_injection=False,
grid_shape=sim.fld.interp[0].Ez.shape,
particle_shape='linear',
use_cuda=sim.use_cuda)
sim.ptcl.append(photons)
print('Initialized photons')
# Activate Compton scattering for electrons of the bunch
elec.activate_compton(target_species=photons,
laser_energy=laser_energy,
laser_wavelength=laser_wavelength,
laser_waist=laser_waist,
laser_ctau=laser_ctau,
laser_initial_z0=laser_initial_z0,
ratio_w_electron_photon=50,
boost=boost)
print('Activated Compton')
# Add diagnostics
if write_hdf5:
sim.diags = [
ParticleDiagnostic(diag_period,
species={
'electrons': elec,
'photons': photons
},
comm=sim.comm)
]
# Get initial total momentum
initial_total_elec_px = (elec.w * elec.ux).sum() * m_e * c
initial_total_elec_py = (elec.w * elec.uy).sum() * m_e * c
initial_total_elec_pz = (elec.w * elec.uz).sum() * m_e * c
### Run the simulation
for species in sim.ptcl:
species.send_particles_to_gpu()
for i_step in range(N_step):
for species in sim.ptcl:
species.halfpush_x()
elec.handle_elementary_processes(sim.time + 0.5 * sim.dt)
for species in sim.ptcl:
species.halfpush_x()
# Increment time and run diagnostics
sim.time += sim.dt
sim.iteration += 1
for diag in sim.diags:
diag.write(sim.iteration)
# Print fraction of photons produced
if i_step % 10 == 0:
for species in sim.ptcl:
species.receive_particles_from_gpu()
simulated_frac = photons.w.sum() / elec.w.sum()
for species in sim.ptcl:
species.send_particles_to_gpu()
print( 'Iteration %d: Photon fraction per electron = %f' \
%(i_step, simulated_frac) )
for species in sim.ptcl:
species.receive_particles_from_gpu()
# Check estimation of photon fraction
check_photon_fraction(simulated_frac)
# Check conservation of momentum (is only conserved )
if elec.compton_scatterer.ratio_w_electron_photon == 1:
check_momentum_conservation(gamma_boost, photons, elec,
initial_total_elec_px,
initial_total_elec_py,
initial_total_elec_pz)
# Transform the photon momenta back into the lab frame
photon_u = 1. / photons.inv_gamma
photon_lab_pz = boost.gamma0 * (photons.uz + boost.beta0 * photon_u)
photon_lab_p = boost.gamma0 * (photon_u + boost.beta0 * photons.uz)
# Plot the scaled angle and frequency
if show:
import matplotlib.pyplot as plt
# Bin the photons on a grid in frequency and angle
freq_min = 0.5
freq_max = 1.2
N_freq = 500
gammatheta_min = 0.
gammatheta_max = 1.
N_gammatheta = 100
hist_range = [[freq_min, freq_max], [gammatheta_min, gammatheta_max]]
extent = [freq_min, freq_max, gammatheta_min, gammatheta_max]
fundamental_frequency = 4 * gamma_bunch_mean**2 * c / laser_wavelength
photon_scaled_freq = photon_lab_p * c / (h * fundamental_frequency)
gamma_theta = gamma_bunch_mean * np.arccos(
photon_lab_pz / photon_lab_p)
grid, freq_bins, gammatheta_bins = np.histogram2d(
photon_scaled_freq,
gamma_theta,
weights=photons.w,
range=hist_range,
bins=[N_freq, N_gammatheta])
# Normalize by solid angle, frequency and number of photons
dw = (freq_bins[1] - freq_bins[0]) * 2 * np.pi * fundamental_frequency
dtheta = (gammatheta_bins[1] - gammatheta_bins[0]) / gamma_bunch_mean
domega = 2. * np.pi * np.sin(
gammatheta_bins / gamma_bunch_mean) * dtheta
grid /= dw * domega[np.newaxis, 1:] * elec.w.sum()
grid = np.where(grid == 0, np.nan, grid)
plt.imshow(grid.T,
origin='lower',
extent=extent,
cmap='gist_earth',
aspect='auto',
vmax=1.8e-16)
plt.title('Particles, $d^2N/d\omega \,d\Omega$')
plt.xlabel('Scaled energy ($\omega/4\gamma^2\omega_\ell$)')
plt.ylabel(r'$\gamma \theta$')
plt.colorbar()
# Plot theory
plt.plot(1. / (1 + gammatheta_bins**2), gammatheta_bins, color='r')
plt.show()
plt.clf()
# Create empty arrays for saving rms bunch sizes
sig_zp = np.zeros(int(N_step / N_show) + 1)
sig_xp = np.zeros(int(N_step / N_show) + 1)
sig_yp = np.zeros(int(N_step / N_show) + 1)
# Set initial bunch sizes
sig_zp[0] = np.std(sim.ptcl[0].z)
sig_xp[0] = np.std(sim.ptcl[0].x)
sig_yp[0] = np.std(sim.ptcl[0].y)
# Create array corresponding to the propagation distance of the bunch
z_prop = np.arange(int(N_step / N_show) + 1) * ((zmax - zmin) / Nz) * N_show
z_prop += -20.e-6
if l_boost:
z_prop, = boost.copropag_length([z_prop], beta_object=boost.beta0)
# Carry out the simulation
for k in range(int(N_step / N_show)):
sim.step(N_show)
sig_zp[k + 1] = np.std(sim.ptcl[0].z)
sig_xp[k + 1] = np.std(sim.ptcl[0].x)
sig_yp[k + 1] = np.std(sim.ptcl[0].y)
if show_fields:
# Show the fields
plt.figure(0)
plt.clf()
sim.fld.interp[0].show('Ez')
plt.figure(1)
plt.clf()
sim.fld.interp[0].show('Er')
def run_simulation(gamma_boost):
"""
Run a simulation with a laser pulse going through a gas jet of ionizable
N5+ atoms, and check the fraction of atoms that are in the N5+ state.
Parameters
----------
gamma_boost: float
The Lorentz factor of the frame in which the simulation is carried out.
"""
# The simulation box
zmax_lab = 20.e-6 # Length of the box along z (meters)
zmin_lab = 0.e-6
Nr = 3 # Number of gridpoints along r
rmax = 10.e-6 # Length of the box along r (meters)
Nm = 2 # Number of modes used
# The particles of the plasma
p_zmin = 5.e-6 # Position of the beginning of the plasma (meters)
p_zmax = 15.e-6
p_rmin = 0. # Minimal radial position of the plasma (meters)
p_rmax = 100.e-6 # Maximal radial position of the plasma (meters)
n_e = 1. # The plasma density is chosen very low,
# to avoid collective effects
p_nz = 2 # Number of particles per cell along z
p_nr = 1 # Number of particles per cell along r
p_nt = 4 # Number of particles per cell along theta
# Boosted frame
boost = BoostConverter(gamma_boost)
# Boost the different quantities
beta_boost = np.sqrt(1. - 1. / gamma_boost**2)
zmin, zmax = boost.static_length([zmin_lab, zmax_lab])
p_zmin, p_zmax = boost.static_length([p_zmin, p_zmax])
n_e, = boost.static_density([n_e])
# Increase the number of particles per cell in order to keep sufficient
# statistics for the evaluation of the ionization fraction
if gamma_boost > 1:
p_nz = int(2 * gamma_boost * (1 + beta_boost) * p_nz)
# The laser
a0 = 1.8 # Laser amplitude
lambda0_lab = 0.8e-6 # Laser wavelength
# Boost the laser wavelength before calculating the laser amplitude
lambda0, = boost.copropag_length([lambda0_lab], beta_object=1.)
# Duration and initial position of the laser
ctau = 10. * lambda0
z0 = -2 * ctau
# Calculate laser amplitude
omega = 2 * np.pi * c / lambda0
E0 = a0 * m_e * c * omega / e
B0 = E0 / c
def laser_func(F, x, y, z, t, amplitude, length_scale):
"""
Function that describes a Gaussian laser with infinite waist
"""
return( F + amplitude * math.cos( 2*np.pi*(z-c*t)/lambda0 ) * \
math.exp( - (z - c*t - z0)**2/ctau**2 ) )
# Resolution and number of timesteps
dz = lambda0 / 16.
dt = dz / c
Nz = int((zmax - zmin) / dz) + 1
N_step = int(
(2. * 40. * lambda0 + zmax - zmin) / (dz * (1 + beta_boost))) + 1
# Get the speed of the plasma
uz_m, = boost.longitudinal_momentum([0.])
v_plasma, = boost.velocity([0.])
# The diagnostics
diag_period = N_step - 1 # Period of the diagnostics in number of timesteps
# Initialize the simulation object
sim = Simulation(
Nz,
zmax,
Nr,
rmax,
Nm,
dt,
p_zmax,
p_zmax, # No electrons get created because we pass p_zmin=p_zmax
p_rmin,
p_rmax,
p_nz,
p_nr,
p_nt,
n_e,
zmin=zmin,
initialize_ions=False,
v_comoving=v_plasma,
use_galilean=False,
boundaries='open',
use_cuda=use_cuda)
sim.set_moving_window(v=v_plasma)
# Add the N atoms
p_zmin, p_zmax, Npz = adapt_to_grid(sim.fld.interp[0].z, p_zmin, p_zmax,
p_nz)
p_rmin, p_rmax, Npr = adapt_to_grid(sim.fld.interp[0].r, p_rmin, p_rmax,
p_nr)
sim.ptcl.append(
Particles(q=e,
m=14. * m_p,
n=0.2 * n_e,
Npz=Npz,
zmin=p_zmin,
zmax=p_zmax,
Npr=Npr,
rmin=p_rmin,
rmax=p_rmax,
Nptheta=p_nt,
dt=dt,
use_cuda=use_cuda,
uz_m=uz_m,
grid_shape=sim.fld.interp[0].Ez.shape,
continuous_injection=False))
sim.ptcl[1].make_ionizable(element='N',
level_start=0,
target_species=sim.ptcl[0])
# Add a laser to the fields of the simulation (external fields)
sim.external_fields = [
ExternalField(laser_func, 'Ex', E0, 0.),
ExternalField(laser_func, 'By', B0, 0.)
]
# Add a field diagnostic
sim.diags = [
ParticleDiagnostic(diag_period, {"ions": sim.ptcl[1]},
write_dir='tests/diags',
comm=sim.comm)
]
if gamma_boost > 1:
T_sim_lab = (2. * 40. * lambda0_lab + zmax_lab - zmin_lab) / c
sim.diags.append(
BoostedParticleDiagnostic(zmin_lab,
zmax_lab,
v_lab=0.,
dt_snapshots_lab=T_sim_lab / 2.,
Ntot_snapshots_lab=3,
gamma_boost=gamma_boost,
period=diag_period,
fldobject=sim.fld,
species={"ions": sim.ptcl[1]},
comm=sim.comm,
write_dir='tests/lab_diags'))
# Run the simulation
sim.step(N_step, use_true_rho=True)
# Check the fraction of N5+ ions at the end of the simulation
w = sim.ptcl[1].w
ioniz_level = sim.ptcl[1].ionizer.ionization_level
# Get the total number of N atoms/ions (all ionization levels together)
ntot = w.sum()
# Get the total number of N5+ ions
n_N5 = w[ioniz_level == 5].sum()
# Get the fraction of N5+ ions, and check that it is close to 0.32
N5_fraction = n_N5 / ntot
print('N5+ fraction: %.4f' % N5_fraction)
assert ((N5_fraction > 0.30) and (N5_fraction < 0.34))
# Check consistency in the regular openPMD diagnostics
ts = OpenPMDTimeSeries('./tests/diags/hdf5/')
last_iteration = ts.iterations[-1]
w, q = ts.get_particle(['w', 'charge'],
species="ions",
iteration=last_iteration)
# Check that the openPMD file contains the same number of N5+ ions
n_N5_openpmd = np.sum(w[(4.5 * e < q) & (q < 5.5 * e)])
assert np.isclose(n_N5_openpmd, n_N5)
# Remove openPMD files
shutil.rmtree('./tests/diags/')
# Check consistency of the back-transformed openPMD diagnostics
if gamma_boost > 1.:
ts = OpenPMDTimeSeries('./tests/lab_diags/hdf5/')
last_iteration = ts.iterations[-1]
w, q = ts.get_particle(['w', 'charge'],
species="ions",
iteration=last_iteration)
# Check that the openPMD file contains the same number of N5+ ions
n_N5_openpmd = np.sum(w[(4.5 * e < q) & (q < 5.5 * e)])
assert np.isclose(n_N5_openpmd, n_N5)
# Remove openPMD files
shutil.rmtree('./tests/lab_diags/')