def main():
conf = toml.load(sys.argv[1])
common.setup_plot()
with PdfPages(conf['Output']) as output:
for page_conf in conf['Pages']:
dump(output, conf, page_conf)
def main():
common.setup_plot()
output = PdfPages('quadrupole-trajectory-diagram.pdf')
plot_quadrupole(output)
output.close()
output = PdfPages('sextupole-trajectory-diagram.pdf')
plot_sextupole(output)
output.close()
def main():
common.setup_plot()
plot.rc('figure.subplot', right=0.96, top=0.97, bottom=0.15, left=0.13)
output = PdfPages('comparison.pdf')
for thickness in common.thickness_vals:
for energy in common.energy_vals:
for particle in ['e-']:
desc = '{:.3f}mm_{:.3f}MeV'.format(round(thickness / mm, 3),
round(energy / MeV, 3))
filename = 'out/' + desc + '.h5'
plot_run(output, filename, thickness, energy, particle)
output.close()
def main():
args = get_args()
conf = args.conf
fin = h5py.File(conf['Files']['Input'], 'r')
run_index = tuple(conf['Files']['RunIndex'])
edep = fin['edep'][run_index] * eV
xbin = fin['xbin'][:] * mm
ybin = fin['ybin'][:] * mm
zbin = fin['zbin'][:] * mm
common.setup_plot()
out_fname = conf['Files']['Output']
os.makedirs(os.path.dirname(out_fname), exist_ok=True)
output = PdfPages(out_fname)
plot_deposition_2d(output, conf, edep, xbin, ybin, zbin)
output.close()
return 0
def main():
common.setup_plot()
with PdfPages('collimation-output.pdf') as output:
dump(
output, 'results/W-5-p45-10.h5', 'results/uncollimated.h5',
'Tungsten collimator\n' +
r'$a = 5\,{\rm mm}, r = 0.45\,a, d = 10\,{\rm mm}$')
dump(
output, 'results/W-4-p43-12.h5', 'results/uncollimated.h5',
'Tungsten collimator\n' +
r'$a = 4\,{\rm mm}, r = 0.43\,a, d = 12\,{\rm mm}$')
dump(
output, 'results/W-5-p42-15.h5', 'results/uncollimated.h5',
'Tungsten collimator\n' +
r'$a = 5\,{\rm mm}, r = 0.42\,a, d = 15\,{\rm mm}$')
dump(
output, 'results/Al-5-p45-10.h5', 'results/uncollimated.h5',
'Aluminum collimator\n' +
r'$a = 5\,{\rm mm}, r = 0.45\,a, d = 10\,{\rm mm}$')
def main():
fin = h5py.File('d2W.h5', 'r')
common.setup_plot()
os.makedirs('figs', exist_ok=True)
# PWFA
if True:
pwfa_drive_datasets = [
'E300 PWFA/Unmatched Trailing (s=0.17)/drive',
'E300 PWFA/Unmatched Trailing (s=0.14)/drive',
'E300 PWFA/Matched Trailing (s=0.06)/drive',
'E300 PWFA/Unmatched Trailing (s=0.01)/drive']
pwfa_trailing_datasets = [
'E300 PWFA/Unmatched Trailing (s=0.17)/trailing',
'E300 PWFA/Unmatched Trailing (s=0.14)/trailing',
'E300 PWFA/Matched Trailing (s=0.06)/trailing',
'E300 PWFA/Unmatched Trailing (s=0.01)/trailing']
output = PdfPages('figs/pwfa.pdf')
plot_spectral_photon_density(
output, fin, pwfa_trailing_datasets[1:2],
(MeV, 'MeV'),
(1/MeV, '1/MeV'),
(-0.1, 2.0), (1e7, 1e13))
plot_d2W(
output, fin, pwfa_trailing_datasets[1],
)
for x in pwfa_trailing_datasets:
plot_double_differential(
output, fin, x, 8,
(MeV, 'MeV'), (mrad, 'mrad'),
(uJ/(mrad*MeV), r'uJ/(mrad$^\cdot$MeV)'),
(0.01, 1.0), (-2.0, 2.0))
output.close()
def main():
args = get_args()
conf = args.conf
fin = h5py.File(conf['Files']['Input'], 'r')
run_index = tuple(conf['Files']['RunIndex'])
num_events = fin['num_events'][run_index]
gin = fin[conf['Files']['Group']]
edep = gin['edep'][run_index] * MeV
xbin = gin['xbin'][:] * mm
ybin = gin['ybin'][:] * mm
zbin = gin['zbin'][:] * mm
common.setup_plot()
filename = conf['Files']['Output']
path = os.path.dirname(filename)
if path != '':
os.makedirs(path, exist_ok=True)
# output = PdfPages(filename)
plot_deposition_2d(filename, conf, edep, num_events, xbin, ybin, zbin)
# output.close()
return 0
def main():
common.setup_plot()
energy = []
position = []
theta0 = 28.0 * deg
M = compton.build_transformation(
[['TranslateX', 'RotateY', 'TranslateZ'], [-40.1, -28.0, -30.0]], mm,
deg)
with h5py.File('trajectories/trajectories.h5', 'r') as fin:
for gin in fin.values():
E0 = get_energy(gin)
x0 = gin['x'][0] * meter
x1 = gin['x'][-1] * meter
if x0[1] != 0.0:
continue
x1 = compton.transform(M, x1)
energy.append(E0)
position.append(x1[2])
energy = np.array(energy)
position = np.array(position)
args = np.argsort(energy)
energy = energy[args]
position = position[args]
mod = lmfit.Model(fit_func)
params = mod.make_params(c0=0.0, c1=0.0, c2=0.0)
result = mod.fit(data=energy / MeV, x=position, params=params)
print(result.fit_report())
v = result.params.valuesdict()
print(v['c0'])
print(v['c1'])
print(v['c2'])
x_fit = np.linspace(position[0], position[-1], 200)
fig = plot.figure(figsize=(244.0 / 72, 120.0 / 72))
ax = fig.add_subplot(1, 1, 1)
ax.semilogy(x_fit / mm, result.eval(x=x_fit), linewidth=0.6)
ax.semilogy(position / mm,
energy / MeV,
marker='.',
ls='',
markersize=0.001,
color='k')
ax.text(0.05,
0.8,
r'$E(z_s)/{\rm MeV} = \exp (c_0 + c_1 z_s + c_2 z_s^2)$',
fontsize=7.0,
transform=ax.transAxes)
text = r'$c_0 = {:.3}$'.format(v['c0']) + '\n'
text += r'$c_1 = {:.3}'.format(num2tex(v['c1'] * mm))
text += r'\;{\rm mm}^{-1}$' + '\n'
text += r'$c_2 = {:.3}'.format(num2tex(v['c2'] * mm**2))
text += r'\;{\rm mm}^{-2}$' + '\n'
print(text)
ax.text(0.5, 0.2, text, transform=ax.transAxes)
ax.set_xlabel(r'$z_s$ (mm)', labelpad=-1.0)
ax.set_ylabel(r"Energy (MeV)", labelpad=2.0)
# ax.set_xlim(0, 250)
ax.set_ylim(0.1, 20)
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
filename = 'out/energy-scale.pdf'
os.makedirs(os.path.dirname(filename), exist_ok=True)
plot.savefig(filename, transparent=True)
def plot_homing_map(filename, homing_map, hull):
os.makedirs(os.path.dirname(filename), exist_ok=True)
output = PdfPages(filename)
common.setup_plot()
plot.rc('figure.subplot', right=0.99, top=0.99, bottom=0.09, left=0.10)
mesh_dx = 0.5 * mm
Y, Z = np.meshgrid(
np.arange(hull.min_bound[0] - mesh_dx, hull.max_bound[0] + mesh_dx,
mesh_dx),
np.arange(hull.min_bound[1] - mesh_dx, hull.max_bound[1] + mesh_dx,
mesh_dx))
smoothed_xi = homing_map(Y, Z)[:, :, 0]
smoothed_phi = homing_map(Y, Z)[:, :, 1]
fig = plot.figure(figsize=(244 / 72, 100 / 72))
ax = fig.add_subplot(1, 1, 1, aspect=1.0)
ax.contour(Z / mm,
Y / mm,
smoothed_xi,
levels=np.linspace(-1.0, 1.0, 20),
linewidths=0.3,
colors='#0083b8',
linestyles='solid',
zorder=0)
ax.fill(hull.points[hull.vertices, 1] / mm,
hull.points[hull.vertices, 0] / mm,
fill=False,
linewidth=0.5,
edgecolor='k',
zorder=10)
scale_limits(ax.xaxis, 1.1)
scale_limits(ax.yaxis, 1.1)
plot.title(r'$\xi(z, y)$')
plot.xlabel(r'$z_{\rm scint}$ (mm)', labelpad=0.0)
plot.ylabel(r'$y_{\rm scint}$ (mm)', labelpad=0.0)
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
output.savefig(fig, transparent=True)
fig = plot.figure(figsize=(244 / 72, 100 / 72))
ax = fig.add_subplot(1, 1, 1, aspect=1.0)
ax.contour(Z / mm,
Y / mm,
smoothed_phi,
levels=20,
linewidths=0.3,
colors='#0083b8',
linestyles='solid',
zorder=0)
ax.fill(hull.points[hull.vertices, 1] / mm,
hull.points[hull.vertices, 0] / mm,
fill=False,
linewidth=0.5,
edgecolor='k',
zorder=10)
scale_limits(ax.xaxis, 1.1)
scale_limits(ax.yaxis, 1.1)
plot.title(r'$\phi(z, y)$')
plot.xlabel(r'$z_{\rm scint}$ (mm)', labelpad=0.0)
plot.ylabel(r'$y_{\rm scint}$ (mm)', labelpad=0.0)
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
output.savefig(fig, transparent=True)
output.close()
def main():
fin = h5py.File('d2W.h5', 'r')
common.setup_plot()
os.makedirs('figs', exist_ok=True)
# PWFA
if True:
pwfa_drive_datasets = [
'E300 PWFA/Unmatched Trailing (s=0.17)/drive',
'E300 PWFA/Unmatched Trailing (s=0.14)/drive',
'E300 PWFA/Matched Trailing (s=0.06)/drive',
'E300 PWFA/Unmatched Trailing (s=0.01)/drive'
]
pwfa_trailing_datasets = [
'E300 PWFA/Unmatched Trailing (s=0.17)/trailing',
'E300 PWFA/Unmatched Trailing (s=0.14)/trailing',
'E300 PWFA/Matched Trailing (s=0.06)/trailing',
'E300 PWFA/Unmatched Trailing (s=0.01)/trailing'
]
output = PdfPages('figs/pwfa.pdf')
plot_spectral_energy_density(output, fin, pwfa_drive_datasets,
(MeV, 'MeV'), (mJ / MeV, 'mJ/MeV'),
(-0.1, 2.0), (-0.05, 2.6))
plot_spectral_energy_density(output, fin, pwfa_trailing_datasets,
(MeV, 'MeV'), (mJ / MeV, 'mJ/MeV'),
(-0.1, 2.0), (-0.05, 2.6))
plot_spectral_photon_density(output, fin, pwfa_drive_datasets,
(MeV, 'MeV'), (1 / MeV, '1/MeV'),
(-0.1, 2.0), (1e7, 1e13))
plot_spectral_photon_density(output, fin, pwfa_trailing_datasets,
(MeV, 'MeV'), (1 / MeV, '1/MeV'),
(-0.1, 2.0), (1e7, 1e13))
for x in pwfa_drive_datasets:
plot_spot(output, fin, x, 6, (mJ / mrad**2, r'mJ/mrad$^2$'),
(-2.0, 2.0), (-1.5, 1.5))
for x in pwfa_trailing_datasets:
plot_spot(output, fin, x, 6, (mJ / mrad**2, r'mJ/mrad$^2$'),
(-2.0, 2.0), (-1.5, 1.5))
for x in pwfa_drive_datasets:
plot_double_differential(
output, fin, x, 8, (MeV, 'MeV'), (mrad, 'mrad'),
(uJ / (mrad * MeV), r'uJ/(mrad$^\cdot$MeV)'), (0.01, 1.0),
(-2.0, 2.0))
for x in pwfa_trailing_datasets:
plot_double_differential(
output, fin, x, 8, (MeV, 'MeV'), (mrad, 'mrad'),
(uJ / (mrad * MeV), r'uJ/(mrad$^\cdot$MeV)'), (0.01, 1.0),
(-2.0, 2.0))
output.close()
# Filamentation
if True:
output = PdfPages('figs/filamentation.pdf')
plot_spectral_energy_density(output,
fin, ['Filamentation/solid'],
(MeV, 'MeV'), (uJ / MeV, 'uJ/MeV'),
(-4, 60.0), (-5, 100),
display_legend=False)
plot_spectral_photon_density(output,
fin, ['Filamentation/solid'],
(MeV, 'MeV'), (1 / MeV, '1/MeV'),
(-2, 60.0), (1e6, 1e10),
display_legend=False)
plot_spot(output, fin, 'Filamentation/solid', 5,
(mJ / mrad**2, r'mJ/mrad$^2$'), (-0.2, 0.2), (-0.2, 0.2), '')
plot_double_differential(output, fin, 'Filamentation/solid', 8,
(MeV, 'MeV'), (mrad, 'mrad'),
(uJ / (mrad * MeV), r'uJ/(mrad$\cdot$MeV)'),
(1, 30), (-0.2, 0.2), '')
output.close()
# SFQED
if True:
output = PdfPages('figs/sfqed.pdf')
sfqed_datasets = [
'SFQED/MPIK/LCFA_w3.0_xi5.7', 'SFQED/MPIK/LCFA_w2.4_xi7.2',
'SFQED/MPIK/LCS+LCFA_w3.0_xi5.7', 'SFQED/MPIK/LCS+LCFA_w2.4_xi7.2'
]
sfqed_labels = [
r'LCFA ($a_0=5.7, w_0=3.0\;\mu{\rm m}$)',
r'LCFA ($a_0=7.2, w_0=2.4\;\mu{\rm m}$)',
r'QED ($a_0=5.7, w_0=3.0\;\mu{\rm m}$)',
r'QED ($a_0=7.2, w_0=2.4\;\mu{\rm m}$)'
]
plot_spectral_energy_density(output,
fin,
sfqed_datasets, (GeV, 'GeV'),
(mJ / GeV, 'mJ/GeV'), (-0.1, 10.0),
(-0.1, 4),
labels=sfqed_labels)
plot_spectral_energy_density(output,
fin,
sfqed_datasets, (MeV, 'MeV'),
(uJ / MeV, 'uJ/MeV'), (-1, 60.0),
(-0.1, 3),
labels=sfqed_labels)
plot_spectral_photon_density(output,
fin,
sfqed_datasets, (GeV, 'GeV'),
(1 / GeV, '1/GeV'), (-0.1, 10.0),
(1e1, 1e9),
labels=sfqed_labels)
plot_spectral_photon_density(output,
fin,
sfqed_datasets, (MeV, 'MeV'),
(1 / MeV, '1/MeV'), (-1, 60.0),
(2e4, 4e7),
labels=sfqed_labels)
for x, y in zip(sfqed_datasets, sfqed_labels):
plot_double_differential(output,
fin,
x,
8, (MeV, 'MeV'), (mrad, 'mrad'),
(uJ /
(mrad * MeV), r'uJ/(mrad$\cdot$MeV)'),
(1, 30), (-0.5, 0.5),
label=y)
for x, y in zip(sfqed_datasets, sfqed_labels):
plot_spot(output,
fin,
x,
6, (mJ / mrad**2, r'mJ/mrad$^2$'), (-0.3, 0.3),
(-0.1, 0.1),
label=y)
output.close()
def main():
args = get_args()
conf = args.conf
# create safe interpreter for evaluation of configuration expressions
aeval = asteval.Interpreter(use_numpy=True)
for q in common.units.__all__:
aeval.symtable[q] = common.units.__dict__[q]
pconf = conf['Projection']
M = compton.build_transformation(pconf['Transformation'], mm, deg)
prefilter = np.array(pconf['Prefilter']) * mm
postfilter = np.array(pconf['Postfilter']) * mm
energy = []
position = []
x = []
E0 = []
with h5py.File(conf['Files']['Input'], 'r') as fin:
for gin in fin.values():
x.append((gin['x'][0] * meter,
compton.transform(M, gin['x'][-1] * meter)))
E0.append(get_energy(gin))
x = np.array(x)
E0 = np.array(E0)
prefilter_mask = compton.in_volume(prefilter, x[:, 0, :])
x_pre = x[prefilter_mask, :, :]
E0_pre = E0[prefilter_mask]
postfilter_mask = compton.in_volume(postfilter, x_pre[:, 1, :])
x_post = x_pre[postfilter_mask, :, :]
E0_post = E0_pre[postfilter_mask]
energy = E0_post.copy()
position = x_post[:, 1, 2].copy()
args = np.argsort(energy)
energy = energy[args]
position = position[args]
mod = lmfit.Model(fit_func)
params = mod.make_params(c0=0.0, c1=0.0, c2=0.0, c3=0.0)
result = mod.fit(data=np.log(energy / MeV), x=position, params=params)
v = result.params.valuesdict()
x_fit = np.linspace(position[0], position[-1], 200)
common.setup_plot()
fig = plot.figure(figsize=np.array(conf['Plot']['FigSize']) / 72)
ax = fig.add_subplot(1, 1, 1)
axes = [get_axis(aeval, *conf['Plot'][x]) for x in ['XAxis', 'YAxis']]
ax.semilogy(x_fit / axes[0].unit,
np.exp(result.eval(x=x_fit)),
linewidth=0.6)
ax.semilogy(position / axes[0].unit,
energy / axes[1].unit,
marker='.',
ls='',
markersize=2.0,
markeredgewidth=0,
color='k')
aeval.symtable['fitval'] = v
aeval.symtable['num2tex'] = num2tex
plot_annotation(ax, aeval, conf['Plot'])
ax.set_xlabel(axes[0].label, labelpad=-1.0)
ax.set_ylabel(axes[1].label, labelpad=2.0)
ax.set_xlim(*axes[0].xlim)
ax.set_ylim(*axes[1].xlim)
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
filename = conf['Files']['PlotOutput']
path = os.path.dirname(filename)
if path != '':
os.makedirs(path, exist_ok=True)
plot.savefig(filename, transparent=True)
if 'CalcOutput' in conf['Files']:
filename = conf['Files']['CalcOutput']
path = os.path.dirname(filename)
if path != '':
os.makedirs(path, exist_ok=True)
calc_output = {
'EnergyScaleCoefficients': {
'c0': float(v['c0']),
'c1': float(v['c1'] * mm),
'c2': float(v['c2'] * mm**2),
'c3': float(v['c3'] * mm**3)
}
}
with open(filename, 'w') as fout:
toml.dump(calc_output, fout)