def opt_func(sig_t_fs, count_nfev):
global ctr, sp_ctr, plot_ctr, sp, nfev_ctr
sig_t = sig_t_fs / 1e15
bp_wake = tracking.get_gaussian_profile(sig_t, tt_halfrange,
len_profile, charge,
tracker.energy_eV)
screen_recon = tracker.back_and_forward(meas_screen,
meas_screen0,
bp_wake,
gaps,
beam_offsets,
n_streaker,
n_bins,
back_cutoff=0.1)
screen_max_x = screen_recon.x[np.argmax(screen_recon.intensity)]
screen_shift = tracking.ScreenDistribution(
screen_recon.x - screen_max_x, screen_recon.intensity.copy())
screen_shift.cutoff(0.1)
diff = screen_shift.compare(meas_screen_shift)
print(ctr, '%f fs' % sig_t_fs, '%.1e' % diff)
ctr += 1
if plot_ctr == 5:
plot_ctr = 0
if sp_ctr == 7:
ms.figure('Optimization bo %.1e' % beam_offset0)
sp_ctr = 1
sp = subplot(sp_ctr, title='Profile')
sp_ctr += 1
sp.plot(meas_screen_shift.x * 1e3,
meas_screen_shift.intensity,
label='Original')
plot_ctr += 1
sp.plot(screen_shift.x * 1e3,
screen_shift.intensity,
label='%i: %.1f fs %.3e' % (ctr, sig_t_fs, diff))
sp.legend()
plt.show()
plt.pause(0.01)
if count_nfev:
nfev_ctr += 1
if nfev_ctr > max_nfev:
raise StopIteration(sig_t_fs)
opt_func_values.append((float(sig_t), diff))
return diff
timestamp0 = misc.get_timestamp(os.path.basename(p_dict['filename0']))
tracker0 = tracking.Tracker(magnet_file,
timestamp0,
struct_lengths,
n_particles,
n_emittances,
screen_bins,
screen_cutoff,
smoothen,
profile_cutoff,
len_profile,
quad_wake=quad_wake)
bp_test = tracking.get_gaussian_profile(40e-15, tt_halfrange,
len_profile, charge,
tracker0.energy_eV)
screen_sim = tracker0.matrix_forward(bp_test, [10e-3, 10e-3],
[0, 0])['screen']
all_emittances = []
all_beamsizes = []
for proj in projx0:
screen_meas = get_screen_from_proj(proj, x_axis0, invert_x0)
all_beamsizes.append(screen_meas.gaussfit.sigma)
emittance_fit = misc.fit_nat_beamsize(screen_meas,
screen_sim,
n_emittances[0],
smoothen,
print_=False)
#print(screen_meas.gaussfit.sigma)
all_emittances.append(emittance_fit)
def main():
fig_paper = ms.figure('Comparison plots')
subplot = ms.subplot_factory(2, 2)
sp_ctr_paper = 1
#images0 = dict0['projx'][-1]
#x_axis = dict0['x_axis']*1e-6
#if np.diff(x_axis)[0] < 0:
# x_axis = x_axis[::-1]
# invert_x = True
#else:
# invert_x = False
process_dict = {
'Long': {
'filename': file38,
'main_dict': dict38,
'proj0': dict0['projx'][-1],
'x_axis0': dict0['x_axis'] * 1e-6,
'n_offset': None,
'filename0': file0,
'blmeas': blmeas38,
'flipx': False,
},
'Medium': {
'filename': file25,
'main_dict': dict25,
'proj0': dict25['projx'][7],
'x_axis0': dict25['x_axis'] * 1e-6,
'n_offset': 0,
'filename0': file25,
'blmeas': blmeas25,
'flipx': False,
},
}
for main_label, p_dict in process_dict.items():
#if main_label != 'Medium':
# continue
projx0 = p_dict['proj0']
x_axis0 = p_dict['x_axis0']
if np.diff(x_axis0)[0] < 0:
x_axis0 = x_axis0[::-1]
invert_x0 = True
all_mean = []
for proj in projx0:
screen = get_screen_from_proj(proj, x_axis0, invert_x0)
xx, yy = screen._xx, screen._yy
gf = gaussfit.GaussFit(xx, yy)
all_mean.append(gf.mean)
mean0 = np.mean(all_mean)
timestamp0 = misc.get_timestamp(os.path.basename(p_dict['filename0']))
tracker0 = tracking.Tracker(
archiver_dir + 'archiver_api_data/2020-10-03.h5', timestamp0,
struct_lengths, n_particles, n_emittances, screen_bins,
screen_cutoff, smoothen, profile_cutoff, len_profile)
bp_test = tracking.get_gaussian_profile(40e-15, tt_halfrange,
len_profile, charge,
tracker0.energy_eV)
screen_sim = tracker0.matrix_forward(bp_test, [10e-3, 10e-3],
[0, 0])['screen']
all_emittances = []
for proj in projx0:
screen_meas = get_screen_from_proj(proj, x_axis0, invert_x0)
emittance_fit = misc.fit_nat_beamsize(screen_meas, screen_sim,
n_emittances[0])
all_emittances.append(emittance_fit)
new_emittance = np.mean(all_emittances)
print(main_label, 'Emittance [nm]', new_emittance * 1e9)
n_emittances[0] = new_emittance
dict_ = p_dict['main_dict']
file_ = p_dict['filename']
x_axis = dict_['x_axis'] * 1e-6
y_axis = dict_['y_axis'] * 1e-6
n_offset = p_dict['n_offset']
if np.diff(x_axis)[0] < 0:
x_axis = x_axis[::-1]
invert_x = True
else:
invert_x = False
if np.diff(y_axis)[0] < 0:
y_axis = y_axis[::-1]
invert_y = True
else:
invert_y = False
timestamp = misc.get_timestamp(os.path.basename(file_))
tracker = tracking.Tracker(
archiver_dir + 'archiver_api_data/2020-10-03.h5', timestamp,
struct_lengths, n_particles, n_emittances, screen_bins,
screen_cutoff, smoothen, profile_cutoff, len_profile)
blmeas = p_dict['blmeas']
flip_measured = p_dict['flipx']
profile_meas = tracking.profile_from_blmeas(blmeas,
tt_halfrange,
charge,
tracker.energy_eV,
subtract_min=True)
profile_meas.reshape(len_profile)
profile_meas2 = tracking.profile_from_blmeas(blmeas,
tt_halfrange,
charge,
tracker.energy_eV,
subtract_min=True,
zero_crossing=2)
profile_meas2.reshape(len_profile)
if flip_measured:
profile_meas.flipx()
else:
profile_meas2.flipx()
profile_meas.cutoff(1e-2)
profile_meas2.cutoff(1e-2)
beam_offsets = [0., -(dict_['value'] * 1e-3 - mean_struct2)]
distance_um = (gaps[n_streaker] / 2. - beam_offsets[n_streaker]) * 1e6
if n_offset is not None:
distance_um = distance_um[n_offset]
beam_offsets = [beam_offsets[0], beam_offsets[1][n_offset]]
tdc_screen1 = tracker.matrix_forward(profile_meas, gaps,
beam_offsets)['screen']
tdc_screen2 = tracker.matrix_forward(profile_meas, gaps,
beam_offsets)['screen']
plt.figure(fig_paper.number)
sp_profile_comp = subplot(sp_ctr_paper,
title=main_label,
xlabel='t [fs]',
ylabel='Intensity (arb. units)')
sp_ctr_paper += 1
profile_meas.plot_standard(sp_profile_comp,
norm=True,
color='black',
label='TDC',
center='Right')
ny, nx = 2, 4
subplot = ms.subplot_factory(ny, nx)
sp_ctr = np.inf
all_profiles, all_screens = [], []
if n_offset is None:
projections = dict_['projx']
else:
projections = dict_['projx'][n_offset]
for n_image in range(len(projections)):
screen = get_screen_from_proj(projections[n_image], x_axis,
invert_x)
screen.crop()
screen._xx = screen._xx - mean0
gauss_dict = tracker.find_best_gauss(
sig_t_range,
tt_halfrange,
screen,
gaps,
beam_offsets,
n_streaker,
charge,
self_consistent=self_consistent)
best_screen = gauss_dict['reconstructed_screen']
best_screen.cutoff(1e-3)
best_screen.crop()
best_profile = gauss_dict['reconstructed_profile']
if n_image == 0:
screen00 = screen
bp00 = best_profile
best_screen00 = best_screen
best_gauss = gauss_dict['best_gauss']
if sp_ctr > (ny * nx):
ms.figure('All reconstructions Distance %i %s' %
(distance_um, main_label))
sp_ctr = 1
if n_image % 2 == 0:
sp_profile = subplot(sp_ctr, title='Reconstructions')
sp_ctr += 1
sp_screen = subplot(sp_ctr, title='Screens')
sp_ctr += 1
profile_meas.plot_standard(sp_profile,
color='black',
label='Measured',
norm=True,
center='Right')
tdc_screen1.plot_standard(sp_screen, color='black')
color = screen.plot_standard(sp_screen,
label=n_image)[0].get_color()
best_screen.plot_standard(sp_screen, color=color, ls='--')
best_profile.plot_standard(sp_profile,
label=n_image,
norm=True,
center='Right')
sp_profile.legend()
sp_screen.legend()
all_profiles.append(best_profile)
# Averaging the reconstructed profiles
all_profiles_time, all_profiles_current = [], []
for profile in all_profiles:
profile.shift('Right')
#all_profiles_time.append(profile.time - profile.time[np.argmax(profile.current)])
all_profiles_time.append(profile.time)
new_time = np.linspace(min(x.min() for x in all_profiles_time),
max(x.max() for x in all_profiles_time),
len_profile)
for tt, profile in zip(all_profiles_time, all_profiles):
new_current = np.interp(new_time,
tt,
profile.current,
left=0,
right=0)
new_current *= charge / new_current.sum()
all_profiles_current.append(new_current)
all_profiles_current = np.array(all_profiles_current)
mean_profile = np.mean(all_profiles_current, axis=0)
std_profile = np.std(all_profiles_current, axis=0)
average_profile = tracking.BeamProfile(new_time, mean_profile,
tracker.energy_eV, charge)
average_profile.plot_standard(sp_profile_comp,
label='Reconstructed',
norm=True,
center='Right')
ms.figure('Test averaging %s' % main_label)
sp = plt.subplot(1, 1, 1)
for yy in all_profiles_current:
sp.plot(new_time, yy / np.trapz(yy, new_time), lw=0.5)
to_plot = [
('Average', new_time, mean_profile, 'black', 3),
('+1 STD', new_time, mean_profile + std_profile, 'black', 1),
('-1 STD', new_time, mean_profile - std_profile, 'black', 1),
]
integral = np.trapz(mean_profile, new_time)
for pm, ctr, color in [(profile_meas, 1, 'red'),
(profile_meas2, 2, 'green')]:
#factor = integral/np.trapz(pm.current, pm.time)
#t_meas = pm.time-pm.time[np.argmax(pm.current)]
i_meas = np.interp(new_time, pm.time, pm.current)
bp = tracking.BeamProfile(new_time,
i_meas,
energy_eV=tracker.energy_eV,
charge=charge)
bp.shift('Right')
to_plot.append(('TDC %i' % ctr, bp.time, bp.current, color, 3))
for label, tt, profile, color, lw in to_plot:
gf = gaussfit.GaussFit(tt, profile)
width_fs = gf.sigma * 1e15
if label is None:
label = ''
label = (label + ' %i fs' % width_fs).strip()
factor = np.trapz(profile, tt)
sp.plot(tt, profile / factor, color=color, lw=lw, label=label)
sp.legend(title='Gaussian fit $\sigma$')
plt.show()
screen._yy -= screen._yy.min() screen.reshape(1e3) screen.cutoff(0.05) screen.remove0() screen.reshape(1e3) sp_proj = subplot(sp_ctr, title='Screen', xlabel='Position [mm]', ylabel='Intensity (arb. units)') sp_ctr += 1 screen.plot_standard(sp_proj, label='Streaking') screen0.plot_standard(sp_proj, label='No streaking') tracker = tracking.Tracker(archiver_dir + 'archiver_api_data/2020-10-03.h5', timestamp, struct_lengths, n_particles, n_emittances, screen_bins, screen_cutoff, smoothen, profile_cutoff, len_profile) r12 = tracker.calcR12()[1] bp_gauss = tracking.get_gaussian_profile(30e-15, 200e-15, 1e3, charge, tracker.energy_eV) offset_arr = dict_['value']*1e-3 - mean_struct2 wf = bp_gauss.calc_wake(gaps[1], offset_arr[n_offset], struct_lengths[1]) dipole_wake = wf['dipole']['wake_potential'] dipole_t = wf['input']['charge_xx']/c sp_wf = subplot(sp_ctr, title='Wakefield', xlabel='Time [fs]', ylabel='Wake [V/m]') sp_ctr += 1 sp_wf.plot(dipole_t, dipole_wake) sp_deltax = subplot(sp_ctr, title='Wake effect', xlabel='Time [fs]', ylabel='$\Delta$ x') sp_ctr += 1
def opt_func(sig_t_fs, count_nfev, profile_cutoff, screen_cutoff, smoothen):
a = np.array(opt_func_values)
if len(a) > 0 and np.any(a[:, 0] == sig_t_fs):
index = np.argwhere(sig_t_fs == a[:, 0])[0]
return a[index, 1]
global opt_ctr, sp_ctr, plot_ctr, sp, nfev_ctr, sp2
sig_t = sig_t_fs / 1e15
bp_wake = tracking.get_gaussian_profile(sig_t, tt_halfrange, len_profile,
charge, tracker.energy_eV)
baf = tracker.back_and_forward(meas_screen, bp_wake, gaps, beam_offsets,
n_streaker)
if self_consistent:
baf_self = tracker.back_and_forward(meas_screen, baf['beam_profile'],
gaps, beam_offsets, n_streaker)
screen_self = baf_self['screen']
else:
screen_self = baf['screen']
profile = baf['beam_profile']
diff = screen_self.compare(meas_screen)
print(opt_ctr, '%f fs' % sig_t_fs, '%.1e' % diff)
opt_ctr += 1
if opt_plot:
if plot_ctr == 5:
plot_ctr = 0
if sp_ctr == ny * nx + 1:
ms.figure('Optimization %s' % label)
sp_ctr = 1
sp = subplot(sp_ctr, title='Screen')
sp_ctr += 1
sp.plot(meas_screen_shift.x * 1e3,
meas_screen_shift.intensity / meas_screen_shift.integral,
label='Original')
sp2 = subplot(sp_ctr, title='Profile')
sp_ctr += 1
sp2.plot(profile_meas.time * 1e15,
profile_meas.current / profile_meas.integral,
label='Original')
plot_ctr += 1
sp.plot(screen_self.x * 1e3,
screen_self.intensity / screen_self.integral,
label='%i: %.1f fs %.3e' % (opt_ctr, sig_t_fs, diff))
sp2.plot(profile.time * 1e15,
profile.current / profile.integral,
label='%i: %.1f fs %.3e' % (opt_ctr, sig_t_fs, diff))
sp.legend()
sp2.legend()
plt.show()
plt.pause(0.01)
if count_nfev:
nfev_ctr += 1
if nfev_ctr > max_nfev:
raise StopIteration(sig_t_fs)
opt_func_values.append((float(sig_t_fs), diff))
opt_func_screens.append(screen_self)
opt_func_profiles.append(baf['beam_profile'])
return diff
else:
magnet_file = '/afs/psi.ch/intranet/SF/Beamdynamics/Philipp/data/archiver_api_data/2020-07-26.h5'
bl_meas_file = '/sf/data/measurements/2020/02/03/Bunch_length_meas_2020-02-03_15-59-13.h5'
tracker = tracking.Tracker(magnet_file,
timestamp,
struct_lengths,
energy_eV='file')
energy_eV = tracker.energy_eV
profile_meas = tracking.profile_from_blmeas(bl_meas_file,
tt_halfrange,
charge,
energy_eV,
subtract_min=False)
profile_gauss = tracking.get_gaussian_profile(sig_t, tt_halfrange, len_profile,
charge, energy_eV)
meas_screen_dict = {}
for beam_offset0 in beam_offsets_s0_real:
beam_offsets = [beam_offset0, 0.]
meas_screen = tracker.elegant_forward(profile_meas, gaps, beam_offsets,
n_bins)['screen']
meas_screen.smoothen(smoothen)
meas_screen.cut(backtrack_cutoff)
for beam_offset0 in beam_offsets_s0:
beam_offsets = [beam_offset0, 0.]
fab_dict_real = tracker.forward_and_back(profile_meas,
profile_meas,
gaps,
beam_offsets,
hostname = gethostname()
if hostname == 'desktop':
dirname1 = '/storage/data_2020-10-03/'
dirname2 = '/storage/data_2020-10-04/'
archiver_dir = '/storage/Philipp_data_folder/'
elif hostname == 'pc11292.psi.ch':
dirname1 = '/sf/data/measurements/2020/10/03/'
dirname2 = '/sf/data/measurements/2020/10/04/'
elif hostname == 'pubuntu':
dirname1 = '/home/work/data_2020-10-03/'
dirname2 = '/home/work/data_2020-10-04/'
archiver_dir = '/home/work/'
bp_gauss = tracking.get_gaussian_profile(40e-15, 200e-15, len_profile, charge, energy_eV)
flat_current = np.zeros_like(bp_gauss.current)
flat_time = bp_gauss.time
flat_current[np.logical_and(flat_time > -40e-15, flat_time < 40e-15)] = 1
bp_flat = tracking.BeamProfile(flat_time, flat_current, energy_eV, charge)
sig = 5e-15
dhf_current = doublehornfit.DoublehornFit.fit_func(flat_time, -20e-15, 20e-15, sig, sig, sig, sig, 0.5, 1, 1)
dhf_current *= charge / dhf_current.sum()
bp_dhf = tracking.BeamProfile(flat_time, dhf_current, energy_eV, charge)
bp_dhf.cutoff(1e-3)
screen_bins=screen_bins,
smoothen=smoothen,
profile_cutoff=profile_cutoff,
len_screen=len_profile)
energy_eV = tracker.energy_eV
profile_meas = tracking.profile_from_blmeas(bl_meas_file,
tt_halfrange,
charge,
energy_eV,
subtract_min=False)
profile_meas.center()
profile_gauss = tracking.get_gaussian_profile(sig_t,
tt_halfrange,
len_profile,
charge,
tracker.energy_eV,
cutoff=profile_cutoff)
screen0 = tracker.matrix_forward(profile_meas, gaps, [0, 0])['screen']
meas_screen = tracker.matrix_forward(profile_meas, gaps,
beam_offsets)['screen']
ms.figure('')
subplot = ms.subplot_factory(2, 2)
sp_ctr = 1
sp_profile0 = subplot(sp_ctr,
title='Beam profiles',
xlabel='t [fs]',
ylabel='Current (arb. units)')
