if hostname == 'desktop':
magnet_file = '/storage/Philipp_data_folder/archiver_api_data/2020-07-26.h5'
bl_meas_file = '/storage/data_2020-02-03/Bunch_length_meas_2020-02-03_15-59-13.h5'
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,
charge,
struct_lengths,
energy_eV='file')
energy_eV = tracker.energy_eV
profile_meas = tracking.profile_from_blmeas(bl_meas_file,
tt_halfrange,
energy_eV,
subtract_min=False)
profile_back = tracker.forward_and_back(profile_meas, profile_meas, gaps,
beam_offsets, 0)
#track_dict_forward = tracker.elegant_forward(profile_meas, gaps, beam_offsets, [1, 1])
#track_dict_forward0 = tracker.elegant_forward(profile_meas, gaps, [0,0], [1, 1])
#
#wf_dict = profile_meas.calc_wake(gaps[0], beam_offsets[0], 1.)
#wake_effect = profile_meas.wake_effect_on_screen(wf_dict, track_dict_forward0['r12_dict'][0])
#profile_back = tracker.track_backward(track_dict_forward, track_dict_forward0, wake_effect)
#profile_back.reshape(len_profile)
ms.figure('Back and forward with real profile')
struct_lengths,
n_particles,
n_emittances,
screen_bins,
screen_cutoff,
smoothen,
profile_cutoff,
len_profile,
quad_wake=quad_wake,
bp_smoothen=bp_smoothen)
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()
if np.diff(x_axis)[0] < 0:
x_axis = x_axis[::-1]
invert_x = True
else:
invert_x = False
all_mean = []
for proj in projx0:
screen = get_screen_from_proj(proj, x_axis, invert_x0)
xx, yy = screen._xx, screen._yy
gf = gaussfit.GaussFit(xx, yy)
all_mean.append(gf.mean)
mean0 = np.mean(all_mean)
profile_meas = tracking.profile_from_blmeas(p_dict['blmeas'], tt_halfrange,
charge, energy_eV)
profile_dhf = tracking.dhf_profile(profile_meas)
profile_dhf.cutoff(screen_cutoff)
profile_dhf.crop()
#profile_meas.flipx()
for n_proj in range(3):
screen0 = get_screen_from_proj(projections[n_proj], x_axis, invert_x0)
screen0._xx = screen0._xx - mean0
screen0.cutoff(3e-2)
screen0.crop()
ms.figure('Fit quad effect %s' % main_label)
subplot = ms.subplot_factory(1, 2)
sp_ctr = 1
'Passive_data_20201004T223859.mat',
'Passive_data_20201004T163828.mat',
'Passive_data_20201004T221502.mat',
#'Passive_money_20201004T012247.mat',
]
#blmeas_1 = dirname1+'Bunch_length_meas_2020-10-03_15-43-29.h5'
blmeas_1 = dirname1+'129833611_bunch_length_meas.h5'
blmeas_2 = dirname2+'129858802_bunch_length_meas.h5'
ms.figure('Measured beam profiles')
subplot = ms.subplot_factory(2,2)
sp_ctr = 1
sp = subplot(sp_ctr, title='Beam profiles', xlabel='time [fs]', ylabel='Current (arb. units)')
sp_ctr += 1
for blmeas in (blmeas_1, blmeas_2):
for zero_crossing in (1, 2):
bp = tracking.profile_from_blmeas(blmeas, tt_halfrange, charge, energy_eV=1, subtract_min=True, zero_crossing=zero_crossing)
if zero_crossing == 2:
bp.flipx()
label = '%s %i' % (blmeas, zero_crossing)
bp.plot_standard(sp, label=label)
sp.legend()
plt.show()
if np.diff(y_axis)[0] < 0:
y_axis = y_axis[::-1]
image0 = image0[::-1, :]
#image = dict_['Image'][0][0].T
image0 = dict_['Image'][-1][0].T
#meas_screen = get_screen_from_image(image, invert=True)
meas_screen0 = get_screen_from_image(image0, invert=True)
timestamp = get_timestamp(file_)
blmeas_1 = dirname1 + '129833611_bunch_length_meas.h5'
energy_eV = 4491892915.7690735
profile_meas = tracking.profile_from_blmeas(blmeas_1,
tt_halfrange,
charge,
energy_eV,
subtract_min=True)
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)
forward_dict = tracker.matrix_forward(profile_meas, [10e-3, 10e-3], [0., 0.])
forward_dict_ele = tracker.elegant_forward(profile_meas, [10e-3, 10e-3],
[0., 0.])
beam_forward = forward_dict['beam0_at_screen']
image_sim, xedges, yedges = np.histogram2d(beam_forward[0],
beam_forward[2],
bins=(200, 100),
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()
invert_x0 = True
x_axis = dict0['x_axis'][::-1]*1e-6
projx = dict0['projx'][0]
projx0 = dict0['projx'][-1]
all_mean = []
for proj in projx0:
screen = get_screen_from_proj(proj, x_axis, invert_x0)
xx, yy = screen._xx, screen._yy
gf = gaussfit.GaussFit(xx, yy)
all_mean.append(gf.mean)
mean0 = np.mean(all_mean)
profile_meas = tracking.profile_from_blmeas(blmeas38, tt_halfrange, charge, energy_eV)
#profile_meas.flipx()
for n_proj in range(10):
screen0 = get_screen_from_proj(projx[n_proj], x_axis, invert_x0)
screen0._xx = screen0._xx - mean0
screen0.cutoff(3e-2)
screen0.crop()
ms.figure('Fit quad effect')
subplot = ms.subplot_factory(1,2)
sp_ctr = 1
tracker = tracking.Tracker(magnet_file,
timestamp,
struct_lengths,
energy_eV='file',
n_emittances=n_emittances,
screen_bins=screen_bins,
n_particles=n_particles,
smoothen=smoothen,
profile_cutoff=profile_cutoff,
screen_cutoff=screen_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=profile_cutoff)
profile_meas.center()
fdict0 = tracker.elegant_forward(profile_meas, gaps, [0., 0.])
fdict1 = tracker.matrix_forward(profile_meas, gaps, [0., 0.])
screen0 = fdict0['screen']
screen1 = fdict1['screen']
ms.figure('Investigate screen')
subplot = ms.subplot_factory(2, 2)
sp_ctr = 1
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,
n_particles=n_particles,
n_emittances=n_emittances,
n_bins=n_bins,
screen_cutoff=screen_cutoff,
smoothen=smoothen,
profile_cutoff=profile_cutoff,
len_screen=len_profile)
profile = tracking.profile_from_blmeas(bl_meas_file,
tt_halfrange,
charge,
tracker.energy_eV,
subtract_min=True)
tmat0 = time.time()
simulator = elegant_matrix.get_simulator(magnet_file)
mat_dict = simulator.get_streaker_matrices(timestamp)
s1 = mat_dict['start_to_s1']
beta_x = 5.067067
beta_y = 16.72606
alpha_x = -0.5774133
alpha_y = 1.781136