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