def calibrate_screen(self):
self.clear_screen_plots()
n_images = int(self.CalibrateScreenImages.text())
image_dict = daq.get_images(self.screen,
n_images,
dry_run=self.dry_run)
try:
result = analysis.analyze_screen_calibration(
image_dict, True, self.screen_calib_plot_handles)
except:
date = datetime.now()
basename = date.strftime(
'%Y_%m_%d-%H_%M_%S_'
) + 'Screen_Calibration_data_%s.h5' % self.screen.replace(
'.', '_')
filename = os.path.join(self.save_dir, basename)
h5_storage.saveH5Recursive(filename, image_dict)
print('Saved screen calibration data %s' % filename)
raise
x0, beamsize = self.update_screen_calibration(result)
date = datetime.now()
basename = date.strftime(
'%Y_%m_%d-%H_%M_%S_'
) + 'Screen_Calibration_%s.h5' % self.screen.replace('.', '_')
elog_text = 'Screen calibration\nScreen center at %i um\nBeamsize %i um' % (
x0 * 1e6, beamsize * 1e6)
self.elog_and_H5(elog_text, [self.screen_calib_fig],
'Screen center calibration', basename, image_dict)
def calibrate_streaker(self):
self.clear_calib_plots()
start, stop, step = float(self.Range1Begin.text()), float(
self.Range1Stop.text()), int(float(self.Range1Step.text()))
range1 = np.linspace(start, stop, step)
start, stop, step = float(self.Range2Begin.text()), float(
self.Range2Stop.text()), int(float(self.Range2Step.text()))
range2 = np.linspace(start, stop, step)
range_ = np.concatenate([range1, [0], range2
]) * 1e-3 # Convert mm to m
range_.sort()
range_ = np.unique(range_)
streaker = config.streaker_names[self.beamline][self.n_streaker]
n_images = int(self.CalibrateStreakerImages.text())
if daq is None:
raise ImportError('Daq not available')
result_dict = daq.data_streaker_offset(streaker, range_, self.screen,
n_images, self.dry_run)
try:
full_dict = self._analyze_streaker_calib(result_dict)
except:
date = datetime.now()
basename = date.strftime(
'%Y_%m_%d-%H_%M_%S_'
) + 'Calibration_data_%s.h5' % streaker.replace('.', '_')
filename = os.path.join(self.save_dir, basename)
h5_storage.saveH5Recursive(filename, result_dict)
print('Saved streaker calibration data %s' % filename)
raise
date = datetime.now()
basename = date.strftime(
'%Y_%m_%d-%H_%M_%S_') + 'Calibration_%s.h5' % streaker.replace(
'.', '_')
streaker_offset = full_dict['meta_data']['streaker_offset']
self.updateStreakerCenter(streaker_offset)
elog_text = 'Streaker calibration streaker %s\nCenter: %i um' % (
streaker, streaker_offset * 1e6)
self.elog_and_H5(elog_text, [self.streaker_calib_fig],
'Streaker center calibration', basename, full_dict)
self.tabWidget.setCurrentIndex(self.streaker_calib_plot_tab_index)
def elog_and_H5(self, text, figs, title, basename, data_dict):
filename = os.path.join(self.save_dir, basename)
h5_storage.saveH5Recursive(filename, data_dict)
print('Saved %s' % filename)
attachments = []
for num, fig in enumerate(figs):
fig_title = filename.replace('.h5', '_%i.png' % num)
fig_filename = os.path.join(self.save_dir, fig_title)
fig.savefig(fig_filename, bbox_inches='tight', pad_inches=0)
print('Saved %s' % fig_filename)
attachments.append(fig_filename)
text += '\nData saved in %s' % filename
text += '\nBeamline: %s' % self.beamline
text += '\nStreaker: %s' % self.streaker
text += '\nScreen: %s' % self.screen
if elog is None:
print('Cannot save to ELOG')
print('I would post:')
print(text)
elif self.ElogSaveCheck.isChecked():
dict_att = {
'Author': 'Application: PostUndulatorStreakerAnalysis',
'Application': 'PostUndulatorStreakerAnalysis',
'Category': 'Measurement',
'Title': title
}
self.logbook.post(text,
attributes=dict_att,
attachments=attachments)
print('ELOG entry saved.')
else:
print('Save to ELOG is not checked in GUI')
return filename
bl_meas_structure = '/sf/data/measurements/2020/02/03/Bunch_length_meas_2020-02-03_15-51-34.h5'
if load_compact:
bl_meas_compact = loadH5Recursive('./example_current_profile.h5')
energy_eV = bl_meas_compact['energy_eV']
time_profile = bl_meas_compact['time_profile']
current = bl_meas_compact['current']
else:
bl_meas = loadH5Recursive2(bl_meas_structure)
time_profile, current = bl_meas['Meta_data'][
'Time axes'][::-1] * 1e-15, bl_meas['Meta_data'][
'Current profile'][::-1]
energy_eV = bl_meas['Input']['Energy'].squeeze() * 1e6
saveH5Recursive('./example_current_profile.h5', {
'time_profile': time_profile,
'current': current,
'energy_eV': energy_eV
})
rescale_factor = 200e-12 / np.sum(current)
yy_charge = current * rescale_factor
xx_space = time_profile * c
xx_space -= xx_space.min()
#cutoff
#yy_charge[yy_charge < 0.1*yy_charge.max()] = 0
fig = ms.figure('Wake effect')
subplot = ms.subplot_factory(2, 3)
sp_ctr = 1
dirname2 = '/home/work/data_2020-10-04/'
files = [
'Passive_data_20201003T231958.mat',
'Passive_data_20201003T233852.mat',
'Passive_data_20201004T161118.mat',
'Passive_data_20201004T172425.mat',
'Passive_data_20201004T223859.mat',
]
for file_ in files:
try:
file1 = dirname1 + file_
dict_ = mat73.loadmat(file1)
except:
file1 = dirname2 + file_
dict_ = mat73.loadmat(file1)
dict2 = {}
for x, y in dict_.items():
try:
y2 = np.array(y)
if y2.dtype != np.dtype('O'):
dict2[x] = y2
except:
print('Pass for %s' % x)
file2 = file1 + '.h5'
h5_storage.saveH5Recursive(file2, dict2)
print('Saved %s' % file2)
gap_mm = 10
projections = np.zeros(
[len(dict_['Image']),
len(dict_['Image'][0]),
len(x_axis)],
dtype=np.float32)
for a, b in itertools.product(range(len(dict_['Image'])),
range(len(dict_['Image'][0]))):
projections[a, b] = dict_['Image'][a][b].sum(axis=-1)
new_dict = {
'streaker_offsets': new_offsets,
'screen': screen,
'n_images': projections.shape[1],
'streaker': streaker,
'pyscan_result': {
'x_axis_m': x_axis,
'projx': projections,
},
'meta_data': {
streaker + ':GAP': gap_mm,
},
}
h5_storage.saveH5Recursive(matfile.replace('.mat', '_new.h5'), new_dict)
streaker_analysis = analysis.analyze_streaker_calibration(new_dict)
plt.show()
print(w.filename)
if sp_ctr > 9:
ms.figure('Continued')
sp_ctr = 1
sp = subplot(sp_ctr,
title='%i %s' % (sigma * 1e15, label),
scix=True)
sp_ctr += 1
if use_constant_bin_number:
bin_edges = bin_number
else:
bin_edges = np.arange(w['x'].min(),
w['x'].max() + hist_bin_size,
hist_bin_size)
hist, bin_edges, _ = sp.hist(w['x'], density=True, bins=bin_edges)
output['proj_' + label][n_offset,
n_sigma, :len(bin_edges) - 1] = hist
output['axis_' + label][n_offset, n_sigma, :len(bin_edges) -
1] = bin_edges[:-1]
#sim0.__del__()
#sim.__del__()
h5_storage.saveH5Recursive('./bins_200_300e3_370um_larger_beamsize.h5', output)
plt.show()
for key in ['Image', 'knob', 'value', 'x_axis', 'y_axis']:
try:
value = inp_dict[key]
except KeyError:
print('Continue for key %s' % key)
continue
if key == 'Image':
outp = []
if type(value[0]) is list:
for n_offset in range(len(value)):
outp.append([])
for n_image in range(len(value[n_offset])):
img = value[n_offset][n_image].T
outp[n_offset].append(img.sum(axis=0))
else:
for n_image in range(len(value)):
img = value[n_image].T
outp.append(img.sum(axis=0))
outp_dict['projx'] = np.array(outp)
else:
outp_dict[key] = np.array(value)
new_file = dir_+file_+'.h5'
saveH5Recursive(new_file, outp_dict)
print(new_file)
