df, filenames = ut.analysis.get_data_daq(fname, daq_labels, sacla_converter, t0=0, selection=sel)
# get laser on/off tags
is_laser_on_tags = df[df.is_laser == 1].index.tolist()
is_laser_off_tags = df[df.is_laser == 0].index.tolist()
# get spectra from Von Hamos, using laser on / off tags
#roi = [[0, 1024], [325, 335]] # X, Y
ap = ImagesProcessor(facility="SACLA")
ap.add_analysis('get_projection', args={"axis": 1})
ap.add_analysis('get_mean_std')
ap.set_dataset('/run_%s/detector_2d_1' % run)
ap.add_preprocess("set_thr", args={"thr_low": 65})
# get the total spectra
results_on = ap.analyze_images(fname, tags=is_laser_on_tags)
spectrum_on = results_on["get_projection"]["spectra"].sum(axis=0)
results_off = ap.analyze_images(fname, tags=is_laser_off_tags)
spectrum_off = results_off["get_projection"]["spectra"].sum(axis=0)
spectrum_off = spectrum_off / spectrum_off.sum()
spectrum_on = spectrum_on / spectrum_on.sum()
# this is the average image from the Von Hamos
sum_image_on = results_on["get_mean_std"]["images_mean"]
# Plot!
plt.subplot(1, 2, 1)
plt.imshow(sum_image_on)
plt.subplot(1, 2, 2)
plt.plot(spectrum_off, label="laser off")
# add analysis
an.add_analysis("get_projection", args={'axis': 1, 'thr_low': thr,})
an.add_analysis("get_mean_std", args={'thr_low': thr})
bins = np.arange(-150, 300, 5)
an.add_analysis("get_histo_counts", args={'bins': bins})
an.add_analysis(get_line_histos, args={'axis': 0, 'bins': bins})
# set the dataset
an.set_dataset(dataset_name)
# add preprocess steps
#an.add_preprocess("image_set_roi", args={'roi': roi})
#an.add_preprocess("image_set_thr", thr_low=thr)
# run the analysis
results = an.analyze_images(fname, n=1000)
# plot
plt.figure(figsize=(7, 7))
plt.plot(np.nansum(results["spectra"], axis=0),
label="ADU > " + str(thr))
plt.legend(loc='best')
#plt.show()
plt.figure(figsize=(7, 7))
plt.bar(bins[:-1], results["histo_counts"], log=True, width=5)
#plt.show()
plt.figure(figsize=(15, 10))
plt.subplot(121)
plt.imshow(results["histo_counts_line"], vmin=0, vmax=3,
