if __name__ == "__main__":
# set filename and dataset name
#DIR = "/swissfel/photonics/data/2014-11-26_SACLA_ZnO/full_hdf5/256635-257499/"
DIR = "/home/sala/Work/Data/SACLA/"
#fname = "/home/sala/Work/Data/Sacla/ZnO/258706_roi.h5"
fname = DIR + "259408_roi.h5"
dataset_name = "/run_259408/detector_2d_1"
# set up parameters for ROI and threshold
roi = [[0, 1024], [0, 400]]
thr = 65
# create an AnalysisProcessor object
an = ImagesProcessor(facility="SACLA")
# if you want a flat dict as a result
an.flatten_results = True
# 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)
f = h5py.File(DIR + run + "_roi.h5")
if dark_fname is not None:
f_calib = h5py.File(dark_fname, "r")
corr = f_calib["/dark1"][:]
f_calib.close()
# get DAQ quantities (only scalars)
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
def compute_rixs_spectra(dataset_name, df, thr_low=0, thr_hi=999999, ):
# In principle, a single run can contain *multiple mono settings*, so we need to load data from all the runs, and the group them by mono energy. `Pandas` can help us with that...
# We load all data from files, place it in a `DataFrame`, and then add some useful derived quantities. At last, we use `tags` as index for the `DataFrame`
runs = sorted(df.run.unique())
print runs
# label for ascii output dump
out_label = "rixs_" + runs[0] + "-" + runs[-1]
delay = df.delay.unique()
if len(delay) > 1:
print "More than one delay settings in the selected run range, exiting"
sys.exit(-1)
print "\nAvailable energy settings"
print df.photon_mono_energy.unique(), "\n"
# Now we can run the analysis. For each energy value and each run, a *list of tags* is created,
# such that events have the same mono energy and they are part of the same run (as each run is in a separated file).
# For each of these lists, we run the `AnalysisProcessor` and create the required spectra, for laser on and off.
# the mono energies contained in the files
energies_list = sorted(df.photon_mono_energy.unique().tolist())
fnames = [DIR + str(run) +"_roi.h5" for run in runs]
# The AnalysisProcessor
an = ImagesProcessor(facility="SACLA")
# if you want a flat dict as a result
an.flatten_results = True
# add analysis
an.add_analysis("get_projection", args={'axis': 1, 'thr_low': thr_low, 'thr_hi': thr_hi})
an.add_analysis("get_mean_std", args={'thr_low': thr_low})
bins = np.arange(-150, 1000, 5)
an.add_analysis("get_histo_counts", args={'bins': bins})
an.set_dataset("/run_%s/%s" % (str(run), dataset_name))
# run the analysis
n_events = -1
spectrum_on = None
spectrum_off = None
# multiprocessing import
from multiprocessing import Pool
from multiprocessing.pool import ApplyResult
# initialization of the RIXS maps. Element 0 is laser_on_ element 1 is laser_off
rixs_map = [np.zeros((len(energies_list), 1024)), np.zeros((len(energies_list), 1024))]
rixs_map_std = [np.zeros((len(energies_list), 1024)), np.zeros((len(energies_list), 1024))]
n_events = -1
spectrum = [None, None]
total_results = {}
events_per_energy = [{}, {}]
for i, energy in enumerate(energies_list):
async_results = [] # list for results
events_per_energy[0][energy] = 0
events_per_energy[1][energy] = 0
energy_masks = []
# creating the pool
pool = Pool(processes=8)
# looping on the runs
for j, run in enumerate(runs):
df_run = df[df.run == run]
energy_masks.append(df_run[df_run.photon_mono_energy == energy])
# apply the analysis
async_results.append(pool.apply_async(an, (fnames[j], n_events, energy_masks[j].index.values)))
# closing the pool
pool.close()
# waiting for all results
results = [r.get() for r in async_results]
print "Got results for energy", energy
# producing the laser on/off maps
for j, run in enumerate(runs):
if not total_results.has_key(run):
total_results[run] = {}
if not results[j].has_key("spectra"):
continue
df_run = df[df.run == run]
energy_mask = energy_masks[j]
laser_masks = [None, None]
if n_events != -1:
laser_masks[0] = energy_mask.is_laser.values[:n_events]
else:
laser_masks[0] = energy_mask.is_laser.values
laser_masks[1] = ~laser_masks[0]
for laser in [0, 1]:
norm = np.count_nonzero(~np.isnan(results[j]["spectra"][laser_masks[laser]][:, 0]))
events_per_energy[laser][energy] += norm
spectrum = np.nansum((results[j]["spectra"][laser_masks[laser]].T / df_run[laser_masks[laser]].I0.values).T, axis=0)
spectrum_events = np.nansum(results[j]["spectra"][laser_masks[laser]], axis=0)
rixs_map[laser][energies_list.index(energy)] += spectrum
rixs_map_std[laser][energies_list.index(energy)] += spectrum_events
total_results[run][energy] = {}
total_results[run][energy]["results"] = results[j]
total_results[run][energy]["laser_on"] = laser_masks[0]
for laser in [0, 1]:
for energy in events_per_energy[0].keys():
rixs_map[laser][energies_list.index(energy)] /= events_per_energy[laser][energy]
rixs_map_std[laser] = rixs_map[laser] / np.sqrt(rixs_map_std[laser])
np.savetxt("%s_map_%s_%dps.txt" % (out_label, "on" if laser==0 else "off", delay), rixs_map[laser])
#np.savetxt("%s_map_%dps_energies.txt" % (out_label, delay), sorted(events_per_energy[0].keys()))
return rixs_map, rixs_map_std, total_results
