def setDetectors(run,
camera=None,
ebeam=None,
gasdetector=None,
eventid=None,
xtcavpars=None):
""" sets detector and data objects
"""
o = Empty()
o._camera = camera if camera is not None else run.Detector(
cons.DETNAME) # 'xtcav'
o._ebeam = ebeam if ebeam is not None else run.Detector(
cons.EBEAM) # 'ebeam'
o._gasdetector = gasdetector if gasdetector is not None else run.Detector(
cons.GAS_DETECTOR) # 'gasdetector'
o._eventid = eventid if eventid is not None else run.Detector(
cons.EVENTID) # 'eventid'
o._xtcavpars = xtcavpars if xtcavpars is not None else run.Detector(
cons.XTCAVPARS) # 'xtcavpars'
o._camraw = xtup.get_attribute(o._camera, 'raw')
o._valsebm = xtup.get_attribute(o._ebeam, 'valsebm')
o._valsgd = xtup.get_attribute(o._gasdetector, 'valsgd')
o._valseid = xtup.get_attribute(o._eventid, 'valseid')
o._valsxtp = xtup.get_attribute(o._xtcavpars, 'valsxtp')
if None in (o._camraw, o._valsebm, o._valsgd, o._valseid, o._valsxtp):
sys.error(
'FATAL ERROR IN THE DETECTOR INTERFACE: MISSING ATTRIBUTE MUST BE IMPLEMENTED'
)
return o
def data_xtcavpars(valsxtp, evt):
"""
ROI_SIZE_X_names = ['XTCAV_ROI_sizeX', 'ROI_X_Length', 'OTRS:DMP1:695:SizeX']
ROI_SIZE_Y_names = ['XTCAV_ROI_sizeY', 'ROI_Y_Length', 'OTRS:DMP1:695:SizeY']
ROI_START_X_names = ['XTCAV_ROI_startX', 'ROI_X_Offset', 'OTRS:DMP1:695:MinX']
ROI_START_Y_names = ['XTCAV_ROI_startY', 'ROI_Y_Offset', 'OTRS:DMP1:695:MinY']
UM_PER_PIX_names = ['XTCAV_calib_umPerPx','OTRS:DMP1:695:RESOLUTION']
STR_STRENGTH_names = ['XTCAV_strength_par_S','Streak_Strength','OTRS:DMP1:695:TCAL_X']
RF_AMP_CALIB_names = ['XTCAV_Amp_Des_calib_MV','XTCAV_Cal_Amp','SIOC:SYS0:ML01:AO214']
RF_PHASE_CALIB_names = ['XTCAV_Phas_Des_calib_deg','XTCAV_Cal_Phase','SIOC:SYS0:ML01:AO215']
DUMP_E_names = ['XTCAV_Beam_energy_dump_GeV','Dump_Energy','REFS:DMP1:400:EDES']
DUMP_DISP_names = ['XTCAV_calib_disp_posToEnergy','Dump_Disp','SIOC:SYS0:ML01:AO216']
o.XTCAV_calib_umPerPx = valsxtp.XTCAV_calib_umPerPx(evt)
o.XTCAV_strength_par_S = valsxtp.XTCAV_strength_par_S(evt)
...
a = valsxtp.getattr(valsxtp, 'OTRS:DMP1:695:SizeX', None)
o.OTRS_DMP1_695_SizeX = None if a is None else a(evt)
"""
o = Empty()
for lstname, names in cons.xtcav_varname.items():
for name in names:
a = xtup.get_attribute(valsxtp, name)
if a is not None:
value = a(evt)
o.setattr(name.replace(':', '_'), value)
#convert name like 'UM_PER_PIX_names' to 'umperpix'
varname = lstname.rstrip('_names').replace('_', '').lower()
if value is not None:
o.setattr(varname, value)
return o
def getLasingOffShot(lon, fname_loff):
results=lon._pulse_characterization
loff = lon._lasingoffreference
ibunch = 0
group = results.groupnum[ibunch]
profs = loff.averaged_profiles
ds = DataSource(files=fname_loff)
run = next(ds.runs())
print('XXXX dir(run)', dir(run))
print('XXXX max_events', run.max_events)
print('XXXX esm', run.esm)
print('XXXX timestamp', run.timestamp)
print('XXXX dm', run.dm)
print('XXXX smd_dm', run.smd_dm)
#====================
sys.exit('TEST EXIT')
#====================
camera = run.Detector(cons.DETNAME)
camraw = xtup.get_attribute(camera,'raw')
#LCLS1 stuff does not work here
#times = run.times()
#time = profs.eventTime[ibunch][group]
#fid = profs.eventFid[ibunch][group]
#et = EventTime(int(time),int(fid))
#evt_loff = run.event(et)
if camraw is None:
print('No lasing off image found for unixtime',time,'and fiducials',fid)
print('Found lasing off shot in run',loff.parameters.run)
return camraw(evt_loff)
def __init__(self, args):
"""
"""
#self.args = args
fname = getattr(
args, 'fname',
'/reg/g/psdm/detector/data2_test/xtc/data-amox23616-r0131-e000200-xtcav-v2.xtc2'
)
experiment = getattr(args, 'experiment', 'amox23616')
run_number = getattr(args, 'run_number', 131)
max_shots = getattr(args, 'max_shots',
401) #Maximum number of shots to process
validity_range = getattr(args, 'validity_range', None)
save_to_file = getattr(args, 'save_to_file', True)
calibration_path = getattr(args, 'calibration_path', '')
start_image = getattr(args, 'start_image', 0)
dark_reference_path = getattr(args, 'dark_reference_path',
None) #Dark reference information
num_bunches = getattr(args, 'num_bunches', 1) #Number of bunches
num_groups = getattr(args, 'num_groups',
None) #Number of profiles to average together
snr_filter = getattr(args, 'snr_filter',
10) #Number of sigmas for the noise threshold
roi_expand = getattr(args, 'roi_expand',
1) #Parameter for the roi location
roi_fraction = getattr(args, 'roi_fraction', cons.ROI_PIXEL_FRACTION)
island_split_method = getattr(
args, 'island_split_method',
cons.DEFAULT_SPLIT_METHOD) #Method for island splitting
island_split_par1 = getattr(
args, 'island_split_par1', 3.0
) #Ratio between number of pixels between largest and second largest groups when calling scipy.label
island_split_par2 = getattr(
args, 'island_split_par2', 5.
) #Ratio between number of pixels between second/third largest groups when calling scipy.label
PLOT_IMAGE = getattr(args, 'plot_image', False)
if PLOT_IMAGE:
self.fig, self.axim, self.axcb = gr.fig_img_cbar_axes(fig=None,\
win_axim=(0.05, 0.05, 0.87, 0.93),\
win_axcb=(0.923, 0.05, 0.02, 0.93)) #, **kwargs)
#if type(run_number) == int:
# run_number = str(run_number)
self.parameters = LasingOffParameters(
experiment=experiment,
max_shots=max_shots,
run_number=run_number,
start_image=start_image,
validity_range=validity_range,
dark_reference_path=dark_reference_path,
num_bunches=num_bunches,
num_groups=num_groups,
snr_filter=snr_filter,
roi_expand=roi_expand,
roi_fraction=roi_fraction,
island_split_method=island_split_method,
island_split_par2=island_split_par2,
island_split_par1=island_split_par1,
calibration_path=calibration_path,
fname=fname,
version=1)
if rank == 0:
print('Lasing off reference')
print('\t File name: %s' % self.parameters.fname)
print('\t Experiment: %s' % self.parameters.experiment)
print('\t Runs: %s' % self.parameters.run_number)
print('\t Number of bunches: %d' % self.parameters.num_bunches)
print('\t Valid shots to process: %d' % self.parameters.max_shots)
print('\t Dark reference run: %s' %
self.parameters.dark_reference_path)
#Loading the data, this way of working should be compatible with both xtc and hdf5 files
#ds = psana.DataSource("exp=%s:run=%s:idx" % (self.parameters.experiment, self.parameters.run_number))
ds = DataSource(files=fname)
run = next(ds.runs()) # run = ds.runs().next()
#env = SimulatorEnvironment() # ds.env()
#Camera for the xtcav images, Ebeam type, eventid, gas detectors
camera = run.Detector(cons.DETNAME) # psana.Detector(cons.DETNAME)
ebeam = run.Detector(
cons.EBEAM) #SimulatorEBeam() # psana.Detector(cons.EBEAM)
eventid = run.Detector(
cons.EVENTID) #SimulatorEventId() # evt.get(psana.EventId)
gasdetector = run.Detector(
cons.GAS_DETECTOR
) #SimulatorGasDetector() # psana.Detector(cons.GAS_DETECTOR)
xtcavpars = run.Detector(cons.XTCAVPARS)
# Empty list for the statistics obtained from each image, the shot to shot properties,
# and the ROI of each image (although this ROI is initially the same for each shot,
# it becomes different when the image is cropped around the trace)
list_image_profiles = []
#dark_background = self._getDarkBackground(env)
dark_data, dark_meta = xtup.get_calibconst(camera, 'xtcav_pedestals',
cons.DETNAME, experiment,
run_number)
logger.debug('==== dark_meta:\n%s' % str(dark_meta))
dark_background = xtu.xtcav_calib_object_from_dict(dark_data)
logger.debug('==== dir(dark_background):\n%s' %
str(dir(dark_background)))
logger.debug('==== dark_background.ROI:\n%s' %
str(dark_background.ROI))
logger.debug(
info_ndarr(dark_background.image, '==== dark_background.image:'))
print('\n', 100 * '_', '\n')
camraw = xtup.get_attribute(camera, 'raw')
valsxtp = xtup.get_attribute(xtcavpars, 'valsxtp')
valsebm = xtup.get_attribute(ebeam, 'valsebm')
valseid = xtup.get_attribute(eventid, 'valseid')
valsgd = xtup.get_attribute(gasdetector, 'valsgd')
if None in (camraw, valsxtp, valsebm, eventid, valsgd):
sys.error(
'FATAL ERROR IN THE DETECTOR INTERFACE: MISSING ATTRIBUTE MUST BE IMPLEMENTED'
)
#times = run.times()
#image_numbers = xtup.divideImageTasks(first_event, len(times), rank, size)
roi_xtcav, global_calibration, saturation_value = None, None, None
num_processed = 0 #Counter for the total number of xtcav images processed within the run
for nev, evt in enumerate(run.events()):
#logger.info('Event %03d'%nev)
img = camraw(evt)
if img is None: continue
if roi_xtcav is None:
# get calibration values needed to process images.
resp = self._getCalibrationValues(nev, evt, camraw, valsxtp)
if resp is None: continue
roi_xtcav, global_calibration, saturation_value = resp
#Obtain the shot to shot parameters necessary for the retrieval of the x and y axis in time and energy units
shot_to_shot = xtup.getShotToShotParameters(
evt, valsebm, valsgd, valseid)
#logger.debug('shot_to_shot: %s' % str(shot_to_shot))
if not shot_to_shot.valid: continue
image_profile, _ = xtu.processImage(img, self.parameters,
dark_background,
global_calibration,
saturation_value, roi_xtcav,
shot_to_shot)
#logger.debug(info_ndarr(image_profile, 'LasingOffReference image_profile'))
if not image_profile:
continue
#Append only image profile, omit processed image
list_image_profiles.append(image_profile)
num_processed += 1
self._printProgressStatements(num_processed)
if num_processed >= np.ceil(
self.parameters.max_shots / float(size)):
break
if PLOT_IMAGE:
nda = img
mean, std = nda.mean(), nda.std()
aran = (mean - 3 * std, mean + 5 * std)
self.axim.clear()
self.axcb.clear()
imsh = gr.imshow(self.axim, nda, amp_range=aran, extent=None, interpolation='nearest',\
aspect='auto', origin='upper', orientation='horizontal', cmap='inferno')
cbar = gr.colorbar(self.fig,
imsh,
self.axcb,
orientation='vertical',
amp_range=aran)
gr.set_win_title(self.fig, 'Event: %d' % nev)
gr.draw_fig(self.fig)
gr.show(mode='non-hold')
# here gather all shots in one core, add all lists
#image_profiles = comm.gather(list_image_profiles, root=0)
image_profiles = list_image_profiles
if rank != 0: return
sys.stdout.write('\n')
# Flatten gathered arrays
#image_profiles = [item for sublist in image_profiles for item in sublist]
#for i,ipf in enumerate(image_profiles) :
# print('XXX image_profiles %d:\n %s'%(i,str(ipf)))
#Since there are 12 cores it is possible that there are more references than needed. In that case we discard some
if len(image_profiles) > self.parameters.max_shots:
image_profiles = image_profiles[0:self.parameters.max_shots]
#At the end, all the reference profiles are converted to Physical units, grouped and averaged together
averaged_profiles = xtu.averageXTCAVProfilesGroups(
image_profiles, self.parameters.num_groups)
self.averaged_profiles, num_groups = averaged_profiles
self.n = num_processed
self.parameters = self.parameters._replace(num_groups=num_groups)
logger.debug('self.parameters.validity_range: %s type: %s' %
(self.parameters.validity_range,
type(self.parameters.validity_range)))
logger.debug(
'self.parameters.run_number: %d type: %s' %
(self.parameters.run_number, type(self.parameters.run_number)))
# Set validity range, replace 'end' -> 9999 othervise save does not work...
if not self.parameters.validity_range or not type(
self.parameters.validity_range) == tuple:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.run_number,
9999)) # IT WAS 'end'))
elif len(self.parameters.validity_range) == 1:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.validity_range[0],
9999)) # 'end'))
#=====================
#sys.exit('TEST EXIT')
#=====================
if save_to_file:
#cp = CalibrationPaths(env, self.parameters.calibration_path)
#file = cp.newCalFileName(cons.LOR_FILE_NAME, self.parameters.validity_range[0], self.parameters.validity_range[1])
fname = 'cons-%s-%04d-xtcav-lasingoff.data' % (run.expt, run.runnum
) # , cons.DETNAME)
self.save(fname)
def __init__(self, args):
#self.args = args
fname = getattr(
args, 'fname',
'/reg/g/psdm/detector/data2_test/xtc/data-amox23616-r0104-e000400-xtcav-v2.xtc2'
)
experiment = getattr(args, 'experiment', 'amox23616')
run_number = getattr(args, 'run_number', 104)
max_shots = getattr(args, 'max_shots', 400)
validity_range = getattr(args, 'validity_range', None)
save_to_file = getattr(args, 'save_to_file', True)
calibration_path = getattr(args, 'calibration_path', '')
start_image = getattr(args, 'start_image', 0)
self.image = None
self.ROI = None
self.n = 0
self.parameters = DarkBackgroundParameters(
experiment=experiment,
max_shots=max_shots,
run_number=run_number,
validity_range=validity_range,
calibration_path=calibration_path)
"""
After setting all the parameters, this method has to be called to generate the dark reference and
save it in the proper location.
"""
logger.info('dark background reference')
logger.info('\t Data file: %s' % fname)
logger.info('\t Experiment: %s' % self.parameters.experiment)
logger.info('\t Run: %s' % self.parameters.run_number)
logger.info('\t Valid shots to process: %d' %
self.parameters.max_shots)
logger.info('\t Detector name: %s' % cons.DETNAME)
#Loading the dataset from the "dark" run, this way of working should be compatible with both xtc and hdf5 files
ds = DataSource(files=fname)
run = next(ds.runs())
logger.info('\t RunInfo expt: %s runnum: %d\n' %
(run.expt, run.runnum))
#Camera and type for the xtcav images
camera = run.Detector(cons.DETNAME)
#ebeam = run.Detector(cons.EBEAM)
#eventid = run.Detector(cons.EVENTID)
#gasdetector = run.Detector(cons.GAS_DETECTOR)
xtcavpars = run.Detector(cons.XTCAVPARS)
#Stores for environment variables
#configStore=dataSource.env().configStore()
#epicsStore=dataSource.env().epicsStore()
print('\n', 100 * '_', '\n')
camraw = xtup.get_attribute(camera, 'raw')
valsxtp = xtup.get_attribute(xtcavpars, 'valsxtp')
#valsebm = xtup.get_attribute(ebeam, 'valsebm')
#valseid = xtup.get_attribute(eventid, 'valseid')
#valsgd = xtup.get_attribute(gasdetector, 'valsgd')
if None in (camraw, valsxtp): # valsebm, eventid, valsgd) :
sys.error(
'FATAL ERROR IN THE DETECTOR INTERFACE: MISSING ATTRIBUTE MUST BE IMPLEMENTED'
)
accumulator_xtcav = None
n = 0 #Counter for the total number of xtcav images processed
for nev, evt in enumerate(run.events()):
#print('Event %03d'%nev, end='')
img = camera.raw(evt)
if img is None: continue
if self.ROI is None:
if not self._getImageROI(nev, evt, camraw, valsxtp): continue
accumulator_xtcav = np.zeros((self.ROI.yN, self.ROI.xN),
dtype=np.float64)
accumulator_xtcav += img
n += 1
if n % 5 == 0:
sys.stdout.write('\r%.1f %% done, %d / %d' %
(float(n) / self.parameters.max_shots * 100,
n, self.parameters.max_shots))
sys.stdout.flush()
if n >= self.parameters.max_shots:
break
#At the end of the program the total accumulator is saved
sys.stdout.write('\nMaximum number of images processed\n')
self.image = accumulator_xtcav / n
if not self.parameters.validity_range or not type(
self.parameters.validity_range) == tuple:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.run_number, 9999)) #'end'))
elif len(self.parameters.validity_range) == 1:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.validity_range[0],
9999)) #'end'))
logger.info(info_ndarr(self.image, 'averaged raw:'))
logger.info('self.parameters: %s' % str(self.parameters))
if save_to_file:
#cp = CalibrationPaths(dataSource.env(), self.parameters.calibration_path)
#fname = cp.newCalFileName(cons.DB_FILE_NAME, self.parameters.validity_range[0], self.parameters.validity_range[1])
fname = 'cons-%s-%04d-xtcav-pedestals.data' % (run.expt, run.runnum
) #, cons.DETNAME)
self.save(fname)